From b311a14b0c0c51357ea9fa6bcc3026c7046158b7 Mon Sep 17 00:00:00 2001 From: Keith Battocchi Date: Tue, 5 Apr 2022 11:20:35 -0400 Subject: [PATCH] Streamline notebook tests --- azure-pipelines.yml | 249 +- ...nd Orthogonal Random Forest Examples.ipynb | 1750 ------------ ...sal Model Selection with the RScorer.ipynb | 662 ----- notebooks/Choosing First Stage Models.ipynb | 649 ----- ...nline Media Company - EconML + DoWhy.ipynb | 1223 -------- ...mentation at An Online Media Company.ipynb | 873 ------ ... at Microsoft via Short-Term Proxies.ipynb | 955 ------- ...nt Attribution at A Software Company.ipynb | 1267 --------- ...line Travel Company - EconML + DoWhy.ipynb | 1312 --------- ... Testing at An Online Travel Company.ipynb | 776 ------ ...f training program - Lalonde dataset.ipynb | 1151 -------- notebooks/Deep IV Examples.ipynb | 372 --- .../Double Machine Learning Examples.ipynb | 2476 ----------------- ...mic Double Machine Learning Examples.ipynb | 778 ------ notebooks/ForestLearners Basic Example.ipynb | 939 ------- notebooks/Generalized Random Forests.ipynb | 1677 ----------- notebooks/Interpretability with SHAP.ipynb | 400 --- notebooks/Metalearners Examples.ipynb | 513 ---- notebooks/OrthoIV and DRIV Examples.ipynb | 1642 ----------- ...licy Learning with Trees and Forests.ipynb | 571 ---- ...erpretation for Boston Housing Price.ipynb | 1628 ----------- ...ation for Employee Attrition Dataset.ipynb | 1283 --------- notebooks/Solutions/tombstone | 0 ...ted Double Machine Learning Examples.ipynb | 737 ----- 24 files changed, 8 insertions(+), 23875 deletions(-) delete mode 100644 notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb delete mode 100644 notebooks/Causal Model Selection with the RScorer.ipynb delete mode 100644 notebooks/Choosing First Stage Models.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb delete mode 100644 notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb delete mode 100644 notebooks/Deep IV Examples.ipynb delete mode 100644 notebooks/Double Machine Learning Examples.ipynb delete mode 100755 notebooks/Dynamic Double Machine Learning Examples.ipynb delete mode 100644 notebooks/ForestLearners Basic Example.ipynb delete mode 100644 notebooks/Generalized Random Forests.ipynb delete mode 100644 notebooks/Interpretability with SHAP.ipynb delete mode 100644 notebooks/Metalearners Examples.ipynb delete mode 100644 notebooks/OrthoIV and DRIV Examples.ipynb delete mode 100644 notebooks/Policy Learning with Trees and Forests.ipynb delete mode 100644 notebooks/Solutions/Causal Interpretation for Boston Housing Price.ipynb delete mode 100644 notebooks/Solutions/Causal Interpretation for Employee Attrition Dataset.ipynb create mode 100644 notebooks/Solutions/tombstone delete mode 100644 notebooks/Weighted Double Machine Learning Examples.ipynb diff --git a/azure-pipelines.yml b/azure-pipelines.yml index ff9b84a82..4d06e672e 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -6,84 +6,8 @@ trigger: - main -jobs: -- job: 'EvalChanges' - displayName: 'Analyze changed files to determine which job to run' - pool: - vmImage: 'macOS-10.15' - steps: - # We want to enforce the following rules for PRs: - # * if all modifications are to README.md - # no testing is needed - # * if there are modifications to docs/* or to any code - # then docs need to be built to verify consistency - # * if there are modifications to notebooks/* or to any code - # then notebooks need to be run to verify consistency - # * for any code changes (or changes to metadata files) - # linting and testing should be run - # For a PR build, HEAD will be the merge commit, and we want to diff against the base branch, - # which will be the first parent: HEAD^ - # (For non-PR changes, we will always perform all CI tasks) - - powershell: | - if ($env:BUILD_REASON -eq 'PullRequest') { - $editedFiles = git diff HEAD^ --name-only - $editedFiles # echo edited files to enable easier debugging - $codeChanges = $false - $docChanges = $false - $nbChanges = $false - $changeType = "none" - foreach ($file in $editedFiles) { - switch -Wildcard ($file) { - "README.md" { Continue } - "econml/_version.py" { Continue } - "prototypes/*" { Continue } - "images/*" { Continue } - "doc/*" { $docChanges = $true; Continue } - "notebooks/*" { $nbChanges = $true; Continue } - default { $codeChanges = $true; Continue } - } - } - } - Write-Host "##vso[task.setvariable variable=buildDocs;isOutput=true]$(($env:BUILD_REASON -ne 'PullRequest') -or ($docChanges -or $codeChanges))" - Write-Host "##vso[task.setvariable variable=buildNbs;isOutput=true]$(($env:BUILD_REASON -ne 'PullRequest') -or ($nbChanges -or $codeChanges))" - Write-Host "##vso[task.setvariable variable=testCode;isOutput=true]$(($env:BUILD_REASON -ne 'PullRequest') -or $codeChanges)" - name: output - displayName: 'Determine type of code change' - -- template: azure-pipelines-steps.yml - parameters: - versions: ['3.6'] - images: ['ubuntu-18.04'] - package: '-e .[all]' - job: - job: 'Docs' - displayName: 'Build documentation' - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.buildDocs'], 'True') - steps: - - script: 'sudo apt-get -yq install graphviz' - displayName: 'Install graphviz' - - - script: 'pip install sklearn-contrib-lightning' - displayName: 'Install lightning' - - - script: 'pip install git+https://github.com/slundberg/shap.git@d1d2700acc0259f211934373826d5ff71ad514de' - displayName: 'Install specific version of shap' - - - script: 'pip install sphinx sphinx_rtd_theme' - displayName: 'Install sphinx' - - - script: 'python setup.py build_sphinx -W' - displayName: 'Build documentation' - - - publish: 'build/sphinx/html' - artifact: 'Documentation' - displayName: 'Publish documentation as artifact' - - - script: 'python setup.py build_sphinx -b doctest' - displayName: 'Run doctests' - +jobs: - template: azure-pipelines-steps.yml parameters: versions: ['3.8'] @@ -91,15 +15,19 @@ jobs: package: '-e .[tf,plt]' job: job: 'Notebooks_cust' - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.buildNbs'], 'True') displayName: 'Notebooks (Customer Solutions)' steps: # Work around https://github.com/pypa/pip/issues/9542 - script: 'pip install -U numpy~=1.21.0' displayName: 'Upgrade numpy' - - script: 'pip install pytest pytest-runner jupyter jupyter-client nbconvert nbformat seaborn xgboost tqdm && pip list && python setup.py pytest' + # shap 0.39 and sklearn 1.0 interact badly in these notebooks + # shap 0.40 has a bug in waterfall (https://github.com/slundberg/shap/issues/2283) that breaks our main tests + # but fixes the interaction here... + - script: 'pip install -U shap~=0.40.0' + displayName: 'Upgrade shap' + + - script: 'pip install pytest pytest-runner jupyter jupyter-client nbconvert nbformat seaborn xgboost tqdm && pip freeze && python setup.py pytest' displayName: 'Unit tests' env: PYTEST_ADDOPTS: '-m "notebook"' @@ -119,8 +47,6 @@ jobs: package: '-e .[tf,plt]' job: job: 'Notebooks_noncust' - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.buildNbs'], 'True') displayName: 'Notebooks (except Customer Solutions)' steps: # Work around https://github.com/pypa/pip/issues/9542 @@ -145,162 +71,3 @@ jobs: testResultsFiles: '**/test-results.xml' testRunTitle: 'Notebooks' condition: succeededOrFailed() - - -# - job: 'AutoML' -# dependsOn: 'EvalChanges' -# condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') -# variables: -# python.version: '3.6' -# pool: -# vmImage: 'ubuntu-18.04' -# steps: -# - template: azure-pipelines-steps.yml -# parameters: -# body: -# - task: AzureCLI@2 -# displayName: 'AutoML tests' -# inputs: -# azureSubscription: 'automl' -# scriptLocation: 'inlineScript' -# scriptType: 'pscore' -# powerShellIgnoreLASTEXITCODE: '' # string for now due to https://github.com/microsoft/azure-pipelines-tasks/issues/12266 -# inlineScript: | -# $env:SUBSCRIPTION_ID = az account show --query id -o tsv -# python setup.py pytest -# env: -# WORKSPACE_NAME: 'testWorkspace' -# RESOURCE_GROUP: 'testingAutoMLEconML' -# PYTEST_ADDOPTS: '-m "automl" -n 0' -# COVERAGE_PROCESS_START: 'setup.cfg' - -# - task: PublishTestResults@2 -# displayName: 'Publish Test Results **/test-results.xml' -# inputs: -# testResultsFiles: '**/test-results.xml' -# testRunTitle: 'AutoML' -# condition: succeededOrFailed() -# package: '.[automl]' - -- template: azure-pipelines-steps.yml - parameters: - versions: ['3.8'] - images: ['macOS-10.15'] - job: - job: 'Linting' - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') - steps: - - script: 'pip install pycodestyle && pycodestyle econml' - failOnStderr: true - displayName: Linting - -- template: azure-pipelines-steps.yml - parameters: - package: '-e .[tf,plt]' - job: - job: Tests_main - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') - displayName: 'Run tests (main)' - steps: - - script: 'pip install pytest pytest-runner && python setup.py pytest' - displayName: 'Unit tests' - env: - PYTEST_ADDOPTS: '-m "not (notebook or automl or dml or serial or cate_api)" -n 2' - COVERAGE_PROCESS_START: 'setup.cfg' - - task: PublishTestResults@2 - displayName: 'Publish Test Results **/test-results.xml' - inputs: - testResultsFiles: '**/test-results.xml' - testRunTitle: 'Python $(python.version), image $(imageName)' - condition: succeededOrFailed() - - - task: PublishCodeCoverageResults@1 - displayName: 'Publish Code Coverage Results' - inputs: - codeCoverageTool: Cobertura - summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' - -- template: azure-pipelines-steps.yml - parameters: - package: '-e .[tf,plt]' - job: - job: Tests_dml - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') - displayName: 'Run tests (DML)' - steps: - - script: 'pip install pytest pytest-runner && python setup.py pytest' - displayName: 'Unit tests' - env: - PYTEST_ADDOPTS: '-m "dml"' - COVERAGE_PROCESS_START: 'setup.cfg' - - task: PublishTestResults@2 - displayName: 'Publish Test Results **/test-results.xml' - inputs: - testResultsFiles: '**/test-results.xml' - testRunTitle: 'Python $(python.version), image $(imageName)' - condition: succeededOrFailed() - - - task: PublishCodeCoverageResults@1 - displayName: 'Publish Code Coverage Results' - inputs: - codeCoverageTool: Cobertura - summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' - -- template: azure-pipelines-steps.yml - parameters: - package: '-e .[tf,plt]' - job: - job: Tests_serial - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') - displayName: 'Run tests (Serial)' - steps: - - script: 'pip install pytest pytest-runner && python setup.py pytest' - displayName: 'Unit tests' - env: - PYTEST_ADDOPTS: '-m "serial" -n 1' - COVERAGE_PROCESS_START: 'setup.cfg' - - task: PublishTestResults@2 - displayName: 'Publish Test Results **/test-results.xml' - inputs: - testResultsFiles: '**/test-results.xml' - testRunTitle: 'Python $(python.version), image $(imageName)' - condition: succeededOrFailed() - - - task: PublishCodeCoverageResults@1 - displayName: 'Publish Code Coverage Results' - inputs: - codeCoverageTool: Cobertura - summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' - -- template: azure-pipelines-steps.yml - parameters: - package: '-e .[tf,plt]' - job: - job: Tests_CATE_API - dependsOn: 'EvalChanges' - condition: eq(dependencies.EvalChanges.outputs['output.testCode'], 'True') - displayName: 'Run tests (Other)' - steps: - - script: 'pip install pytest pytest-runner' - displayName: 'Install pytest' - - script: 'python setup.py pytest' - displayName: 'CATE Unit tests' - env: - PYTEST_ADDOPTS: '-m "cate_api" -n auto' - COVERAGE_PROCESS_START: 'setup.cfg' - - task: PublishTestResults@2 - displayName: 'Publish Test Results **/test-results.xml' - inputs: - testResultsFiles: '**/test-results.xml' - testRunTitle: 'Python $(python.version), image $(imageName)' - condition: succeededOrFailed() - - - task: PublishCodeCoverageResults@1 - displayName: 'Publish Code Coverage Results' - inputs: - codeCoverageTool: Cobertura - summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml' \ No newline at end of file diff --git a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb deleted file mode 100644 index 6497af314..000000000 --- a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb +++ /dev/null @@ -1,1750 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Orthogonal Random Forest and Causal Forest: Use Cases and Examples\n", - "\n", - "Causal Forests and Generalized Random Forests are a flexible method for estimating treatment effect heterogeneity with Random Forests. Orthogonal Random Forest (ORF) combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests. Due to the orthogonalization aspect of this method, the ORF performs especially well in the presence of high-dimensional confounders. For more details, see [this paper](https://arxiv.org/abs/1806.03467) or the [EconML docummentation](https://econml.azurewebsites.net/).\n", - "\n", - "The EconML SDK implements the following OrthoForest variants:\n", - "\n", - "* DMLOrthoForest: suitable for continuous or discrete treatments\n", - "\n", - "* DROrthoForest: suitable for discrete treatments\n", - "\n", - "* CausalForest: suitable for both discrete and continuous treatments\n", - "\n", - "In this notebook, we show the performance of the ORF on synthetic and observational data. \n", - "\n", - "## Notebook Contents\n", - "\n", - "1. [Example Usage with Continuous Treatment Synthetic Data](#1.-Example-Usage-with-Continuous-Treatment-Synthetic-Data)\n", - "2. [Example Usage with Binary Treatment Synthetic Data](#2.-Example-Usage-with-Binary-Treatment-Synthetic-Data)\n", - "3. [Example Usage with Multiple Treatment Synthetic Data](#3.-Example-Usage-with-Multiple-Treatment-Synthetic-Data)\n", - "4. [Example Usage with Real Continuous Treatment Observational Data](#4.-Example-Usage-with-Real-Continuous-Treatment-Observational-Data)" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "import econml" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "# Main imports\r\n", - "from econml.orf import DMLOrthoForest, DROrthoForest\r\n", - "from econml.dml import CausalForestDML\r\n", - "from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper, WeightedLasso, WeightedLassoCV\r\n", - "\r\n", - "# Helper imports\r\n", - "import numpy as np\r\n", - "from itertools import product\r\n", - "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV\r\n", - "import matplotlib.pyplot as plt\r\n", - "\r\n", - "%matplotlib inline" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# 1. Example Usage with Continuous Treatment Synthetic Data" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 1.1 DGP \n", - "We use the data generating process (DGP) from [here](https://arxiv.org/abs/1806.03467). The DGP is described by the following equations:\n", - "\n", - "\\begin{align}\n", - "T =& \\langle W, \\beta\\rangle + \\eta, & \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y =& T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, &\\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim& \\text{Normal}(0,\\, I_{n_w})\\\\\n", - "X \\sim& \\text{Uniform}(0,1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders and $\\beta, \\gamma$ have high sparsity.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta(x) = \\exp(2\\cdot x_1).\n", - "\\end{align}" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "# Treatment effect function\r\n", - "def exp_te(x):\r\n", - " return np.exp(2*x[0])" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# DGP constants\r\n", - "np.random.seed(123)\r\n", - "n = 1000\r\n", - "n_w = 30\r\n", - "support_size = 5\r\n", - "n_x = 1\r\n", - "# Outcome support\r\n", - "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\r\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\r\n", - "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\r\n", - "# Treatment support \r\n", - "support_T = support_Y\r\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\r\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \r\n", - "\r\n", - "# Generate controls, covariates, treatments and outcomes\r\n", - "W = np.random.normal(0, 1, size=(n, n_w))\r\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\r\n", - "# Heterogeneous treatment effects\r\n", - "TE = np.array([exp_te(x_i) for x_i in X])\r\n", - "T = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\r\n", - "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\r\n", - "\r\n", - "# ORF parameters and test data\r\n", - "subsample_ratio = 0.3\r\n", - "lambda_reg = np.sqrt(np.log(n_w) / (10 * subsample_ratio * n))\r\n", - "X_test = np.array(list(product(np.arange(0, 1, 0.01), repeat=n_x)))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 1.2. Train Estimator\n", - "\n", - "**Note:** The models in the final stage of the estimation (``model_T_final``, ``model_Y_final``) need to support sample weighting. \n", - "\n", - "If the models of choice do not support sample weights (e.g. ``sklearn.linear_model.LassoCV``), the ``econml`` packages provides a convenient wrapper for these models ``WeightedModelWrapper`` in order to allow sample weights." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "est = DMLOrthoForest(\r\n", - " n_trees=1000, min_leaf_size=5,\r\n", - " max_depth=50, subsample_ratio=subsample_ratio,\r\n", - " model_T=Lasso(alpha=lambda_reg),\r\n", - " model_Y=Lasso(alpha=lambda_reg),\r\n", - " model_T_final=WeightedLasso(alpha=lambda_reg),\r\n", - " model_Y_final=WeightedLasso(alpha=lambda_reg),\r\n", - " global_residualization=False,\r\n", - " random_state=123)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "To use the built-in confidence intervals constructed via Bootstrap of Little Bags, we can specify `inference=\"blb\"` at `fit` time or leave the default `inference='auto'` which will automatically use the Bootstrap of Little Bags." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "est.fit(Y, T, X=X, W=W, inference=\"blb\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 21.6s\n", - "[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 22.6s\n", - "[Parallel(n_jobs=-1)]: Done 816 tasks | elapsed: 25.6s\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 26.5s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 368 tasks | elapsed: 1.7s\n", - "[Parallel(n_jobs=-1)]: Done 984 tasks | elapsed: 4.7s\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 4.7s finished\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# Calculate treatment effects\r\n", - "treatment_effects = est.effect(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 21.6s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 23.9s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "# Calculate default (95%) confidence intervals for the test data\r\n", - "te_lower, te_upper = est.effect_interval(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 3.3s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.6s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "res = est.effect_inference(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 3.4s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.4s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "res.summary_frame().head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.161 0.183 6.339 0.0 0.802 1.520\n", - "1 1.171 0.177 6.628 0.0 0.825 1.518\n", - "2 1.182 0.171 6.925 0.0 0.847 1.516\n", - "3 1.192 0.165 7.228 0.0 0.869 1.515\n", - "4 1.202 0.160 7.533 0.0 0.890 1.515" - ], - "text/html": [ - "
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.715 1.187 6.276
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.739 1.079 6.525


Note: The stderr_mean is a conservative upper bound." - ] - }, - "metadata": {}, - "execution_count": 11 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "Similarly we can estimate effects and get confidence intervals and inference results using a `CausalForest`." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, - "source": [ - "est2 = CausalForestDML(model_t=Lasso(alpha=lambda_reg),\r\n", - " model_y=Lasso(alpha=lambda_reg),\r\n", - " n_estimators=4000, min_samples_leaf=5,\r\n", - " max_depth=50,\r\n", - " verbose=0, random_state=123)\r\n", - "est2.tune(Y, T, X=X, W=W)\r\n", - "est2.fit(Y, T, X=X, W=W)\r\n", - "treatment_effects2 = est2.effect(X_test)\r\n", - "te_lower2, te_upper2 = est2.effect_interval(X_test, alpha=0.01)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 1.3. Performance Visualization" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "plt.figure(figsize=(15, 5))\r\n", - "plt.subplot(1, 2, 1)\r\n", - "plt.title(\"ContinuousOrthoForest\")\r\n", - "plt.plot(X_test, treatment_effects, label='ORF estimate')\r\n", - "expected_te = np.array([exp_te(x_i) for x_i in X_test])\r\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\r\n", - "plt.fill_between(X_test[:, 0], te_lower, te_upper, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.subplot(1, 2, 2)\r\n", - "plt.title(\"CausalForest\")\r\n", - "plt.plot(X_test, treatment_effects2, label='ORF estimate')\r\n", - "expected_te = np.array([exp_te(x_i) for x_i in X_test])\r\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\r\n", - "plt.fill_between(X_test[:, 0], te_lower2, te_upper2, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# 2. Example Usage with Binary Treatment Synthetic Data" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 2.1. DGP \n", - "We use the following DGP:\n", - "\n", - "\\begin{align}\n", - "T \\sim & \\text{Bernoulli}\\left(f(W)\\right), &\\; f(W)=\\sigma(\\langle W, \\beta\\rangle + \\eta), \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y = & T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, & \\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim & \\text{Normal}(0,\\, I_{n_w}) & \\\\\n", - "X \\sim & \\text{Uniform}(0,\\, 1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders, $\\beta, \\gamma$ have high sparsity and $\\sigma$ is the sigmoid function.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta(x) = \\exp( 2\\cdot x_1 ).\n", - "\\end{align}" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 14, - "source": [ - "# DGP constants\r\n", - "np.random.seed(1234)\r\n", - "n = 1000\r\n", - "n_w = 30\r\n", - "support_size = 5\r\n", - "n_x = 1\r\n", - "# Outcome support\r\n", - "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\r\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\r\n", - "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\r\n", - "# Treatment support\r\n", - "support_T = support_Y\r\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\r\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \r\n", - "\r\n", - "# Generate controls, covariates, treatments and outcomes\r\n", - "W = np.random.normal(0, 1, size=(n, n_w))\r\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\r\n", - "# Heterogeneous treatment effects\r\n", - "TE = np.array([exp_te(x_i) for x_i in X])\r\n", - "# Define treatment\r\n", - "log_odds = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\r\n", - "T_sigmoid = 1/(1 + np.exp(-log_odds))\r\n", - "T = np.array([np.random.binomial(1, p) for p in T_sigmoid])\r\n", - "# Define the outcome\r\n", - "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\r\n", - "\r\n", - "# ORF parameters and test data\r\n", - "subsample_ratio = 0.4\r\n", - "X_test = np.array(list(product(np.arange(0, 1, 0.01), repeat=n_x)))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 2.2. Train Estimator " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 15, - "source": [ - "est = DROrthoForest(\r\n", - " n_trees=200, min_leaf_size=10,\r\n", - " max_depth=30, subsample_ratio=subsample_ratio,\r\n", - " propensity_model = LogisticRegression(C=1/(X.shape[0]*lambda_reg), penalty='l1', solver='saga'),\r\n", - " model_Y = Lasso(alpha=lambda_reg),\r\n", - " propensity_model_final=LogisticRegression(C=1/(X.shape[0]*lambda_reg), penalty='l1', solver='saga'), \r\n", - " model_Y_final=WeightedLasso(alpha=lambda_reg)\r\n", - ")" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, - "source": [ - "est.fit(Y, T, X=X, W=W)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 26.6s\n", - "[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 27.6s\n", - "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed: 27.8s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=-1)]: Done 185 out of 200 | elapsed: 1.0s remaining: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed: 1.0s finished\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 16 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, - "source": [ - "# Calculate treatment effects for the default treatment points T0=0 and T1=1\r\n", - "treatment_effects = est.effect(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 37.4s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 41.0s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, - "source": [ - "# Calculate default (95%) confidence intervals for the default treatment points T0=0 and T1=1\r\n", - "te_lower, te_upper = est.effect_interval(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 1.8s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 3.5s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, - "source": [ - "est2 = CausalForestDML(model_y=Lasso(alpha=lambda_reg),\r\n", - " model_t=LogisticRegression(C=1/(X.shape[0]*lambda_reg)),\r\n", - " n_estimators=200, min_samples_leaf=5,\r\n", - " max_depth=50, max_samples=subsample_ratio/2,\r\n", - " discrete_treatment=True,\r\n", - " random_state=123)\r\n", - "est2.fit(Y, T, X=X, W=W, cache_values=True)\r\n", - "treatment_effects2 = est2.effect(X_test)\r\n", - "te_lower2, te_upper2 = est2.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, - "source": [ - "est2.summary()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Population summary of CATE predictions on Training Data\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Uncertainty of Mean Point Estimate \n", - "================================================================\n", - "mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper\n", - "----------------------------------------------------------------\n", - " 3.088 0.157 19.677 0.0 2.78 3.396\n", - " Distribution of Point Estimate \n", - "=========================================\n", - "std_point pct_point_lower pct_point_upper\n", - "-----------------------------------------\n", - " 1.757 0.846 6.962\n", - " Total Variance of Point Estimate \n", - "==========================================\n", - "stderr_point ci_point_lower ci_point_upper\n", - "------------------------------------------\n", - " 1.764 0.774 6.951\n", - " Doubly Robust ATE on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATE 3.158 0.082 38.551 0.0 2.997 3.318\n", - " Doubly Robust ATT(T=0) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 3.1 0.096 32.322 0.0 2.912 3.288\n", - " Doubly Robust ATT(T=1) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 3.218 0.134 23.965 0.0 2.955 3.481\n", - "---------------------------------------------------------\n", - "\n", - "Note: The stderr_mean is a conservative upper bound.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
3.088 0.157 19.677 0.0 2.78 3.396
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.757 0.846 6.962
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.764 0.774 6.951
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Doubly Robust ATE on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATE 3.158 0.082 38.551 0.0 2.997 3.318
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Doubly Robust ATT(T=0) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 3.1 0.096 32.322 0.0 2.912 3.288
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Doubly Robust ATT(T=1) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 3.218 0.134 23.965 0.0 2.955 3.481


Note: The stderr_mean is a conservative upper bound." - ] - }, - "metadata": {}, - "execution_count": 20 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 2.3. Performance Visualization" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 21, - "source": [ - "plt.figure(figsize=(15, 5))\r\n", - "plt.subplot(1, 2, 1)\r\n", - "plt.title(\"DiscreteTreatmentOrthoForest\")\r\n", - "plt.plot(X_test, treatment_effects, label='ORF estimate')\r\n", - "expected_te = np.array([exp_te(x_i) for x_i in X_test])\r\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\r\n", - "plt.fill_between(X_test[:, 0], te_lower, te_upper, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.subplot(1, 2, 2)\r\n", - "plt.title(\"CausalForest\")\r\n", - "plt.plot(X_test, treatment_effects2, label='ORF estimate')\r\n", - "expected_te = np.array([exp_te(x_i) for x_i in X_test])\r\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\r\n", - "plt.fill_between(X_test[:, 0], te_lower2, te_upper2, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# 3. Example Usage with Multiple Treatment Synthetic Data" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 3.1. DGP \n", - "We use the following DGP:\n", - "\n", - "\\begin{align}\n", - "Y = & \\sum_{t=1}^{n_{\\text{treatments}}} 1\\{T=t\\}\\cdot \\theta_{T}(X) + \\langle W, \\gamma\\rangle + \\epsilon, \\; \\epsilon \\sim \\text{Unif}(-1, 1), \\\\\n", - "\\text{Pr}[T=t \\mid W] \\propto & \\exp\\{\\langle W, \\beta_t \\rangle\\}, \\;\\;\\;\\; \\forall t\\in \\{0, 1, \\ldots, n_{\\text{treatments}}\\} \n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders, $\\beta_t, \\gamma$ are sparse.\n", - "\n", - "For this particular example DGP we used $n_{\\text{treatments}}=3$ and \n", - "\\begin{align}\n", - "\\theta_1(x) = & \\exp( 2 x_1 ),\\\\\n", - "\\theta_2(x) = & 3 \\cdot \\sigma(100\\cdot (x_1 - .5)),\\\\\n", - "\\theta_3(x) = & -2 \\cdot \\sigma(100\\cdot (x_1 - .25)),\n", - "\\end{align}\n", - "where $\\sigma$ is the sigmoid function." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 22, - "source": [ - "def get_test_train_data(n, n_w, support_size, n_x, te_func, n_treatments):\r\n", - " # Outcome support\r\n", - " support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\r\n", - " coefs_Y = np.random.uniform(0, 1, size=support_size)\r\n", - " epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\r\n", - " # Treatment support \r\n", - " support_T = support_Y\r\n", - " coefs_T = np.random.uniform(0, 1, size=(support_size, n_treatments))\r\n", - " eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \r\n", - " # Generate controls, covariates, treatments and outcomes\r\n", - " W = np.random.normal(0, 1, size=(n, n_w))\r\n", - " X = np.random.uniform(0, 1, size=(n, n_x))\r\n", - " # Heterogeneous treatment effects\r\n", - " TE = np.array([te_func(x_i, n_treatments) for x_i in X])\r\n", - " log_odds = np.dot(W[:, support_T], coefs_T)\r\n", - " T_sigmoid = np.exp(log_odds)\r\n", - " T_sigmoid = T_sigmoid/np.sum(T_sigmoid, axis=1, keepdims=True)\r\n", - " T = np.array([np.random.choice(n_treatments, p=p) for p in T_sigmoid])\r\n", - " TE = np.concatenate((np.zeros((n,1)), TE), axis=1)\r\n", - " Y = TE[np.arange(n), T] + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\r\n", - " X_test = np.array(list(product(np.arange(0, 1, 0.01), repeat=n_x)))\r\n", - "\r\n", - " return (Y, T, X, W), (X_test, np.array([te_func(x, n_treatments) for x in X_test]))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 23, - "source": [ - "import scipy.special\r\n", - "def te_func(x, n_treatments):\r\n", - " return [np.exp(2*x[0]), 3*scipy.special.expit(100*(x[0] - .5)) - 1, -2*scipy.special.expit(100*(x[0] - .25))]\r\n", - "\r\n", - "np.random.seed(123)\r\n", - "(Y, T, X, W), (X_test, te_test) = get_test_train_data(2000, 3, 3, 1, te_func, 4)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 3.2. Train Estimator" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 24, - "source": [ - "est = DROrthoForest(n_trees=500, model_Y = WeightedLasso(alpha=lambda_reg))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 25, - "source": [ - "est.fit(Y, T, X=X, W=W)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 31.2s\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 32.9s\n", - "[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 35.5s\n", - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 39.5s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 0.3s\n", - "[Parallel(n_jobs=-1)]: Done 208 tasks | elapsed: 3.4s\n", - "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 7.9s finished\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 25 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 26, - "source": [ - "# Calculate marginal treatment effects\r\n", - "treatment_effects = est.const_marginal_effect(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 20.6s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 22.8s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 27, - "source": [ - "# Calculate default (95%) marginal confidence intervals for the test data\r\n", - "te_lower, te_upper = est.const_marginal_effect_interval(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 2.7s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 5.1s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 28, - "source": [ - "res = est.const_marginal_effect_inference(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 4.9s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 29, - "source": [ - "res.summary_frame()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X T \n", - "0 T0_1 1.013 0.159 6.360 0.000 0.701 1.325\n", - " T0_2 -0.989 0.149 -6.636 0.000 -1.281 -0.697\n", - " T0_3 0.034 0.226 0.152 0.879 -0.408 0.477\n", - "1 T0_1 1.018 0.160 6.379 0.000 0.705 1.331\n", - " T0_2 -0.987 0.147 -6.717 0.000 -1.276 -0.699\n", - "... ... ... ... ... ... ...\n", - "98 T0_2 1.967 0.210 9.345 0.000 1.554 2.379\n", - " T0_3 -2.021 0.163 -12.414 0.000 -2.340 -1.702\n", - "99 T0_1 6.867 0.244 28.194 0.000 6.390 7.344\n", - " T0_2 1.966 0.212 9.276 0.000 1.551 2.382\n", - " T0_3 -2.017 0.163 -12.352 0.000 -2.337 -1.697\n", - "\n", - "[300 rows x 6 columns]" - ], - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 29 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 30, - "source": [ - "est2 = CausalForestDML(model_y=Lasso(alpha=lambda_reg),\r\n", - " model_t=LogisticRegression(C=1/(X.shape[0]*lambda_reg)),\r\n", - " n_estimators=4000, min_samples_leaf=5,\r\n", - " max_depth=50, max_samples=subsample_ratio/2,\r\n", - " discrete_treatment=True,\r\n", - " random_state=123)\r\n", - "est2.fit(Y, T, X=X, W=W)\r\n", - "treatment_effects2 = est2.const_marginal_effect(X_test)\r\n", - "te_lower2, te_upper2 = est2.const_marginal_effect_interval(X_test, alpha=.01)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 3.3. Performance Visualization" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 31, - "source": [ - "plt.figure(figsize=(15, 5))\r\n", - "plt.subplot(1, 2, 1)\r\n", - "plt.title(\"DiscreteTreatmentOrthoForest\")\r\n", - "y = treatment_effects\r\n", - "colors = ['b', 'r', 'g']\r\n", - "for it in range(y.shape[1]):\r\n", - " plt.plot(X_test[:, 0], te_test[:, it], '--', label='True effect T={}'.format(it), color=colors[it])\r\n", - " plt.fill_between(X_test[:, 0], te_lower[:, it], te_upper[:, it], alpha=0.3, color='C{}'.format(it))\r\n", - " plt.plot(X_test, y[:, it], label='ORF estimate T={}'.format(it), color='C{}'.format(it))\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.subplot(1, 2, 2)\r\n", - "plt.title(\"CausalForest\")\r\n", - "y = treatment_effects2\r\n", - "colors = ['b', 'r', 'g']\r\n", - "for it in range(y.shape[1]):\r\n", - " plt.plot(X_test[:, 0], te_test[:, it], '--', label='True effect T={}'.format(it), color=colors[it])\r\n", - " plt.fill_between(X_test[:, 0], te_lower2[:, it], te_upper2[:, it], alpha=0.3, color='C{}'.format(it))\r\n", - " plt.plot(X_test, y[:, it], label='ORF estimate T={}'.format(it), color='C{}'.format(it))\r\n", - "plt.ylabel(\"Treatment Effect\")\r\n", - "plt.xlabel(\"x\")\r\n", - "plt.legend()\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# 4. Example Usage with Real Continuous Treatment Observational Data\n", - "\n", - "We applied our technique to Dominick’s dataset, a popular historical dataset of store-level orange juice prices and sales provided by University of Chicago Booth School of Business. \n", - "\n", - "The dataset is comprised of a large number of covariates $W$, but researchers might only be interested in learning the elasticity of demand as a function of a few variables $x$ such\n", - "as income or education. \n", - "\n", - "We applied the `ContinuousTreatmentOrthoForest` to estimate orange juice price elasticity\n", - "as a function of income, and our results, unveil the natural phenomenon that lower income consumers are more price-sensitive." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 4.1. Data" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 32, - "source": [ - "# A few more imports\r\n", - "import os\r\n", - "import pandas as pd\r\n", - "import urllib.request\r\n", - "from sklearn.preprocessing import StandardScaler" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 33, - "source": [ - "# Import the data\r\n", - "file_name = \"oj_large.csv\"\r\n", - "\r\n", - "if not os.path.isfile(file_name):\r\n", - " print(\"Downloading file (this might take a few seconds)...\")\r\n", - " urllib.request.urlretrieve(\"https://msalicedatapublic.blob.core.windows.net/datasets/OrangeJuice/oj_large.csv\", file_name)\r\n", - "oj_data = pd.read_csv(file_name)\r\n", - "oj_data.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " store brand week logmove feat price AGE60 EDUC ETHNIC \\\n", - "0 2 tropicana 40 9.018695 0 3.87 0.232865 0.248935 0.11428 \n", - "1 2 tropicana 46 8.723231 0 3.87 0.232865 0.248935 0.11428 \n", - "2 2 tropicana 47 8.253228 0 3.87 0.232865 0.248935 0.11428 \n", - "3 2 tropicana 48 8.987197 0 3.87 0.232865 0.248935 0.11428 \n", - "4 2 tropicana 50 9.093357 0 3.87 0.232865 0.248935 0.11428 \n", - "\n", - " INCOME HHLARGE WORKWOM HVAL150 SSTRDIST SSTRVOL CPDIST5 \\\n", - "0 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "1 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "2 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "3 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "4 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "\n", - " CPWVOL5 \n", - "0 0.376927 \n", - "1 0.376927 \n", - "2 0.376927 \n", - "3 0.376927 \n", - "4 0.376927 " - ], - "text/html": [ - "
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storebrandweeklogmovefeatpriceAGE60EDUCETHNICINCOMEHHLARGEWORKWOMHVAL150SSTRDISTSSTRVOLCPDIST5CPWVOL5
02tropicana409.01869503.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
12tropicana468.72323103.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
22tropicana478.25322803.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
32tropicana488.98719703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
42tropicana509.09335703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
\n", - "
" - ] - }, - "metadata": {}, - "execution_count": 33 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 34, - "source": [ - "# Prepare data\r\n", - "Y = oj_data['logmove'].values\r\n", - "T = np.log(oj_data[\"price\"]).values\r\n", - "scaler = StandardScaler()\r\n", - "W1 = scaler.fit_transform(oj_data[[c for c in oj_data.columns if c not in ['price', 'logmove', 'brand', 'week', 'store']]].values)\r\n", - "W2 = pd.get_dummies(oj_data[['brand']]).values\r\n", - "W = np.concatenate([W1, W2], axis=1)\r\n", - "X = oj_data[['INCOME']].values" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 4.2. Train Estimator" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 35, - "source": [ - "# Define some parameters\r\n", - "n_trees = 1000\r\n", - "min_leaf_size = 50\r\n", - "max_depth = 20\r\n", - "subsample_ratio = 0.04" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 36, - "source": [ - "est = DMLOrthoForest(\r\n", - " n_trees=n_trees, min_leaf_size=min_leaf_size, max_depth=max_depth, \r\n", - " subsample_ratio=subsample_ratio,\r\n", - " model_T=Lasso(alpha=0.1),\r\n", - " model_Y=Lasso(alpha=0.1),\r\n", - " model_T_final=WeightedLassoCVWrapper(cv=3), \r\n", - " model_Y_final=WeightedLassoCVWrapper(cv=3)\r\n", - " )" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 37, - "source": [ - "est.fit(Y, T, X=X, W=W)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 20.3s\n", - "[Parallel(n_jobs=-1)]: Done 152 tasks | elapsed: 21.0s\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 22.5s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=-1)]: Done 888 tasks | elapsed: 1.6s\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 2.1s finished\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 37 - } - ], - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "execution_count": 38, - "source": [ - "min_income = 10.0 \r\n", - "max_income = 11.1\r\n", - "delta = (max_income - min_income) / 100\r\n", - "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1, 1)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 39, - "source": [ - "# Calculate marginal treatment effects\r\n", - "treatment_effects = est.const_marginal_effect(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 23.0s\n", - "[Parallel(n_jobs=-1)]: Done 101 out of 101 | elapsed: 35.2s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 40, - "source": [ - "# Calculate default (95%) marginal confidence intervals for the test data\r\n", - "te_upper, te_lower = est.const_marginal_effect_interval(X_test)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 6.1s\n", - "[Parallel(n_jobs=-1)]: Done 101 out of 101 | elapsed: 21.3s finished\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 41, - "source": [ - "est2 = CausalForestDML(model_y=WeightedLassoCVWrapper(cv=3),\r\n", - " model_t=WeightedLassoCVWrapper(cv=3),\r\n", - " n_estimators=n_trees, min_samples_leaf=min_leaf_size, max_depth=max_depth,\r\n", - " max_samples=subsample_ratio/2,\r\n", - " random_state=123)\r\n", - "est2.fit(Y, T, X=X, W=W)\r\n", - "treatment_effects2 = est2.effect(X_test)\r\n", - "te_lower2, te_upper2 = est2.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## 4.3. Performance Visualization" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 42, - "source": [ - "# Plot Orange Juice elasticity as a function of income\r\n", - "plt.figure(figsize=(15, 5))\r\n", - "plt.subplot(1, 2, 1)\r\n", - "plt.plot(X_test.flatten(), treatment_effects, label=\"OJ Elasticity\")\r\n", - "plt.fill_between(X_test.flatten(), te_lower, te_upper, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.xlabel(r'$\\log$(Income)')\r\n", - "plt.ylabel('Orange Juice Elasticity')\r\n", - "plt.legend()\r\n", - "plt.title(\"Orange Juice Elasticity vs Income: ContinuousTreatmentOrthoForest\")\r\n", - "plt.subplot(1, 2, 2)\r\n", - "plt.plot(X_test.flatten(), treatment_effects2, label=\"OJ Elasticity\")\r\n", - "plt.fill_between(X_test.flatten(), te_lower2, te_upper2, label=\"95% BLB CI\", alpha=0.3)\r\n", - "plt.xlabel(r'$\\log$(Income)')\r\n", - "plt.ylabel('Orange Juice Elasticity')\r\n", - "plt.legend()\r\n", - "plt.title(\"Orange Juice Elasticity vs Income: CausalForest\")\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.6.6 64-bit" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "interpreter": { - "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/notebooks/Causal Model Selection with the RScorer.ipynb b/notebooks/Causal Model Selection with the RScorer.ipynb deleted file mode 100644 index b8bded33d..000000000 --- a/notebooks/Causal Model Selection with the RScorer.ipynb +++ /dev/null @@ -1,662 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Selection for Causal Effect Model with the RScorer" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import econml" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "## Ignore warnings\n", - "import warnings\n", - "warnings.filterwarnings('ignore') " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Main imports\n", - "from econml.dml import DML, LinearDML, SparseLinearDML, NonParamDML\n", - "from econml.metalearners import XLearner, TLearner, SLearner, DomainAdaptationLearner\n", - "from econml.dr import DRLearner\n", - "\n", - "import numpy as np\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from sklearn.linear_model import LassoCV\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Example Usage with Single Binary Treatment Synthetic Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1. DGP \n", - "We use the following DGP:\n", - "\n", - "\\begin{align}\n", - "T \\sim & \\text{Bernoulli}\\left(f(W)\\right), &\\; f(W)=\\sigma(\\langle W, \\beta\\rangle + \\eta), \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y = & T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, & \\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim & \\text{Normal}(0,\\, I_{n_w}) & \\\\\n", - "X \\sim & \\text{Uniform}(0,\\, 1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders, $\\beta, \\gamma$ have high sparsity and $\\sigma$ is the sigmoid function.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta(x) = 1\\{x_0 > .5\\}\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Treatment effect function\n", - "def exp_te(x):\n", - " return x[:, 0] > 0.5\n", - "\n", - "np.random.seed(123)\n", - "n = 5000\n", - "support_size = 5\n", - "n_x = 10\n", - "# Outcome support\n", - "support_Y = np.random.choice(range(n_x), size=support_size, replace=False)\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\n", - "epsilon_sample = lambda n:np.random.uniform(-1, 1, size=n)\n", - "# Treatment support\n", - "support_T = support_Y\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n", - "\n", - "# Generate controls, covariates, treatments and outcomes\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\n", - "# Heterogeneous treatment effects\n", - "TE = exp_te(X)\n", - "# Define treatment\n", - "log_odds = np.dot(X[:, support_T], coefs_T) + eta_sample(n)\n", - "T_sigmoid = 1/(1 + np.exp(-log_odds))\n", - "T = np.array([np.random.binomial(1, p) for p in T_sigmoid])\n", - "# Define the outcome\n", - "Y = TE * T + np.dot(X[:, support_Y], coefs_Y) + epsilon_sample(n)\n", - "\n", - "# get testing data\n", - "X_test = np.random.uniform(0, 1, size=(n, n_x))\n", - "X_test[:, 0] = np.linspace(0, 1, n)\n", - "expected_te_test = exp_te(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "reg = lambda: RandomForestRegressor(min_samples_leaf=10)\n", - "clf = lambda: RandomForestClassifier(min_samples_leaf=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_val, T_train, T_val, Y_train, Y_val = train_test_split(X, T, Y, test_size=.4)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "models = [('ldml', LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " linear_first_stages=False, cv=3)),\n", - " ('sldml', SparseLinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", - " linear_first_stages=False, cv=3)),\n", - " ('xlearner', XLearner(models=reg(), cate_models=reg(), propensity_model=clf())),\n", - " ('dalearner', DomainAdaptationLearner(models=reg(), final_models=reg(), propensity_model=clf())),\n", - " ('slearner', SLearner(overall_model=reg())),\n", - " ('tlearner', TLearner(models=reg())),\n", - " ('drlearner', DRLearner(model_propensity=clf(), model_regression=reg(),\n", - " model_final=reg(), cv=3)),\n", - " ('rlearner', NonParamDML(model_y=reg(), model_t=clf(), model_final=reg(),\n", - " discrete_treatment=True, cv=3)),\n", - " ('dml3dlasso', DML(model_y=reg(), model_t=clf(), model_final=LassoCV(), discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=3),\n", - " linear_first_stages=False, cv=3))\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 4 out of 9 | elapsed: 35.0s remaining: 43.8s\n", - "[Parallel(n_jobs=-1)]: Done 9 out of 9 | elapsed: 54.0s finished\n" - ] - } - ], - "source": [ - "from joblib import Parallel, delayed\n", - "\n", - "def fit_model(name, model):\n", - " return name, model.fit(Y_train, T_train, X=X_train)\n", - "\n", - "models = Parallel(n_jobs=-1, verbose=1)(delayed(fit_model)(name, mdl) for name, mdl in models)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.score import RScorer\n", - "\n", - "scorer = RScorer(model_y=reg(), model_t=clf(),\n", - " discrete_treatment=True, cv=3,\n", - " mc_iters=3, mc_agg='median')\n", - "scorer.fit(Y_val, T_val, X=X_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "rscore = [scorer.score(mdl) for _, mdl in models]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "expected_te_val = exp_te(X_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "rootpehe = [np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(X_val).flatten())**2)) for _, mdl in models]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(rootpehe, rscore)\n", - "plt.xlabel('rpehe')\n", - "plt.ylabel('rscore')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(16, 16))\n", - "rows = int(np.ceil(len(models) / 3))\n", - "for it, (name, mdl) in enumerate(models):\n", - " plt.subplot(rows, 3, it + 1)\n", - " plt.title('{}. RScore: {:.3f}, Root-PEHE: {:.3f}'.format(name, rscore[it], rootpehe[it]))\n", - " plt.plot(X_test[:, 0], mdl.effect(X_test), label='{}'.format(name))\n", - " plt.plot(X_test[:, 0], expected_te_test, 'b--', label='True effect')\n", - " plt.ylabel('Treatment Effect')\n", - " plt.xlabel('x')\n", - " plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Getting the Best Model" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "mdl, score = scorer.best_model([mdl for _, mdl in models])\n", - "rootpehe_best = np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(X_val).flatten())**2))\n", - "plt.figure()\n", - "plt.title('RScore: {:.3f}, Root-PEHE: {:.3f}'.format(score, rootpehe_best))\n", - "plt.plot(X_test[:, 0], mdl.effect(X_test), label='best')\n", - "plt.plot(X_test[:, 0], expected_te_test, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Getting an Ensemble based on Scores" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "mdl, score = scorer.ensemble([mdl for _, mdl in models])\n", - "rootpehe_ensemble = np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(X_val).flatten())**2))\n", - "plt.figure()\n", - "plt.title('RScore: {:.3f}, Root-PEHE: {:.3f}'.format(score, rootpehe_ensemble))\n", - "plt.plot(X_test[:, 0], mdl.effect(X_test), label='ensemble')\n", - "plt.plot(X_test[:, 0], expected_te_test, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Semi-Synthetic Data" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "reg = lambda: RandomForestRegressor(min_samples_leaf=10, random_state=123)\n", - "clf = lambda: RandomForestClassifier(min_samples_leaf=10, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.data.dgps import ihdp_surface_B, ihdp_surface_A\n", - "Y, T, X, expected_te = ihdp_surface_B(random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_val, T_train, T_val,\\\n", - "Y_train, Y_val, expected_te_train, expected_te_val = train_test_split(X, T, Y, expected_te, test_size=.3, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "models = [('ldml', LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " linear_first_stages=False, cv=3)),\n", - " ('xlearner', XLearner(models=reg(), cate_models=reg(), propensity_model=clf())),\n", - " ('dalearner', DomainAdaptationLearner(models=reg(), final_models=reg(), propensity_model=clf())),\n", - " ('slearner', SLearner(overall_model=reg())),\n", - " ('tlearner', TLearner(models=reg())),\n", - " ('drlearner', DRLearner(model_propensity=clf(), model_regression=reg(),\n", - " model_final=reg(), cv=3)),\n", - " ('rlearner', NonParamDML(model_y=reg(), model_t=clf(), model_final=reg(),\n", - " discrete_treatment=True, cv=3)),\n", - " ('dml3dlasso', DML(model_y=reg(), model_t=clf(), model_final=LassoCV(), discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=2, interaction_only=True, include_bias=False),\n", - " linear_first_stages=False, cv=3))\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 out of 8 | elapsed: 1.4s remaining: 4.3s\n", - "[Parallel(n_jobs=-1)]: Done 8 out of 8 | elapsed: 10.2s finished\n" - ] - } - ], - "source": [ - "from joblib import Parallel, delayed\n", - "\n", - "def fit_model(name, model):\n", - " print(\"Training: \", name)\n", - " model.fit(Y_train, T_train, X=X_train)\n", - " print(\"Done training: \", name)\n", - " return name, model\n", - "\n", - "models = Parallel(n_jobs=-1, verbose=1)(delayed(fit_model)(name, mdl) for name, mdl in models)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.score import RScorer\n", - "\n", - "scorer = RScorer(model_y=reg(), model_t=clf(),\n", - " discrete_treatment=True, cv=3,\n", - " mc_iters=3, mc_agg='median')\n", - "scorer.fit(Y_val, T_val, X=X_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "rscore = [scorer.score(mdl) for _, mdl in models]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "rootpehe = [np.sqrt(np.mean((expected_te.flatten() - mdl.effect(X).flatten())**2)) for _, mdl in models]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(rootpehe, rscore)\n", - "plt.xlabel('rpehe')\n", - "plt.ylabel('rscore')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7393917952588073" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "best, score = scorer.best_model([mdl for _, mdl in models])\n", - "rootpehe_best = np.sqrt(np.nanmean((expected_te_val.flatten() - best.effect(X_val).flatten())**2))\n", - "rootpehe_best" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7346069084804471" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ensemble, score = scorer.ensemble([mdl for _, mdl in models])\n", - "rootpehe_ensemble = np.sqrt(np.nanmean((expected_te_val.flatten() - ensemble.effect(X_val).flatten())**2))\n", - "rootpehe_ensemble" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Visualization of bias distribution\n", - "plt.figure(figsize=(15, 5))\n", - "plt.violinplot([np.abs(mdl.effect(X).flatten() - expected_te) for _, mdl in models] + \n", - " [np.abs(best.effect(X).flatten() - expected_te)] +\n", - " [np.abs(ensemble.effect(X).flatten() - expected_te)], showmeans=True)\n", - "plt.ylabel(\"Bias distribution\")\n", - "plt.xticks(np.arange(1, len(models) + 3), [name for name, _ in models] + ['best', 'ensemble'])\n", - "plt.show()" - ] - } - ], - "metadata": { - "file_extension": ".py", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Choosing First Stage Models.ipynb b/notebooks/Choosing First Stage Models.ipynb deleted file mode 100644 index 8f61f5d0c..000000000 --- a/notebooks/Choosing First Stage Models.ipynb +++ /dev/null @@ -1,649 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Choosing First Stage Models in EconML Estimators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Choosing first stage models for the various EconML estimators can seem like a daunting task. However, there are several ways to choose suitable first stage models, depending on the problem you are trying to solve. In this notebook, we go through the various types of crossvalidation and hyperparameter tuning used to select the first stage models. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 1;\n", - " var nbb_formatted_code = \"import warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\n\\n# Imports\\nimport numpy as np\\nimport scipy.special\\nfrom econml.dml import LinearDML\\nfrom sklearn.linear_model import Lasso, LassoCV\\nfrom sklearn.ensemble import GradientBoostingRegressor\\nfrom sklearn.model_selection import GridSearchCV\\nfrom sklearn.preprocessing import PolynomialFeatures\\nimport matplotlib.pyplot as plt\\nimport matplotlib\\n\\n%matplotlib inline\\n%load_ext nb_black\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Imports\n", - "import numpy as np\n", - "import scipy.special\n", - "from econml.dml import LinearDML\n", - "from sklearn.linear_model import Lasso, LassoCV\n", - "from sklearn.ensemble import GradientBoostingRegressor\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 2;\n", - " var nbb_formatted_code = \"# Data generation with quadratic treatment effect\\nnp.random.seed(123)\\nn = 2000\\np = 10\\nW = np.random.uniform(size=(n, p))\\nX = np.random.uniform(size=(n, 1))\\ntrue_effect = lambda x: x[:, 0] ** 2\\nT = W[:, 0] + W[:, 1] ** 2 + np.random.uniform(-1, 1, size=n)\\nY = (\\n true_effect(X) * T\\n + W @ np.random.uniform(size=p)\\n + np.random.uniform(-1, 1, size=n)\\n)\\nX_test = np.arange(0, 1, 0.02).reshape(-1, 1)\\ntest_effect = true_effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Data generation with quadratic treatment effect\n", - "np.random.seed(123)\n", - "n = 2000\n", - "p = 10\n", - "W = np.random.uniform(size=(n, p))\n", - "X = np.random.uniform(size=(n, 1))\n", - "true_effect = lambda x: x[:, 0] ** 2\n", - "T = W[:, 0] + W[:, 1] ** 2 + np.random.uniform(-1, 1, size=n)\n", - "Y = (\n", - " true_effect(X) * T\n", - " + W @ np.random.uniform(size=p)\n", - " + np.random.uniform(-1, 1, size=n)\n", - ")\n", - "X_test = np.arange(0, 1, 0.02).reshape(-1, 1)\n", - "test_effect = true_effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Using cross-validated estimators as first stage models\n", - "\n", - "The most straightforward way to choose a first stage model is to not choose one at all and instead let the EconML estimators do the work for you. To achieve this, you can pass in a cross-validated estimator such as `sklearn`'s `LassoCV` or `GridSearchCV` as the first stage models. The EconML estimator will internally run the cross-validation step and select the best models for the first stage. \n", - "\n", - "**Advantages:** \n", - "\n", - "* Requires little to no boilerplate code, you can just pass in a CV estimator along with a hyperparameter grid.\n", - "\n", - "**Disadvantages:**\n", - "\n", - " * The EconML estimator will take longer to run due to an internal cross-validation step for computing the residuals. Further, the CV estimator will be trained on $n_{samples}/\\text{cv}$ data points which might not be suitable for small datasets. \n", - " * Requires special CV estimator to choose among many classes of estimators (e.g. Lasso and GradientBoostingForest, see section 2.2. for workaround)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 3;\n", - " var nbb_formatted_code = \"model_y = LassoCV(max_iter=10000)\\nmodel_t = LassoCV(max_iter=10000)\\nest = LinearDML(\\n model_y=model_y,\\n model_t=model_t,\\n featurizer=PolynomialFeatures(degree=2),\\n fit_cate_intercept=False,\\n)\\nest.fit(Y, T, X=X, W=W)\\nte_pred_lasso = est.effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model_y = LassoCV(max_iter=10000)\n", - "model_t = LassoCV(max_iter=10000)\n", - "est = LinearDML(\n", - " model_y=model_y,\n", - " model_t=model_t,\n", - " featurizer=PolynomialFeatures(degree=2),\n", - " fit_cate_intercept=False,\n", - ")\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred_lasso = est.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 4;\n", - " var nbb_formatted_code = \"first_stage = lambda: GridSearchCV(\\n estimator=GradientBoostingRegressor(),\\n param_grid={\\\"max_depth\\\": [3, 5, None], \\\"n_estimators\\\": (50, 100, 200)},\\n cv=2,\\n n_jobs=-1,\\n)\\nest = LinearDML(\\n model_y=first_stage(),\\n model_t=first_stage(),\\n featurizer=PolynomialFeatures(degree=2),\\n linear_first_stages=False,\\n)\\nest.fit(Y, T, X=X, W=W)\\nte_pred_gbr = est.effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "first_stage = lambda: GridSearchCV(\n", - " estimator=GradientBoostingRegressor(),\n", - " param_grid={\"max_depth\": [3, 5, None], \"n_estimators\": (50, 100, 200)},\n", - " cv=2,\n", - " n_jobs=-1,\n", - ")\n", - "est = LinearDML(\n", - " model_y=first_stage(),\n", - " model_t=first_stage(),\n", - " featurizer=PolynomialFeatures(degree=2),\n", - " linear_first_stages=False,\n", - ")\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred_gbr = est.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 5;\n", - " var nbb_formatted_code = \"plt.plot(X_test, test_effect, \\\"--\\\", label=\\\"Truth\\\")\\nplt.plot(X_test, te_pred_lasso, label=\\\"DML with LassoCV\\\")\\nplt.plot(X_test, te_pred_gbr, label=\\\"DML with GradientBoostingRegressor\\\")\\nplt.legend()\\nplt.xlabel(\\\"X\\\")\\nplt.ylabel(\\\"Effect\\\")\\nplt.show()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(X_test, test_effect, \"--\", label=\"Truth\")\n", - "plt.plot(X_test, te_pred_lasso, label=\"DML with LassoCV\")\n", - "plt.plot(X_test, te_pred_gbr, label=\"DML with GradientBoostingRegressor\")\n", - "plt.legend()\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Effect\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Performing first stage model selection outside of EconML\n", - "\n", - "An alternative to passing in CV models to EconML is to perform model selection outside of the EconML estimators and then pass in the pre-selected models to EconML. This is the preferred method for first stage model selection due to its statistical and computational advantages.\n", - "\n", - "**Advantages:** \n", - "\n", - "* Faster runtimes of the EconML estimators and more flexible selection of first stage models.\n", - "\n", - "* As long as $\\log(\\text{#hyperparameters}) << O(n_{samples})$, this approach maintains statisical validity of the resulting inference results.\n", - "\n", - "**Disadvantages:** Requires more boilerplate code and manual training, scoring and selection of the first stage models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1. Tuning hyperparameters within the same estimator class\n", - "\n", - "Here we select the best estimator within a given class (e.g. Lasso or GradientBoostingForest). This is done by conventional hyperparameter tuning. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 6;\n", - " var nbb_formatted_code = \"XW = np.hstack([X, W])\\nmodel_y_alpha = LassoCV(max_iter=10000).fit(XW, Y).alpha_\\nmodel_t_alpha = LassoCV(max_iter=10000).fit(XW, T).alpha_\\nmodel_y = Lasso(alpha=model_y_alpha, max_iter=10000)\\nmodel_t = Lasso(alpha=model_t_alpha, max_iter=10000)\\nest = LinearDML(\\n model_y=model_y,\\n model_t=model_t,\\n featurizer=PolynomialFeatures(degree=2),\\n fit_cate_intercept=False,\\n)\\nest.fit(Y, T, X=X, W=W)\\nte_pred_lasso = est.effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "XW = np.hstack([X, W])\n", - "model_y_alpha = LassoCV(max_iter=10000).fit(XW, Y).alpha_\n", - "model_t_alpha = LassoCV(max_iter=10000).fit(XW, T).alpha_\n", - "model_y = Lasso(alpha=model_y_alpha, max_iter=10000)\n", - "model_t = Lasso(alpha=model_t_alpha, max_iter=10000)\n", - "est = LinearDML(\n", - " model_y=model_y,\n", - " model_t=model_t,\n", - " featurizer=PolynomialFeatures(degree=2),\n", - " fit_cate_intercept=False,\n", - ")\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred_lasso = est.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 7;\n", - " var nbb_formatted_code = \"model_y = first_stage().fit(XW, Y).best_estimator_\\nmodel_t = first_stage().fit(XW, T).best_estimator_\\nest = LinearDML(\\n model_y=model_y,\\n model_t=model_t,\\n featurizer=PolynomialFeatures(degree=2),\\n linear_first_stages=False,\\n)\\nest.fit(Y, T, X=X, W=W)\\nte_pred_gbr = est.effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model_y = first_stage().fit(XW, Y).best_estimator_\n", - "model_t = first_stage().fit(XW, T).best_estimator_\n", - "est = LinearDML(\n", - " model_y=model_y,\n", - " model_t=model_t,\n", - " featurizer=PolynomialFeatures(degree=2),\n", - " linear_first_stages=False,\n", - ")\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred_gbr = est.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 8;\n", - " var nbb_formatted_code = \"plt.plot(X_test, test_effect, \\\"--\\\", label=\\\"Truth\\\")\\nplt.plot(X_test, te_pred_lasso, label=\\\"DML with LassoCV\\\")\\nplt.plot(X_test, te_pred_gbr, label=\\\"DML with GradientBoostingRegressor\\\")\\nplt.legend()\\nplt.xlabel(\\\"X\\\")\\nplt.ylabel(\\\"Effect\\\")\\nplt.show()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(X_test, test_effect, \"--\", label=\"Truth\")\n", - "plt.plot(X_test, te_pred_lasso, label=\"DML with LassoCV\")\n", - "plt.plot(X_test, te_pred_gbr, label=\"DML with GradientBoostingRegressor\")\n", - "plt.legend()\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Effect\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Choosing amongst different classes of estimators\n", - "\n", - "Here we select among different classes of estimators. This is essentially a two-step process where we first do in-class parameter tuning and then we choose among the optimized models. EconML offers the `GridSearchCVList` utility class to perform this type of model selection." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_formatted_code = \"from econml.sklearn_extensions.model_selection import GridSearchCVList\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from econml.sklearn_extensions.model_selection import GridSearchCVList" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_formatted_code = \"first_stage = lambda: GridSearchCVList(\\n [Lasso(max_iter=10000), GradientBoostingRegressor()],\\n param_grid_list=[\\n {\\\"alpha\\\": [0.001, 0.01, 0.1, 1, 10]},\\n {\\\"max_depth\\\": [3, 5, None], \\\"n_estimators\\\": [50, 100, 200]},\\n ],\\n cv=2,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "first_stage = lambda: GridSearchCVList(\n", - " [Lasso(max_iter=10000), GradientBoostingRegressor()],\n", - " param_grid_list=[\n", - " {\"alpha\": [0.001, 0.01, 0.1, 1, 10]},\n", - " {\"max_depth\": [3, 5, None], \"n_estimators\": [50, 100, 200]},\n", - " ],\n", - " cv=2,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_formatted_code = \"model_y = first_stage().fit(XW, Y).best_estimator_\\nmodel_t = first_stage().fit(XW, T).best_estimator_\\nest = LinearDML(\\n model_y=model_y, model_t=model_t, featurizer=PolynomialFeatures(degree=2)\\n)\\nest.fit(Y, T, X=X, W=W)\\nte_pred = est.effect(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model_y = first_stage().fit(XW, Y).best_estimator_\n", - "model_t = first_stage().fit(XW, T).best_estimator_\n", - "est = LinearDML(\n", - " model_y=model_y, model_t=model_t, featurizer=PolynomialFeatures(degree=2)\n", - ")\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred = est.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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However, rather than learning whether to take action for a specific intervention for all users, people are increasingly interested in understanding the different responses from different users to the two alternatives. Identifying the characteristics of users having the strongest response for the intervention could help make rules to segment the future users into different groups. This can help optimize the policy to use the least resources and get the most profit.\n", - "\n", - "In this case study, we will use a personalized pricing example to explain how the [EconML](https://aka.ms/econml) and [DoWhy](https://github.com/microsoft/dowhy) libraries could fit into this problem and provide robust and reliable causal solutions.\n", - "\n", - "### Summary\n", - "\n", - "1. [Background](#background)\n", - "2. [Data](#data)\n", - "3. [Create Causal Model and Identify Causal Effect with DoWhy](#identify)\n", - "4. [Get Causal Effects with EconML](#estimate)\n", - "5. [Test Estimate Robustness with DoWhy](#robustness)\n", - " 1. [Add Random Common Cause](#random-common-cause)\n", - " 2. [Add Unobserved Common Cause](#unobserved-common-cause)\n", - " 3. [Replace Treatment with a Random (Placebo) Variable](#placebo-variable)\n", - " 4. [Remove a Random Subset of the Data](#subset)\n", - "6. [Understand Treatment Effects with EconML](#interpret)\n", - "7. [Make Policy Decisions with EconML](#policy)\n", - "8. [Conclusions](#conclusion)\n", - "\n", - "\n" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Background \n", - "\n", - "\n", - "\n", - "The global online media market is growing fast over the years. Media companies are always interested in attracting more users into the market and encouraging them to buy more songs or become members. In this example, we'll consider a scenario where one experiment a media company is running is to give small discount (10%, 20% or 0) to their current users based on their income level in order to boost the likelihood of their purchase. The goal is to understand the **heterogeneous price elasticity of demand** for people with different income level, learning which users would respond most strongly to a small discount. Furthermore, their end goal is to make sure that despite decreasing the price for some consumers, the demand is raised enough to boost the overall revenue.\n", - "\n", - "The EconML and DoWhy libraries complement each other in implementing this solution. On one hand, the DoWhy library can help [build a causal model, indentify the causal effect](#identify) and [test causal assumptions](#robustness). On the other hand, EconML’s `DML` based estimators can be used to take the discount variation in existing data, along with a rich set of user features, to [estimate heterogeneous price sensitivities](#estimate) that vary with multiple customer features. Then, the `SingleTreeCateInterpreter` provides a [presentation-ready summary](#interpret) of the key features that explain the biggest differences in responsiveness to a discount, and the `SingleTreePolicyInterpreter` recommends a [policy](#policy) on who should receive a discount in order to increase revenue (not only demand), which could help the company to set an optimal price for those users in the future." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "# Some imports to get us started\r\n", - "import warnings\r\n", - "warnings.simplefilter('ignore')\r\n", - "\r\n", - "# Utilities\r\n", - "import os\r\n", - "import urllib.request\r\n", - "import numpy as np\r\n", - "import pandas as pd\r\n", - "from networkx.drawing.nx_pydot import to_pydot\r\n", - "from IPython.display import Image, display\r\n", - "\r\n", - "# Generic ML imports\r\n", - "from sklearn.preprocessing import PolynomialFeatures\r\n", - "from sklearn.ensemble import GradientBoostingRegressor\r\n", - "\r\n", - "# EconML imports\r\n", - "from econml.dml import LinearDML, CausalForestDML\r\n", - "from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter\r\n", - "\r\n", - "import matplotlib.pyplot as plt\r\n", - "\r\n", - "%matplotlib inline" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Data \n", - "\n", - "\n", - "The dataset* has ~10,000 observations and includes 9 continuous and categorical variables that represent user's characteristics and online behaviour history such as age, log income, previous purchase, previous online time per week, etc. \n", - "\n", - "We define the following variables:\n", - "\n", - "Feature Name|Type|Details \n", - ":--- |:---|:--- \n", - "**account_age** |W| user's account age\n", - "**age** |W|user's age\n", - "**avg_hours** |W| the average hours user was online per week in the past\n", - "**days_visited** |W| the average number of days user visited the website per week in the past\n", - "**friend_count** |W| number of friends user connected in the account \n", - "**has_membership** |W| whether the user had membership\n", - "**is_US** |W| whether the user accesses the website from the US \n", - "**songs_purchased** |W| the average songs user purchased per week in the past\n", - "**income** |X| user's income\n", - "**price** |T| the price user was exposed during the discount season (baseline price * small discount)\n", - "**demand** |Y| songs user purchased during the discount season\n", - "\n", - "**To protect the privacy of the company, we use the simulated data as an example here. The data is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*\n", - "\n", - "\n", - "The treatment and outcome are generated using the following functions:\n", - "$$\n", - "T = \n", - "\\begin{cases}\n", - " 1 & \\text{with } p=0.2, \\\\\n", - " 0.9 & \\text{with }p=0.3, & \\text{if income}<1 \\\\\n", - " 0.8 & \\text{with }p=0.5, \\\\\n", - " \\\\\n", - " 1 & \\text{with }p=0.7, \\\\\n", - " 0.9 & \\text{with }p=0.2, & \\text{if income}\\ge1 \\\\\n", - " 0.8 & \\text{with }p=0.1, \\\\\n", - "\\end{cases}\n", - "$$\n", - "\n", - "\n", - "\\begin{align}\n", - "\\gamma(X) & = -3 - 14 \\cdot \\{\\text{income}<1\\} \\\\\n", - "\\beta(X,W) & = 20 + 0.5 \\cdot \\text{avg_hours} + 5 \\cdot \\{\\text{days_visited}>4\\} \\\\\n", - "Y &= \\gamma(X) \\cdot T + \\beta(X,W)\n", - "\\end{align}\n", - "\n" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "# Import the sample pricing data\r\n", - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/Pricing/pricing_sample.csv\"\r\n", - "train_data = pd.read_csv(file_url)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# Data sample\r\n", - "train_data.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " account_age age avg_hours days_visited friends_count has_membership \\\n", - "0 3 53 1.834234 2 8 1 \n", - "1 5 54 7.171411 7 9 0 \n", - "2 3 33 5.351920 6 9 0 \n", - "3 2 34 6.723551 0 8 0 \n", - "4 4 30 2.448247 5 8 1 \n", - "\n", - " is_US songs_purchased income price demand \n", - "0 1 4.903237 0.960863 1.0 3.917117 \n", - "1 1 3.330161 0.732487 1.0 11.585706 \n", - "2 1 3.036203 1.130937 1.0 24.675960 \n", - "3 1 7.911926 0.929197 1.0 6.361776 \n", - "4 0 7.148967 0.533527 0.8 12.624123 " - ], - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "# Define estimator inputs\r\n", - "train_data[\"log_demand\"] = np.log(train_data[\"demand\"])\r\n", - "train_data[\"log_price\"] = np.log(train_data[\"price\"])\r\n", - "\r\n", - "Y = train_data[\"log_demand\"].values\r\n", - "T = train_data[\"log_price\"].values\r\n", - "X = train_data[[\"income\"]].values # features\r\n", - "confounder_names = [\"account_age\", \"age\", \"avg_hours\", \"days_visited\", \"friends_count\", \"has_membership\", \"is_US\", \"songs_purchased\"]\r\n", - "W = train_data[confounder_names].values" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "# Get test data\r\n", - "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\r\n", - "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Create Causal Model and Identify Causal Effect with DoWhy \n", - "\n", - "We define the causal assumptions with DoWhy. For example, we can include features we believe as confounders and features we think will influence the heterogeneity of the effect. With these assumptions defined, DoWhy can generate a causal graph for us, and use that graph to first identify the causal effect.\n", - "\n" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# initiate an EconML cate estimator\r\n", - "est = LinearDML(model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor(),\r\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "# fit through dowhy\r\n", - "est_dw = est.dowhy.fit(Y, T, X=X, W=W, outcome_names=[\"log_demand\"], treatment_names=[\"log_price\"], feature_names=[\"income\"],\r\n", - " confounder_names=confounder_names, inference=\"statsmodels\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n", - "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n", - "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['log_price'] on outcome ['log_demand']\n", - "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n", - "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n", - "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "# Visualize causal graph\r\n", - "try:\r\n", - " # Try pretty printing the graph. Requires pydot and pygraphviz\r\n", - " display(\r\n", - " Image(to_pydot(est_dw._graph._graph).create_png())\r\n", - " )\r\n", - "except:\r\n", - " # Fall back on default graph view\r\n", - " est_dw.view_model() " - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "image/png": 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" - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "identified_estimand = est_dw.identified_estimand_\r\n", - "print(identified_estimand)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Estimand type: nonparametric-ate\n", - "\n", - "### Estimand : 1\n", - "Estimand name: backdoor1 (Default)\n", - "Estimand expression:\n", - " d \n", - "────────────(Expectation(log_demand|is_US,has_membership,days_visited,age,inco\n", - "d[log_price] \n", - "\n", - " \n", - "me,account_age,avg_hours,songs_purchased,friends_count))\n", - " \n", - "Estimand assumption 1, Unconfoundedness: If U→{log_price} and U→log_demand then P(log_demand|log_price,is_US,has_membership,days_visited,age,income,account_age,avg_hours,songs_purchased,friends_count,U) = P(log_demand|log_price,is_US,has_membership,days_visited,age,income,account_age,avg_hours,songs_purchased,friends_count)\n", - "\n", - "### Estimand : 2\n", - "Estimand name: iv\n", - "No such variable found!\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Get Causal Effects with EconML \n", - "\n", - "Based on the identified causal effect above, we fit the model as follows using EconML:\n", - "\n", - "\n", - "\\begin{align}\n", - "log(Y) & = \\theta(X) \\cdot log(T) + f(X,W) + \\epsilon \\\\\n", - "log(T) & = g(X,W) + \\eta\n", - "\\end{align}\n", - "\n", - "\n", - "where $\\epsilon, \\eta$ are uncorrelated error terms. \n", - "\n", - "\n", - "The models we fit here aren't an exact match for the data generation function above, but if they are a good approximation, they will allow us to create a good discount policy. Although the model is misspecified, we hope to see that our `DML` based estimators can still capture the right trend of $\\theta(X)$ and that the recommended policy beats other baseline policies (such as always giving a discount) on revenue. Because of the mismatch between the data generating process and the model we're fitting, there isn't a single true $\\theta(X)$ (the true elasticity varies with not only X but also T and W), but given how we generate the data above, we can still calculate the range of true $\\theta(X)$ to compare against." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, - "source": [ - "# Define underlying treatment effect function given DGP\r\n", - "def gamma_fn(X):\r\n", - " return -3 - 14 * (X[\"income\"] < 1)\r\n", - "\r\n", - "def beta_fn(X):\r\n", - " return 20 + 0.5 * (X[\"avg_hours\"]) + 5 * (X[\"days_visited\"] > 4)\r\n", - "\r\n", - "def demand_fn(data, T):\r\n", - " Y = gamma_fn(data) * T + beta_fn(data)\r\n", - " return Y\r\n", - "\r\n", - "def true_te(x, n, stats):\r\n", - " if x < 1:\r\n", - " subdata = train_data[train_data[\"income\"] < 1].sample(n=n, replace=True)\r\n", - " else:\r\n", - " subdata = train_data[train_data[\"income\"] >= 1].sample(n=n, replace=True)\r\n", - " te_array = subdata[\"price\"] * gamma_fn(subdata) / (subdata[\"demand\"])\r\n", - " if stats == \"mean\":\r\n", - " return np.mean(te_array)\r\n", - " elif stats == \"median\":\r\n", - " return np.median(te_array)\r\n", - " elif isinstance(stats, int):\r\n", - " return np.percentile(te_array, stats)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, - "source": [ - "# Get the estimate and range of true treatment effect\r\n", - "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\") # estimate\r\n", - "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95) # upper level\r\n", - "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5) # lower level" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Parametric heterogeneity\n", - "First of all, we can try to learn a **linear projection of the treatment effect** assuming a polynomial form of $\\theta(X)$. We use the `LinearDML` estimator. Since we don't have any priors on these models, we use a generic gradient boosting tree estimators to learn the expected price and demand from the data." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "lineardml_estimate = est_dw.estimate_\r\n", - "print(lineardml_estimate)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "*** Causal Estimate ***\n", - "\n", - "## Identified estimand\n", - "Estimand type: nonparametric-ate\n", - "\n", - "## Realized estimand\n", - "b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "Target units: ate\n", - "\n", - "## Estimate\n", - "Mean value: -0.9956103906192235\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 14, - "source": [ - "# Get treatment effect and its confidence interval\r\n", - "te_pred = est_dw.effect(X_test).flatten()\r\n", - "te_pred_interval = est_dw.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 15, - "source": [ - "# Compare the estimate and the truth\r\n", - "plt.figure(figsize=(10, 6))\r\n", - "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\r\n", - "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " te_pred_interval[0].flatten(),\r\n", - " te_pred_interval[1].flatten(),\r\n", - " alpha=0.2,\r\n", - " label=\"95% Confidence Interval\",\r\n", - ")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " truth_te_lower,\r\n", - " truth_te_upper,\r\n", - " alpha=0.2,\r\n", - " label=\"True Elasticity Range\",\r\n", - ")\r\n", - "plt.xlabel(\"Income\")\r\n", - "plt.ylabel(\"Songs Sales Elasticity\")\r\n", - "plt.title(\"Songs Sales Elasticity vs Income\")\r\n", - "plt.legend(loc=\"lower right\")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 15 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "From the plot above, it's clear to see that the true treatment effect is a **nonlinear** function of income, with elasticity around -1.75 when income is smaller than 1 and a small negative value when income is larger than 1. The model fits a quadratic treatment effect, which is not a great fit. But it still captures the overall trend: the elasticity is negative and people are less sensitive to the price change if they have higher income." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, - "source": [ - "# Get the final coefficient and intercept summary\r\n", - "est_dw.summary(feature_names=[\"income\"])" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===============================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------------\n", - "income 2.386 0.081 29.485 0.0 2.227 2.545\n", - "income^2 -0.42 0.028 -15.185 0.0 -0.474 -0.366\n", - " CATE Intercept Results \n", - "=====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------------------\n", - "cate_intercept -3.003 0.049 -60.738 0.0 -3.1 -2.906\n", - "---------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
income 2.386 0.081 29.485 0.0 2.227 2.545
income^2 -0.42 0.028 -15.185 0.0 -0.474 -0.366
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept -3.003 0.049 -60.738 0.0 -3.1 -2.906


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {}, - "execution_count": 16 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "`LinearDML` estimator can also return the summary of the coefficients and intercept for the final model, including point estimates, p-values and confidence intervals. From the table above, we notice that $income$ has positive effect and ${income}^2$ has negative effect, and both of them are statistically significant." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Nonparametric Heterogeneity\n", - "Since we already know the true treatment effect function is nonlinear, let us fit another model using `CausalForestDML`, which assumes a fully **nonparametric estimation of the treatment effect**." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, - "source": [ - "# initiate an EconML cate estimator\r\n", - "est_nonparam = CausalForestDML(model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor())\r\n", - "# fit through dowhy\r\n", - "est_nonparam_dw = est_nonparam.dowhy.fit(Y, T, X=X, W=W, outcome_names=[\"log_demand\"], treatment_names=[\"log_price\"],\r\n", - " feature_names=[\"income\"], confounder_names=confounder_names, inference=\"blb\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n", - "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n", - "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['log_price'] on outcome ['log_demand']\n", - "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n", - "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n", - "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, - "source": [ - "# Get treatment effect and its confidence interval\r\n", - "te_pred = est_nonparam_dw.effect(X_test).flatten()\r\n", - "te_pred_interval = est_nonparam_dw.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, - "source": [ - "# Compare the estimate and the truth\r\n", - "plt.figure(figsize=(10, 6))\r\n", - "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\r\n", - "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " te_pred_interval[0].flatten(),\r\n", - " te_pred_interval[1].flatten(),\r\n", - " alpha=0.2,\r\n", - " label=\"95% Confidence Interval\",\r\n", - ")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " truth_te_lower,\r\n", - " truth_te_upper,\r\n", - " alpha=0.2,\r\n", - " label=\"True Elasticity Range\",\r\n", - ")\r\n", - "plt.xlabel(\"Income\")\r\n", - "plt.ylabel(\"Songs Sales Elasticity\")\r\n", - "plt.title(\"Songs Sales Elasticity vs Income\")\r\n", - "plt.legend(loc=\"lower right\")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "We notice that this model fits much better than the `LinearDML`, the 95% confidence interval correctly covers the true treatment effect estimate and captures the variation when income is around 1. Overall, the model shows that people with low income are much more sensitive to the price changes than higher income people." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Test Estimate Robustness with DoWhy " - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Add Random Common Cause \n", - "\n", - "How robust are our estimates to adding another confounder? We use DoWhy to test this!" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, - "source": [ - "res_random = est_nonparam_dw.refute_estimate(method_name=\"random_common_cause\")\r\n", - "print(res_random)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count+w_random | income\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Refute: Add a Random Common Cause\n", - "Estimated effect:-0.9594204479199662\n", - "New effect:-0.9574777656374094\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Add Unobserved Common Cause \n", - "\n", - "How robust are our estimates to unobserved confounders? Since we assume the model is under unconfoundedness, adding an unobserved confounder might bias the estimates. We use DoWhy to test this!" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 21, - "source": [ - "res_unobserved = est_nonparam_dw.refute_estimate(\r\n", - " method_name=\"add_unobserved_common_cause\",\r\n", - " confounders_effect_on_treatment=\"linear\",\r\n", - " confounders_effect_on_outcome=\"linear\",\r\n", - " effect_strength_on_treatment=0.1,\r\n", - " effect_strength_on_outcome=0.1,\r\n", - ")\r\n", - "print(res_unobserved)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:-0.9594204479199662\n", - "New effect:0.20029340691678463\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Replace Treatment with a Random (Placebo) Variable \n", - "\n", - "What happens our estimates if we replace the treatment variable with noise? Ideally, the average effect would be wildly different than our original estimate. We use DoWhy to investigate!" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 22, - "source": [ - "res_placebo = est_nonparam_dw.refute_estimate(\r\n", - " method_name=\"placebo_treatment_refuter\", placebo_type=\"permute\", \r\n", - " num_simulations=3\r\n", - ")\r\n", - "print(res_placebo)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:dowhy.causal_refuters.placebo_treatment_refuter:Refutation over 3 simulated datasets of permute treatment\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~placebo+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~placebo+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~placebo+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "WARNING:dowhy.causal_refuters.placebo_treatment_refuter:We assume a Normal Distribution as the sample has less than 100 examples.\n", - " Note: The underlying distribution may not be Normal. We assume that it approaches normal with the increase in sample size.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Refute: Use a Placebo Treatment\n", - "Estimated effect:-0.9594204479199662\n", - "New effect:-0.0009044538846515711\n", - "p value:0.4246571154416484\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Remove a Random Subset of the Data \n", - "\n", - "Do we recover similar estimates on subsets of the data? This speaks to the ability of our chosen estimator to generalize well. We use DoWhy to investigate this!" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 23, - "source": [ - "res_subset = est_nonparam_dw.refute_estimate(\r\n", - " method_name=\"data_subset_refuter\", subset_fraction=0.8, \r\n", - " num_simulations=3)\r\n", - "print(res_subset)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 8000.0 each\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n", - "INFO:dowhy.causal_estimator:b: log_demand~log_price+is_US+has_membership+days_visited+age+income+account_age+avg_hours+songs_purchased+friends_count | income\n", - "WARNING:dowhy.causal_refuters.data_subset_refuter:We assume a Normal Distribution as the sample has less than 100 examples.\n", - " Note: The underlying distribution may not be Normal. We assume that it approaches normal with the increase in sample size.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Refute: Use a subset of data\n", - "Estimated effect:-0.9594204479199662\n", - "New effect:-0.9571011772201145\n", - "p value:0.19397906736405435\n", - "\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Understand Treatment Effects with EconML \n", - "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", - "\n", - "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users respond strongly to the discount and the in white users respond lightly to the discount." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 24, - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\r\n", - "intrp.interpret(est_nonparam_dw, X_test)\r\n", - "plt.figure(figsize=(25, 5))\r\n", - "intrp.plot(feature_names=[\"income\"], fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Make Policy Decision with EconML \n", - "We want to make policy decisions to maximum the **revenue** instead of the demand. In this scenario,\n", - "\n", - "\n", - "\\begin{align}\n", - "Rev & = Y \\cdot T \\\\\n", - " & = \\exp^{log(Y)} \\cdot T\\\\\n", - " & = \\exp^{(\\theta(X) \\cdot log(T) + f(X,W) + \\epsilon)} \\cdot T \\\\\n", - " & = \\exp^{(f(X,W) + \\epsilon)} \\cdot T^{(\\theta(X)+1)}\n", - "\\end{align}\n", - "\n", - "\n", - "With the decrease of price, revenue will increase only if $\\theta(X)+1<0$. Thus, we set `sample_treatment_cast=-1` here to learn **what kinds of customers we should give a small discount to maximum the revenue**.\n", - "\n", - "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. In the figure below we can see the model recommends to give discount for people with income less than $0.985$ and give original price for the others." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 25, - "source": [ - "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\r\n", - "intrp.interpret(est_nonparam_dw, X_test, sample_treatment_costs=-1)\r\n", - "plt.figure(figsize=(25, 5))\r\n", - "intrp.plot(feature_names=[\"income\"], treatment_names=[\"Discount\", \"No-Discount\"], fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "Now, let us compare our policy with other baseline policies! Our model says which customers to give a small discount to, and for this experiment, we will set a discount level of 10% for those users. Because the model is misspecified we would not expect good results with large discounts. Here, because we know the ground truth, we can evaluate the value of this policy." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 26, - "source": [ - "# define function to compute revenue\r\n", - "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\r\n", - " policy_price = baseline_T * (1 - discount_level1) * policy + baseline_T * (1 - discount_level2) * (1 - policy)\r\n", - " demand = demand_fn(data, policy_price)\r\n", - " rev = demand * policy_price\r\n", - " return rev" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 27, - "source": [ - "policy_dic = {}\r\n", - "# our policy above\r\n", - "policy = intrp.treat(X)\r\n", - "policy_dic[\"Our Policy\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, policy))\r\n", - "\r\n", - "## previous strategy\r\n", - "policy_dic[\"Previous Strategy\"] = np.mean(train_data[\"price\"] * train_data[\"demand\"])\r\n", - "\r\n", - "## give everyone discount\r\n", - "policy_dic[\"Give Everyone Discount\"] = np.mean(revenue_fn(train_data, 0.1, 0, 1, np.ones(len(X))))\r\n", - "\r\n", - "## don't give discount\r\n", - "policy_dic[\"Give No One Discount\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, np.ones(len(X))))\r\n", - "\r\n", - "## follow our policy, but give -10% discount for the group doesn't recommend to give discount\r\n", - "policy_dic[\"Our Policy + Give Negative Discount for No-Discount Group\"] = np.mean(revenue_fn(train_data, -0.1, 0.1, 1, policy))\r\n", - "\r\n", - "## give everyone -10% discount\r\n", - "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 28, - "source": [ - "# get policy summary table\r\n", - "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\r\n", - "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\r\n", - "res" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Revenue Rank\n", - "Our Policy 14.686241 2.0\n", - "Previous Strategy 14.349342 4.0\n", - "Give Everyone Discount 13.774469 6.0\n", - "Give No One Discount 14.294606 5.0\n", - "Our Policy + Give Negative Discount for No-Disc... 15.564411 1.0\n", - "Give Everyone Negative Discount 14.612670 3.0" - ], - "text/html": [ - "
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Our Policy14.6862412.0
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" - ] - }, - "metadata": {}, - "execution_count": 28 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "**We beat the baseline policies!** Our policy gets the highest revenue except for the one raising the price for the No-Discount group. That means our currently baseline price is low, but the way we segment the user does help increase the revenue!" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Conclusions \n", - "\n", - "In this notebook, we have demonstrated the power of using EconML and DoWhy to:\n", - "\n", - "* Estimate the treatment effect correctly even the model is misspecified\n", - "* Test causal assumptions and investigate the robustness of the resulting estimates\n", - "* Interpret the resulting individual-level treatment effects\n", - "* Make the policy decision beats the previous and baseline policies\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [docummentation](https://econml.azurewebsites.net/). \n", - "\n", - "To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/microsoft/dowhy) or [documentation](https://microsoft.github.io/dowhy/index.html).\n" - ], - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.6.6 64-bit" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "interpreter": { - "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb deleted file mode 100644 index ce96e1e5b..000000000 --- a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb +++ /dev/null @@ -1,873 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Customer Segmentation: Estimate Individualized Responses to Incentives\n", - "\n", - "Nowadays, business decision makers rely on estimating the causal effect of interventions to answer what-if questions about shifts in strategy, such as promoting specific product with discount, adding new features to a website or increasing investment from a sales team. However, rather than learning whether to take action for a specific intervention for all users, people are increasingly interested in understanding the different responses from different users to the two alternatives. Identifying the characteristics of users having the strongest response for the intervention could help make rules to segment the future users into different groups. This can help optimize the policy to use the least resources and get the most profit.\n", - "\n", - "In this case study, we will use a personalized pricing example to explain how the [EconML](https://aka.ms/econml) library could fit into this problem and provide robust and reliable causal solutions.\n", - "\n", - "### Summary\n", - "\n", - "1. [Background](#background)\n", - "2. [Data](#data)\n", - "3. [Get Causal Effects with EconML](#estimate)\n", - "4. [Understand Treatment Effects with EconML](#interpret)\n", - "5. [Make Policy Decisions with EconML](#policy)\n", - "6. [Conclusions](#conclusion)\n" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Background \n", - "\n", - "\n", - "\n", - "The global online media market is growing fast over the years. Media companies are always interested in attracting more users into the market and encouraging them to buy more songs or become members. In this example, we'll consider a scenario where one experiment a media company is running is to give small discount (10%, 20% or 0) to their current users based on their income level in order to boost the likelihood of their purchase. The goal is to understand the **heterogeneous price elasticity of demand** for people with different income level, learning which users would respond most strongly to a small discount. Furthermore, their end goal is to make sure that despite decreasing the price for some consumers, the demand is raised enough to boost the overall revenue.\n", - "\n", - "EconML’s `DML` based estimators can be used to take the discount variation in existing data, along with a rich set of user features, to estimate heterogeneous price sensitivities that vary with multiple customer features. Then, the `SingleTreeCateInterpreter` provides a presentation-ready summary of the key features that explain the biggest differences in responsiveness to a discount, and the `SingleTreePolicyInterpreter` recommends a policy on who should receive a discount in order to increase revenue (not only demand), which could help the company to set an optimal price for those users in the future. " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "# Some imports to get us started\r\n", - "# Utilities\r\n", - "import os\r\n", - "import urllib.request\r\n", - "import numpy as np\r\n", - "import pandas as pd\r\n", - "\r\n", - "# Generic ML imports\r\n", - "from sklearn.preprocessing import PolynomialFeatures\r\n", - "from sklearn.ensemble import GradientBoostingRegressor\r\n", - "\r\n", - "# EconML imports\r\n", - "from econml.dml import LinearDML, CausalForestDML\r\n", - "from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter\r\n", - "\r\n", - "import matplotlib.pyplot as plt\r\n", - "\r\n", - "%matplotlib inline" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Data \n", - "\n", - "\n", - "The dataset* has ~10,000 observations and includes 9 continuous and categorical variables that represent user's characteristics and online behaviour history such as age, log income, previous purchase, previous online time per week, etc. \n", - "\n", - "We define the following variables:\n", - "\n", - "Feature Name|Type|Details \n", - ":--- |:---|:--- \n", - "**account_age** |W| user's account age\n", - "**age** |W|user's age\n", - "**avg_hours** |W| the average hours user was online per week in the past\n", - "**days_visited** |W| the average number of days user visited the website per week in the past\n", - "**friend_count** |W| number of friends user connected in the account \n", - "**has_membership** |W| whether the user had membership\n", - "**is_US** |W| whether the user accesses the website from the US \n", - "**songs_purchased** |W| the average songs user purchased per week in the past\n", - "**income** |X| user's income\n", - "**price** |T| the price user was exposed during the discount season (baseline price * small discount)\n", - "**demand** |Y| songs user purchased during the discount season\n", - "\n", - "**To protect the privacy of the company, we use the simulated data as an example here. The data is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*\n", - "\n", - "\n", - "The treatment and outcome are generated using the following functions:\n", - "$$\n", - "T = \n", - "\\begin{cases}\n", - " 1 & \\text{with } p=0.2, \\\\\n", - " 0.9 & \\text{with }p=0.3, & \\text{if income}<1 \\\\\n", - " 0.8 & \\text{with }p=0.5, \\\\\n", - " \\\\\n", - " 1 & \\text{with }p=0.7, \\\\\n", - " 0.9 & \\text{with }p=0.2, & \\text{if income}\\ge1 \\\\\n", - " 0.8 & \\text{with }p=0.1, \\\\\n", - "\\end{cases}\n", - "$$\n", - "\n", - "\n", - "\\begin{align}\n", - "\\gamma(X) & = -3 - 14 \\cdot \\{\\text{income}<1\\} \\\\\n", - "\\beta(X,W) & = 20 + 0.5 \\cdot \\text{avg_hours} + 5 \\cdot \\{\\text{days_visited}>4\\} \\\\\n", - "Y &= \\gamma(X) \\cdot T + \\beta(X,W)\n", - "\\end{align}\n", - "\n" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "# Import the sample pricing data\n", - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/Pricing/pricing_sample.csv\"\n", - "train_data = pd.read_csv(file_url)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "# Data sample\n", - "train_data.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " account_age age avg_hours days_visited friends_count has_membership \\\n", - "0 3 53 1.834234 2 8 1 \n", - "1 5 54 7.171411 7 9 0 \n", - "2 3 33 5.351920 6 9 0 \n", - "3 2 34 6.723551 0 8 0 \n", - "4 4 30 2.448247 5 8 1 \n", - "\n", - " is_US songs_purchased income price demand \n", - "0 1 4.903237 0.960863 1.0 3.917117 \n", - "1 1 3.330161 0.732487 1.0 11.585706 \n", - "2 1 3.036203 1.130937 1.0 24.675960 \n", - "3 1 7.911926 0.929197 1.0 6.361776 \n", - "4 0 7.148967 0.533527 0.8 12.624123 " - ], - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 3 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# Define estimator inputs\n", - "Y = train_data[\"demand\"] # outcome of interest\n", - "T = train_data[\"price\"] # intervention, or treatment\n", - "X = train_data[[\"income\"]] # features\n", - "W = train_data.drop(columns=[\"demand\", \"price\", \"income\"]) # confounders" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "# Get test data\n", - "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\n", - "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Get Causal Effects with EconML \n", - "To learn the price elasticity on demand as a function of income, we fit the model as follows:\n", - "\n", - "\n", - "\\begin{align}\n", - "log(Y) & = \\theta(X) \\cdot log(T) + f(X,W) + \\epsilon \\\\\n", - "log(T) & = g(X,W) + \\eta\n", - "\\end{align}\n", - "\n", - "\n", - "where $\\epsilon, \\eta$ are uncorrelated error terms. \n", - "\n", - "The models we fit here aren't an exact match for the data generation function above, but if they are a good approximation, they will allow us to create a good discount policy. Although the model is misspecified, we hope to see that our `DML` based estimators can still capture the right trend of $\\theta(X)$ and that the recommended policy beats other baseline policies (such as always giving a discount) on revenue. Because of the mismatch between the data generating process and the model we're fitting, there isn't a single true $\\theta(X)$ (the true elasticity varies with not only X but also T and W), but given how we generate the data above, we can still calculate the range of true $\\theta(X)$ to compare against." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "# Define underlying treatment effect function given DGP\n", - "def gamma_fn(X):\n", - " return -3 - 14 * (X[\"income\"] < 1)\n", - "\n", - "def beta_fn(X):\n", - " return 20 + 0.5 * (X[\"avg_hours\"]) + 5 * (X[\"days_visited\"] > 4)\n", - "\n", - "def demand_fn(data, T):\n", - " Y = gamma_fn(data) * T + beta_fn(data)\n", - " return Y\n", - "\n", - "def true_te(x, n, stats):\n", - " if x < 1:\n", - " subdata = train_data[train_data[\"income\"] < 1].sample(n=n, replace=True)\n", - " else:\n", - " subdata = train_data[train_data[\"income\"] >= 1].sample(n=n, replace=True)\n", - " te_array = subdata[\"price\"] * gamma_fn(subdata) / (subdata[\"demand\"])\n", - " if stats == \"mean\":\n", - " return np.mean(te_array)\n", - " elif stats == \"median\":\n", - " return np.median(te_array)\n", - " elif isinstance(stats, int):\n", - " return np.percentile(te_array, stats)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# Get the estimate and range of true treatment effect\r\n", - "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\") # estimate\r\n", - "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95) # upper level\r\n", - "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5) # lower level" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Parametric heterogeneity\n", - "First of all, we can try to learn a **linear projection of the treatment effect** assuming a polynomial form of $\\theta(X)$. We use the `LinearDML` estimator. Since we don't have any priors on these models, we use a generic gradient boosting tree estimators to learn the expected price and demand from the data." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "# Get log_T and log_Y\r\n", - "log_T = np.log(T)\r\n", - "log_Y = np.log(Y)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "# Train EconML model\r\n", - "est = LinearDML(\r\n", - " model_y=GradientBoostingRegressor(),\r\n", - " model_t=GradientBoostingRegressor(),\r\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False),\r\n", - ")\r\n", - "est.fit(log_Y, log_T, X=X, W=W, inference=\"statsmodels\")\r\n", - "# Get treatment effect and its confidence interval\r\n", - "te_pred = est.effect(X_test)\r\n", - "te_pred_interval = est.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "# Compare the estimate and the truth\r\n", - "plt.figure(figsize=(10, 6))\r\n", - "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\r\n", - "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " te_pred_interval[0],\r\n", - " te_pred_interval[1],\r\n", - " alpha=0.2,\r\n", - " label=\"95% Confidence Interval\",\r\n", - ")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " truth_te_lower,\r\n", - " truth_te_upper,\r\n", - " alpha=0.2,\r\n", - " label=\"True Elasticity Range\",\r\n", - ")\r\n", - "plt.xlabel(\"Income\")\r\n", - "plt.ylabel(\"Songs Sales Elasticity\")\r\n", - "plt.title(\"Songs Sales Elasticity vs Income\")\r\n", - "plt.legend(loc=\"lower right\")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "From the plot above, it's clear to see that the true treatment effect is a **nonlinear** function of income, with elasticity around -1.75 when income is smaller than 1 and a small negative value when income is larger than 1. The model fits a quadratic treatment effect, which is not a great fit. But it still captures the overall trend: the elasticity is negative and people are less sensitive to the price change if they have higher income." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, - "source": [ - "# Get the final coefficient and intercept summary\r\n", - "est.summary()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===============================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------------\n", - "income 2.427 0.075 32.189 0.0 2.28 2.575\n", - "income^2 -0.437 0.026 -16.907 0.0 -0.487 -0.386\n", - " CATE Intercept Results \n", - "=====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------------------\n", - "cate_intercept -3.02 0.046 -65.081 0.0 -3.111 -2.929\n", - "---------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
income 2.427 0.075 32.189 0.0 2.28 2.575
income^2 -0.437 0.026 -16.907 0.0 -0.487 -0.386
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept -3.02 0.046 -65.081 0.0 -3.111 -2.929


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {}, - "execution_count": 11 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "`LinearDML` estimator can also return the summary of the coefficients and intercept for the final model, including point estimates, p-values and confidence intervals. From the table above, we notice that $income$ has positive effect and ${income}^2$ has negative effect, and both of them are statistically significant." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Nonparametric Heterogeneity\n", - "Since we already know the true treatment effect function is nonlinear, let us fit another model using `CausalForestDML`, which assumes a fully **nonparametric estimation of the treatment effect**." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, - "source": [ - "# Train EconML model\r\n", - "est = CausalForestDML(\r\n", - " model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor()\r\n", - ")\r\n", - "est.fit(log_Y, log_T, X=X, W=W, inference=\"blb\")\r\n", - "# Get treatment effect and its confidence interval\r\n", - "te_pred = est.effect(X_test)\r\n", - "te_pred_interval = est.effect_interval(X_test)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "# Compare the estimate and the truth\r\n", - "plt.figure(figsize=(10, 6))\r\n", - "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\r\n", - "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " te_pred_interval[0],\r\n", - " te_pred_interval[1],\r\n", - " alpha=0.2,\r\n", - " label=\"95% Confidence Interval\",\r\n", - ")\r\n", - "plt.fill_between(\r\n", - " X_test.flatten(),\r\n", - " truth_te_lower,\r\n", - " truth_te_upper,\r\n", - " alpha=0.2,\r\n", - " label=\"True Elasticity Range\",\r\n", - ")\r\n", - "plt.xlabel(\"Income\")\r\n", - "plt.ylabel(\"Songs Sales Elasticity\")\r\n", - "plt.title(\"Songs Sales Elasticity vs Income\")\r\n", - "plt.legend(loc=\"lower right\")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "We notice that this model fits much better than the `LinearDML`, the 95% confidence interval correctly covers the true treatment effect estimate and captures the variation when income is around 1. Overall, the model shows that people with low income are much more sensitive to the price changes than higher income people." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Understand Treatment Effects with EconML \n", - "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", - "\n", - "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by any of the EconML estimators. In the figure below we can see in dark red users respond strongly to the discount and the in white users respond lightly to the discount." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 14, - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\r\n", - "intrp.interpret(est, X_test)\r\n", - "plt.figure(figsize=(25, 5))\r\n", - "intrp.plot(feature_names=X.columns, fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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WLrvhfWULCyM4KIhilSrj5+vLF/3ep0ihe1KNK1e6NF999CHde7/Dzj278fX2oXLFClS9r9IV86aV8T9N5fnXujsfZy9ajA6tWzHo88/w8vJizNChvPjGm/T96GOKFCrEmKFD8fLyAuDxhzuyZ98+qjZ4AIDH2rfj8Yc7pmk+EREREbn7GN1kQkRERET+ZYxpUKl8+Ymzpk4JcnWWiy1atpynX36FTStuvIh8t5j+62+80qPn7JOnTt3v6iwiIiIikj6oHYSIiIiIiIiIiIhIBqYisIiIiIiIiIiIiEgGpiKwiIiIiKR7NapUVisIEREREZEbpCKwiIiIiIiIiIiISAamIrCIiIiIiIiIiIhIBqYisIiIiIjcVpOmTaf2g83IVTycohUq0fqxziz7668UY8ZMmkxwvgL89MsMAJauXEmu4uHkKh5OzmIlCM5XwPk4V/Fw9h88SJN27clWpGiK9e26dHXFIYqIiIiIpCserg4gIiIiInePb7/7ni8HDeaLD96nXq2aeHl6MnvBAn6d9SdVKlZ0jhs3eQohwcGMn/ITLZs+SNVKlTi4eSMAe/cfoEz1GuzdsA4Pj5Sns5/27ctjHdrf1mMSEREREUnvNBNYRERERG6Ls+fO8eEX/fnsvb40a/QA/n5+eHp60qh+fd7r2cM5bt+BAyxZsYIvP+zHnIULOXrseJpnGTNpMg1btuatd98jb6nSlKlekxWrVjNm0mTCK1elUPkKjJ08xTk+NjaWt9//gJJVqlH43gq80qMn52NiADhz9iztHu/KPeXuJV+pMrR7vCsHDx92PrdJu/a8/9nnNGzZmtwlStLikUc5eepUmh+TiIiIiMjlqAgsIiIiIrfFX3+vISY2lgcbNrziuPFTfqJc6VI0b9yIooUKMXHatFuSZ9XatYQXK8butWto07wZXV94kTXr1vH3gnkM/fILXu/dh8ioKADe+fgTduzezaLfZvL3gvkcOnKET776GoCkpCQ6tmnNhqWL+WfZEnx8vOneu0+KfU2e/jMDPvuE7X+vIi4+nm+GfndLjklERERE5FJUBBYRERGR2+LU6dNkyRySqoXDf43/aSqtmzcHoHXzZoz/6adr3scb7/Qlb6nSzuX9zz6/7Nh8efLwSNs2uLu706Lpgxw4dIjXX3oRb29v6tasiaenJ7v27MVay4ix4/iwdy9CgoMJyJSJV59/nik//wJA5pAQmjduhJ+vr2Nbt24sWb4ixb4ebtOaQgUL4uvjQ4smTdiwadM1H5OIiIiIyM1ST2ARERERuS0yh4Rw8tRpEhISLlsIXv7XKvbu30+rpk0BRxH4vU8/Y/3GTZQOL3HVfXz8Tp9r7gmcNTTU+bOvj49jXVhYinVR0VGcOHmS6PPnqfVg0wtPtpCYmAhA9Pnz9Hj3PWYvWMDZs+cAiIiMJDExEXd3dwCyXfy6vr5ERUVfU0YRERERkbSgIrCIiIiI3BYVy5fD28uLmX/MonmTxpccM27KFKy11GjcONX6aykC3wpZMmfG18eH5X/OImf27Km2fzv0O7bv2sWcadPIljWM9Rs3UbNxE6y1LkgrIiIiIpKa2kGIiIiIyG0RFBjIW/97hdd692bGH7OIPn+e+Ph4/pw3n979PiQmJpapM2fy5Yf9WPTrr87lk77vMHn6dBISElyS283Njcc6tKfHu+9x/MQJAA4dOcKcBQsAiIyKwtfbh6DAQE6fOcPHX33lkpwiIiIiIpejIrCIiIiI3DYvPPUkH7z9Np998y2Fyt1LeJWqfDdiJE0aNGDmrFn4evvQoVVLsmUNcy6PtGtLQkIis5OLrlfSvU8fchUPdy61mjS96nOuRd8336Rg/vzUf6glecJL8VDHR9i+axcAz3btwvnYGO4pV576D7Wgfq1aabJPEREREZG0YnSZmoiIiIj8yxjToFL58hNnTZ0S5OoscmOm//obr/ToOfvkqVP3uzqLiIiIiKQPmgksIiIiIiIiIiIikoGpCCwiIiIiIiIiIiKSgakILCIiIiIiIiIiIpKBqQgsIiIiIiIiIiIikoGpCCwiIiIiIiIiIiKSgakILCIiIiIiIiIiIpKBqQgsIiIiInesAd8Po0iFiuQJL8Xzr71ObGzsVZ/z8VdfE5yvAPMXL3aue/bV1wgrVIRcxcOdS2JionN79PnzvNqzFwXLlidvydI0atP2lhyPiIiIiMitoCKwiIiIiNyR5ixYwJeDBjF97Bg2LF3Mnv37+LD/l1d8zu69e5k+81eyZ82aattLTz/Fwc0bnYu7u7tz28tvvsXps2dYOedPdq9bw4e9e6X14YiIiIiI3DIqAouIiIjIHWnc5J94tF07ihcpQnBQEK+/8AJjJ0++4nNe69Wbd958A08vz2vez7YdO/lt9hy+/LAfoVmy4O7uTtlSpW42voiIiIjIbaMisIiIiIjckTZv30bJ4sWdj0uWKM6x4yc4dfr0JcdPmzkTLy8vGtStc8nt348aTf7SZanVpCnTf/3NuX71urXkyZWLD/t/ScGy5ana4IEU20VERERE0jsPVwcQEREREbkRUVHRBAYEOB//+3NEZBSZQ0JSjI2IjOTdTz5j6uhRl3ytpzt35oO3exIYEMDchYvo0u0FsoWFUbliBQ4dPsKmrVtp2ugBtqxczsq//6bd410pVrgwRQsXunUHKCIiIiKSRjQTWERERETuCBOnTnPetK31Y53x9/cjIjLCuT0iMhKAgEz+qZ77Uf8vadeyBfny5L7ka5ctVZLMISF4eHjQoG4d2jzUnF9+/x0AHx8fPD096f5CN7y8vKheuTLVq1Rm7qJFt+AoRURERETSnorAIiIiInJHaNviIedN2yaPHE7xwkX4Z9Nm5/YNmzaTNSw01SxggAVLlzLkx+EUqVCRIhUqcvDQYTo/140vBw2+5L6MMVjr+LlksWKX3C4iIiIicqdQEVhERERE7kjtW7Vk1MSJbNm2nTNnz/HZN9/SsXXrS479eewYls36g0W//sqiX38lR7ZsfPnhBzzx2KMATJ/5K5FRUSQlJTF34UImTp1Go/vrA1D1vkrkzpmTLwYMJCEhgeV/rWLxsuXUq1nzth2riIiIiMjNUBFYRERERO5I9WvX4sWnn6Zphw6UqlqNPLlz8dYrLzu3V67fgIlTpwGQOSSEbFnDnIubuxvBQUFk8ne0jhj844+UuK8K+UqVoVe/D/nqow+pUaUyAJ6enoz9fih/zptP3pKleenNtxj0xecUKXTP7T5kEREREZEbYuy/17mJiIiIyF3PGNOgUvnyE2dNnRLk6ixyY6b/+huv9Og5++SpU/e7OouIiIiIpA+aCSwiIiIiIiIiIiKSgakILCIiIiIAGGNyAnUTk5J8XZ1Fbk5iYmJ+Y0wFY4y7q7OIiIiIiOt5uDqAiIiIiLiGMSYzUBuoB9QFsgKbjCHRlbkkTXgDI4AcxpgFwFxgDrDZqh+ciIiIyF1HM4FFRERE7hLGmEzGmAeMMZ8aY1YDe4Ank//3YRxF4PfcjFuc61JmDMH5CrBrzx6X7d/d3X2rtTYcCAcmAWWBX4FDxpgxxpguxpj8LgsoIiIiIreVisAiIiIiGZQxxtsYU9MY09cYswg4ArwFRAIvAaHW2kbW2k+ttX9ba9PFDOBJ06ZT+8Fm5CoeTtEKlWj9WGeW/fVXijFjJk0mOF8BfvplBgBLV64kV/FwchUPJ2exEgTnK+B8nKt4OPsPHqRJu/ZkK1I0xfp2Xbpedz5XF3ivh7X2sLV2rLW2q7U2P1ANmAfcD6wwxuw0xnxnjGlvjMnm0rAiIiIicsuoHYSIiIhIBpHc/7UcF9o7VAW24GgF8B6wxFob5bqEV/ftd9/z5aDBfPHB+9SrVRMvT09mL1jAr7P+pErFis5x4yZPISQ4mPFTfqJl0wepWqkSBzdvBGDv/gOUqV6DvRvW4eGR8nT30759eaxD+9t6TOmJtXYXsAv43hhjcMwUrgt0AAYbYw7gaBsxB1horT3jqqwiIiIiknY0E1hERETkDmUcShhjXjDGTAWO4+gDmxsYBOS11la01r5hrZ2V3gvAZ8+d48Mv+vPZe31p1ugB/P388PT0pFH9+rzXs4dz3L4DB1iyYgVfftiPOQsXcvTY8TTPsmvPHhq3bUfekqUpWLY8jz/fDYBGbdoCUP2BxuQqHu6cifz14CEUrVCJYhXvY9SEiWme51awDv9Ya7+21jYHQoEuwGHgBWC/MWaFMeZDY0x9Y4yfSwOLiIiIyA3TTGARERGRO0hyH9d/Z/rWBWJxzNqcBDxnrT3sunQ356+/1xATG8uDDRtecdz4KT9RrnQpmjduRNFChZg4bRovPPVkmmb54PMvqFujBjPGjyMuLp41G9YD8NukiQTnK8Di33+lYP78AMyev4BvvvuO6WPHkC9PHl564600zXK7WGsTgJXJy0fGGG+gCo7/n/UFyhhjVnFhpvBf1tp4V+UVERERkWunmcAiIiIi6ZgxJpsxpkNy39ZdwAqgPo6+rtWstfmT+72OvZMLwACnTp8mS+aQVC0c/mv8T1Np3bw5AK2bN2P8Tz9d8z7eeKcveUuVdi7vf/b5Jcd5eHiw/+BBDh89io+Pd4pWFP81dcZMHm7ThhJFi+Lv58ebr7x0zXnSM2ttrLV2vrW2t7W2GpAD+AQIAgYAJ4wxM40xrxpjyhlj9NlCREREJJ3SiZqIiIhIOmKMCTbGNDfGfGWM+QfYCrQHNgDNgOzW2g7W2u+T+7tmGJlDQjh56jQJCQmXHbP8r1Xs3b+fVk2bAo4i8KYtW1m/cdM17ePjd/qwb8N65/L2a69ecty7b72FtZZ6zR6icv0GV2zxcOTYUXLlyOF8nCdXrmvKcqex1kZYa3+11r5mrS0H3AP8mPy/44FjxpjJxphnjTFFknsOi4iIiEg6oHYQIiIiIi6U3Ge1Go5L7usBxYHlOC637wL8nXyZfoZXsXw5vL28mPnHLJo3aXzJMeOmTMFaS43GjVOtLx1eIs2yZMsaxtcffwTAsr/+4qGHH6HafZWcLSBSjs3KwcMXJmEfOHQozXKkZ9baE8Dk5AVjTG4utCnp4Vhl5uC4MeFca+1+V2UVERERudtpJrCIiIjIbWSM8TTGVDXG9DLGzAeOAe8A8cDrQKi19n5r7UfW2pV3SwEYICgwkLf+9wqv9e7NjD9mEX3+PPHx8fw5bz69+31ITEwsU2fO5MsP+7Ho11+dyyd932Hy9OlXnEF8vabNnOks7AYHBWEwuLk5Tp2zhoWyZ98+59gWTZowdvJktmzbTvT583z85VdpluNOYq09YK0daa3tDOQF6gDLgCbA38aYbcaYwcaYNsaYUFdmFREREbnbqAgsIiIicgsZY9yS+6W+aoyZCZzA0U81CEd/1RzW2mrJfVfnW2tjXRrYxV546kk+ePttPvvmWwqVu5fwKlX5bsRImjRowMxZs/D19qFDq5ZkyxrmXB5p15aEhERmL1hw1dfv3qcPuYqHO5daTZpectzf69ZTv3kLchUPp0PXJ/nond7kz5sXgDdffpln//caeUuVZuqMGdxfpzbPdulCs44dKV+zNjWqVk3LX8kdyTpst9YOtta2BbIBbYBtQCdgpzFmrTHmc2NME2NMgEsDi4iIiGRwxlrr6gwiIiIiGUZyH9TCOFo71ANqAydxtHeYC8xPvow+XTLGNKhUvvzEWVOnBLk6i9yY6b/+xis9es4+eerU/a7OcjnGGE+gAo5/I3WBSsB6Lvw7WWatjXFdQhEREZGMRT2BRURERG6SMSYPF3qh1gMsjmLWz8DL1toDLownku5Ya+NxtIpYBrxvjPEFquL49/MhEG6MWcGFovDqu6k1ioiIiEhaUxFYRERE5Dol9zOtw4VZjCHAPBzFqveBHVaXW4lcM2vteRwF3zkAxpggoCaOf2PfA3mMMQu5UBT+R//GRERERK6disAiIiIiV5Hcr7QmF2b6FgAW4ShGDQI2WGuTXJdQJGOx1p4FfkleMMZkw9FapR7wAhBgjJnHhaLwLhWFRURERC5PRWARERGR/zDG+ABVuFD0LQ2sxFFsehZYlXw5u4jcBtbao8CE5AVjTD4u/Pt8F4g1xszFURSeZ6095KqsIiIiIumRisAiIiJy1zPGeAD3cqGodB+wEUfRtxewNPlydRFJB6y1e4EfgR+Tb8ZYDMe/31bAN8aYIzj+/c4BFlhrT7ksrIiIiEg6oCKwiIiI3HWSi0YluVD0rQnsw1E0+gpYmHw5uoikc8ltIDYnLwOMMe5AWRz/tp8GRhhjtnGhKLzYWhvlorgiIiIiLuHm6gAiIiIit5pxuMcY86QxZjxwBJgKhANjgKLW2tLW2pettb+oAJz+nT5zhoefepqcxUpQsmo1Jk2bftmxazf8Q6M2bclVPJzC91Zg0A8/ptg+6IcfKV2tBjmLlaBS3frs2LXLue3EyZM88cJL5C1ZmnylyvDkiy/fqkOSNGKtTbTWrrbWfmKtfQAIBV4BooCewFFjzEJjzDvGmBrGGC+XBhYRERG5DTQTWERERDIkY0xOoA6O2YB1AS8cMwH/AN5Ivpxc7lCv9eqNl6cn21b/xYZNm2j3eFdKlihO8SJFUow7eeoUrTt1pl+vt2neuBFx8fEcOnzEuX3kuPGMmjCBiT/+QNHChdizbx/BQUHO7Y8+/QzlSpdhw9LF+Pn6smnrttt2jJI2rLVxOG7kuAjoa4zxB6rjeG/oDxQxxizlwkzhtdbaRFflFREREbkVVAQWERGRDMEYkxmoxYWib3ZgPo6izqfAluTLxuUOFxUdzc+//c6yWb+Tyd+fKhUr8kD9ekz4aSrvvPlGirEDvh9G3Zo1aNviIQC8vb0pWrgQAElJSXz81dcM/OxTihUpDECBfPmcz527cCEHDh9mxoTxuLu7A1CmZPhtOEK5lZJbQfyRvPz3vWMUkN0YM58LRWG9d4iIiMgdT+0gRERE5I5kjPE3xjQ0xnxijFkF7MHR/3Mv8CgQZq1taa0dYK3drCJOxrFj12483N0pVLCgc12p4sXZvC31LN2//l5DSHAwDVq0olD5CrTr0pX9Bw8CcPDwYQ4ePsymbdsIr1yV0tVq0O+L/iQlJTmeu2YthQsW5Nn/vUqBMuWo07Q5i5cvvz0HKbeNtfaUtXaqtbabtbYEjn7hU4DywO/AQWPMaGNMF2NMviu+mIiIiEg6pSKwiIiI3BGMMV7J/TvfMcYsBI7i6O8ZhaPfZ6i19gFr7afJ/UB1OXcGFRUdRUBAphTrAgMDiIxKfa+vQ0cOM27KFD56pzf/LF1Cvjx5eOKFlxzbkttCzFu4iCV//M4v48cx5eefGTVhQvL2w8xduIjqVaqwbdVKuj35BB2ffIqTp07d4iMUV7LWHrLWjrHWdgHyAzWABUAD4C9jzE5jzFBjTHtjTFZXZhURERG5VioCi4iISLpkjHE3xtxrjHndGPM7cAJH/05f4AMgm7W2prW2r7V2UXLfT7kL+Pv5ExERmWLduYhIMvn7pxrr4+PDgw0bUr5MGXx8vHnz5ZdYsXo1Z8+dw8fHB4AXn3mK4KBA8uXJTeeOHZk1b77zuXlz5+ax9u3w9PSkVbOm5M6Rk+WrVt3yY5T0wTrstNZ+Z61tD2QDHgI2Ah2BbcaYDcaYL40xzYwxQVd6PRERERFXURFYRERE0gXjUNwY080Y8xNwHEd/zjzAECC/tbaCtfYNa+0fyX095S5UqGABEhIT2bl7t3PdP5s3p7opHEB4sWIYjPOxMRd+LnxPQby8vFKsu/jn8GLFUjx2DEiLI5A7VXJReIO19itrbTMgFHgCx5UJL+JoHbHCGNPPGFPfGOPr0sAiIiIiyVQEFhEREZcxxuRL7rM5GjgI/AaUw9GPM9xaW8Ja+0Jyv05dgy8A+Pv50fSBhvT7oj9R0dEs/2sVv/05m3YtW6Qa+3CbNsz44w/Wb9xEfHw8n3z9DVUqViAoMBA/X19aPNiErwcPJSIykoOHDzNi3Dga1q0LQNMHGnLm7FnGTp5CYmIi02f+yqHDR6hcocLtPmRJp6y1CdbaFdbaD6219XEUhd8EEoF3gePGmHnGmLeNMVWNMZ4uDSwiIiJ3LRWBRURE5LYxxmRN7qM51BizE1gJ3I+j32Z1oIC1tmtyP87DLg0r6drn77/H+ZgYCpevwBMvvsTn779H8SJFWLpyJbmKhzvH1apWld6vd6fd410oVL4Cu/bs4buvv3Ju//Tdvvj7+1G8UmUatGhF6+bNebRdWwBCgoMZN+w7vh36HXlLlqb/oMGM/X4oWTJnvu3HK3cGa22MtXaetbaXtbYqkAP4DAgBBgInjDEzjTH/M8aUNcbo85iIiIjcFkY3yhaRO5kxxg+4B/BydRYBIBbYpt6s8q/k/pi1gLpAPRytHRYCc5KXjVYnI+mKMaZBpfLlJ86aOkW9Te9Q03/9jVd69Jx98tSp+12dRVIyxoQCdbjwnpgZmIfj/XAusF3vifIvY0xeIMzVOQQACxyx1h5ydRARkRvl4eoAIiI3whjj5efl+Z2nu1vbYD/fOE93d31gSgdiExLczp2Pdff39hoaHRf/qrU2ydWZ5PZK7n9ZjQsFjhLAchzFjSeA1dbaBNclFBFxHWvtCWBS8oIxJg+O98u6wNuANcbMJbkobK094Kqs4jrGmFqBgYHDAwMDs4WFhcW5uWnCuKslJSaaI0ePegUHB+8+e/ZsB2vtOldnEhG5XioCi8gdyc/Lc1jxHNla/a9BTZ8gP18fV+eRC05ERPH+jNlPHjh15jzQw9V55NZK7m9ZkQtF34rAehwFjDeA5dbaGNcllBuQGJ8Q7+oMchMSEhKw1uo/4h3AWrsfGAGMMI67EBbG8V7aFPjCGHMSx5doc4D5yUVkycCMMYX9/Pxmjho50v/BBx/Ezc1NNxdMJxISEhg9Zkyx5557boExppD+PYrInUZfKYrIHccY4x+fmNjm1Ya1fIP8dF6c3oQG+PNqw1r+Fp5Rr8OMxxjjltzH8n/GmJnACWAAjn6XnwE5rLVVk/thzlcB+I60Y/vOXd4JCZqwfada+88/8dHnz29wdQ65PtZhm7V2kLW2DZAVaAfsAB4Hdhpj1hhjPjfGNDbGBLg0sNwSXl5ej3Xt2tWzWbNmaAZw+uLh4UHnTp3M/fff7wG0dHUeEZHrpb8qInInKpzZ3y82UBOA0608mYMxBl/Ux+6OZxyKGGOeMcZMAo4BE3HMVvsRuMdaW85a+6q1dqa1NsKlgeWmWWv3GmO2fz1kaIJak955tu/cyfAxY+NjY2PHujqL3BxrbZK1dq219nNrbRMgFHgeOAN0Bw4bY5YYY94zxtQ2xujEKAMICAioVrtWLd3rIh27v359/4CAgCquziEicr3UDkJE7kRel+oB/NyoKTxTpyqlc+dwRSb5D09398SY+AR9iLkDGWNyc6G9Q13A4LgUeQbwv+TLlyUDi4iMbPL5twMWfD9yVNh9Fe41Pt4+xtWZ5MqSkpLYvW9fwtoNG7ySkpK6qV9lxpPc4mNp8vJe8s1xq+J4r/4YKGGM+bcH+xzgb/Vgv/MYY7x9fFLW88NLlmTAt99Su3Zt14SSFHx9ffFwd9fliCJyx1ERWEQyjIGPtnJ1hHRt7b5DDJ6/lOMRkRTJnpVX7q9B1sArX0m64cBh3pryK+0qluHRqhUAsNYyetlq/ty0nZj4eAqGZeHZOlXJlyXkdhyG3ALJd6uvzYWibxYcd6ufC3yA7lZ/17HW7jfG3BMVHV1h6oyZJQFPV2eSq7I4ZurPsdZGujqM3HrW2mhgdvKCMSYYqInjvXwYkNsYs5ALReGNei+/M2385x9XR0i3Nm3axGOdOrFz504A7r33Xr7+6itKlChxxedt376dUqVL07p1a0aPGuVcf/z4cV56+WVmzpyJm5sbjRs3Zszo0bf0GEREbhcVgUVE0jlrLefOx3Az/Y/Pno+h38zZvFi/BpUK5GH0sr/5+Ld5fN6u2WWfk5CYxNAFyymaPWVHh8Xbd/Pnpu180qYJYQGZGLVsNV/8sYCvOj50w/nk9kruI1mDC0XfgsBiHEWCIcB6a22S6xJKepBcLPoreRGRdM5aewb4OXnBGJMNqIPjvf5FIJMxZi7JRWFr7S4XRRUBHOe4J06cICzsxruH5cyZk8mTJpEvXz6SkpIYMGAA7Tt0YP26K18M8Xy3blSsWDHV+patWlGxQgX27d2Ln58f/6gALyIZiHoCi0iG0eWHCazddxCAMcv/5qNf5/L5HwtoM3Akz42awvajx51jj0dE8sGM2XQcOoYOQ0YzaN5SAJKsZfzKNTz+w3geHjqGz/9YQFRsHABHz0Xw4FfD+HPjNjoPG0+7waP4df1mth05TrfRP9Fu0Cjn6/xr1sZtPDNyMu0Gj6LX1N85du7a26UeOXuOMcv+puuPE/lz07ab+t0s27GHvFlCqF64AF4eHnSsXI7dx0+x/9SZyz5n6t8bKJcvF7lDglOsP3oughI5s5E9KBB3NzfqFCvEviu8jrieMcYnuV/ke8aYJcBhHP0kz+DoLxlqrW1irf0iuf+kCsAiInc4a+1Ra+14a+2T1tp7gPuAP3Fc+bHEGLPbGDPMGPOwMUa9tNKx/AUKMHv2bADeeecd2rZrx2OdOhEQGEh4yZKsWrXKOXb//v20bNWKsKxZyRIaSrdu3QBHy5j333+ffPnzkzVbNh7r1ImzZ88CsGfPHoybGz/++CN58uYlJHNmBg8ezF9//UXpMmUIDglxvs6/fvjhB4qXKEFI5sw0fOAB9u7de83Hs2vXLvr06UOBggX54Ycfbup3ExwcTP78+THGYK3F3d2dHTt2XPE548ePJzg4mHp166ZYP2vWLPbv38+nn35KUFAQnp6elCtX7qbyiYikJyoCi0iGtWLXPmoWKcj4Zx6hUsG8DJ6/DIDEpCT6/vwnWQMy8cPjbRnRtT01ixQEYPam7czZtJ0PWzXm+85tiYmPZ/D8lIXdrUeOM7RTa95oVIfvFq5g4l9reb9lIwY82pLF23ez4cBhAJbv3Mukv9bS88H6jH3qYcJzZeOT3+dfMXNMfAJzNm/nrSm/8sr4nzlz/jxvNK5D6wplnGPaDRp12WXSX5ee9bD31GkKhGZ2Pvbx9CR7cAD7Tp6+5Phj5yL4c9M2OlRKfeJbs0hBDp89x8HTZ0lITGLO5u3cmy/XFY9Lbi9jjIcxppIx5i1jzJ/AcRz9Ij2APkBWa20da+171tqlyX0mRUQkA7PW7rHW/mCtfRjICTQB1gCtgU3GmE3GmG+MMS2MMerxlI79/PPPtG/XjjOnT9OsaVO6vfACAImJiTzYtCn58uZlz+7dHDxwgPbt2wMwfPhwho8Ywby5c9m1cyeRkZHO5/1rxYoVbN+2jQnjx/PyK6/wQb9+zP7zTzb+8w8TJ01iwYIFAEyfPp1+H37IT1OmcPzYMWpUr06Hjh2vmDk6OpqRI0dSp25dKlaqxLFjx5gwfjxvvPGGc0xwSMhll48++uiKrx8cEoKPry8vvPgiPd5667Ljzp07R+8+ffji889TbVu+fDlFixalU+fOZAkNpWKlSs5jFhHJCNQOQkQyrBI5s1GxQB4A6hYrxM9rNgKw7ehxTkVF06VGJdzdHN+FhefKDsD8LTt4qFxJsgcFAtCpWgWeH/0TL99f0/m67e8ri5eHB+Xz5cbH04OaRe8hOLlVQ3jO7Ow6fpJSuXPw24YttKlQhjyZgwFoW7Esk/5ax7FzEZfsxfv17EUs3bGHotmz0rhUMSoXzIenh3uqcROeffS6fxcxcQkE+aa8yYi/lxfn4y9d+xuyYDmPVLkXX6/UbUBD/P0okTMbT4+cjJsxhAX480HLxtedSdKOMcYA4Vxo71ATOIDjkt9vgDbJlwmLiIj82+5lU/LyrTHGHSiH42/IM8BIY8wWLvQTXmKtjXJVXkmpevXqNG7sOPd69NFH+fKrrwBYuXIlhw4d4tNPP8XDw8M5FmDM2LH875VXKFjQMfHhw379KFmqFD9eNBO3V69e+Pj40KBBA/z9/enQvj1Zs2YFoEaNGqxZs4ZatWoxeMgQ3nrzTYoXLw5Ajx496Pfhh+zdu5d8+fKlyvvEE0/w09SpVK5cmWefeYbmzZvj7e2datyZ05eenHAtzpw+TVRUFCNGjLhkhouPsWuXLuTOnTvVtgMHDjBr1iy+/+47fvzhB6ZMmULzhx5ix/bthIaG3nA2EZH0QkVgEcmwQi7qoevt6UFcYiKJSUmciIgia0AmZwH4YqeioskamMn5OGtAJhKTLKejz1/ydb083J0F4H8f/1tYPRYRydCFyxm2aKVzu7VwMjL6kkXgvSdP4+HuTsGwLOQPzXzJAvCN8vHyIDouLsW66Lh4fD1TF3lX7NrH+bh45+zo/xq/Yg3bj55geJf2hPj7Mm/LDnr+9CsDHmmFj6f+rNwuxpiCXCj61gUicXxYHwc8Za096sJ4IiJyB7HWJgKrkpdPjDHeONpH1AV6AeWMMau5UBReaa2Nu9zrya2VPXt2589+fn7ExMSQkJDA/v37yZcvn7MAfLFDhw6lKI7my5ePhIQEjh69cLqQLVs258++vr6pHkdGOu45uXfvXl56+WVefe0153ZrLQcPHrxkAfafjRvx8vKibJkylC5d+pIF4LTg7+/PM888Q1jWrGzetMlZwP7X2rVrmT1nDmv+/vuSz/f19SV//vx07doVgPbt2/NBv34sWbKE5s2b35LMIiK3kz6ti8hdJzTAn+MRkSQmJaUqBGf29+PYuQs3VT8eEYW7myHEz5cTkdc3ASY0kz9tK5ahTrFC1zT+83bN2H/qDH9u2kbPn34jSyY/6hYvTK0iBQm8aBZv64EjLvsabSuUoW2lsqnW58scwpzN252PY+LjOXL2HHmzpL7ac93+Q2w/doJHvhsLQHRsHG5uhj0nT9Or6f3sOn6KGoULEBrgD0D9EkX4bsEK9p86TeFsN35jD7my5H6NdS9afHB8GP8TeMtau8d16UREJCOx1sYCC5OXd4wxmYDqOP7+fAUUTu4x/29ReF1yIVlcKE+ePOzbt4+EhIRUheCcOXOm6Nu7b98+PDw8yJYtGwcOHLju/fTs0YOHH374msYvX7aMzZs38+OPP1K3Xj1y587NY48+SocOHciSJYtzXKaA1JMk/tXjrbfo0aPHVfeVlJREdHQ0Bw8eTFUEnj9/Pnv27CFvcqE6MjKSxMRENm3axN+rV1O6dGl+mTEjxXMcF1uJiGQMKgKLyF2nSLYwQvz9GL7kLx6uXB43Y9hx7CQlcmajVtF7mLxqPffmz0OQrw8jlq6iRuGCl5w1fDWNShVj9PLVFAzLQr4sIUTFxrFm30GqFy5w2efkyRxMl+qV6FS1An/vPcDsTdsZuWQVT9a8jwYliwIw+blO152lyj35+GHxSpZs303FAnkYt2IN+UMzO1tVXOyRKuVpU6G08/HQBcvJ7O9H+/sc/YELZwtlyfY91CxyD0F+PszfsoOEpCRyBAdedy65vOR+jLVxfOCuB+QA5uP4sP05sDn5cl4REZFbylobCfyevGCMycyFv1FjgKzGmPlcKApv1d+o269SpUrkyJGDN998k759++Lu7s7q1aupVq0aHdq35+NPPqFRo0aEhYXRo2dP2rVrd8lZw1fzzNNP06t3b8qWLUt4eDhnz55l1qxZtGnT5rLPKV68OJ988gkffvghv//+O8NHjOCtHj34sn9/58zbyIhrv4Hyv/78809CQ0MpXbo0UVFRvP3224SEhDhbVVzsqaeecvZIBvjss8/Ys3cvgwYOBKBFixa81r07I0aM4JFHHmHq1KkcOHCAatWqXXcuEZH0SEVgEbnruLu50bvp/QxdsIzHf5iAAWoVvYcSObNxf3gRTkVF8+bkmcQlJlI+by6eqV3lhvZTtVB+YuLj+eS3eRyLiMTfy4uyeXNesQh8ccaKBfJSsUBeImJiOR0VfUMZ/hXk50uPJvUYPH8Zn/+xgCLZw3i9UR3n9m/nLAGgW71q+Hl54efl5dzm5eGBj6cHAT6OS/daVyjN2fMxvDh2KjEJCeQMCqRHk3pkukWX9t0tjDH+XJhlVQ8oCizF8WH6MWCNZlmJiEh6YK09BfyUvGCMycmFK1VeBzyMMXNJLgpba/e5KuvdxN3dnV9+/pkXX3qJvPnyYYyhY4cOVKtWjS5dunDo0CFq1qpFTEwMDRs25Juvv76h/bRo0YLIyEjad+jA3r17CQoK4v769a9YBL44Y5MmTWjSpAmnTp3iyJEjN5ThX2fOnOGFF1/kwIED+Pr6UqlSJX7/7Td8fBxX0fXr149Fixfz26+/4ufnh5+fn/O5mTJlwsfHh7Awx5VsmTNn5ufp03nu+ed5vls3ihUrxvRp09QPWEQyDKMvaEXkTmOMqZQ7JGjW4MdaB7k6i1xehyGjoyJiYotba/e7Okt6ZIzx4kK/xXpAeRx3aZ+D40PziuTLcUVERO4YyTcr/W/f+nNc+Ps2z1p7zHUJ07ewsLAlI4YPr/rvjd8k/Rk2bBjdu3efcOr06fZXHy0ikn5oJrCIiMhtkHzn9bJcKPpWBbbj+FDcD1icfLmtiIjIHSu5DcTO5GWoMcYNCMfxt+9hYIgxZh8XisILrLXnXJVXRETkbqEisIiIyC2QPBOqGBeKvrWBozg+9A4FOiZfTisiIpJhWWuTgA3Jy5fGGA/gXhx/G18CxhpjNnKhKLzUWnveVXlFREQyKhWBRURE0ogxJh8Xir51gXgcH2p/ArpZaw+5MJ6IiIjLWWsTgBXJSz9jjA9QBcffzveA0saYlVwoCv+V/BwRERG5CSoCi4iI3CBjTFagDheKvoE4PrDOBfoAu3R3dBERkcuz1sYA85IXjDGBQA0cf1sHAQWMMYu4UBTekDy7WERERK6DisAiIrdJ/1kLCc3kx6NVK7g6itwgY0wQUJMLRd+8wEIcH0q/ATbqg6mIiMiNS+4PPDN5wRgThqOlUj3gWSDEGDOPC0XhHfrC1fU6P/44uXPl4v3333d1FBERuQwVgUVE7kLDFq1g+c59nIk+T5ZMfrSpWIZ6xQs7t+86fpKvZi/iwKkz5M4czEv1a1AwLIsLE7uGMcYXxw3c/i36huO4fHUu8CSwWpeoioiI3DrW2uPApOQFY0xeLlyF0xtINMbMJbkobK096Kqskj6cOnWKZ597jtmzZ2OMoWHDhgwaOJDAwEAA9uzZw+NdurBixQry5s3Lt998Q/369V2cWkTk1nNzdQAREbn9fDw86d3sfiY8+yivNKjJ0AXL2XzoKADxiYm898uf1ClaiPFPP0q94oV575c/iU9MdHHqW88Y42GMqWKM6Zn8gfI4jv6EScBbQJi1tr61tp+1doUKwCIiIreXtXaftXaEtfYxIDdwP44vaJsD640xW4wxA40xrYwxd9832MLbb7/N6dOn2b1rFzt37ODo0aO88847zu0dOnakXNmynDxxgg/ef5/Wbdpw/Phx1wUWEblNVAQWkbvC5FXreOz7cbQZOJKnR0xm7T7H/bm2HjnOqxN+pt2gUTz63VgGzVuaotj54FfDmLluE08On0SbgSMZtWw1h8+c47WJv9Bm0Eg++nWuc/z6A4fpNGwcE1eupeOQ0XT5YQLztuy4bKaVu/bxwpiptBs0itcm/sLu46eumjetPFylPHkyB+NmDEWzZyU8Z3Y2HzkGwIYDh0lMsjQvF46nhzvNyoaDhfX7D6dphvTAGONmjCljjHnFGDMDOImj/2AW4HMgp7W2qrX2bWvtvOS+hSIiIpIOWIet1tpB1trWQBjQAdgJdAV2G2P+NsZ8ZoxpZIzJ5NLAt8jHH39Mrty5CQgMpGixYsyZMweAlStXUqVqVYJDQsiRMyfdunUjLi7O+Tzj5sbAgQMpXKQIAYGB9OrVi507d1K1WjUCg4Jo266dc/z8+fPJnScP/fr1IzQsjPwFCjBmzJjLZpoxYwZly5UjOCSEqtWqsX79+qvmTSu79+zhoebNCQwMJCgoiBYPPcTGTZsA2LZtG3///Td9+/bF19eXVq1aUapUKaZMmZKmGURE0iO1gxCRDO/A6TPMWLeZ/u2bkSWTP0fPRZCU5Ggd524MT9asTOFsoZyIjKLPtD/4df1mmpcr6Xz+3/sO8lWH5hyPjOKlcdPYfOgorzWsRYCPD69N/IWFW3dRr4SjlcLpqPOcjYlhRNcObDlyjHemz6JwtlByhwSnyLTz2Am+mr2I3s3up1DWUOZv2cl7v/zJkMdaczQi4rJ5/2vSX+uYvGr9JbcBTHj20av+fmITEth+9DhNShcHYN/JMxQIzYwxxjkmf2hm9p46zb35c1/19dIz4zioQjhaO9TDcTnpaRztHUYAjydfdioiIiJ3mOS+/GuSl8+NMZ5AJRx/998AJhlj1uL4uz8HWG6tjXVR3DSxdetWvh0wgL9WriRnzpzs2bOHxOQJCu7u7vT/4gsqVKjAgQMHaNS4MQMHDuTll192Pv+PWbNYvWoV+/fvp/y997J02TJGjxpFlixZqFK1KuPGjaNTp04AHDlyhBMnTnDwwAGWL19O4yZNqFChAkWLFk2Rac2aNXTp2pVffv6ZChUqMHr0aJo1b87WLVvYs2fPZfP+10cffcRHH3982WM/c/r0Jdc//9xzDBw0iA4dOgAw5aefaNa0KQAbN26kYMGCBAQEOMeXKV2ajRs3XuU3LSJy51MRWEQyPDfjRnxiIvtPnSHI15dsgRdO+gplC3X+nC0wgEalirHh4JEUReBW95bGz9uLfN5e5MsSQvl8ucge5Ogpdm++3Ow8fpJ6XOin+2iVe/H0cKdU7hxULJCHRdt20+G+ciky/f7PVh4oVYyi2bMCUK9EYSb+tY4tR46RJZP/ZfP+V5uKZWhTscxN/X4GzF1CgbDMlM+XC4Dz8fH4eXumGOPn7cX5uPib2o+rGGNycaHoWxdwx/HBbybwmrV2nwvjiYiIyC1irY0HliQv7xlj/IBqOM4HPgWKG2OWcaEo/Le19o7qf+Xu7k5sbCybNm0iLCyM/PnzO7fde++9zp/z58/P0089xYKFC1MUgV/v3p3AwEDCw8MpWbIkDe6/n4IFCwLQ6IEHWLNmjbMIDPDee+/h7e1NrVq1aNKkCRMnTqRXr14pMg0dOpSnn3qK++67D4BOnTrR78MPWb58Obly5bps3v968803efPNN6/7d1K+fHni4uLIEuo4z69Xrx7PPfccAJGRkQQFBaUYHxQUxMFDaXvVnYhIeqQisIhkeDmDA3myZmXGLl/D3lNzKZ8vF0/UuI8smfw5ePos3y9cwfZjJ4hNSCApKYl7soameH6wn6/zZ293j5SPPdw5HX3e+TiTjzc+nhcKqFkDMnEqKjpVpmPnIpm7eTsz1m5yrktISuRUVDSlcue4bN609sOilew9eZoPWzZ2zvz19fQkOjZlwTc6Lg5fL89LvUS6k9z/rzYXir5hwL93Ef8Q2Ka7iIuIiNx9rLXRwJ/JC8aYYKAWjvOFH4FcxpgFXCgKb0rv5wyFChXiy/79eadvXzZu3EjDhg354vPPyZkzJ9u2beN/r77KqlWriI6OJiEhIUVhGCBbtmzOn319fVM9PnLkiPNxSEgI/v4Xzkfz5c3LoUsUT/fu28eIkSP55ttvnevi4uI4dOgQtWrVumzetNK2XTtKlyrF9GnTsNby2muv8cijjzJxwgQyZcrEuXPnUow/d+4cAZkyZKcQEZEUVAQWkbtC7WL3ULvYPUTHxvHt3CUMX/IXrzaszYC5S7gnaxa6N6qNn5cX09f8w5Lte254P5ExscTExzsLwccjIsmbJSTVuLAAf9pWLEu7SmWvK+9/TVy5lomr1l02z+TnOl1225hlf7Nq734+atUEP28v5/q8WYKZumYD1lpnYXjPiVM8WLrEZV/LlZL7+9XgQtG3ELAYx4e374B1yZeHioiIiDhZa88A05MXjDHZcbSKqgu8DPgl3yh2LjDHWrvbNUmvrGPHjnTs2JFz587x9DPP8MabbzJq5Eiefe45ypUty7ixYwkICODLL79k8k30vj19+jRRUVHOQvC+/fspGR6ealye3Lnp2aMHPXv2vK68/9WvXz/6ffjhZfNERkRccv3atWsZ8O23zpzPPPMM1WvUACA8PJxdu3YRERHhbAmxbv16Oia3jhARych0YzgRyfAOnD7Duv2HiE9IxNPDHS8Pd2dx83x8PH5eXvh6erL/1Bl+Xb/lpvc3ZvnfxCcm8s/BI6zcvZ/qhQukGtOwZFF+27CFrUeOYa0lJj6ev3bvIzou7op5/6ttpbJMfq7TZZfLmfjXOuZv3ckHLRoR6OuTYlup3DlwM4af124kPiGRX9Y5ZiuXzpPjJn4raccY422MqWWMedcYsxg4gqPP31ngBSCLtbaxtfZza+0aFYBFRETkWlhrj1hrx1lrn7TWFgSq4PhSuQ6wzBiz2xjzvTGmY3LB2OW2bt3K3LlziY2NxcfHB18fH9zcHB/zIyIiCAwMJFOmTGzZsoVBgwff9P769OlDXFwcixYtYsaMGbRp0ybVmCeffJLBQ4awYsUKrLVERUUxc+ZMIiIirpj3v3r06EFkRMRll8upWLEi33//PefPn+f8+fMMHTqU0qVLA1CkSBHKli1L3759iYmJYerUqaxfv55WrVrd9O9GRCS900xgEcnw4hOSGL7kLw6cOou7m6F4jmx0q1cNgK41KvHtnCVMWb2egmFZqFGkAOv3H77hfYX4+5LJ25tO34/D28OD5+tWJU/m4FTjCmcL44V61Rk8bxmHzpzDy8OdEjmzEZ4r+xXzppWRS1fh4e7GkyMmOde1rVCGtpXK4unuztsP1ufrOYsZsWQVeTIH8/aD9fF0d0/TDNfKGOMOlOfCTN8qwGYcH8r6AkuSL+8UERERSTPJM3+HAcOSby5bHMf5SBtggDHmEI7zkbnA/OSZxbdVbGwsb771Fps3b8bT05OqVasydMgQAD779FOeevppPvn0U8qVK0e7tm2ZO2/eDe8re/bshISEkDNXLvz8/Bg8aBDFihVLNa5ChQp8N3Qo3V54ge3bt+Pr60v16tWpWbPmFfOmlR+GDePFl14id548WGupVKkSI4YPd24fP24cnR9/nJDMmcmbNy+TJ00iLCwsTTOIiKRHJp23OBIRScUYUyl3SNCswY+1Drr66Ntn/YHDfP7HfEZ01eVkAB2GjI6KiIktbq3d/99txphmQGZr7fBLbDNACS4UfWsBB7nwIWuBKz5kiYiIiPwr+Uvqclw4X6kKbOHC+criS31JbYzJjaPVRE9rbex/t4eFhS0ZMXx41caNG9/C9Ndv/vz5PPLooxzYn+q07q4zbNgwunfvPuHU6dPtXZ1FROR6aCawiIjcVsaYl4DXgQcvWleACx+i6gLROD5ETQCesdYeucRLiYiIiLiEtTYRWJW8fGyM8Qbuw3E+0wsoZ4xZzYWi8EprbRxwHMgP/GGMaWGtPe2K/CIicvdREVhERG6L5BkznwENgYeA4saY53F8WPLlwp24e6bXG6+IiIiIXEryrN6FyUufi25cWxf4GihkjFmC41znQ6AjsMQY09hau8c1qUVE5G6iIrCISBopnTuHWkFchjEmAPgDyAtEALOABTg+CPUHNln1JxIREZEMwlobCfyWvGCMyQLUxlEU7gpkBfYDa40xHay1v7ko6jWpXbu2WkGIiNzhVAQWEZHboTGOm7slAHuA8cAKYLn6+4qIiEhGZ609aYyZBuwGNgB1gGqAP/AmycViERGRW8XN1QFERCTjs9ZOsNb6AEWBz3G0f3gdGOzSYCIiIiK3T2NgJFAJRxuspoCftbaWS1OJiMhdQTOBRUSSzd+yk2lr/uHA6TP4enpSMCwLbSuWITxXdueY2Zu28eWfi3i9UR1qFinIPweP8M70PwCwFmITEvDxvPDWOvCRVnwxawFbjxzH3c0415fKnYM+zRrcvoNLJ6y1B4GpyYuIiIjIXcNa+wvwy+3e79ixY/mif3+2bNlCQEAAZcuWpWePHlSvXt05Zvjw4TzepQvjx42jXbt2LFq0iEaNG/+bm+joaPz9/Z3jN23cyGOdOrF8+XI8PC6c+9apU4dffv759h2ciIhcMxWBRUSAqX9vYPKq9Txftxrl8+XCw82d1XsPsGLXvhRF4DmbdxDg483czdupWaQgJXNlZ/JznQA4ei6Crj9OZMIzj+LulvJCi2dqV6FhyaK39ZhERERE5O72xRdf8NHHHzN40CAaNmyIl5cXv//+O9OnT09RBB4xciSZM2dm5KhRtGvXjho1ahAZEQHAnj17KFCwIGdOn05R8AX49ptveOKJJ27rMYmIyI1REVhE7npRsXGMWf43L99fk6qF8jvX31cwL/cVzOt8fOxcBP8cOMybjevy8W/zOB0VTYi/X5pmmb1pG3/8s5Ui2cKYvWk7mXy8ea1hLQ6eOcvoZX8Tn5hIl+qVqFeiMADxCYmMXLaKxdt2E5+YSOV78vNkrfvw9vAgMiaWz/9YwNajx0lMSqJEjmw8X7caoQGOWRxvTp5JeK7srN9/iD0nTlMsRxivPVCHIF+fND0mEREREbn9zp49S+8+ffjxhx9o2bKlc33Tpk1p2rSp8/HevXtZsGABkyZOpF379hw5coTs2bNf6iVv2PDhw/nu+++pVLEiPw4fTubMmRk9ahTbtm2jV+/exMbG8uknn9Cpk2NyRWxsLD179mTipEnExsbS4qGH6N+/P76+vpw+fZpHH3uMFStWkJCQQLVq1Rg8aBC5c+cGoHadOtSoXp258+axfv16qlSpwtgxYwgNDU3TYxIRudOoJ7CI3PW2HD5GXEIiVe7Jd8VxczbvoFC2UKoVLkCezMHM27rzluTZeuQ4+UMzM/bph6ldtCCf/DaP7UdP8F2nNrzasBaD5y/jfFw8AMOX/MXB0+f4+uEWDO3chpNRUYxbsQaAJGupX6IwPzzejh+7tMPLw53B85em2NeCrTt5+f6ajH6qI/GJSUxdveGWHJOIiIiI3F7Lli0jJiaGFi1aXHHcyJEjqVChAq1ataJ48eKMGTPmluRZsWIFpUuX5uSJE3Ts0IH2HTrw119/sWP7dkaPGkW3F14gMjISgDfffJNt27ezds0admzfzsFDh3j33XcBSEpK4vHOndm7Zw/79u7F19eXbi+8kGJfY8eN48cffuDY0aPExcXx2Wef3ZJjEhG5k6gILCJ3vXMxMQT6+qRq4fBfczfvoHbRewCoVfQe5m7ecc37GLpgGe0GjXIuo5atvuzYbIEB3B9eBHc3N2oUKcjxyCjaVyqHp4c75fPlxsPdjcNnz2Gt5fd/tvJkzfsI8PHGz8uLthXLsGjbLgACfX2oVrgAPp4e+Hl50a5SWTYcPJJiX/VLFCZXSBDeHh7UKFyAXSdOXvMxiYiIiEj6dfLkSUJDQ1O1cPivkaNG0bFDBwA6dujAyFGjrnkfL770EsEhIc6lV69elx1boEABHn/8cdzd3WnXrh379++nd+/eeHt706BBA7y8vNixYwfWWoZ+9x39v/iCzJkzExAQQI+33mL8hAkAZMmShVatWuHn50dAQAA9e/RgwYIFKfb1eOfOFClSBF9fX9q2acPadeuu+ZhERDIqtYMQkbteoI8P587HkJiUdNlC8KZDRzl6LoKaRQoCULtoQUYtXcWu4ycpGJblqvt4qta19wQO8fN1/uyVfNIe4n/xOnfOx8Vz9nwMsQkJvDxuunObxZJkLQAx8Ql8v3A5q/ceIDI2DoDzcfEpjjPE70I7C29PD2LiEq4po4iIiIikb1myZOHEiRMkJCRcthC8ZMkSdu/eTfv27QHo2LEjPd9+m7Vr11K2bNmr7uPrr7665p7A2bJlc/7s6+t7yXWRkZEcP36c6Oho7q1QwbnNWktiYiIA0dHRvPLKK/z+xx+cPn0agIiICBITE3F3dwdI0c7Cz8/POcNYRORupiKwiNz1iuXIiqe7O8t27qV64QKXHDNn83YAXhg7LeX6TdspWOvqReBbIdDXB28PdwY82pLQTP6ptk/9ewMHTp/li3bNCPH3Y9fxk7z4n/wiIiIikjFVqVIFb29vpk2bRuvWrS85ZsSIEVhrKVuuXKr111IEvhVCQ0Px9fVl4z//kCtXrlTbP//8c7Zu28aK5cvJnj07a9eupVz58tjkiRAiInJpagchInc9f28vHq5SnsHzlrJs5x5i4hNISExi1Z79/LB4JXEJCSzetptudavxTceHnMvTtauwYOtOEpOSXJLbzRgalCzK9wtXcCb6PAAnIqNYvfcAAOfj4/H28MDf24uImFjGJvcKFhEREZGMLygoiHf79uX5bt2YNm0a0dHRxMfH89tvv/H6668TExPDxEmTGDpkCGvXrHEu33z9NWPHjSMhwTVXiLm5ufHkE0/wyv/+x7FjxwA4ePAgf/zxB+CY9evr60twcDCnTp2ib3KvYBERuTIVgUVEgJblS9G15n1MWLmOh4eOofMP45mxbhNVCuZj2c69eHm4U7d4YUL8/ZzL/eFFSLSW1XsOXPX1B89fRuuBI5zLS+OmpUnux6tVJEdQIK9O+IU2g0by9k+/cfD0WQCalw0nNiGBjkPH8OqEn7k3X+402aeIiIiI3BleffVVvvj8c97/4APCsmYlT968fDtgAA899BDTpk3D19eXxx57jOzZszuXLl26kJCQwO+//37V1+/2wgtkCghwLhe3cLgZH3/8MYXuuYfKVaoQGBRE/fvvZ+vWrQC8/PLLnD9/ntCwMCpXqcIDDRumyT5FRDI6o0smROROY4yplDskaNbgx1oHuTqLXF6HIaOjImJii1tr97s6i4iIiMidICwsbMmI4cOrNm7c2NVR5DKGDRtG9+7dJ5w6fbq9q7OIiFwPzQQWERERERERERERycBUBBYRERERERERERHJwFQEFhEREREREREREcnAVAQWERERERERERERycBUBBYRERERERERERHJwDxcHUBEJCPZc+IUwxatZOexE5yLiWXGS10vO/afg0d4Z/ofKdbFxCfwVuO6VCtcAGsto5et5s9N24mJj6dgWBaerVOVfFlCAOg/ayELtu7Ew/3C93kTnnkUdzd9vyciIiIiaa9///58/MknREdH07pVKwYNGoS3t3eqcXFxcXR8+GFWrVrF3r17mTd3LrVr13Zu//TTTxkxciR79+4lNDSU5559lu7duzu316lbl3/++YfY2FgKFCjAu3370rx589txiCIiGZaKwCIiacjD3Y0ahQvQpHRx3p8x+4pjS+bKzuTnOjkfrz9wmPd+/pN78+cGYPH23fy5aTuftGlCWEAmRi1bzRd/LOCrjg85n9Pq3lI8WrXCLTkWEREREZF//fHHH3z08cfMnTOHnDlz0qJlS/r06cNHH310yfHVq1Xj5Zdeok3btqm2WWsZOWIEpUuXZufOnTRo2JA8efLQvn17AL768ktKlCiBh4cHK1asoP7997Nt61Zy5MhxS49RRCQj03QxEZE0lDskmAYli5I3ebbu9Zi7aTvVCuXHx9MTgKPnIiiRMxvZgwJxd3OjTrFC7Dt1Jo0Ti4iIiIhc3YiRI+napQvh4eGEhITQ6+23GT5ixCXHenl58fLLL1O9enXc3d1TbX/99dcpX748Hh4eFC1alObNmrFkyRLn9tKlS+Ph4ZizZowhPj6e/fv335oDExG5S6gILCKSDsTEx7Nkxx7qlijsXFezSEEOnz3HwdNnSUhMYs7m7dybL1eK581cv5n2g0fx0rhpLNm++3bHFhEREZG7xMaNGylTpozzcZkyZTh69CgnT568qde11rJo8WLCw8NTrH+waVN8fH25r3JlateuTYUKuvpNRORmqB2EiEg6sHTHHgJ9vSmVK7tzXYi/HyVyZuPpkZNxM4awAH8+aNnYub1p2RJ0rVEJf28v/t57kE9+m+d8joiIiIhIWoqMjCQoKMj5+N+fIyIiyJIlyw2/7jvvvENSUhKPP/54ivUzfvmF+Ph4Zs+ezebNm3HTfS9ERG6K3kVFRG7CvC07aD1wBK0HjqDPtD+u/oTLmLN5B3WLFcYY41w3fsUath89wfAu7ZnarTMd7itHz59+JSY+AYBCWUMJ9PXB3c2NigXyUKvoPSzdsedmD0lEREREhDFjxpApIIBMAQE0atyYTJkyce7cOef2f38OCAi44X18++23jBw1ipkzZlzyBnOenp40atSIWX/+yc8//3zD+xERERWBRURuSp1ihZj8XCcmP9eJvg81vKHXOB4RyYYDh6lbvFCK9buOn6JG4QKEBvjj7uZG/RJFiIyJY/+p05d8HWPAYm8og4iIiIjIxR5++GEiIyKIjIjgt19/JTw8nHXr1jm3r1u3jmzZst3wLOAffviBjz7+mDmzZ5M7d+4rjk1ISGDnzp03tB8REXFQEVhEJA1Za4lLSCAhMRGAuIQE4hMSr/iceZt3UDxHVnIEB6ZYXzhbKEu27+F01HmSrGXu5u0kJCU5xy3evpvzcfEkWcvfew8wf8tO7iuY79YcmIiIiIjc1R579FGG/fADmzZt4syZM7z/wQd07tTpsuNjY2OJiYkBIC4ujpiYGKx1TFgYM2YMPXr25M9ZsyhYsGCK523ZsoXffvuN8+fPEx8fz+jRo1m4cCG1atW6dQcnInIXUE9gEZE0dCwikq4/TnQ+bjlgBFkDMvFDl3YA9Jn2B+E5s9G2UlnnmLlbdtCyfKlUr9W6QmnOno/hxbFTiUlIIGdQID2a1CNT8qVyP6/dyNezF2GBbIEBdKtXjdK5c9zS4xMRERGRu9MDDzzA6927U6duXc6fP0+rVq3o27evc3t4yZL0eOstHn74YQCKFivG3r17AWj4wAMA7N61i/z58/N2r16cPHmSipUqOZ//yMMPM3jwYKy1vNO3L5vatcPd3Z3ChQszYfx4ypcvfxuPVkQk4zH/fhMnInKnMMZUyh0SNGvwY62Drj5aXKXDkNFRETGxxa21+12dRUREROROEBYWtmTE8OFVGzdufPXB4hLDhg2je/fuE06dPt3e1VlERK6H2kGIiIiIiIiIiIiIZGAqAouIiIiIiIiIiIhkYCoCi4iIiIiIiIiIiGRgKgKLiIiIiIiIiIiIZGAqAouI3CUe/GoYh86cc3UMEREREZE0Zdzc2LFjh6tjiIikax6uDiAicieYv2Un09b8w4HTZ/D19KRgWBbaVixDeK7szjGzN23jyz8X8XqjOtQsUpB/Dh7hnel/AGAtxCYk4ON54W134COt+GLWArYeOY67m3GuL5U7B32aNbiufA9+NYyhndqQMzjwJo9URERERO4mY8eO5Yv+/dmyZQsBAQGULVuWnj16UL16deeY4cOH83iXLowfN4527dqxaNEiGjVuDIC1lujoaPz9/Z3jN23cyGOdOrF8+XI8PC6c/9apU4dffv75uvIZNze2b9tGoUKFbvJIRUTubioCi4hcxdS/NzB51Xqer1uN8vly4eHmzuq9B1ixa1+KIvCczTsI8PFm7ubt1CxSkJK5sjP5uU4AHD0XQdcfJzLhmUdxd0t5EcYztavQsGTR23pMIiIiIiJffPEFH338MYMHDaJhw4Z4eXnx+++/M3369BRF4BEjR5I5c2ZGjhpFu3btqFGjBpEREQDs2bOHAgULcub06RQFX4Bvv/mGJ5544rYek4iIXJraQYiIXEFUbBxjlv/Ns3WqUrVQfnw8PfFwd+O+gnnpUqOSc9yxcxH8c+Aw3epW4++9BzkdFZ3mWQ6dOcebk2fSdtBIOg4Zzce/zgXgjUkzAHhhzFRaDxzBwm27AJiyej2PfjeWx74fx6yN29I8j4iIiIjcuc6ePUvvPn0Y8O23tGzZEn9/fzw9PWnatCmffvqpc9zevXtZsGABQ4cM4Y8//uDIkSNpnmXHjh3Uql2boOBgQsPCaNe+PQA1a9UCoEzZsmQKCGDChAkAfPrpp+TImZOcuXLxww8/pHkeEZGMSDOBRUSuYMvhY8QlJFLlnnxXHDdn8w4KZQulWuEC5Fmxhnlbd9KyfKk0zTJ62WrK5c1Fv1aNSUhMZPvREwB83OZBHvxqGN883MLZDmL1ngNMXb2B91s2IntQAN/MXpymWURERETkzrZs2TJiYmJo0aLFFceNHDmSChUq0KpVK4oXL86YMWN49dVX0zRLr969aXD//cybO5e4uDhWrVoFwMIFCzBubqxbu9bZDuL333/ns88/Z87s2RQoUIAnn3oqTbOIiGRUmgksInIF52JiCPT1SdXC4b/mbt5B7aL3AFCr6D3M3XztN6YYumAZ7QaNci6jlq2+5Dh3NzeORURyKjIaLw+PFK0o/mvR9l3UL1GE/KGZ8fH0pGPl8tecR0REREQyvpMnTxIaGpqqhcN/jRw1io4dOgDQsUMHRo4adc37ePGllwgOCXEuvXr1uuQ4T09P9u7dy6FDh/Dx8UnRiuK/Jk6cyOOdO1OyZEn8/f15p0+fa84jInI3UxFYROQKAn18OHc+hsSkpMuO2XToKEfPRVCzSEEAahctyN4Tp9h1/OQ17eOpWlWY8OyjzuXRKvdeclyX6hWx1vK/CT/z3KgpV2zxcCoqmtCACzfnyBqQ6ZqyiIiIiMjdIUuWLJw4cYKEhITLjlmyZAm7d++mfXJ7ho4dO7JhwwbWrl17Tfv4+quvOHP6tHN57733Ljnuk48/xlpLpfvuI7xkySu2eDh0+DB58uRxPs6X78pX7ImIiIPaQYiIXEGxHFnxdHdn2c69VC9c4JJj5mzeDsALY6elXL9pOwVrZUmzLCH+frxYvwYAGw8e4e2pv1MyV3ZnC4iLZfbz40RElPPxsYjINMshIiIiIne+KlWq4O3tzbRp02jduvUlx4wYMQJrLWXLlUu1vmzZsmmWJXv27Hz33XcALF68mPr330/NmjWdLSAuliNHDvbv3+98vG/fvjTLISKSkWkmsIjIFfh7e/FwlfIMnreUZTv3EBOfQEJiEqv27OeHxSuJS0hg8bbddKtbjW86PuRcnq5dhQVbd15xBvH1Wrx9t7Owm8nHGwO4Gce2YD9fjpw95xxbvUgBZm/ezr6Tp4mJT2DcijVplkNERERE7nxBQUG827cvz3frxrRp04iOjiY+Pp7ffvuN119/nZiYGCZOmsTQIUNYu2aNc/nm668ZO27cFWcQX69JkyZx4MABAEJCQjDG4Jbcji1btmzs2rXLObZtmzYMHzGCTZs2ER0dTd93302zHCIiGZlmAouIXEXL8qUI8fNlwsp1fPb7Any9PCmUNQvtKpZl2c69eHm4U7d4YTzcL3yvdn94EcYs/5vVew5QqWDeK77+4PnL+G7hcufjXCFBfNXhoVTjth09ztAFy4mOiyPYz5enalUme5BjFnDH+8rRf9ZC4hIS6VavGjWKFKR52XB6/PQbbsbwSJV7mb91Z9r8QkREREQkQ3j11VfJnj0773/wAQ8/8ggBAQHce++99OzRg2nTpuHr68tjjz2Gp6en8zldunShd58+/P777zz44INXfP1uL7zAy6+84nxctGhRViff9O1if/31Fy+/8gpnz54lW7ZsfPXllxQs6Gi19k6fPnTq3Jnz588zdMgQ2rZty8svvUTdevVwc3Pj/ffeY8yYMWn0GxERybiMtdbVGURErosxpmqukKDZQx5r7evqLHJ57QePio2MjStqrd3r6iwiIiIid4LQ0NA1I0eMKNu4cWNXR5HLGDZsGK++9tqMM2fONHV1FhGR66F2ECJyxzDGeBhjOgOTsNbzauPF5TyBWcaYVsYY4+owIiIiIumVMeYeY8yohISEcFdnkatLSEhoYIwZYIzJ6eosIiLXSkVgEUn3jDFuxpi2wD/A48A7xpioqzxNXMxgzgMfAD2BVcaYRioGi4iIiFxgjMltjBkCrAC2e3h4/O3qTHJ1nh4evwLngX+MMZ8ZY0JdnUlE5GpUBBaRdMs4PAj8DXQHXgRqA+tcmUuukaPcOw+oAHwIfA4sNMbUcmEqEREREZczxmQ1xvTHcV57GihqrX3XGJPo4mhyDYwxsdba14CSgB+w1RjT1xgT5OJoIiKXpSKwiKRLxpi6wFIcxcN3gErW2llWjczvONbaJGvtZKAUMBT4wRgzyxhT0cXRRERERG4rY0yIMeYDYDPgDoRba9+01p50cTS5AdbaQ9ba53BMesgHbDfGvGGM8XdxNBGRVFQEFpF0xRhTxRgzBxgCfAOUtdZOU/H3zmetTbTWjgKKAVOAqcaYqcaYUi6OJiIiInJLGWMyGWN6AtuAbEB5a+2L1tojLo4macBau9ta2xmoBdyLoxj8gjHG27XJREQuUBFYRNIFY0xZY8wvwARgHFDCWjvWWntHXRIXERPL+zNm02rACB7/YTzzt+y85DhrLT8uXkmHIaPpMGQ0Py5eyb917n8OHqH1wBEplge/GsaS7bsBiE9I5LsFy3ns+3G0GzyKgXOXkJCYdNuO8WZZa+OttUOAwsAiYLYxZqwxprCLo4mIiIikKWOMjzHmFWAHEA5Us9Y+Ya3d6+Jo1+XUqVO0aNkS/0yZyJc/P2PHjr3kOGstb7zxBllCQ8kSGsobb7zBxXM5nnrqKYoWK4abuzvDhw9P8dzx48dTtFgxgoKDyZotG506d+bcuXO38rDSnLV2s7W2LdAEaAhsM8Y8YYzxcHE0EREVgUXEtYwxxYwxE4DfgD+BItba76218S6OdkMGzVuKp5sbo5/syGsNazNw3hL2njydatzv/2xl+c59fNOxBd883IKVu/fz24YtAJTMlZ3Jz3VyLr2bNcDX05N78+cGYNKqdWw/doIBj7Rk6GOt2Xn8JBNWrr2dh5kmrLXnrbVfAIWATcAyY8z3xpi8Lo4mIiIiclOMMZ7GmKeB7Thmh95vre1ord3m4mg35Plu3fDy8uLokSOMGT2aZ597jo0bN6YaN3ToUKZNn866tWtZv24dv8yYwZAhQ5zby5Qpw8ABAyhfvnyq51arVo0lixdz9swZdu3cSUJCAm+//fYtPa5bxVq7xlr7INAe6AhsNsZ0MMaoBiMiLqM3IBFxCWNMfmPMjzhmgq4BCllrv7bWxrg42g2LiY9n6Y49PFLlXny9PAnPlZ37CuZl3uYdqcbO2bydFuVLEhrgT2gmf1qUK8mczdsv+bpzN22nWqH8+Hh6ArBy936alQ0nwMebID9fmpYJ589Nd+TnCQCstRHW2vdxzAw+CqwxxnxtjMnu4mgiIiIi18UY426MeRTYArQCWllrH7LWbnBxtBsWFRXFlClTeO/dd8mUKRPVq1enWbNmjBo1KtXYESNH8ur//kfu3LnJlSsXr/7vfwwfMcK5/fnnn6devXr4+Pikem6ePHkIDQ11PnZ3d2fHzktfVXensNYus9bWBZ4BXgLWGmOaG2OMi6OJyF1IRWARua2MMTmNMQOA1cB+oLC19iNrbZSLo920g6fP4u5myBVy4abABUKzsPdU6pnA+06epkBY5gvjwjKz7+SZVONi4uNZsmMPdUuk7JRw8WV1FsuJyCiiYuPS4Chcx1p72lrbEygBJAEbjTEfGmMyX+WpIiIiIi5lHFoB64GngS7W2gbW2pUujnbTtm3bhoeHB0WKFHGuK1O6NBs3bUo1duPGjZQpU+bCuDJlLjlj+HIWL15MUHAwAYGBTJkyhZdfeunmwqcT1to5QBWgJ/AusMIY00DFYBG5nVQEFpHbwhgTaoz5FPgHOA8Us9b2ttaecW2ytHM+PgFfL68U6/y8PTkfl7qzRUx8An4XjfXz8uJ8fHyK4i7A0h17CPT1plSuC5Ni782Xi5/XbuRs9HlOR0Xzy1rHCXhsQkJaHo7LWGuPWmtfBsoCWXD0UuttjAlwaTARERGR/0gu/jYCVgE9gNeAGtbaBa5NlnYiIyMJDAxMsS4oKIiIiIhLjg0KCkoxLjIyMtU57uVUr16ds2fOcGD/frq/9hr58+e/qezpiXX4BSgHfI7jJtjzjDHVXJtMRO4WKgKLyC1ljAkyxvQFtgL+QClr7WvW2uMujpbmfD09OB+XcjZudFw8vl6eqcb6eHoQfVFx+HxcPL6envx3MsCczTuoW6xwivVtK5WlYFgWXhg7jdcmzqDyPfnwcHMj2M83jY/Itay1+621TwGVgSLADmPMa8aYjHWgIiIickcyxtTC0drsc6AfUMFa+5u91ornHSJTpkypbtB27tw5AgJSfz//37Hnzp0jU6ZMqc5xryZXrlw88MADtO/Q4cZCp2PW2iRr7QQcNwocAYwxxvxqjEndKFlEJA2pCCwit4Qxxt8Y8waOm2Hkw3FS/Jy19qCLo90yuUKCSEyyHDx91rlu9/FT5Msckmps3iwh7D5x0vl414lT5M0SnGLM8YhINhw4TN3ihVKs9/bw4Nk6VRn5RAeGPd6WQB9v7smaBbcMejWZtXaHtfYRoB5QFUcx+FljjNdVnioiIiKS5owxFY0xs4AfgCE4JjlMyWjF338VKVKEhIQEtm+/cP+KdevXE16iRKqx4eHhrFu37sK4desIDw+/of0mJCSw8w7vCXwl1toEa+2PQFFgJvCLMWayMSb1L1ZEJA2oCCwiacoY422MeQFH8fdeoJa1trO1dreLo91yPp6eVCmUjzHL/yYmPp5Nh46yYtde6vyniAtQt1ghpv39DyciozgZGcW0vzdQr3jKvr/zNu+geI6s5AhOefndv8+x1rLl8DHGr1zLw5Uz/sQBa+0/1tqWQPPkZYsxppMxxsPF0UREROQuYIwpZYyZBkwFpuBobzbKWpvo2mS3lr+/Py1btqR3nz5ERUWxZMkSpk+fzqOPPppq7GOPPsoX/ftz8OBBDh06xOdffEHnTp2c2+Pi4oiJicFaS3x8PDExMSQlJQEwZswY9u3bB8DevXvp+fbb1KtX7/YcpAtZa2OttQNw3CR5BTDfGDPSGHOPi6OJSAajIrCIpAljjIcxpiuwDWgINLHWtrXWbnZxtNvquTrViEtI4OGhY/n0t3k8V6ca+bKE8M/BI7QeeOHOyI1KFaNSwbx0G/0Tz4/+iQoF8tCoVLEUrzV3y45UhWGAI2fP0X3iDFoPHEH/WQvpVK0C5fPlvuXHll5Ya1dZax8AOgFdgQ3GmLbGGP1NExERkTRnjClsjBkL/AkswHFj4yHW2tQ3fsigBg4YwPnz58maLRsdOnZk0MCBhIeHs2jRIjJd1Bbi6aefpumDD1KqdGlKlipFk8aNefrpp53bGzRsiK+fH0uXLuWpp5/G18+PhQsXArBp0yaqVquGf6ZMVKtenaJFivDd0KG3/VhdxVobba39FCgE7MRx87jBxpi750RfRG4pk0GvWBGR2yS58NYO6AscAN621i69xfuslDskaNbgx1oHXX20uEqHIaOjImJii1tr99+qfSTfUbkB8D7gAfQCZmbUyzFFRETk9jHG5AV647gC6UvgK2tt5K3cZ1hY2JIRw4dXbdy48a3cjdyEYcOG0b179wmnTp9ufyv3Y4zJArwOPIGjd/BH1tpjt3KfIpKxadaUiNyQ5DshNwfWAi8Bz1pr697qArDIxZLvsvwHUAl4F/gIWGKMqePaZCIiInKnMsZkN8Z8DawBjgJFrLUf3OoCsMjFrLUnrbVvACUBT2CzMeZ9Y0ywa5OJyJ1KRWARuS7Jxd/7geU4im49gSrW2jmuTSbpjbX2tt2pLrkYPBUoAwwAvjPGzDbGVL5dGUREROTOZozJbIz5CNgIJALFrbU9rbWnb1cGay26oCl9s9Zi4bb9R7LWHrbWvgCUB3IA240xPYwxmW5XBhHJGFQEFpFrZoypBswDvgW+AMpZa39xwaX30efj4vX+lY4lWUtcQqIHEHU792utTbTWjgGKAxOAicaYX4wxZW5nDhEREblzGGMCjDG9cdzbIgQoa619xRWX3ltrI8+dO3e7dyvX4dy5c8THx5+93fu11u611nYFqgGlgB3GmJeNMT63O4uI3JlURBGRqzLGlDfG/AqMwdGPKtxaO8Fam+SiSFsiYmM5fEYnyOnVpkNH8XB3O2KtPeWK/Vtr46213wFFgNnA78aY8caYoq7IIyIiIumPMcbXGPMasAPHOUNla+3Tt/J+Bldz5syZ36ZPn37eVfuXK7PWMmny5IioqCiXXQVprd1mre2A474YdXDMDH7KGOPpqkwicmdQEVhELssYU8IYMxn4BZgJFLXW/mitTXBlLmttgsF83Gva71Fr9x0kLsGlcSSZtZaY+HhW7NrHhzPnRMclJPZJB5lirLVf4bjL8jpgsTHmB2NM/v+ONcaE3u58IiIicvsZY7yMMc/hKP5WAepaax+x1u5wcTSSkpLG/DJjRmSv3r2T9u3bR1KSq+ZcyMUSExPZsWMH3V54If6fDRtO4Phs5FLW2vXW2uZAa6ANjp7Bjxhj3C8eZ4wJU4FYRACM+g2JyH8ZYwoC7wAPAJ8CA6y10S4N9R/GGONuzJM+Xp7/Ox8XXyjJWverP0tuNTdjEvy8PDdGxcb1S7J2oqvz/FfyjTReBZ4DxgEfWGsPG2P8gL1AHWvtPy6MKCIiIreIMcYDeAToA2wB3rbWrnZtqtSMMfkDAwM/jo+Pb3T+/PkAV+cRAKyfn99ZNze3qZGRkW9Ya4+7OtB/GWNqAx8AwUAvYKq11hpjPgGCrLVPuy6diKQHKgKLiJMxJjfwNo5vk78B+ltr1XNBMhxjTBjwJvA4MAz4GGiKo0Bc0VqryzBFRETuQMmzIHNYaw9ctM4Nx/ntu8AxoKe1dpGLIorcMsYYAzQC3geScHy2Wwr8DbxprZ3swngi4mIqAmdgySc7mQEvV2eRWy4BOGmtTbyRJxtjsuIoiHUCvgc+sdaeTMN8IumSMSYXjpPjtji++AgHjltrn7vMeD8csytELicRx/ux+tSIpCGd10qyq57zGmP6ATmttZ2TC2JNgPeSn/s2MMsFNzUWua2S3zNb4vji4yQwCkdhuKK1du8lxrvjeI9V2wi5kihr7W2/KaKkHRWBMyBjjJuPh8d7Fp4B6+/h5n5DhUG5cyTaJLekJJvo4e42Pjou/oVLzWI0xgQCZS6e9ZB8afxrwLPAWKCftfbw7cotkl4kt0DpAzTG0S//OWvthIu2V87k7fVtTHxCaW8Pj3iMq5JKepeUZE1CUhKe7m7TouPin9HVFCI3xxjj5u/v/x7wjLXW38vLS+e1d7GEhAS3hISERC8vr/Hnzp1Ldc5rjKkHjATKASVxFL0CcFwaP13FX7nbJBd3H8bR6i8eOA9U+PfLamOMT0BAwFfx8fEd3d3dPTw9PdWEWi7JWsv58+c9fX19D0RHR78dFxc31tWZ5PqpCJwB+Xl5fZkrJPDJV+rX8MubOdjVceQ2OXYuku8Wr4xZf+DwoqjYuAYXb0ueBTEO2G+t7W6MyQS8CLwC/Ay8e6lvhEUyOmNMWWABjsvl/p25GQxYwDu5j1pRbw+PVd3qVc9Uo0hBvDw8XBNW7hgnI6P4cfHK2OU796yPjou/T0UHkRsXGBj4ZZEiRZ4cMXy4X4kSJVwdR9KBvXv38sr//hczb968RWfOnHGe8ya3elqDo8XTQ0BeHF/wTrjRq+VE7mTGmN+B2jjOcRMAD8Af+Mha+xZAcHDwLzVq1Kj3zTff+ObPn99VUeUOkZSUxIIFC2jTps35kydPPmytnerqTHJ9VATOYIwx3p7u7qe+e7SVX5ZMfq6OI7dZQmISHb4fGxMTn1Ds4qKuMaYL8DJQE+iMo/XDPKCPtXabK7KKpBfGmAAcJ8UXL8Zauw/A28Pj06Zlw1/qWrOyLo+Ta5ZkLQ8PGRV19nxMFWvtBlfnEbkTGWO8fXx8Tu3Yvt0vZ86cro4j6Uh8fDyZs2SJiYqKKmat3Zs84WE+kAtHy5B3gRFAgr6Ik7tV8ixgTy6c33oC3sBha22iMSaHn5/f7pMnT3r7+Pi4MqrcYX766SeeeOKJv06dOlXJ1Vnk+ri5OoCkuaLBvj4JKgDfnTzc3SiePWsc4HwzNsYUwzEjYgqwAagDNLDWdlABWASstRHW2tPW2uPW2sPW2v3/FoABvD096pbLl1sFYLkubsZQNm8uA1R2dRaRO1jRrFmzJqgALP/l6elJtapVLz7nfRLHZIc8QBZgABADrHdNQhHXs9YmWmtjrLWR1tozyee6By6aGV+xYsWKMSoAy/WqV68ekZGRZVydQ66fisAZj4+3p0eG/7a7/+xFjFr+t6tjpEu+Xp5ugA+AMcYX+ANwB+4H/gf0ANTMXeTa+fh43t0tIL74Yz4jlvzl6hh3HD8vL+f7sYjcEB8/P78Mf157sccff5y3e/VydYw7QqaAgIvfY78DQoGsyUsgjhnBpV2TTuSO4JMpU6a7+k4XnTt35u2333Z1jDuOv78/CQkJmiRzB1IRWOQmLdq+m+6TZ9Jq8Cje+um3VNt3HT/JyxN+odXgUbw84Rd2HT95S/P85694OyAbEAfkwHFzjInAwFsaQkTkFlm4dSevjp9Oi2+G8cakX1Jt33nsBC+O+YkW3wzjxTE/sfPYCRekdLirP1WJSIZx6tQpsmbLRo2aNVOs//777ylcpAgBgYE0atSIQ4cO3dZcjg4QDtbhpLX2rLU2ylobmzwL8q76EkFE7lyvv/46efLkITAwkHz58tGvX78U25966imKFi2Km5sbw4cPd03IZBe//8qdRUVgkZsU4ONNszIlaF2+VKpt8YmJvP/rXGoXLcj4JztSt9g9vP/rXOITb8+9Kay1w621Ptba7Nbae6y1Ra214dbaJrclgIhIGgvw8aZ5uZK0qVg21bb4xETe/XkWdYoXYuKznalXogjv/jzrtr3niohkRG+8+SbFixdPsW7+/Pn0fPttpk2dyskTJ8hfoAAdH37YRQlFRO58Xbt2ZcuWLZw7d46lS5cyZswYfvrpJ+f2MmXKMHDgQMqXL+/ClHKnUxFYrtvk1Rvo9ONE2g4ZzTOjf2Ldfse3/tuOHue1STNpP3QMj/0wgcELlqf44N302+HM3LCFp0ZNoe2Q0Yxe/jeHz56j++SZtB0yho9+n+8cv+HAYTr/OJGJq9bT8ftxdB0xiflbd14208rd+3lx/HTaDx1D98kz2X3i1FXzppWyeXJSo3ABMvun7sO84eAREpMszcuUwNPdnWZlSmAtrD9wOE0ziEjGNemvtTw6dDStvv2RJ4dPYO2+gwBsPXKM/42fRpuBw3l4yCgGzl2c4j23cf+hzFi3kSd+HE+rb39k5NK/OHzmHK+On06rAT/Sb8Zs5/j1+w/x6HdjmLByDe0HjaDzsLHM27z9splW7NpLt9FTaDNwOK+On87ui65wuFzetFIuX25qFr2HzP7+qbZtOHCYpKQkHipXCk8Pd5qXKwnYNH/fF5GM6eNPPiF3njwEBgVRrHhx5syZA8DKlSupWq0aIZkzkzNXLrq98AJxcXHO57m5uzNw0CCKFC1KYFAQvXr3ZufOnVSrXp2g4GDatWvnHD9//nzy5M1Lvw8/JCxrVgoULMiYMWMum2nGjBmUK1+ekMyZqVa9OuvXX2hxe7m8aWnp0qVs3LiRzp06pcw1cyatW7cmPDwcLy8ver39NgsXLmTnzsufr4uIXOzjjz8mV65cBAQEULRo0RTvuVWqVCE4OJgcOXLQrVu3FO+5xhgGDhxI4cKFCQgIoFevXuzcuZOqVasSGBhI27ZtU7zn5s6dm379+hEaGkr+/Pmv+p5btmxZgoODqVq1asr33MvkTStFixbF/6LzWzc3N3bs2OF8/Pzzz1OvXj3Uw1luxt3d5FCu24HTZ5m5YTNftHmQLJn8OHougqTkq6zcjOGJGhUpnDWUE5FRvPPLbH7dsIXmZcOdz1+z7yBftmvK8YgoXp7wC5uPHOfV+2sS4ONN98kzWbhtN/WKFwLgdPR5zp2PYcTjbdly5Dh9f5lNoayh5A4JSpFp5/GTfD13Cb2a1KNQ1izM37qL92fOYfAjLTl6LvKyef9r0ur1TFl9+Ru4j3/q+mc37Dt1hvxZQlJcLpE/Swj7Tp3h3ny5r/v1ROTucuDUGX5Zu5EvO7YgSyZ/jp5N+Z77VK0q/L+9+w6L4mr7OP5dYIGlNwURRRRFxRJ7711jbBEVu0k0scUk75OiscSYPPqYaGIviQoItmg0mtiiWGJvWEBFRbCj9LosC/v+gVlBukFRc3+ua66L3Tk7c8+Y/BjOnjlT1bEMUYnJTNu6k9/Ph9A7210JZ8PvsMC7L48Sk5gQsIXL9yL5v67tsFKZ8vH6rRy8coOOntUAiE1OIT5Vjd97Q7jyIJJpv+6iqmMZXOxsctR042EUP+w5yPReXanq6EDg5et89dtuVg4fQGRCYr71Pm3jySA2nQ7K99g3jR1R7PMVER1DpTL2OTPXwZ5b0bE0rFSh2NsTQvx7XL16lcWLF3PyxAmcnZ0JDw8n4/EXZYaGhsz7/nsaNmzInTt36N6jB0uWLmXShx/qP79n925OnzrF7du3adCwIceOHcPP1xd7e3uat2jBunXrGP64I/XBgwdERUVx5/Ztjh8/To8336Rhw4Z4eHjkqOncuXO88+67/LZtGw0bNmTt2rX06t2bK5cvEx4enm+9T5s9Zw5z5szJ99hjY2LyfD8jI4MJEyeyYvlyLl7MfY2cfaaFv3++dOkSVapUyXdfQggBWZm7aNEiTp06lWfmzp8/X5+53bp1Y8mSJUyaNEn/+d27d3PmzBlu375N/fr1OXr0KGvXrsXe3p5mzZrlmbl3797l+PHjdO/ePd/MHTVqFNu3b9dn7ltvvcXVq1cJDw/Pt96nzZ49m9mzZ+d77HFxcfmumz17NrNmzSI5ORk3Nze8vb2LeEaFKBoZCSyKxUChID0jk9uxcWgzMnG0sqSctRUA7mUdqO5UFkMDAxytLOnqWY1L9yJzfL5v/VqYGRvjam+Lq70t9So442RtibmJMQ1cXQiLyjlf7pCm9VAaGlK7vBMNK7nw1/XwXDXtDg6lq2c1PJzKYGhgQIca7igNDbny4FGB9T6tf4M6rB89ON/lWajTtZib5Jwv3dzEmFRN+jNtTwjx72JgoCA9I4Nb0bFZGWZtSTmbrAyr6liG6uUcszLX2pJutWtw8am7DPo1rIuZiTGuDnZUsrejvqsL5WysMDcxpmGlCtx4lHO+3GHNG6I0MqS2izON3CpyKDQsV007L16mW50aVC+XlfcdPas9ztzIAut9mlfjN9g0dkS+y7NI1WgxN86duSnZRo8IIUReDA0NSUtLIyQkhPT0dCpVqqTvzGzQoAFNmzbFyMiISpUqMXr0aA4dPJjj8//5z3+wsrLC09OTWrVq0alTJypXroy1tTVdu3blXFBQjvZfz5yJiYkJbdq0oUf37mzctClXTStWrmT06NE0adIEQ0NDhg8fjomJCcePHy+w3qd9/tlnxMbE5LvkZ8HChTRu3JgGDRrkWte1Sxc2bdrEhQsXSE1NZebXX6NQKEhJSSnsVAshRLEyd8yYMRx8KnM//fTTHJnbuXNnfeZ269aNc+fO5Wj/9ddfP8ncHj3YuHFjrppWrFjBmDFj/nnmfv45cXFx+S4F+fzzz0lMTOTs2bMMHToUa2vrAtsLUVwyElgUi7ONFe+2bEzAySBuxcRRv6Iz77RojL2FGXdj4/npr1NcfxRFWnoGGbpM3MvY5/i8jUql/9nYyBAbM9Mcr2NTnvyhbmFijKnyyR/zZS3NiUnOfWH5MDGJfVeus+PCZf176ZmZxCSnULu8U771vgimSiNSnurwTdFoUBnLgzSFEIVztrFmdNvm+B8/w63oP6nv6sJ7bZphb2HOndg4Vh48zvXIR6i1WjIzM3EvWybH523Nn87c7K+NiM32x7qFqUmOzHW0siAmOTlXTQ8TktgXEsr2oGD9e9qMDKKTUqjt4pxvvS+CyjiPzE3TYGZs/EL2L4R4dbm7uzN/3jy+mjmT4OBgunTuzPfff4+zszOhoaF88sknnD5zhpSUFLRaba6OUUdHR/3PKpUKx7Jlc7x+8OCB/rWtrW2OW34rurrm+VC1WxER+Pr6smjRIv17Go2Ge/fu0aZNm3zrLQn37t1j4cKFnD51Ks/1HTt2ZMaMGbzdvz8JCQl8+OGHWFpa4uIid7oJIQrn7u7ODz/8wIwZM7IyrEsX5s2bp8/cjz/+mNOnTxc9c596XVDmuuaTuREREfj4+LBw4UL9e9kzN796S5pCoaBevXrs3r2b6dOnM2/evBLfh/j3kk5gUWxtPSrT1qMyKRoNiwOPsebYaT7p1JolB49T2cGO/3Rpg5mxkm1BwRy5EfHM+0lK06BOT9d3SjxKTMbV3jZXOwcLc7wa1mFAw7rFqvdpG09fYNOZC3lsIcumMUOKfQwV7WzYei4YnU6nvz05PDqWHrWrF3tbQoh/p3bV3WlX3Z2UNA0L9x1m1eET/Kdbexbv+4sqZR34rHt7zIyN2Xr2In9dyz1yt6iS1Gk5MvdhYhKu9na52pWxNGdA43oMbJL3Qynyq/dpG06eY8PJc3lsIcuW8aOKfQyu9nZsOXMxR+bejIrhzWzTEgkhRH68vb3x9vYmISGB999/n88//xxfX1/GjhvHG2+8QUBAAJaWlvzw449s3rz5mfcTGxtLcnKyvlPi9q1beNaqlaudS4UKTJ48mSmTJxer3qd9+9//8t///jffehITEnK9d/LkSe7fv6+vKzU1ldTUVMo5O3Pn9m0MDQ0ZN3Ys48aOBSA0NJRvvvmGWnkchxBC5CV7ho0ZM4bPPvsMPz8/PvjgA+rVq8e6deuyMveHH/jll1+eeT9PZ+6tW7fyzKoKFSowZcoUpkyZUqx6n/btt9/y7bff5ltPUlJSkerWarUyz7oocdIJLIrlTmw80ckp1CxXFqWhIcZGhmRmZs0BlqpJx8xYiUppxO3YOHZeuoqV6p9NWu5/IohhzeoTGhnFqfA7eDd5I1ebLjWr8e3O/bzh4kw1RwfStFou3n2Ap7MTMckp+db7NK+GdfBqWKfYNWZkZpKRmUmmLhMdoNFqMVAYYGRoQO3yThgYKNh+4TLdanmwOzgUgDou5Yq9HyHEv8+dmDiikpLxdHZCaZRf5iq5HRPH7xdCsP6Hmbv22BmGt2jE1fsPORl2iyHNGuZq06VWDWZt38MbFV3wcCpDmlbLhdv3qeXiRExSSr71Pm1A43oMaFyv2DX+nblZuavLmbku5TBQKNh27hI96tRk16WsO0TqVij5URpCiNfL1atXuXv3Li1atMDU1BRTlYrMx/M9JiYmYmVlhYWFBVeuXGHZsmWUKVOmkC0WbPqMGXz7zTecOHGCHb//zowZM3K1ee/dd+nbrx8dO3SgcePGpKSkcODAAVq3bs29e/fyrfdpk7/4gslffFGs+rp168bNsCdfLG7YuJF169ax9ddfMTQ0RK1Wc/36dTw9Pbl9+zZj3n+fiRMnYmube8CGEEI87enMValU+jl2n87cpUuX/vPMnT6db7/9Nitzd+zgq6++ytXmvffeo0+fPnTs2LHQzM1e79MmT57M5Hy+vMtPZmYmK1euxMvLCxsbG06dOsXixYv5Ilt2azQaMjMz0el0pKeno1arMTY2xsBAZnkVRSedwKJY0jMy8Dl6hjuxcRgaGFDdqSzj2zUHYFSLhiwKPMqWc5eo7GBHy6puXHhqfsrisDVTYWFqzPDVGzExMmJs22ZUsLXJ1a6qowPj2zVn2aHj3I9LwNjIiJrlyuLp7FRgvSUl8OoNftx3RP+637K1tK9ehY86tkJpaMiU7u1ZuP8oPkfP4GJnzZTu7VEaGpZoDUKI11N6RgZr/jrJ7ZisDKvh7MjEjq0AeKd1Uxb+eYhfTp+nShkHWlerwvnbd595X7bmZliYGDN0xVpMlEaM79CSCk89FA6gmlMZJnZqzdLAI9yLi8fY0BDP8k7UcnEqsN6Ssv/yNebveTIvXO+Fq+hYsxofd2mL0tCQaW915se9h1jz10kq2Nsw7a3OkrlCiEKlpaXxxeTJXL58GaVSSfNmzVi+fDkAc//3P8a8/z5z586lXr16eHl5ERgY+Mz7cnJywtbWlvIuLpiZmbF0yRKqV899l1jDhg1ZsXw5EyZO5Nq1a6hUKlq2aEHr1q0LrLckmJiY4OTkpH9tbWWFUqnUv6dWqxk8ZAg3btzA0tKSESNG8PXMmSW2fyHE6y0tLY3PP//8SYY1b86KFSsA+O677xg9ejT/+9//qFevHgMGDGD//v3PvK+/M9fZ2RkzMzOWLVuWb+auXLmS8ePHP8ncli31mZtfvSXl119/5YsvvkCj0eDs7MyECROYMGGCfn3nzp31cyMfPXqU0aNHExgYSNu2bUu0DvF6U+jyeWq3eDUpFIrGLrbWe5YO7vNKzyB+8c59vt97mDUjvUq7lFfO7J2BSUduRIzV6XS5700RQhSblco0eHqvLjVrOjsV3vgVdeH2PebuCsTvvWd7CKbI26I/D6v/uHj5U51Ot7Dw1kKIpykUisbVq1ffExIc/Epf12Z34MABhg4bxu1bt0q7lFdefy+vpM2bN8s1rxDPSKFQePXo0WPljh078n6K72vgwIEDDBkyhDt37pR2Ka+VjIwMlEqlLjMzU4Yhv2LkH0wIIYQQQgghhBBCCCFeY9IJLIQQQgghhBBCCCGEEK8x6QQWL6XaLuVkKgghhHhB6lRwlqkghBDiBWjbtq1MBSGEEC9I27ZtZSoIIbKRTmAhhBBCCCGEEEIIIYR4jUknsBBCCCGEEEIIIYQQQrzGjEq7APH6OXA1jG1BwdyJi0elVOLmYIdXwzp4Ojvq2/x5+Ro/7jvCp13a0KqqG8H3IpmxfS8AOh2kabWYKp/857nYuzfz9x7mauQjDA2efHdRu7wT097s+OIOTgghXjKBV67z65kL3ImNQ6VUUrmsAwMb18OzvJO+zd7gq8zfc5DPunegjUcVLt25z7StO4G8M3fZMC++3x3IlfsPMTRQ6N+v4+LMjN5dX9zBCSHESyYgIID5P/zAlStXsLS05I26dZk8eTItW7bUt1mzZg2j3nmHdQEBDBgwgMOHD9O9Rw8AdDodKSkpmJub69sHX7rE8BEjOH78OEZGT7K4Xdu2/Pbbby/u4IQQ4iUSEBDAvHnznuTtG28wZcqUXHk7cuRI1q9fr8/bbt26AXnnbUhICMOGDcudt+3asX379hd3cEKUEukEFiVq67lgfjl7kbFtm1G/ojNGBoacvXWXEzdv5egE3n/lBpYmJuy/coNWVd3wdHZk05ghAEQmJPKu72bWv+edo8MXYEzrpnTxrPZCj0kIIV5WW85cYNOpIMZ3aEWDSi4YGRhyJvw2x2+E5+gE3hcSiqWpCfsvh9LGowq1XMqxZfwoACLjExm5ah2bxo7IlbkftGtB19rVX+gxCSHEy2re/PnMmTOHpUuW0KVLF4yNjdm1axfbfvstR6eEr58fdnZ2+Pn5MWDAAFq1akViQgIA4eHhVK5ShdiYmBwdEAALFyzg3XfffaHHJIQQL6N58+Yxe/Zsli1bljNvt23Lkbc+Pj7Y2dnh6+urz9ukpCQgK2/d3NyIi4vLlbeLFi2SvBX/StIJLEpMcpoG/5Pn+LBDS5pXcdW/39itAo3dKuhfP0xI4tLdB3zWtS3/232Q2OQUbM3NSrSWPy9fY0/wNao6OrDv8jUsTE34pFNr7sbF43/iHOkZmYxs3pAONdwBSM/IwPfYWf66Ho42I4OmlSvybqvGmBgZkaRO4/u9hwmNfESGTkcNp7KMa9cMB4usbxS/2LITT2dHLtx5QHh0DB5OZfm/zq2xVpmW6DEJIUR2yWka1h47zUed29Kiqpv+/SZVXGmSLYMjExK5eOc+X7zZkdm/7yMmOQW7Es7cvcFX2XXxCh5OZdgbEoqliQn/160dd2Pj8Tt6mvSMDN5p1ZSOj7/ES9dm4HP0FIdDb5CekUmzKpUY3bYZJkZGJKrT+G5XIFcfPCQzM5Mazk5M6NASB0sLAD7btB3P8k6cv3WP8KgYqpcry6fdO0jmCiGeq/j4eKZPn86qn3+mb9+++vd79uxJz5499a8jIiI4ePAgGzdsYOCgQTx48AAnJ6e8NvnM1qxZw08//0yjRo1Ys2ZNVoezry+hoaFMmz6dtLQ0/jdnDsOHDwcgLS2NKV9+yaZNm0hLS6N3797MnzcPlUpFbGwsw4YN48TJk2i1Wlo0b87SpUtxcXEBoF379rRs2ZLAwEAuXLhAs6ZN8ff3x8HBoUSPSQgh/hYfH8+0adNYvXp1kfJ206ZNDBgw4Lnl7cqVK2ncuDGrV6/Gzs6OtWvXEhoaytSpU0lLS2Pu3Lk583bKFDZu3EhaWhp9+vRh/vz5+rwdOnQoJ06cyMrbFi1YtmyZPm/btm1Lq1at2L9/f1beNmtGQECA5K0oUTInsCgxVx48RKPNoFnligW223/1Bu5lHWjhXokKttYcCA17LvVcjXyEm70t/u8Ook21yvxv90GuPYxmxdB+fNypFcsPHSdVkw7AmqNnuBeXwIKBb7F8aD+ik1NYf/I8AJk6HR1ruPPz8P6sGt4fEyNDlh08nmNfB0Nv8mGHFvi9MxBtRga/nrv0XI5JCCH+dvl+JBptBs3dKxXYbl/INao6lqFl1cpUsLPhwJXrz6Weqw8eUqmMPevfH0ab6u7M+WMfoZGP+HnkQP7TrT1LA4/oM3f1Xye4GxvHoiH9+GnkAKKTkgk4fhbIunWvk2c11rzjzZp3vTExMmRJ4JEc+zpw5TofdWlDwPtD0WZmsuX0+edyTEII8bdjx46hVqvp06dPge18/fxo2LAh/fr1o0aNGvgHBDyXek6cOEGd2rWJevSIQYMGMcjbm1OnT3MtNBQ/X18mTJyoHw33+RdfcC00lHNnz3ItNJR7d+8y8+uvAcjMzGTEiBGE37xJRHg4KpWKCRMm5NjXunXrWPXzz0Q+eIAmPZ3vvv/+uRyTEEJAMfLW1zdn3vr7P5d6Tpw4QZ06dYiOjsbb25uBAwdy6tQprl+/ztq1axk/fvyTvP38c0JDQwkKCuL69evcvXuXmTNnAll5O3LkSCIiIrh16xYqlYrx48fn2FdAQACrV6/m4cOHaDQavvvuu+dyTOLfSzqBRYlJVKdhpTLJdTvx0/ZfuU6balmj1tpUq8z+KzeKvI8Vh08wcIW/fln7uNMgL45WFnSsWRVDAwNaubsRlZTMwEZ1URoaUr9ieYwMDLkfn4BOp2N3cCjvtmqEpakJZsZKvBrU4dC1mwBYqUxp4V4JU6VR1rqGdbh0LzLHvjrWcKe8rTUmRka0rOrGzaiYIh+TEEI8i8RUNdYq08Iz93Iobatn3fXQtro7+0JCi7yP5QeO0H/JGv3ie/RUvm2drC3p7OmBoYEBrT2q8CgxGe8m9VEaGVLf1QUjQwPuxcWj0+nYefEKo9s0x9LUFDNjYwY0rsehq1m/C6xUprSsWvlx5matu3Tnfo59dfL0wMXWBhMjI1pVq0zYo+giH5MQQjyL6OhoHBwcct1S/DQ/Pz8GDRoEwKBBg/Dz8yvyPj6cNAlbOzv9MnXatHzburm5MXLkSAwNDRng5cXt27eZNnUqJiYmdO7cGWNjY65fv45Op2PlypXMmzcPOzs7LC0t+eKLL9iwYQMA9vb29OvXDzMzMywtLZk8eTIHDx3Ksa8RI0ZQrVo1VCoV/fv353xQUJGPSQghiquoeevr64u3tzcA3t7e+Pr6FnkfEydOxMbGRr9MnTo137Y58nbAgKy8nTYtz7xdsWIF8+fP1+ft5MmTWb9+PZA7b6dMmcLBgwdz7GvkyJH6vPXy8iJI8laUMJkOQpQYS1MTElLTyMjMzLdTIuR+JJEJSbSu+qQT2O/4WcIeRVO5jH2h+xjdqkmR5wS2MVPpfzY2MgTA9qn3UtO1xKeqSdNq+WjDk4ngdWSNAAZQp2v56a+TnL11lyS1BoDU9PQcx5l9XyaPtyuEEM+TpcqU+FR1gZkbfPcBD+ITae1RBcjqBPY9coobD6OoUrbwW8vGtC36nMBP5yCQY6ofYyMj1Nkyd2LAFv06nU6XI3NXHjzK6fA7JKWlAZCqyZm5tmZPtmtiZERqenqRahRCiGdlb29PVFQUWq02346JI0eOcPPmTQYOGACA96BBfPnllwQFBfHGG28Uuo8ff/ihyHNUOjo+edaGSqXK872kpCQePXpESkoKDRs10q/T6XRkZGQAkJKSwkcff8zu3buJjY0FIDExkYyMDAwNs7LcKdt2zVQqkpKTi1SjEEI8i2Ll7cCBQFYn8JQpU4qctwuKMQd7cfO2QYMG+nW58vajj9i1a1f+eZttOgszMzP9CGMhSop0AosSU92pLEpDQ46H3aJFPrcn77+cNdJr4oacTzred+VGkTqBnwcrlSnGRoYs9u6NvYV5rvVbg4K5GxvP92/3wNbcjLBH0Xy4QZ4cKoQoXTXKOaI0NOTY9XBaVqucZ5u/R/1OWLs51/tF6QR+HqxUppgYGbJ0WH/93OrZbTlzgTux8cwf1Bs7czNuPIxigv+WPLYkhBAvTrNmzTAxMWHr1q28/fbbebbx8fVFp9NRr379XO8XpVPieXBwcEClUnHp4kXKly+fa/338+YRGhrK8WPHcHJyIigoiPoNGqB7/MWcEEK8aEXKWx8fdDpdrmz18fEp9bwNDg7OO2+//56rV69y4sQJfd7Wq1dP8la8UDIdhCgx5ibGDG7yBssOHudYWATqdC3ajExOR9xh9ZHTaLRa/rp+k3HtmrFgwFv6ZUzrJhwKDSMjM7NU6jZQKOhSsxor/zpFXEoqANFJyZyNuAtkjUAzNjLC3MSYRHUa607J3JNCiNJnbmLM0OYNWRJ4hKPXw/WZe+rmLX4+dByNVsvha2FM6NiKhUP66Zf327XgwNUbpZu5tWqw4sAxfeZGJSVzJvw2AKkaDcZGRliYGJOoVuvnChZCiNJkbW3NV199xfgJE9i6dSspKSmkp6ezc+dOPv3sM9RqNZs2bWL5smWcO3tWvyxYsIB169ah1ZbOXWIGBga8++67fPzxxzx8+BCAu3fvsnv3biBrFJrK1BQbGxtiYmL0c1cKIURpsba2ZubMmYwbNy533n76KWq1mo0bN7JixQqCgoL0y8KFCwkICCjVvH3vvff46KOP8s9blUqft1999VWp1Cn+3aQTWJSoPvVq8U7LRmw8dYEhP69npM9Gfr9whaaVK3I87BbGRka093DH1txMv3SsWZWMTB1nHne6FmT5oeP0X75Wv0wqoRG5I5o3wNnakv/75Xe8lvvz5bY93ImLB+CtujXRaLUM/nk9/7fpd+pXzP2tnhBClIa+DerwXuumrD9xlkHLfBn+kz87zgfTzL0Sx26EY2xoSIca1bAzN9MvnWt5kJGZyenHna4FWRp4hL6LVumXiSU0IndUq8Y421jx8bqt9Fu8mimbf+dObFbm9q5fG41Wy8Blvny0bhsNKrmUyD6FEOKf+uTjj/n+u+/45ttvKevoSEVXVxYvXkzvXr3YunUrKpWKYcOG4eTkpF9GjRyJVqtl165dhW5/wsSJWFpZ6ZfsUzj8E3Nmz6aKuzvNmjfH2saGTp07czU0606RSR9+SKpaTZmyZWnWvDldunQpkX0KIcQ/8cknnzBv3jxmzZpFmTJlqFChAosWLaJ379755+2oUUXO2/Hjx2NhYaFfsk/h8E/MmTMHd3d3mjZtipWVFR07duTq1asATJo0idTUVBwcHGjatCldu3YtkX0KURwKGXr+elEoFI1dbK33LB3cx7q0axGlY/bOwKQjNyLG6nS6oj+JRAiRLyuVafD0Xl1q1nR2KryxENks+vOw+o+Llz/V6XQLS7sWIV5FCoWicfXq1feEBAfLda3Ipb+XV9LmzZvlmleIZ6RQKLx69OixcseOHValXYt4tWRkZKBUKnWZmZkysPQVI/9gQgghhBBCCCGEEEII8RqTTmAhhBBCCCGEEEIIIYR4jUknsBBCCCGEEEIIIYQQQrzGpBNYCCGEEEIIIYQQQgghXmPSCSxeOukZGYz1/5WY5JTSLuWlFZuSygf+v5KekVHapQghXnJnwm8z87fdpV3Ga+u3c5dYdfhEaZchhChFLVu14ty5c6VdxmspMjKSmp6epKWllXYpQoiXUEhICA0bNkSn05V2Ka+l7du3M2DAgNIuQ5Qg6QT+l4qIjmXatj14/7SOnovWFNr+/J37fLjhN7yW+/Ou7y/sunRVvy4mOYWvd+xj+KoN9Fy0hsiExByfHRuwlf7L1+qXXot9mLnjz3z3tetSKJ7OjtiZm+W5/sqDh0zdtptBKwMY/NM6Zu8MLLDDOFGdxjd/7OftZWsZ5bOJA1fDCj3e/BR2rHn57XwI7/j8wtvL1vKB/6/cjY0H4MKd+4wP2MrAFf54r1zHN3/sJzopWf+51UdOM3LNRryW+zPKZxMbT1/Qr7M1U1G7vBO7LoU+87EIIUpOeFQMX275g4FLfeg+f0WBbS/duU/fRatyLN3nr+Cva1nZtDf4Km/+sDLH+gu37+XazsU79+g+fwU+R04VuD+fI6fwavRGvuvP377H55u28/bi1Yz4OaDQY9118QrvrFpP30WrmLrljxy5NfXXnTnqfuvHn/jAd9Mz116Q4pxzgO7zV9Bn4ZPafth7UL/u17MXGPXzOvotXs2QFWtZceAoGZmZRaq7a+3qBF65TlxK6jMfixDin5v/ww+Uc3bG2saGUe+8k2+noUajoX///rhVroyBoSEHDhzItZ0q7u5Y29hQ3sWFjz7+GK1Wm+9+t2/fjqWFBfXq1cu3TVBQEA0bNcLcwoKGjRoRFBSUb9tFixfTqHFjTFUqRo4cmWNdeHg4BoaGWFpZ6ZevZ83Sr9+4cSMtWrbE3MKCdu3b57uPoiqpugH27dtHjZo1MbewoH2HDkREROjX3b17l969e2Pv4ECFihVZtmyZfp2joyNt27ZlxcqV//h4hBDPbv78+Tg5OWFlZcWoUaMKzNi3336bSpUqoVAocmVsYGAg7dq1w9ramkqVKuVY9/DhQwYNGoSzszPW1ta0aNGCEycK/qJ96tSp/N///R8KheK51z537lxq1aqFpaUlbm5uzJ079x/VXpiAgABcXV0xNzend+/exMTE5Nt29OjReHh4YGBgwJo1a3Ktz+8cFFZ3z549CQ4O5sKFC7m2KV5N0gn8L2VoYEDLqpWY2L5FoW21GZl8+8d+unp6sGG0N592acvPR05xMyorhBQKBfVdy/NFt3Z5fn6Jd282jRnCpjFD2Dh6MA4W5rR0r5Tv/nYGX6Vd9Sr5rk9K09DF04Ofh7/Nz8P7ozJW8uO+v/Jtv+zgcYwMDPAbNYBPOrVm6cFjRETHFnrceSnsWJ+2OziUvSHXmN6zI5vGDGbamx2wUpkCUMHOhq/e6sz60YPxGeWFs7UVSw4c13+2U82qLB3ch41jBjO3X3cOhIZx9MaTC+a2HpXZFXw11z6FEC+ekYEBrapV5sPObQptW8ulHFvGj9IvM3p1RaVU0rBSBX2b6uXK5mhTp4Jzjm1oMzJZfuAYHk5lC9xX6IOHpGg0VC/nmG8bU6URnTw9eKdV00Jrv3D7Hj5HTjLtrc5s+GA4jtaWzPljv37913265ai7hrMjrapVfqbaC1Occ/63xUP76Wub1OnJ55pWrsSCwX3ZPG4kS4e9TVhUDNvOXSpS3cZGRjSsVIF9IfKlnBClZffu3cyZM4c/9+4l/OZNbt68yfQZM/Jt36JlS/x8fXFycsq17q2ePTlz+jTxcXFcvHCBCxcusGDhwny3tXz5coYMGZLveo1GQ+8+fRg8eDAx0dEMGzaM3n36oNFo8mzvXK4cUyZPzrMj9W+xMTEkJiSQmJDA1C+/1L9vZ2fHhxMn8tlnn+X72aIqybqjoqLo9/bbzPzqK6KjomjQoAEDBw3Srx86bBiV3Nx4cP8+O7ZvZ8qXXxIYGKhfP9jbmxUrCv+yTwjxfOzevZvZs2ezb98+IiIiCAsLY/r06fm2b9myJWvXrs0zY83NzRk1alSODtS/JSUl0ahRI86cOUNMTAzDhw+nR48eJCUl5bmf+/fvExgYSO/evV9I7TqdDl9fX2JjY9m1axeLFi1i/fr1z1R7YYKDgxkzZgx+fn5ERkZiZmbG2LFj821ft25dlixZQv369XOtK+gcFKXuQYMGSQa/RqQT+F/KxdaazjWrUdHOptC2iWlppGjSaedRBYVCQTVHB1xsrbkVEwdkjUrtUbs6VR0dCt3WpXuRJKrVNK/imuf6h4lJRMYn4uFYJt9tNHR1oaV7JcyMjTFVGtGjdg0u33+YZ1t1ejpHb0QwpEk9VMZKPJ0daexWgcCrNwqtNS/FOdZMnY71p4J4t2UjKtrZoFAoKGdthaWpiX5b9hZPRjsbKBTcj0/Qv3axtcZUqXyyHnKs93Asw4OERB4mPNsvFiFEyXGxs6FLreq42tsW+7N/hoTSoqpbjv/fC7PlzAXqVSxPhUIy/HT4bWq5lCuwjYdTWTrUrIaTtWWh+z158xYtq1XG1cEOpaEhg5rU59Ld+9yPS8jVNjI+keC7D+hQo9oz1V6Yf3LOn1bOxgqLx9ms04EBilzHVFDddVzKcermrX9chxDi2fj6+jJq1Cg8PT2xtbXlyylT8PHxybOtsbExkz78kJYtW2JoaJhrfZUqVbCxsQGy/uA3MDDg+vXreW5Lo9GwPzCQNm3y/zLqwIEDaLVaJn34ISYmJkycMAGdTsf+/fvzbN+3b9+sUbH29oUcdW4dO3bEy8sL53IF535RlGTdW7ZswdPTk/79+2NqasqM6dM5f/48V65cISkpiQMHDjBl8mSUSiV169alX79+rF69Wv/5Jk2aEBYWlmP0sBDixfHx8eGdd97RZ+zUqVPzHG0KjzN20qR8M7Zx48YMHTqUypUr51pXuXJlPv74Y8qVK4ehoSGjR49Go9Fw9WreA5/27t1L/fr1MTU1fSG1f/rpp9SvXx8jIyM8PDzo1asXR44ceabaC+Pv70/Pnj1p3bo1FhYWfP3112zZsoXExLzvRB43bhwdOnTI81wUdA6KUnfbtm35/fffn+k4xMtHOoFFoWzNVLSu6sa+y9fIyMzkyv2HPEpMpmYBI8vys//KdZpVcc23syMiOhZHa0sMDYr+n2bwvQf5dmbfjUvAwEBBeVtr/Xtu9nb6DuznKSopmaikFCJi4hi5ZiPv+PyC/4lzZGabr+hhYhIDV/jTb6kfvwZdom/9Wjm2senMBfovX8uINZtQa7W0yTaiztDAAGdrK25G539biBDi5aZOT+fItZt0rJmzo/TGw2gGLvXh3dUbCDh+NsfUBJEJiewJvop30waFbj88KgYXW5uSLVqX+8fwPHJo3+VQPMs74Zitc7k4tT8Pn27czuDlfszavofI+JwX0YFXrtNv8WoGLvMlLCqabnVq6NcVVncFO1vCHkkWC1FagkNCqFunjv513bp1iYyMJDo6+pm2FxAQgLWNDWXKluX8+fOMGT06z3bXrl3DwMAAFxeXAmurU7t2jluV69SuTXBIyDPVBlDJzY0KFSsyatQooqKinnk7BSnJuoNDQqiT7d/H3NycKlWqEBwcrJ/HM/t8njqdjkvBwfrXRkZGuLu7c/78+Wc5FCHEPxQcHEzdunX1r/9pxhZVUFAQGo0Gd3f3PNdfvHgRDw+PArfxvGrX6XQcPnwYT0/PPNcXVnthnq67SpUqGBsbExpa/DvPinMO8qq7Ro0ahIeHk5CQe9CHePVIJ7AokjbVKrP+1Hn6LvXjsy07Gdq0PmUszYu1DXW6liPXI+hYPf8gTE7TYKY0KvI2b0bFsP7UeUa2aJTvPs2Mc3Y4m5sYk6pJL/I+nlV0UtY8xedu3WPhoF5826cLh0Jvsjfkmr5NWUsL1o8ejP+7gxjSpD4u2TqrAfo3qMPG0YP5YUBP2nlUwczYOMd6lVJJclret+UJIV5+R67dxEplSu1so3VrlS/H0mFvE/D+MKb07MTBq9f55fSTP3yXBx5laPOGqIwLHzmclKZBVYwRxoVp4OrC4dAb3HwUTZpWy7rjZ1AAaem558vcF3ItV+d2cWovaXP692T1O4NYPtwLO3MzZmzblaNzvV11dzaPG8nKEQPoXqcGNmaqItetMlaSks8t0kKI5y8pKQlr6yfXUH//nN+IqcJ4e3sTHxfH1StXGDNmDI6OeQ98iIuLw9Ky4Lsonq7t7/qepTYHBwdOnjhB+M2bnD51isTExAKnovgnSrLugrZlaWlJixYt+HrWLNRqNWfPnmXLli2kpOR83oelpSVxcXHF3rcQ4p8r6YwtioSEBIYOHcr06dNz5cffniWDS6r2GTNmkJmZmecUOEWpvTDPM4PzOwf51f33OZYMfj1IJ/C/wIGrN/QPZZv+295if/52bBz/232Qjzq24texw1js3ZvNZy9yKvx2sbZzLCwCS1NjapXPPb/O3yxMTEjJ1pnwMDEpx0PlsrsXl8CM7X/yXqsmeDrnfXFuqjQi5akO3xSNpkgdEMH3IvX7HRuwtdD2TzM2yrqFpG/9WliYmOBoZUnXWtU4HXEnV1tLUxM6VK/CN7/vz/UwIoVCQZUy9hgbGRJwIueTp1PT0zE3ydkxLIR4vgIvX9M/XGzqrzv/0bb2hVyjQ42qOUZalbOxwsnaCgOFAjcHO7yb1ufItZsAnLgRQUp6Om088p83PTsLExNS059k4IaT5/S1L/zzcLHrrefqwuBmDflmx15G/ryOslaWqIyVOFjk/FIw+O4DYlNSaFn1yd0Lxa09u5I457VdyqE0NMTC1IQxbZvzID4xz7tCytta42pvy5L9fxW57lRNeq4v6YQQz4e/v7/+oWjdu3cHwMLCIscIpb9/LqxzoDBVq1bFs2ZNxo0bl+d6W1vbXH9EZ39o261bt3LV9nd9z1KbhYUFDRs2xMjICEdHRxYuXMievXtLpCPmeddd0LbW+vkRfvMmFV1dGTtuHIMHD841ujoxMVE/TYcQ4vnx9/fHwsICCwsLunXrBjy/jM1PamoqPXv2pGnTpnzxxRf5tns6g19U7YsWLcLX15fff/8dExOTZ6o9u8OHD+vr/ntk8fPM4LzOQUF1/32OJYNfD0UfcileWW09qtD2Gf7o/tut6DjK21hR37U8kDVXbaNKLpyJuEujbA8yKsz+K9dpV929wCd3VnKwJTIhkYzMTAwNDChracGmMblHODxMSGLqtt0MbFSH9gU8RK68jRWZmTruxSXgbGMFwM2o2CLNhezp7JjnvouqvI01RgYGZD9aBfkfe4ZOR1yqmhRNun7e4OwyM3U8SHjySy4jM5N78Qm42ds9c41CiOJrV6Mq7WpU/cfbeZSYxIU795jQsVUhLRXoHk+8EHT7LtciHzF4uR+QdfeEgYGCiKgYpvXqkuuTbmXsuBsbr389oHE9BjTO/wn2RdHzDU96vpF1gXonNo71J87h6pAzh/4MCaW5u1uOL9yKW3t2JXXOs1MoyJoAOA8ZmTr9nMBFqft2TCyVy0gWC/EiDB48mMGDB+d4z7NmTc5fuICXlxcA58+fx9HR8Znm1X2aVqvlRlhYnuvc3d3R6XTcvXuX8uWzrpMTn/qj3bNmTebNm4dOp9NfA1+4eLHAB/wU1d/by3xqAMGzeJ51e9asia+fn/51cnIyN27c0Hd2uLq6sn37dv36wYMH06jRk7v8tFot169fz3E7sxDi+cgzYz09OX/+/HPJ2KelpaXRu3dvXFxcWL58eYFt69Spk2P+9xdR+6pVq5g9ezaHDh3K9WVVcWrPrlWrVrkeIPd33X8LCwsjLS2NatWqPf3xQhV2Dgqr+/Lly1SqVAkrK6ti71u8fGQk8L+UTqdDo9WifXzRqNFqSc/IyLNt5TJ23ItP4Pyd++h0Ou7HJ3Aq/A6Vsj2MJ/vn0zMy0Whz3hoclZTMhTsP6FBAhy2Ag4U5ztZWhEbmP79ZdFIyU7bupkftGnSrVb3A7ZkqlTSrUhH/E+dQp6cTcj+SEzdv0e5xp3hkQiI9F60hMqHoIygKO9Yn+zaiVVU3Np+9RIomnaikZHYFX6VRpaxfFkdvRHAnNp5MnY74VDU//XWKymXssDQ1IVOnY+elqySp09DpdIRGPuL3i1eok+2W8dDIKBwtLShrZVHk2oUQz4c+UzOyZao270z92/7L16jh7Eg5m5wXVKdu3iI2Oes22Nsxcaw/cZamlSsBMLR5Q1aOGMDCIf1YOKQfTaq40rVWdT7qkvdDiRpVqsjFO/cLrCMz2+8Dna7g3wcarZbwqBh0Oh0PE5JY+OdhetWrleOLqzStlsOhN3JNBVFY7Rdu36P7/KI/ebg45zwiKoYbD6PIyMwkVZPOT4eOY29hTgW7rN9juy5eIS4lFYBb0bFsPBlE3Yrli1Q3wMU792lQjC9FhRAla+jQoaxatYqQkBDi4uL45ttvGT58eL7t09LSUKvVQNbD3dRqtX5O2p9++omHD7MeOBwSEsLsOXNo3759ntsxNjamY4cOHDx4MN99tW3bFkNDQxYsXEhaWhqLFi8GyHebWq0WtVpNRkYGGRkZqNVqtI+vNU+cOMHVq1fJzMwkOjqaDz/8kLZt2+pv283ePjMzE7VaTXq2u0HcKlfO94FIz7PuPn36cOnSJTZv3oxarWbm119Tp04dqlfPuo6/fPkyiYmJaDQa1q5dy569e/n4o4/02z558iSVKlXC1TXvB0sLIZ6vYcOG8fPPP+szdtasWYwYMSLf9gVlbPZs0ul0qNVqNI+n1EpPT+ftt99GpVLh4+ODQSHPCerUqRNnz57V7+t51+7v78/kyZPZu3dvrgfbFVZ7eHg4CoWC8PDwAo/pb4MHD2b79u0cPnyY5ORkpk2bRt++ffMdCZy91vT0dNRqtf4LwoLOQVHO+cGDB/Ujq8WrTzqB/6UeJibRb9laxj2e5qDfsrW8v3aLfv303/ay8fQFAMpZWzGxfQtWHDrBgBX+fLFlF82ruNLZ88kf+P2WraX/cn8APvD/lX7Lck7dEHjlBtWdylDOuvBvj7p6ViPw6o181+8JucaDhETWnQrKc6qIjacv5Jj24oM2zUjTahny8wbm7j7EB22a6Z8mH5WUQllLc+zNiz6/cUHHujjwKIsDj+pfv9+mCSpjI0as3sB/fvmdNtUq0+nxaLbopBSm/7YXr+X+jF+3FQMFTOn25ML6eFgE7/ltxmuFP9/vOcybdWrQM9uDig6EhtG1VsET4QshXoyHCUn0XriK9303AdB74Sre89mgXz/1151sOJlzOpd9IaG5OkoBzt++x7i1m+mzcBXTf91Jc3c3/ehdM2Nj7MzN9IuJkREmSiWW+TwV2d3RAXMTY67cf5hv7Zfu3Kf3wlVM37qLR4lZx/Hllj/069/32UTg5ay5zDXaDP63cz99F63mo3W/Ur2cI0ObN8yxvWPXwzE3MaFuBecc7xdW+6PEJGoU44GjxTnnsSmpzP5jH28vXsOoVeuITEhkRq+uGBlmXQaF3HvAWL9f6LNwFdO27qSRWwVGPJ5rvrC6NVotp8Jv5/lvKYR4Mbp27cp//vMf2nfogGulSrhWrMhXM2bo19eqXRt/f3/96+o1amBmbs7du3fp2q0bZubmREREAHD06FHq1K2LhaUlPd58k27duvHtN9/ku+/Ro0ezdu3afNcbGxvz65Yt+Pn5YWtnx+rVq/l1yxaMH08h8+1//6uf1gJg1jffYGZuzpw5c1jr74+ZuTmzHu8/LCyMbt27Y2VtTe06dTAxMSEg23H5+flhZm7O2HHjOHz4MGbm5ox+/FA7jUZDdHQ0TZs2LdI5Lcm6y5Qpwy+bNvHl1KnY2dtz8uRJ1gUE6D+7e88eqri7Y2dvz/IVK9j5xx+UKVNGv94/IIAxY8YUqW4hRMnr2rUrn376Ke3ataNixYq4urry1Vdf6dd7enrmyFgPDw9UKhV3796lS5cuqFQqfcYeOnQIlUpF9+7duXXrFiqVis6dOwNZ+btjxw727NmDjY2NfoqEw4fznsLM0dGR9u3bs23bthdS+5dffkl0dDSNGjXS1/b+++8Xqfbbt2/j6uqqv2ukMJ6enixbtozBgwdTtmxZEhMTWbJkiX59t27d+Pbbb/WvO3fujEql4ujRo4wePRqVSsWhQ4cKPQdFOefr1q2TDH6NKHT53AopXk0KhaKxi631nqWD+zzbDOQvgfSMDD5c/xuzenfBztzsue5rw6nzWKlM6faKdabGpaTyxa+7+HFAT4yNcs7qMntnYNKRGxFjdTqdXz4fF0IUg5XKNHh6ry41azrnP5/5y+xsxB12nA9m2lsFT7lQ2n7Ye5BWVSu/ciNqfzt3iUeJSbzTOnfHyqI/D6v/uHj5U51Ot7AUShPiladQKBpXr159T0hw8Et9XduyVSsWLlhAvXr/bLqd5+mvv/5iyZIlBGTrfH0VPHz4kLbt2nH2zBlMn/rCs7+XV9LmzZvlmleIZ6RQKLx69OixcseOHa/sff4hISEMHz6ckydPFjjtZGmbNWsWZcqUeeU6U7dv346fnx8bN27M8X5GRgZKpVKXmZkpA0tfMTInsHjpKA0NWTK4zwvZ14BGr+bcYjZmKpa+oHMkhHi11Xd1ob6rS+ENS9mkTnlPafGye6terdIuQQhRyv7KZ5Tay6Rly5a0bNmytMsotrJlyxISHFzaZQghXlI1a9bk1KlTpV1Gob788svSLuGZ9OzZk549e5Z2GaIESa+9EEIIIYQQQgghhBBCvMakE1gIIYQQQgghhBBCCCFeY9IJLIQQQgghhBBCCCGEEK8x6QQW4hn0XLSGe3EJpV2GEEL8K3Sfv4J7cfGlXYYQQrz2DAwNuX79emmXIYQQ/woKhUIyV7xQ8mA48cIduBrGtqBg7sTFo1IqcXOww6thHTydHfVt/rx8jR/3HeHTLm1oVdWN4HuRzNi+FwCdDtK0WkyVT/7zXezdm/l7D3M18hGGBk++26hd3olpb3YsVn09F61h+ZC+ONu8sg9JFUIIvcAr1/n1zAXuxMahUiqpXNaBgY3r4VneSd9mb/BV5u85yGfdO9DGowqX7txn2tadQN6Zu2yYF9/vDuTK/YcYGjx5EnMdF2dm9O5arPq6z1/BTyMH4Gxj/Q+PVAghSldAQADzf/iBK1euYGlpyRt16zJ58uQcD2Rbs2YNo955h3UBAQwYMIDDhw/TvUcPAHQ6HSkpKZibm+vbB1+6xPARIzh+/DhGRk9yuF3btvz222/Fqs/A0JDQq1dxd3f/h0cqhBClLyAggHnz5j3J3DfeYMqUKbkyd+TIkaxfv16fud26dQPyztyQkBCGDRuWO3PbtWP79u3Fqk+hUHDt2jXJXPFSkU5g8UJtPRfML2cvMrZtM+pXdMbIwJCzt+5y4uatHJ3A+6/cwNLEhP1XbtCqqhuezo5sGjMEgMiERN713cz697xzdPgCjGndlC6e1V7oMQkhxMtqy5kLbDoVxPgOrWhQyQUjA0POhN/m+I3wHJ3A+0JCsTQ1Yf/lUNp4VKGWSzm2jB8FQGR8IiNXrWPT2BG5MveDdi3oWrv6Cz0mIYR4Gc2bP585c+awdMkSunTpgrGxMbt27WLbb7/l6JDw9fPDzs4OPz8/BgwYQKtWrUhMyLq7LDw8nMpVqhAbE5Oj8wFg4YIFvPvuuy/0mIQQ4mU1b948Zs+ezbJly3Jm7rZtOTLXx8cHOzs7fH199ZmblJQEZGWum5sbcXFxuTJ30aJFkrnitSTTQYgXJjlNg//Jc7zfpinNq7hiqlRiZGhAY7cKjGrRSN/uYUISl+4+YFy7Zpy9dZfY5JQSr+VeXAKfb9nJgBX+eP+0jjm7DgDw+ZaskW8T1/9G/+VrOXztJgBbzl5i2KoNDF+1gb0h10q8HiGEKGnJaRrWHjvN2PYtaVHVTZ+5Taq48k7rpvp2kQmJXLxznwkdW3Em/A4xzyVz4/l043beXryagUt9+O/vfwLwn41Zo9jG+W2m76JVHLx6A4BfTp9n8HI/hqxYy55LV0q8HiGEKEnx8fFMnz6dRQsX0rdvX8zNzVEqlfTs2ZO5//ufvl1ERAQHDx5k+bJl7N6zhwcPHpR4LdevX6dtu3bY2NpSpmxZBg4cCECbtm0BeKNePSytrNiwYQMAc7/7Dufy5Snv4sKqVatKvB4hhChp8fHxTJs2jcWLF+fO3Llz9e3+ztwVK1awe/fu55a5bdq0wdraGgcHBwYMGABA69atAahbty4WFhZPMnfuXMqVK4ezs7NkrigVMhJYvDBXHjxEo82gWeWKBbbbf/UG7mUdaOFeiQongzgQGkaferVKtBb/E+eoV9GZb/t0RZuRwbWH0QDM7tuNnovWsGDgW/rpIM5E3OHXc5eY1bsLjlYWLNx/tERrEUKI5+Hy/Ug02gyau1cqsN2+kGtUdSxDy6qVqWB3hgNXrtO3QZ0SrcXv6Gnqu5Zndv83szI3MgqAuV5v0X3+ChYP7aefDuJ0+G22nLnAt/164GRtyY97D5VoLUIIUdKOHTuGWq2mT58+Bbbz9fOjYcOG9OvXjxo1auAfEMAnH39corVMmzaNTp06sX/fPjQaDadPnwbg4IEDGBgaEnTunP7W5F27dvH999/z5969uLm5MXr06BKtRQghnociZ66vb87M9ffnk08+KdFapk6dSufOnQkMDMyRuYcOHUKhUHD+/Pkcmfvdd9+xb98+3NzceO+990q0FiGKQkYCixcmUZ2Glcok1+3ET9t/5TptqrkB0KZaZfZfuVHkfaw4fIKBK/z1y9rjZ/NsZ2hgwKOEZGKSUzA2MsoxFcXT/roeToca7rja22KqVOLd+I0i1yOEEKUlMVWNtcq08My9HErb6lkXp22ru7MvJLTI+1h+4Aj9l6zRL75HT+XZztDAgIcJScQkJWdlbrapKJ52OPQGnWpWo5KDHaZKJYObNShyPUIIURqio6NxcHDIdTvx0/z8/Bg0aBAAgwYNws/Pr8j7+HDSJGzt7PTL1GnT8mynVCqJiIjg3r17mJqa5rgt+mkbN21ixIgR1KpVC3Nzc6ZPn17keoQQorQUNXN9fX3x9vYGwNvbG19f3yLvY+LEidjY2OiXqVOn5tmuWJm7cSMjR47UZ+6MGTOKXI8QJUU6gcULY2lqQkJqGhmZmfm2CbkfSWRCEq2rPukEjoiOJexRdJH2MbpVE9aPHqxfhjStn2e7kc0boEPHJxt3MDZga4FTPMQkp1DG4slk8WWtLIpUixBClCZLlSnxqeoCMzf47gMexCfS2qMKkNUJHB4Vw42HUUXax5i2Ldg0doR+Gda8UZ7tRrVqgg6YtG4r7/tsKnCKh5ikFBwsn+Sso6VlkWoRQojSYm9vT1RUFFqtNt82R44c4ebNmwx8fKuw96BBXLx4kaCgoCLt48cffiA2Jka/fD1zZp7t5syZg06no0nTptSqXbvA243v37tHhQoV9K9dXV2LVIsQQpSmYmXu4ylxvL29i5W5CxYsIC4uTr98/fXXebb73//+h06no3Hjxnh6ehaYufckc8VLQKaDEC9MdaeyKA0NOR52ixb53J68/3LWqN+JG3I+7XjflRtULmNfYrXYmpsxoX0LAILvRTJ12248nR31U0DkaGtmxqOkZP3rR4nJudoIIcTLpkY5R5SGhhy7Hk7LapXzbPP3qN8Jazfner9KWYcSq8XO3IwPO2XNjRZ89wGTN/9OLZdy+ikgsrM1NyMqMUn/+mG2n4UQ4mXUrFkzTExM2Lp1K2+//XaebXx8fdHpdNSrXz/X+2+88UaJ1eLk5MTKFSsA+Ouvv+jUuTOtW7fO8+n05cqV4/bt2/rXt27dKrE6hBDieSlS5vr4oNPpcuWrj49PyWfuypVAVuZ27NhRMle81GQksHhhzE2MGdzkDZYdPM6xsAjU6Vq0GZmcjrjD6iOn0Wi1/HX9JuPaNWPBgLf0y5jWTTgUGlbgaLbi+ut6OFGPO3YtTIxRoMBAoQDAxsyUBwmJ+rYtq1Zi35Xr3IqJQ52uZd3JoBKrQwghnhdzE2OGNm/IksAjHL0ers/cUzdv8fOh42i0Wg5fC2NCx1YsHNJPv7zfrgUHrt4o0cw9HBqm79jNylxQkJW5tmYqHsQ/ydxW1SrzZ0got6JjUadrCTh+psTqEEKI58Ha2pqvvvqK8RMmsHXrVlJSUkhPT2fnzp18+tlnqNVqNm3axPJlyzh39qx+WbBgAevWrStwNFtxbdq0iTt37gBga2uLQqHA4PG0QI6OjoSFhenb9u/fHx8fH0JCQkhJSWFmPqOLhRDiZWJtbc3MmTMZN25c7sz99FPUajUbN25kxYoVBAUF6ZeFCxcSEBBQapnr5eXFmjVr9Jn71VdflVgdQhSVjAQWL1SferWwNVOx8dQFvt9zGJWxEe5lHPBqWIfjYbcwNjKivYc7RoZPvp/oWLMq/ieCOBNxl8ZuFQrYOiw/dJyf/jqpf13expofBvTM1e5aZBQrD58kRaPBRqXivVaNcbLOuuXYu/Eb/PDnX2i0Wsa1a06rqm68VbcmU7buwgAFQ5rW50BoWK5tCiHEy6ZvgzrYmqlYf+Isc3fux8xYibujAwMa1+PYjXCMDQ3pUKNajsztXMuDtcdOczr8Nk0qF3yb2tLAI6w4+ORhmS62NiwY3DdXu9AHD1lx4CjJGg02ZirGtG1Oucd3Xng3bcD3uwLRZGQwoUMrWntUoVe92nzxyw4UCgXDmjck8Mr1EjojQgjxfHzy8cc4OTryzbffMmToUCwtLWlQvz6TJ09m69atqFQqhg0bhlKp1H9m1MiRTJ8+nV27dvHmm28WuP0JEyfyUbaHyHl4eHD6VO552E+dPs1HH39MfHw8jo6O/DB/PpUrZ90NMn3aNEaMHElqairLly3Dy8uLDz/8kA4dO2JgYMDXM2fiHxBQQmdECCGen08++QQnJydmzZrF4MGDszK3QQOmTJmSf+aOGsW0adOKlLnjx49n0qRJ+tceHh6cOZN7YMKpU6eYNGmSPnN//PFHfebOmDGD4cOHk5qayooVK/Dy8mLSpEm0b98eAwMDZs2ahb+/f8mcECGKSKHT6Uq7BlGCFApFYxdb6z1LB/fJfY+t+FeYvTMw6ciNiLE6na7oTxsRQuTLSmUaPL1Xl5o1nfN/mJkQeVn052H1Hxcvf6rT6RaWdi1CvIoUCkXj6tWr7wkJDpbrWpFLfy+vpM2bN8s1rxDPSKFQePXo0WPljh07cs+JKEQBMjIyUCqVuszMTJld4BUj/2BCCCGEEEIIIYQQQgjxGpNOYCGEEEIIIYQQQgghhHiNSSewEEIIIYQQQgghhBBCvMakE1gIIYQQQgghhBBCCCFeY9IJLIQQQgghhBBCCCGEEK8x6QQWr4REdRrf/LGft5etZZTPJg5cDcuznU6nY83R03ivXIf3ynWsOXoanU6nX99z0RreXraW/suzlgX7j7yoQxBCiJdWolrN17/toc/CVQz/KYDAK9fzbKfT6Vh1+AQDlvowYKkPqw6fyJGxQbfuMsF/M/0Wr2bUz+vYeeFyjs+uP3GW4T/502/xamb//icpaZrnfmxCCPGyiYmJoW/fvlhYWlLJzY2AgIA82+l0Oj77/HMcypTBoUwZPvv88xyZO3rMGKrXqIGhkRFr1qzJ8VkfHx8aNmqEtY0NFSpW5NPPPkOr1T7PwxJCiJdSTEwMffr0wdzcHFdX14Iz97PPsLe3x97ens8++yzndW5QEA0aNMDMzIwGDRoQFBSkXzd37lxq1aqFpaUlbm5uzJ0793kflhDPxKi0CxCiKJYdPI6RgQF+owYQFhXDzB1/4uZgi6u9bY52u4JDOR52iwWD3kIBTN22B0crC7rVqq5vs2DgWzjbWL3gIxBCiJfXkv1HMDI0IGDMUMIeRTN9604qO9jh6mCXo93Oi5c5diOcxUP6gULBlM2/42hlSY+6NdFmZDJr+x5GtWpCt9o1uBb5iM9/2YFHubJULmPPvpBr7L98je8G9MLCxIS5O/ezNPAIn3RtV0pHLYQQpWP8+PEYGxvz4P59goKCeLNnT+rWrYunp2eOditWrGDbtm0EnTuHQqGgc5cuuFWqxPvvvw9A3bp1GeDlxedffJFrHykpKcyfN48mTZrw6NEjevXuzXfff8/nn332Qo5RCCFeFuPGjcPY2JjIyEiCgoLo0aNHvpm7detWzp8/j0KhoFOnTri5ufH++++j0Wjo1asXkyZNYuzYsSxfvpxevXpx7do1jI2N0el0+Pr6UqdOHW7cuEHnzp2pUKECAwcOLKWjFiJvMhJYvPTU6ekcvRHBkCb1UBkr8XR2pLFbBQKv3sjVdv+V6/R+wxMHC3PsLczpXc+TfZfzHtEmhBAiK2OPXLvJ0OYNszK2vBNNKruy//K1XG33hVyjb/06OFha4GBhTt8GdfgzJBTIGk2cokmnfY1qKBQKqjmVpYKdLbeiYwE4ERZB51rVKWNpgcpYyduN6nIoNAx1uoxME0L8eyQnJ7N5yxZmzpyJhYUFLVu25K2ePfFbuzZXW18/Pz7+6CNcXFwoX748H3/0ET6+vvr148aOpUOHDpiamub67AcffECrVq0wNjamfPnyeHt7c/SI3AEnhPh3SU5OZvPmzXz99ddPMvett/Dz88vV1sfHh08++USfuZ988on+LosDBw6g1WqZNGkSJiYmTJw4EZ1Ox/79+wH49NNPqV+/PkZGRnh4eNCrVy+OSOaKl5B0AouX3t24BAwMFJS3tda/52Zvx62YuFxtb8XE4ZZt5Fpe7b7YspOhq9bz7R/7iUxIfF5lCyHEK+FubDyGBgpcbG3071UuY0/E487b7CKiY3ArY69/7eZgr+/ktTU3o41HFfYGXyUjM5PL9yJ5mJCIZ3knffvst9TpdJCekcG9uPjncFRCCPFyCg0NxcjIiGrVqunfq1O3LiHBwbnaBgcHU7duXf3runXrEpxHu6I4fOgQNZ8a9SaEEK+7vDI3vywtKHODg4OpU6cOCoVCv75OnTp5bken03H48OFcI42FeBnIdBDipadO12JmrMzxnrmJMama9LzbmihztkvXotPpUCgU/LdPVzycypCm1bL2+Dlm7tjHgoFvYWgg34cIIf6dUjXpmBkb53gvKzvzzlhzE+Nc7f7O2LYe7vz45yGWHzgKwPgOLSljaQFAg0oV+OX0eVpXq4KFqQm/nA4CIE1GAgsh/kWSkpKwsso5LZm1lRWJSUl5trW2fjIIwtramqSkJH3mFtWqVas4feYMK1eufPbChRDiFZRn5lpbk5iYezBYQZn79LqCtjNjxgwyMzMZOXJkCR2FECVHOoHFS89UaUTKUx2+KRoNqqc6hv9um71zOEWjQaU00l8o13o8Ik1paMh7rRozYEUAt2PiqeRgm2tbQgjxb6AyVpKiyfmAthRNOipl3hmbvW1WxipRKBTcjolj9h/7+LJnJ+q5unAvNp4Z23ZhZ25O48oV6VzLg6jEJD77ZTsZmTr6NqjNibBbOFiaP/djFEKIl4WFhQUJCQk53ktITMTSwqLQtgkJCVhYWBSrA3jr1q1MnjKFvXv24ODg8OyFCyHEKyjPzE1IwNLSstC22TO3qNtZtGgRvr6+HD58GBMTkxI8EiFKhgx/FC+98jZWZGbquBf3JHRvRsVS0c4mV9uKdjbcjIottN3fFArQoct3vRBCvO7K21qTkanjbuyTaRnCHkXnevAmgKu9HTcfRetf33wUTcXH7SKiYihva02DShUwUChwsbOhkVtFToffAsBAoWBI84aseccbv/cG42pvh/3j+duFEOLfolq1ami1Wq5dezLv+oXz5/OcqsHT05Pz58/rX58/f75Ytxfv2rWL0WPG8Nu2bdSuXfufFS6EEK+gvDI3vywtKHM9PT25cOFCjqnNLly4kGM7q1atYvbs2ezbtw8XF5fncThC/GPSCSxeeqZKJc2qVMT/xDnU6emE3I/kxM1btPOokqtt++pV2BoUTHRSMtFJKfwaFEyHGu4ARETHEvYomozMTFI16aw6cgp7czMqZJsHUwgh/m1MlUqau1di7bHTqNPTCb77gOM3wmlfo2qutu1rVOXXsxeJSkomOimZLWcu0rFm1hxrVco6cC8unqBbd9HpdNyPS+Bk2C3cHLLmEE5Uq7kfl4BOp+NWdCwrDx7Du0l9DIoxok0IIV515ubm9O3Th+nTp5OcnMyRI0fY9ttvDB0yJFfboUOGMP+HH7h79y737t1j3vz5DB82TL9eo9GgVqvR6XSkp6ejVqvJzMwEYP/+/QwZOpRfNm2icePGL+z4hBDiZWJubk7fvn2ZNm3ak8zdto2hQ4fmajts2DDmzZunz9zvv/+eESNGANC2bVsMDQ1ZsGABaWlpLFq0CID27dsD4O/vz+TJk9m7dy+VK1d+YccnRHHJdBDilfBBm2b8uO8vhvy8AUtTEz5o0wxXe1uC70UyY/teNo3JunDu6unBg/gkxq/bBkDnmtXo6ukBQFyqmqUHjhGVlIKp0ojqTmWY9mZHjAzluxAhxL/buA4tmb/nIIOW+WGlMmFch1a4Othx6c59pm3dyZbxowDoXqcGD+ITGOv7CwBdannQvU4NAMrZWDGpUxuWHzjKw4QkzEyMaVfdnS61qwMQn6rmq227iUpMwkqlole9WnR7/FkhhPg3Wbx4Me+88w6OTk7Y29uzZPFiPD09OXz4MN179CDx8S3HY8aMIezmTeo8flDRO++8w5gxY/Tb6dK1KwcPHgTg6NGjjHn/ffbv20fbtm2Z9c03xMfH0+PNN/XtW7VsyR9//PECj1QIIUrfkiVLGDVqFGXLlsXe3p6lS5fqM7dbt24kPZ6TfcyYMYSFhenvnHj33Xf1mWtsbMzWrVt59913+fzzz6lRowZbt27F+PFzNb788kuio6Np1KiRfr9Dhgxh2bJlL/hohSiYIvtwdvHqUygUjV1srfcsHdzHuvDW4nU0e2dg0pEbEWN1Op1fadcixOvASmUaPL1Xl5o1nZ1KuxTxiln052H1Hxcvf6rT6RaWdi1CvIoUCkXj6tWr7wkJDpbrWpFLfy+vpM2bN8s1rxDPSKFQePXo0WPljh07rApvLcQTGRkZKJVKXWZmpoyoe8XIP5gQQgghhBBCCCGEEEK8xqQTWIjXjIztF0KIl4PksRBCPD9yR6sQQpQOyd9Xl3QCv37UaelaecrOv1iqJj0TUJd2HUK8RtTqdG1p1yBeQSkajeSxEP+MOiUlRa5rRZ6SEhMlY4X4Z9RJSUnSmyeKLTk5GSMjo/TSrkMUn3QCv36uxqWqjaKTUkq7DlEK0jMyuPzgoTFwsrRrEeJ1kZau3X8u4o5c5IhiydTpOHfrrg44Xtq1CPEKu/rw4UOje/fulXYd4iWj0Wg4cvSoXPMK8c+cOnXqlKlaLd+liOLZt28fFhYW50u7DlF80gn8mtHpdGlGBgYrZ/2xLyUiOra0yxEv0MOEJP63+6DaQKE4rNPpIkq7HiFeF5qMjJ92nA9J2xcSikYrI4JF4aKSkpm3+0CaNiMjBLhU2vUI8arS6XRpSqVyZa/evVOCg4NLuxzxkoiIiGDgoEFqIyMjueYV4h/Q6XT3lUrl3v79+6fevHmztMsRr4CMjAwCAwMZPXp0amxs7H9Lux5RfAqZy+P1o1AoDEyNjL7WwfugMzcyMMwo7ZrE85WhyzTIzNRpDQ0MNqSmp0/Q6XSppV2TEK8ThULR1MLEeKE6XVvX2MhQq1Ao5JenyFNGps4gIzMzU2louDVFo/lAp9MllHZNQrzKFAqFgbm5+dfA+zqdztzY2Fiua//FtFqtgVar1SqVyg2JiYlyzSvEP6RQKEwtLS1/1Gq1gwwMDJRKpTKztGsSLyedTkdqaqpSpVLdTklJmarRaAJKuyZRfNIJ/BpTKBQKwB4wLu1axHOnBaJ1Op38YSTEc6RQKMwAa0DmqBT50QIxOp1Oho0LUYLkulY8Jte8QjwHCoXCELADlKVdi3ipJet0uvjSLkI8O+kEFkIIIYQQQgghhBBCiNeYzAkshBBCCCGEEEIIIYQQrzHpBBZCCCGEEEIIIYQQQojXmHQCCyGEEEIIIYQQQgghxGtMOoGFEEIIIYQQQgghhBDiNSadwEIIIYQQQgghhBBCCPEak05gIYQQQgghhBBCCCGEeI1JJ7AQQgghhBBCCCGEEEK8xqQTWAghhBBCCCGEEEIIIV5j0gkshBBCCCGEEEIIIYQQrzHpBBZCCCGEEEIIIYQQQojX2P8DGL3XbWzh+6IAAAAASUVORK5CYII=" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Make Policy Decision with EconML \n", - "We want to make policy decisions to maximum the **revenue** instead of the demand. In this scenario,\n", - "\n", - "\n", - "\\begin{align}\n", - "Rev & = Y \\cdot T \\\\\n", - " & = \\exp^{log(Y)} \\cdot T\\\\\n", - " & = \\exp^{(\\theta(X) \\cdot log(T) + f(X,W) + \\epsilon)} \\cdot T \\\\\n", - " & = \\exp^{(f(X,W) + \\epsilon)} \\cdot T^{(\\theta(X)+1)}\n", - "\\end{align}\n", - "\n", - "\n", - "With the decrease of price, revenue will increase only if $\\theta(X)+1<0$. Thus, we set `sample_treatment_cast=-1` here to learn **what kinds of customers we should give a small discount to maximum the revenue**.\n", - "\n", - "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. In the figure below we can see the model recommends to give discount for people with income less than $0.985$ and give original price for the others." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 15, - "source": [ - "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\r\n", - "intrp.interpret(est, X_test, sample_treatment_costs=-1)\r\n", - "plt.figure(figsize=(25, 5))\r\n", - "intrp.plot(feature_names=X.columns, treatment_names=[\"Discount\", \"No-Discount\"], fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "markdown", - "source": [ - "Now, let us compare our policy with other baseline policies! Our model says which customers to give a small discount to, and for this experiment, we will set a discount level of 10% for those users. Because the model is misspecified we would not expect good results with large discounts. Here, because we know the ground truth, we can evaluate the value of this policy." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, - "source": [ - "# define function to compute revenue\r\n", - "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\r\n", - " policy_price = baseline_T * (1 - discount_level1) * policy + baseline_T * (1 - discount_level2) * (1 - policy)\r\n", - " demand = demand_fn(data, policy_price)\r\n", - " rev = demand * policy_price\r\n", - " return rev" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, - "source": [ - "policy_dic = {}\r\n", - "# our policy above\r\n", - "policy = intrp.treat(X)\r\n", - "policy_dic[\"Our Policy\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, policy))\r\n", - "\r\n", - "## previous strategy\r\n", - "policy_dic[\"Previous Strategy\"] = np.mean(train_data[\"price\"] * train_data[\"demand\"])\r\n", - "\r\n", - "## give everyone discount\r\n", - "policy_dic[\"Give Everyone Discount\"] = np.mean(revenue_fn(train_data, 0.1, 0, 1, np.ones(len(X))))\r\n", - "\r\n", - "## don't give discount\r\n", - "policy_dic[\"Give No One Discount\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, np.ones(len(X))))\r\n", - "\r\n", - "## follow our policy, but give -10% discount for the group doesn't recommend to give discount\r\n", - "policy_dic[\"Our Policy + Give Negative Discount for No-Discount Group\"] = np.mean(revenue_fn(train_data, -0.1, 0.1, 1, policy))\r\n", - "\r\n", - "## give everyone -10% discount\r\n", - "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, - "source": [ - "# get policy summary table\r\n", - "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\r\n", - "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\r\n", - "res" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Revenue Rank\n", - "Our Policy 14.686241 2.0\n", - "Previous Strategy 14.349342 4.0\n", - "Give Everyone Discount 13.774469 6.0\n", - "Give No One Discount 14.294606 5.0\n", - "Our Policy + Give Negative Discount for No-Disc... 15.564411 1.0\n", - "Give Everyone Negative Discount 14.612670 3.0" - ], - "text/html": [ - "
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RevenueRank
Our Policy14.6862412.0
Previous Strategy14.3493424.0
Give Everyone Discount13.7744696.0
Give No One Discount14.2946065.0
Our Policy + Give Negative Discount for No-Discount Group15.5644111.0
Give Everyone Negative Discount14.6126703.0
\n", - "
" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "**We beat the baseline policies!** Our policy gets the highest revenue except for the one raising the price for the No-Discount group. That means our currently baseline price is low, but the way we segment the user does help increase the revenue!" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Conclusions \n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* Estimate the treatment effect correctly even the model is misspecified\n", - "* Interpret the resulting individual-level treatment effects\n", - "* Make the policy decision beats the previous and baseline policies\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ], - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.6.6 64-bit" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "interpreter": { - "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb b/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb deleted file mode 100644 index 35882ad10..000000000 --- a/notebooks/CustomerScenarios/Case Study - Long-Term Return-on-Investment at Microsoft via Short-Term Proxies.ipynb +++ /dev/null @@ -1,955 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Long-Term Return-on-Investment at Microsoft via Short-Term Proxies\n", - "\n", - "\n", - "Policy makers typically face the problem of wanting to estimate the treatment effect of some new incentives on long-run downstream interests. However, we only have historical data of older treatment options, and we haven't seen the long-run play out yet. We assume access to a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies. Our work combines three major recent techniques in the causal machine learning literature: **surrogate indices**, **dynamic treatment effect estimation** and **double machine learning**, in a unified\n", - "pipeline. For more details, see this paper [here](https://arxiv.org/pdf/2103.08390.pdf).\n", - "\n", - "In this case study, we will show you how to apply this unified pipeline to a ROI estimation problem at Microsoft. These methodologies have already been implemented into our [EconML](https://aka.ms/econml) library and you could do it with only a few lines of code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary\n", - "\n", - "1. [Background](#Background)\n", - "2. [Data](#Data)\n", - "3. [Do Dynamic Adjustment with EconML](#Do-Dynamic-Adjustment-with-EconML)\n", - "4. [Train Surrogate Index](#Train-Surrogate-Index)\n", - "5. [Run DML to Learn ROI with EconML](#Run-DML-to-Learn-ROI-with-EconML)\n", - "6. [Model Evaluation](#Model-Evaluation)\n", - "7. [Extensions -- Including Heterogeneity in Effect](#Extensions----Including-Heterogeneity-in-Effect)\n", - "8. [Conclusions](#Conclusions)" - ] - }, - { - "attachments": { - "causal_graph.PNG": { - "image/png": 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" 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "Microsoft provides multiple montetary and resource investments to enterprice customers in support of products adoption, the sales manager would like to know which of these programs (\"investments\") are more successful than others? Specifically, we are interested in identifying the average treatment effect of each investment at some period $t$, on the cumulative outcome in the subsequent $m$ months. \n", - "\n", - "There are a few challenges to answer this question. First of all, we haven't fully observed the long-term revenue yet and we don't want to wait that long to evaluate a program. In addition, a careful causal modeling is required to correctly attribute the long-term ROI of multiple programs in a holistic manner, avoiding the biased estimate coming from confounding effect or double counting issues. \n", - "\n", - "The causal graph below shows how to frame this problem:\n", - "\n", - "![causal_graph.PNG](attachment:causal_graph.PNG)\n", - "\n", - "**Methodology:** Our proposed adjusted surrogate index approach could address all the chanllenges above by assuming the long-term effect is channeled through some short-term observed surrogates and employing a dynamic adjustment step (`DynamicDML`) to the surrogate model in order to get rid of the effect from future investment, finally applying double machine learning (`DML`) techniques to estimate the ROI. \n", - "\n", - "The pipeline below tells you how to solve this problem step by step:\n", - "![pipeline.PNG](attachment:pipeline.PNG)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "from econml.data.dynamic_panel_dgp import SemiSynthetic\n", - "from sklearn.linear_model import LassoCV, MultiTaskLassoCV\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data\n", - "\n", - "The **semi-synthetic data*** is comprised of 4 components:\n", - " * **Surrogates:** short-term metrics that could represent long-term revenue\n", - " * **Treatments:** different types of monetary investments to the end customers\n", - " * **Outcomes:** cumulative long-term revenue\n", - " * **Controls:** lagged surrogates and treatments, other time-invariant controls (e.g. demographics)\n", - "\n", - "To build the semi-synthetic data we estimate a series of moments from a real-world dataset: a full covariance matrix of\n", - "all surrogates, treatments, and controls in one period and a series of linear prediction models (lassoCV) of each surrogate and\n", - "treatment on a set of 6 lags of each treatment, 6 lags of each surrogate, and time-invariant controls. Using these values, we draw new parameters from distributions matching the key characteristics of each family of parameters. Finally, we use these new\n", - "parameters to simulate surrogates, treatments, and controls by drawing a set of initial values from the covariance matrix and\n", - "forward simulating to match intertemporal relationships from the transformed prediction models. We use one surrogate to be the outcome of interests. Then we consider the effect of each treatment in period $t$ on the cumulative sum of outcome from following 4 periods. We can calculate the true treatment effects in the semi-synthetic data as a function of parameters from the linear prediction models.\n", - "\n", - "The input data is in a **panel format**. Each panel corresponds to one company and the different rows in a panel correspond to different time period. \n", - "\n", - "Example:\n", - "\n", - "||Company|Year|Features|Controls/Surrogates|T1|T2|T3|AdjRev|\n", - "|---|---|---|---|---|---|---|---|---|\n", - "|1|A|2018|...|...|\\$1,000|...|...|\\$10,000|\n", - "|2|A|2019|...|...|\\$2,000|...|...|\\$12,000|\n", - "|3|A|2020|...|...|\\$3,000|...|...|\\$15,000|\n", - "|4|A|2021|...|...|\\$3,000|...|...|\\$18,000|\n", - "|5|B|2018|...|...|\\$0|...|...|\\$5,000|\n", - "|6|B|2019|...|...|\\$1,000|...|...|\\$10,000|\n", - "|7|B|2020|...|...|\\$0|...|...|\\$7,000|\n", - "|8|B|2021|...|...|\\$1,200|...|...|\\$12,000|\n", - "|9|C|2018|...|...|\\$1,000|...|...|\\$20,000|\n", - "|10|C|2019|...|...|\\$1,500|...|...|\\$25,000|\n", - "|11|C|2020|...|...|\\$500|...|...|\\$18,000|\n", - "|12|C|2021|...|...|\\$500|...|...|\\$20,000|\n", - " \n", - " **For confidentiality reason, the data used in this case study is synthetically generated and the feature distributions don't exactly correspond to real distributions.*" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# generate historical dataset (training purpose)\n", - "np.random.seed(43)\n", - "dgp = SemiSynthetic()\n", - "dgp.create_instance()\n", - "n_periods = 4\n", - "n_units = 5000\n", - "n_treatments = dgp.n_treatments\n", - "random_seed = 43\n", - "thetas = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", - "\n", - "panelX, panelT, panelY, panelGroups, true_effect = dgp.gen_data(\n", - " n_units, n_periods, thetas, random_seed\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Outcome shape: (5000, 4)\n", - "Treatment shape: (5000, 4, 3)\n", - "Controls shape: (5000, 4, 71)\n" - ] - } - ], - "source": [ - "# print panel data shape\n", - "print(\"Outcome shape: \", panelY.shape)\n", - "print(\"Treatment shape: \", panelT.shape)\n", - "print(\"Controls shape: \", panelX.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# generate new dataset (testing purpose)\n", - "thetas_new = np.random.uniform(0, 2, size=(dgp.n_proxies, n_treatments))\n", - "panelXnew, panelTnew, panelYnew, panelGroupsnew, true_effect_new = dgp.gen_data(\n", - " n_units, n_periods, thetas_new, random_seed\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n" - ] - } - ], - "source": [ - "# print true long term effect\n", - "true_longterm_effect = np.sum(true_effect_new, axis=0)\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Do Dynamic Adjustment with EconML\n", - "From the causal graph above, we could see we want to first remove the effects of future incentives from the historical outcomes to create an **adjusted long-term revenue** as if those future incentives never happened.\n", - "\n", - "EconML's `DynamicDML` estimator is an extension of Double Machine Learning approach to **dynamically estimate the period effect of treatments assigned sequentially over time period**. In this scenario, it could help us to adjust the cumulative revenue by subtracting the period effect of all of the investments after the target investment.\n", - "\n", - "For more details about `DynamicDML`, please read this [paper](https://arxiv.org/pdf/2002.07285.pdf). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper function to reshape the panel data\n", - "def long(x): # reshape the panel data to (n_units * n_periods, -1)\n", - " n_units = x.shape[0]\n", - " n_periods = x.shape[1]\n", - " return (\n", - " x.reshape(n_units * n_periods)\n", - " if np.ndim(x) == 2\n", - " else x.reshape(n_units * n_periods, -1)\n", - " )\n", - "\n", - "\n", - "def wide(x): # reshape the panel data to (n_units, n_periods * d_x)\n", - " n_units = x.shape[0]\n", - " return x.reshape(n_units, -1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", - " [1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00],\n", - " [2.000e+00, 2.000e+00, 2.000e+00, 2.000e+00],\n", - " ...,\n", - " [4.997e+03, 4.997e+03, 4.997e+03, 4.997e+03],\n", - " [4.998e+03, 4.998e+03, 4.998e+03, 4.998e+03],\n", - " [4.999e+03, 4.999e+03, 4.999e+03, 4.999e+03]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panelGroups" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.35952949 1.92451605 0.34684417]\n", - "[0.74662029 1.13138969 0.25069193 1.30585143 1.79051531 0.34597602]\n", - "[0.46734394 0.74952179 0.16292026 0.67056612 0.92299133 0.23686006\n", - " 1.36311063 1.91659314 0.34728767]\n" - ] - } - ], - "source": [ - "# on historical data construct adjusted outcomes\n", - "from econml.dynamic.dml import DynamicDML\n", - "\n", - "panelYadj = panelY.copy()\n", - "\n", - "est = DynamicDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=2\n", - ")\n", - "for t in range(1, n_periods): # for each target period 1...m\n", - " # learn period effect for each period treatment on target period t\n", - " est.fit(\n", - " long(panelY[:, 1 : t + 1]),\n", - " long(panelT[:, 1 : t + 1, :]), # reshape data to long format\n", - " X=None,\n", - " W=long(panelX[:, 1 : t + 1, :]),\n", - " groups=long(panelGroups[:, 1 : t + 1]),\n", - " )\n", - " print(est.intercept_)\n", - " # remove effect of observed treatments\n", - " T1 = wide(panelT[:, 1 : t + 1, :])\n", - " panelYadj[:, t] = panelY[:, t] - est.effect(\n", - " T0=np.zeros_like(T1), T1=T1\n", - " ) # reshape data to wide format" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train Surrogate Index\n", - "Once we have the adjusted outcome, we'd like to train any ML model to learn the relationship between short-term surrogates and long-term revenue from the historical dataset, assuming the treatment effect of investments on long-term revenue could **only** go through short-term surrogates, and the **relationship keeps the same** between the historical dataset and the new dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_formatted_code = \"# train surrogate index on historical dataset\\nXS = np.hstack(\\n [panelX[:, 1], panelYadj[:, :1]]\\n) # concatenate controls and surrogates from historical dataset\\nTotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\\nadjusted_proxy_model = LassoCV().fit(\\n XS, TotalYadj\\n) # train proxy model from historical dataset\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# train surrogate index on historical dataset\n", - "XS = np.hstack(\n", - " [panelX[:, 1], panelYadj[:, :1]]\n", - ") # concatenate controls and surrogates from historical dataset\n", - "TotalYadj = np.sum(panelYadj, axis=1) # total revenue from historical dataset\n", - "adjusted_proxy_model = LassoCV().fit(\n", - " XS, TotalYadj\n", - ") # train proxy model from historical dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_formatted_code = \"# predict new long term revenue\\nXSnew = np.hstack(\\n [panelXnew[:, 1], panelYnew[:, :1]]\\n) # concatenate controls and surrogates from new dataset\\nsindex_adj = adjusted_proxy_model.predict(XSnew)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# predict new long term revenue\n", - "XSnew = np.hstack(\n", - " [panelXnew[:, 1], panelYnew[:, :1]]\n", - ") # concatenate controls and surrogates from new dataset\n", - "sindex_adj = adjusted_proxy_model.predict(XSnew)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run DML to Learn ROI with EconML\n", - "Finally we will call `LinearDML` estimator from EconML to learn the treatment effect of multiple investments on the adjusted surrogate index in new dataset. `LinearDML` is a two stage machine learning models for estimating **(heterogeneous) treatment effects** when all potential confounders are observed, it leverages the machine learning power to deal with **high dimensional dataset** and still be able to construct **confidence intervals**. \n", - "\n", - "For more details, please read this [paper](https://arxiv.org/pdf/1608.00060.pdf). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858
cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733
cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 0.83 0.015 57.214 0.0 0.802 0.858\n", - "cate_intercept|T1 0.677 0.028 23.767 0.0 0.621 0.733\n", - "cate_intercept|T2 2.438 0.035 69.711 0.0 2.369 2.507\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nadjsurr_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on surrogate index on new dataset\n", - "from econml.dml import LinearDML\n", - "\n", - "adjsurr_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "adjsurr_est.fit(sindex_adj, panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "adjsurr_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 12;\n", - " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\nadjsurr_point_est = adjsurr_est.intercept_\\nadjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# save the treatment effect and confidence interval\n", - "adjsurr_point_est = adjsurr_est.intercept_\n", - "adjsurr_conf_int_lb, adjsurr_conf_int_ub = adjsurr_est.intercept__interval(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Evaluation\n", - "Now we want to compare the proposed **adjusted surrogate index** approach with estimation from realized long-term outcome. Below we train another `LinearDML` model on the realized cumulative revenue directly, without any adjustment. And then we visualize the two models output, comparing with the ground truth." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304
cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004
cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 2.227 0.039 56.865 0.0 2.15 2.304\n", - "cate_intercept|T1 1.561 0.226 6.911 0.0 1.118 2.004\n", - "cate_intercept|T2 4.335 0.209 20.748 0.0 3.926 4.745\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 13;\n", - " var nbb_formatted_code = \"# learn treatment effect on direct outcome\\nfrom econml.dml import LinearDML\\n\\ndirect_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\ndirect_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\ndirect_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on direct outcome\n", - "from econml.dml import LinearDML\n", - "\n", - "direct_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "direct_est.fit(np.sum(panelYnew, axis=1), panelTnew[:, 0], X=None, W=panelXnew[:, 0])\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "direct_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 14;\n", - " var nbb_formatted_code = \"# save the treatment effect and confidence interval\\ndirect_point_est = direct_est.intercept_\\ndirect_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# save the treatment effect and confidence interval\n", - "direct_point_est = direct_est.intercept_\n", - "direct_conf_int_lb, direct_conf_int_ub = direct_est.intercept__interval(alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Error bar plot of treatment effect from different models')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_formatted_code = \"# plot the error bar plot of different models\\nplt.figure(figsize=(18, 6))\\nplt.subplot(1, 2, 1)\\n\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\n\\nplt.subplot(1, 2, 2)\\nplt.errorbar(\\n np.arange(n_treatments) - 0.04,\\n true_longterm_effect,\\n fmt=\\\"o\\\",\\n alpha=0.6,\\n label=\\\"Ground truth\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments),\\n adjsurr_point_est,\\n yerr=(\\n adjsurr_conf_int_ub - adjsurr_point_est,\\n adjsurr_point_est - adjsurr_conf_int_lb,\\n ),\\n fmt=\\\"o\\\",\\n label=\\\"Adjusted Surrogate Index\\\",\\n)\\nplt.errorbar(\\n np.arange(n_treatments) + 0.04,\\n direct_point_est,\\n yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\\n fmt=\\\"o\\\",\\n label=\\\"Direct Model\\\",\\n)\\nplt.xticks(np.arange(n_treatments), [\\\"T0\\\", \\\"T1\\\", \\\"T2\\\"])\\nplt.ylabel(\\\"Effect\\\")\\nplt.legend()\\nplt.suptitle(\\\"Error bar plot of treatment effect from different models\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot the error bar plot of different models\n", - "plt.figure(figsize=(18, 6))\n", - "plt.subplot(1, 2, 1)\n", - "\n", - "plt.errorbar(\n", - " np.arange(n_treatments) - 0.04,\n", - " true_longterm_effect,\n", - " fmt=\"o\",\n", - " alpha=0.6,\n", - " label=\"Ground truth\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments),\n", - " adjsurr_point_est,\n", - " yerr=(\n", - " adjsurr_conf_int_ub - adjsurr_point_est,\n", - " adjsurr_point_est - adjsurr_conf_int_lb,\n", - " ),\n", - " fmt=\"o\",\n", - " label=\"Adjusted Surrogate Index\",\n", - ")\n", - "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", - "plt.ylabel(\"Effect\")\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.errorbar(\n", - " np.arange(n_treatments) - 0.04,\n", - " true_longterm_effect,\n", - " fmt=\"o\",\n", - " alpha=0.6,\n", - " label=\"Ground truth\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments),\n", - " adjsurr_point_est,\n", - " yerr=(\n", - " adjsurr_conf_int_ub - adjsurr_point_est,\n", - " adjsurr_point_est - adjsurr_conf_int_lb,\n", - " ),\n", - " fmt=\"o\",\n", - " label=\"Adjusted Surrogate Index\",\n", - ")\n", - "plt.errorbar(\n", - " np.arange(n_treatments) + 0.04,\n", - " direct_point_est,\n", - " yerr=(direct_conf_int_ub - direct_point_est, direct_point_est - direct_conf_int_lb),\n", - " fmt=\"o\",\n", - " label=\"Direct Model\",\n", - ")\n", - "plt.xticks(np.arange(n_treatments), [\"T0\", \"T1\", \"T2\"])\n", - "plt.ylabel(\"Effect\")\n", - "plt.legend()\n", - "plt.suptitle(\"Error bar plot of treatment effect from different models\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could see the **adjusted surrogate index** approach does a good job overcomes a common data limitation when considering long-term effects of novel treatments and expands the surrogate approach to consider a common, and previously\n", - "problematic, pattern of serially correlated treatments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extensions -- Including Heterogeneity in Effect\n", - "\n", - "Finally, I will show that our EconML's `DynamicDML` and `LinearDML` estimators could not only learn Average Treatment Effect (ATE), but also **Heterogeneous Treatment Effect (CATE)**, which will return the treatment effect as a function of interested characteristics. In the example below, I will use first control variable as feature to learn effect heterogeneity, and retrain the final `LinearDML` model. Similarly, you could train `DynamicDML` with feature $X$ as well." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Long-term Effect for each investment: [0.90994672 0.709811 2.45310877]\n", - "Average treatment effect for each investment: [0.82738185 0.71610965 2.56087599]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031
X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098
X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856
cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779
cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------\n", - "X0|T0 0.009 0.011 0.76 0.447 -0.014 0.031\n", - "X0|T1 0.037 0.031 1.218 0.223 -0.023 0.098\n", - "X0|T2 -0.072 0.151 -0.478 0.633 -0.369 0.224\n", - " CATE Intercept Results \n", - "=======================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------------------\n", - "cate_intercept|T0 0.827 0.015 56.625 0.0 0.799 0.856\n", - "cate_intercept|T1 0.716 0.032 22.466 0.0 0.654 0.779\n", - "cate_intercept|T2 2.56 0.237 10.82 0.0 2.096 3.024\n", - "-----------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_formatted_code = \"# learn treatment effect on surrogate index on new dataset\\nfrom econml.dml import LinearDML\\n\\nadjsurr_est = LinearDML(\\n model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\\n)\\n# fit treatment_0 on total revenue from new dataset\\nadjsurr_est.fit(\\n sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\\n)\\n# print treatment effect summary\\nprint(\\\"True Long-term Effect for each investment: \\\", true_longterm_effect)\\nprint(\\n \\\"Average treatment effect for each investment: \\\",\\n adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\\n)\\nadjsurr_est.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# learn treatment effect on surrogate index on new dataset\n", - "from econml.dml import LinearDML\n", - "\n", - "adjsurr_est = LinearDML(\n", - " model_y=LassoCV(max_iter=2000), model_t=MultiTaskLassoCV(max_iter=2000), cv=3\n", - ")\n", - "# fit treatment_0 on total revenue from new dataset\n", - "adjsurr_est.fit(\n", - " sindex_adj, panelTnew[:, 0], X=panelXnew[:, 0, :1], W=panelXnew[:, 0, 1:]\n", - ")\n", - "# print treatment effect summary\n", - "print(\"True Long-term Effect for each investment: \", true_longterm_effect)\n", - "print(\n", - " \"Average treatment effect for each investment: \",\n", - " adjsurr_est.const_marginal_ate(panelXnew[:, 0, :1]),\n", - ")\n", - "adjsurr_est.summary(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary table above, none of the coefficient for feature $X0$ is significant, that means there is no effect heterogeneity identified, which is consistent with the data generation process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusions\n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* estimate treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes\n", - "* correct the bias coming from auto-correlation of the historical treatment policy\n", - "* use Machine Learning to enable estimation with high-dimensional surrogates and controls\n", - "* solve a complex problem using an unified pipeline with only a few lines of code\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb deleted file mode 100644 index 4d36d9d7d..000000000 --- a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb +++ /dev/null @@ -1,1267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "

Multi-investment Attribution: Distinguish the Effects of Multiple Outreach Efforts

\n", - "\n", - "A startup that sells software would like to know whether its multiple outreach efforts were successful in attracting new customers or boosting consumption among existing customers. They would also like to distinguish the effects of several incentives on different kinds of customers. In other words, they would like to learn the **heterogeneous treatment effect** of each investment on customers' software usage. \n", - "\n", - "In an ideal world, the startup would run several randomized experiments where each customer would receive a random assortment of investments. However, this can be logistically prohibitive or strategically unsound: the startup might not have the resources to design such experiments or they might not want to risk losing out on big opportunities due to lack of incentives.\n", - "\n", - "In this customer scenario walkthrough, we show how tools from the [EconML](https://aka.ms/econml) library can use historical investment data to learn the effects of multiple investments." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Summary\n", - "\n", - "1. [Background](#Background)\n", - "2. [Data](#Data)\n", - "3. [Get Causal Effects with EconML](#Get-Causal-Effects-with-EconML)\n", - "4. [Understand Treatment Effects with EconML](#Understand-Treatment-Effects-with-EconML)\n", - "5. [Make Policy Decisions with EconML](#Make-Policy-Decisions-with-EconML)\n", - "6. [Conclusions](#Conclusions)" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Background\n", - "\n", - "\n", - "\n", - "In this scenario, a startup that sells software provides two types of incentives to its customers: technical support and discounts. A customer might be given one, both or none of these incentives. \n", - "\n", - "The startup has historical data on these two investments for 2,000 customers, as well as how much revenue these customers generated in the year after the investments were made. They would like to use this data to learn the optimal incentive policy for each existing or new customer in order to maximize the return on investment (ROI).\n", - "\n", - "The startup faces two challenges: 1) the dataset is biased because historically the larger customers received the most incentives and 2) the observed outcome combines effects from two different investments. Thus, they need a causal model that can accommodate multiple concurrent interventions. \n", - "\n", - "**Solution:** EconML’s `Doubly Robust Learner` model jointly estimates the effects of multiple discrete treatments. The model uses flexible functions of observed customer features to filter out spurious correlations in existing data and deliver the causal effect of each intervention on revenue.\n" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "# Some imports to get us started\r\n", - "import warnings\r\n", - "warnings.simplefilter('ignore')\r\n", - "# Utilities\r\n", - "import os\r\n", - "import urllib.request\r\n", - "import numpy as np\r\n", - "import pandas as pd\r\n", - "\r\n", - "# Generic ML imports\r\n", - "from xgboost import XGBRegressor, XGBClassifier\r\n", - "\r\n", - "# EconML imports\r\n", - "from econml.dr import LinearDRLearner\r\n", - "\r\n", - "import matplotlib.pyplot as plt\r\n", - "import seaborn as sns\r\n", - "\r\n", - "%matplotlib inline" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Data\n", - "\n", - "The data* contains ~2,000 customers and is comprised of:\n", - "\n", - "* Customer features: details about the industry, size, revenue, and technology profile of each customer.\n", - "* Interventions: information about which incentive was given to a customer.\n", - "* Outcome: the amount of product the customer bought in the year after the incentives were given.\n", - "\n", - "Feature Name | Type | Details \n", - ":--- |:--- |:--- \n", - "**Global Flag** | W | whether the customer has global offices\n", - "**Major Flag** | W | whether the customer is a large consumer in their industry (as opposed to SMC - Small Medium Corporation - or SMB - Small Medium Business)\n", - "**SMC Flag** | W | whether the customer is a Small Medium Corporation (SMC, as opposed to major and SMB)\n", - "**Commercial Flag** | W | whether the customer's business is commercial (as opposed to public secor)\n", - "**IT Spend** | W | \\\\$ spent on IT-related purchases \n", - "**Employee Count** | W | number of employees\n", - "**PC Count** | W | number of PCs used by the customer\n", - "**Size** | X | customer's size given by their yearly total revenue \n", - "**Tech Support** | T | whether the customer received tech support (binary)\n", - "**Discount** | T | whether the customer was given a discount (binary)\n", - "**Revenue** | Y | \\\\$ Revenue from customer given by the amount of software purchased\n", - "\n", - "**To protect the privacy of the startup's customers, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "# Import the sample multi-attribution data\n", - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/ROI/multi_attribution_sample.csv\"\n", - "multi_data = pd.read_csv(file_url)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "# Data sample\n", - "multi_data.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", - "0 1 0 1 0 45537 \n", - "1 0 0 1 1 20842 \n", - "2 0 0 0 1 82171 \n", - "3 0 0 0 0 30288 \n", - "4 0 0 1 0 25930 \n", - "\n", - " Employee Count PC Count Size Tech Support Discount Revenue \n", - "0 26 26 152205 0 1 17688.36300 \n", - "1 107 70 159038 0 1 14981.43559 \n", - "2 10 7 264935 1 1 32917.13894 \n", - "3 40 39 77522 1 1 14773.76855 \n", - "4 37 43 91446 1 1 17098.69823 " - ], - "text/html": [ - "
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Global FlagMajor FlagSMC FlagCommercial FlagIT SpendEmployee CountPC CountSizeTech SupportDiscountRevenue
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\n", - "
" - ] - }, - "metadata": {}, - "execution_count": 3 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# Define estimator inputs\n", - "T_bin = multi_data[\n", - " [\"Tech Support\", \"Discount\"]\n", - "] # multiple interventions, or treatments\n", - "Y = multi_data[\"Revenue\"] # amount of product purchased, or outcome\n", - "X = multi_data[[\"Size\"]] # heterogeneity feature\n", - "W = multi_data.drop(\n", - " columns=[\"Tech Support\", \"Discount\", \"Revenue\", \"Size\"]\n", - ") # controls" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "We investigate below whether the number of investments given is correlated with the size of the customer. We note that the average customer size is larger for more incentives given. " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "# Average customer size per incentive combination\n", - "multi_data[[\"Size\", \"Tech Support\", \"Discount\"]].groupby(\n", - " by=[\"Tech Support\", \"Discount\"], as_index=False\n", - ").mean().astype(int)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Tech Support Discount Size\n", - "0 0 0 70943\n", - "1 0 1 96466\n", - "2 1 0 108978\n", - "3 1 1 171466" - ], - "text/html": [ - "
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Tech SupportDiscountSize
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" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "The data was generated using the following underlying treatment effect function:\n", - "\n", - "$$\n", - "\\text{treatment_effect(Size)} = (5,000 + 2\\% \\cdot \\text{Size}) \\cdot I_\\text{Tech Support} + (5\\% \\cdot \\text{Size}) \\cdot I_\\text{Discount}\n", - "$$\n", - "\n", - "Therefore, the treatment effect depends on the customer's size as follows: tech support provides an consumption boost of \\$5,000 + 2\\% Size and a discount provides an consumption boost of 5\\% Size.**This is the relationship we seek to learn from the data.**" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "# Define underlying treatment effect function\n", - "TE_fn = lambda X: np.hstack([5000 + 2 / 100 * X, 5 / 100 * X])\n", - "true_TE = TE_fn(X)\n", - "\n", - "# Define true coefficients for the three treatments\n", - "# The third coefficient is just the sum of the first two since we assume an additive effect\n", - "true_coefs = [2 / 100, 5 / 100, 7 / 100]\n", - "true_intercepts = [5000, 0, 5000]\n", - "treatment_names = [\"Tech Support\", \"Discount\", \"Tech Support & Discount\"]" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Get Causal Effects with EconML" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "To get causal effects, we use EconML's `LinearDRLearner`* estimator. This estimator requires a set of discrete treatments $T$ that corresponds to different types of interventions. Thus, we first map the binary interventions tech support and discount into one categorical variable:\n", - "\n", - "Tech support| Discount| Treatment encoding| Details\n", - ":--- |:--- |:--- |:---\n", - "0 | 0 | 0 | no incentive\n", - "1 | 0 | 1 | tech support only\n", - "0 | 1 | 2 | discount only\n", - "1 | 1 | 3 | both incentives\n", - "\n", - "The estimator takes as input the outcome of interest $Y$ (amount of product purchased), a discrete treatment $T$ (interventions given), heterogeneity features $X$ (here, customer's size) and controls $W$ (all other customer features).\n", - "\n", - "\n", - "The LinearDRLearner also requires two auxiliary models to model the relationships $T\\sim (W, X)$ (`model_propensity`) and $Y \\sim (W, X)$(`model_regression`). These can be generic, flexible classification and regression models, respectively. \n", - "\n", - "\n", - "**This estimator assumes a linear relationship between the treatment effect and a transformation of the features $X$ (e.g. a polynomial basis expansion). For more generic forms of the treatment effect, see the `DRLearner` estimator.*" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# Transform T to one-dimensional array with consecutive integer encoding\r\n", - "def treat_map(t):\r\n", - " return np.dot(t, 2 ** np.arange(t.shape[0]))\r\n", - "\r\n", - "\r\n", - "T = np.apply_along_axis(treat_map, 1, T_bin).astype(int)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "# Train EconML model with generic helper models\r\n", - "model = LinearDRLearner(\r\n", - " model_regression=XGBRegressor(learning_rate=0.1, max_depth=3),\r\n", - " model_propensity=XGBClassifier(learning_rate=0.1, max_depth=3, objective=\"multi:softmax\"),\r\n", - " random_state=1,\r\n", - ")\r\n", - "# Specify final stage inference type and fit model\r\n", - "model.fit(Y=Y, T=T, X=X, W=W, inference=\"statsmodels\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[06:14:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", - "[06:14:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Understand Treatment Effects with EconML\n", - "\n", - "We can obtain a summary of the coefficient values as well as confidence intervals by calling the `summary` function on the fitted model for each treatment." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "for i in range(model._d_t[0]):\r\n", - " print(f\"Investment: {treatment_names[i]}\")\r\n", - " print(f\"True treatment effect: {true_intercepts[i]} + {true_coefs[i]}*Size\")\r\n", - " display(model.summary(T=i + 1))" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Investment: Tech Support\n", - "True treatment effect: 5000 + 0.02*Size\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "Size 0.021 0.012 1.749 0.08 -0.002 0.044\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 5326.611 845.551 6.3 0.0 3669.361 6983.861\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
Size 0.021 0.012 1.749 0.08 -0.002 0.044
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 5326.611 845.551 6.3 0.0 3669.361 6983.861


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Investment: Discount\n", - "True treatment effect: 0 + 0.05*Size\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "Size 0.052 0.012 4.371 0.0 0.029 0.075\n", - " CATE Intercept Results \n", - "=====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------------------\n", - "cate_intercept 358.699 848.771 0.423 0.673 -1304.861 2022.258\n", - "---------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
Size 0.052 0.012 4.371 0.0 0.029 0.075
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 358.699 848.771 0.423 0.673 -1304.861 2022.258


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Investment: Tech Support & Discount\n", - "True treatment effect: 5000 + 0.07*Size\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "Size 0.074 0.012 6.292 0.0 0.051 0.096\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 4899.208 851.54 5.753 0.0 3230.22 6568.196\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
Size 0.074 0.012 6.292 0.0 0.051 0.096
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 4899.208 851.54 5.753 0.0 3230.22 6568.196


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "From the summary panels, we see that the learned coefficients/intercepts are close to the true coefficients/intercepts and the p-values are small for most of these. \n", - "\n", - "We further use the `coef_, coef__interval` and the `intercept_, intercept__interval` methods to obtain the learned coefficient values and build confidence intervals. We compare the true and the learned coefficients through the plots below." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "# Compare learned coefficients with true model coefficients\r\n", - "# Aggregate data\r\n", - "coef_indices = np.arange(model._d_t[0])\r\n", - "coefs = np.hstack([model.coef_(T=i) for i in 1 + coef_indices])\r\n", - "intercepts = np.hstack([model.intercept_(T=i) for i in 1 + coef_indices])\r\n", - "\r\n", - "# Calculate coefficient error bars for 95% confidence interval\r\n", - "coef_error = np.hstack([model.coef__interval(T=i) for i in 1 + coef_indices])\r\n", - "coef_error[0, :] = coefs - coef_error[0, :]\r\n", - "coef_error[1, :] = coef_error[1, :] - coefs\r\n", - "\r\n", - "# Calculate intercept error bars for 95% confidence interval\r\n", - "intercept_error = np.vstack(\r\n", - " [model.intercept__interval(T=i) for i in 1 + coef_indices]\r\n", - ").T\r\n", - "intercept_error[0, :] = intercepts - intercept_error[0, :]\r\n", - "intercept_error[1, :] = intercept_error[1, :] - intercepts" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, - "source": [ - "# Plot coefficients\r\n", - "plt.figure(figsize=(6, 5))\r\n", - "ax1 = plt.subplot(2, 1, 1)\r\n", - "plt.errorbar(\r\n", - " coef_indices,\r\n", - " coefs,\r\n", - " coef_error,\r\n", - " fmt=\"o\",\r\n", - " label=\"Learned values\\nand 95% confidence interval\",\r\n", - ")\r\n", - "plt.scatter(coef_indices, true_coefs, color=\"C1\", label=\"True values\", zorder=3)\r\n", - "plt.xticks(coef_indices, treatment_names)\r\n", - "plt.setp(ax1.get_xticklabels(), visible=False)\r\n", - "plt.title(\"Coefficients\")\r\n", - "plt.legend(loc=(1.05, 0.65))\r\n", - "plt.grid()\r\n", - "\r\n", - "# Plot intercepts\r\n", - "plt.subplot(2, 1, 2)\r\n", - "plt.errorbar(coef_indices, intercepts, intercept_error, fmt=\"o\")\r\n", - "plt.scatter(coef_indices, true_intercepts, color=\"C1\", zorder=3)\r\n", - "plt.xticks(coef_indices, treatment_names)\r\n", - "plt.title(\"Intercepts\")\r\n", - "plt.grid()\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Make Policy Decisions with EconML\n", - "\n", - "Investments such as tech support and discounts come with an associated cost. Thus, we would like to know what incentives to give to each customer to maximize the profit from their increased engagement. This is the **treatment policy**.\n", - "\n", - "In this scenario, we define a cost function as follows:\n", - "* The cost of `tech support` scales with the number of PCs a customer has. You can imagine that if the software product needs tech support to be installed on each machine, there is a cost (\\\\$100 here) per machine.\n", - "* The cost of `discount` is a fixed \\\\$7,000. Think of this as giving the customer the first \\\\$7,000 worth of product for free.\n", - "* The cost of `tech support` and `discount` is the sum of the cost of each of these. Note that this might not be the case in every business application: it is possible that managing multiple incentive programs can add overhead. " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, - "source": [ - "# Define cost function\r\n", - "def cost_fn(multi_data):\r\n", - " t1_cost = multi_data[[\"PC Count\"]].values * 100\r\n", - " t2_cost = np.ones((multi_data.shape[0], 1)) * 7000\r\n", - " return np.hstack([t1_cost, t2_cost, t1_cost + t2_cost])" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "We use the model's `const_marginal_effect` method to find the counterfactual treatment effect for each possible treatment. We then subtract the treatment cost and choose the treatment which the highest return. That is the recommended policy." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "# Get roi for each customer and possible treatment\r\n", - "potential_roi = model.const_marginal_effect(X=X.values) - cost_fn(multi_data)\r\n", - "# Add a column of 0s for no treatment\r\n", - "potential_roi = np.hstack([np.zeros((X.shape[0], 1)), potential_roi])" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 14, - "source": [ - "all_treatments = np.array([\"None\"] + treatment_names)\r\n", - "recommended_T = np.argmax(potential_roi, axis=1)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 15, - "source": [ - "ax1 = sns.scatterplot(\r\n", - " x=X.iloc[:, 0].values.flatten(),\r\n", - " y=multi_data[\"PC Count\"].values,\r\n", - " hue=all_treatments[recommended_T],\r\n", - " hue_order=all_treatments,\r\n", - " cmap=\"Dark2\",\r\n", - " s=40,\r\n", - ")\r\n", - "plt.legend(title=\"Investment Policy\")\r\n", - "plt.setp(\r\n", - " ax1,\r\n", - " xlabel=\"Customer Size\",\r\n", - " ylabel=\"PC Count\",\r\n", - " title=\"Optimal Investment Policy by Customer\",\r\n", - ")\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "markdown", - "source": [ - "We compare different policies: the optimal policy we learned, the current policy, and the policy under which each customer is given all incentives. We note that the optimal policy has a much higher ROI than the alternatives. " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, - "source": [ - "roi_current = potential_roi[np.arange(X.shape[0]), T].sum()\r\n", - "roi_optimal = potential_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n", - "roi_bothT = potential_roi[:, -1].sum()\r\n", - "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n", - "Y_baseline = (Y - model.effect(X=X.values, T1=T)).sum()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, - "source": [ - "pd.DataFrame(\r\n", - " {\r\n", - " \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n", - " \"ROI ($)\": all_rois,\r\n", - " \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n", - " }\r\n", - ")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Policy ROI ($) ROI (% of baseline Y)\n", - "0 Optimal 9.735966e+06 64.0\n", - "1 Current 2.535076e+06 16.7\n", - "2 All Investments 9.683787e+05 6.4" - ], - "text/html": [ - "
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PolicyROI ($)ROI (% of baseline Y)
0Optimal9.735966e+0664.0
1Current2.535076e+0616.7
2All Investments9.683787e+056.4
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" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Performance of policies based on ground truth" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, - "source": [ - "true_roi = np.zeros((X.shape[0], 4))\r\n", - "true_roi[:, 1:3] = TE_fn(X.iloc[:, [0]])\r\n", - "true_roi[:, 3] = np.sum(true_roi[:, 1:3], axis=1)\r\n", - "true_roi[:, 1:] -= cost_fn(multi_data)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, - "source": [ - "roi_current = true_roi[np.arange(X.shape[0]), T].sum()\r\n", - "roi_optimal = true_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n", - "roi_bothT = true_roi[:, -1].sum()\r\n", - "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n", - "Y_baseline = (Y - model.effect(X=X.values, T1=T)).sum()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, - "source": [ - "pd.DataFrame(\r\n", - " {\r\n", - " \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n", - " \"ROI ($)\": all_rois,\r\n", - " \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n", - " }\r\n", - ")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Policy ROI ($) ROI (% of baseline Y)\n", - "0 Optimal 8920500.30 58.6\n", - "1 Current 1829938.41 12.0\n", - "2 All Investments 373176.80 2.5" - ], - "text/html": [ - "
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PolicyROI ($)ROI (% of baseline Y)
0Optimal8920500.3058.6
1Current1829938.4112.0
2All Investments373176.802.5
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" - ] - }, - "metadata": {}, - "execution_count": 20 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Policy Decisions Using Doubly Robust Policy Learning" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 21, - "source": [ - "from econml.policy import DRPolicyForest\r\n", - "\r\n", - "X = multi_data[['Size', 'PC Count']]\r\n", - "est = DRPolicyForest(random_state=1,\r\n", - " min_propensity=1e-3,\r\n", - " min_impurity_decrease=0.1,\r\n", - " min_samples_leaf=40,\r\n", - " max_samples=.6,\r\n", - " honest=True,\r\n", - " max_depth=4)\r\n", - "costs = np.hstack([np.zeros((X.shape[0], 1)), cost_fn(multi_data)])\r\n", - "est.fit(Y - costs[np.arange(X.shape[0]), T], T, X=X)\r\n", - "recommended_T = est.predict(X.values)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 22, - "source": [ - "ax1 = sns.scatterplot(\r\n", - " x=X.iloc[:, 0].values.flatten(),\r\n", - " y=multi_data[\"PC Count\"].values,\r\n", - " hue=all_treatments[recommended_T],\r\n", - " hue_order=all_treatments,\r\n", - " cmap=\"Dark2\",\r\n", - " s=40,\r\n", - ")\r\n", - "plt.legend(title=\"Investment Policy\")\r\n", - "plt.setp(\r\n", - " ax1,\r\n", - " xlabel=\"Customer Size\",\r\n", - " ylabel=\"PC Count\",\r\n", - " title=\"Optimal Investment Policy by Customer\",\r\n", - ")\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" 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{ - "text/plain": [ - " Policy ROI ($) ROI (% of baseline Y)\n", - "0 Optimal 8735944.79 57.4\n", - "1 Current 1829938.41 12.0\n", - "2 All Investments 373176.80 2.5" - ], - "text/html": [ - "
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PolicyROI ($)ROI (% of baseline Y)
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" - ] - }, - "metadata": {}, - "execution_count": 25 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Conclusions\n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* Learn the effects of multiple concurrent interventions\n", - "* Interpret the resulting individual-level treatment effects\n", - "* Build investment policies around the learned effects\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.6.6 64-bit" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "interpreter": { - "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb deleted file mode 100644 index 22a1fb160..000000000 --- a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb +++ /dev/null @@ -1,1312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "\n", - "# Recommendation A/B Testing: Experimentation with Imperfect Compliance\n", - "\n", - "An online business would like to test a new feature or offering of their website and learn its effect on downstream revenue. Furthermore, they would like to know which kind of users respond best to the new version. We call the user-specific effect a **heterogeneous treatment effect**. \n", - "\n", - "Ideally, the business would run A/B tests between the old and new versions of the website. However, a direct A/B test might not work because the business cannot force the customers to take the new offering. Measuring the effect in this way will be misleading since not every customer exposed to the new offering will take it.\n", - "\n", - "The business also cannot look directly at existing data as it will be biased: the users who use the latest website features are most likely the ones who are very engaged on the website and hence spend more on the company's products to begin with. Estimating the effect this way would be overly optimistic." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "In this customer scenario walkthrough, we show how tools from the [EconML](https://aka.ms/econml) and [DoWhy](https://github.com/microsoft/dowhy) libraries can still use a direct A/B test and mitigate these shortcomings.\n", - "\n", - "### Summary\n", - "\n", - "1. [Background](#Background)\n", - "2. [Data](#Data)\n", - "3. [Create Causal Model and Identify Causal Effect with DoWhy](#Create-Causal-Model-and-Identify-Causal-Effect-with-DoWhy)\n", - "4. [Get Causal Effects with EconML](#Get-Causal-Effects-with-EconML)\n", - "5. [Test Estimate Robustness with DoWhy](#Test-Estimate-Robustness-with-DoWhy)\n", - " 1. [Add Random Common Cause](#Add-Random-Common-Cause)\n", - " 2. [Add Unobserved Common Cause](#Add-Unobserved-Common-Cause)\n", - " 3. [Replace Treatment with a Random (Placebo) Variable](#Replace-Treatment-with-a-Random-(Placebo)-Variable)\n", - " 4. [Remove a Random Subset of the Data](#Remove-a-Random-Subset-of-the-Data)\n", - "6. [Understand Treatment Effects with EconML](#Understand-Treatment-Effects-with-EconML)\n", - "7. [Make Policy Decisions with EconML](#Make-Policy-Decisions-with-EconML)\n", - "8. [Conclusions](#Conclusions)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Background\n", - "\n", - "\n", - "\n", - "In this scenario, a travel website would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. \n", - "\n", - "A direct A/B test is infeasible because the website cannot force users to become members. Likewise, the travel company can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. \n", - "\n", - "**Solution:** The company had run an earlier experiment to test the value of a new, faster sign-up process. Instrumental variable (IV) estimators can exploit this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. This is known as an **intent-to-treat** setting: the intention is to give a random group of users the \"treatment\" (access to the easier sign-up process), but not all users will actually take it. \n", - "\n", - "The EconML and DoWhy libraries complement each other in implementing this solution. On one hand, the DoWhy library can help [build a causal model, identify the causal effect](#Create-Causal-Model-and-Identify-Causal-Effect-with-DoWhy) and [test causal assumptions](#Test-Estimate-Robustness-with-DoWhy). On the other hand, EconML's `IntentToTreatDRIV` estimator can [estimate heterogeneous treatment effects](#Get-Causal-Effects-with-EconML) by taking advantage of the fact that not every customer who was offered the easier sign-up became a member to learn the effect of membership rather than the effect of receiving the quick sign-up. Furthermore, EconML provides users tools to [understand causal effects](#Understand-Treatment-Effects-with-EconML) and [make causal policy decisions](#Make-Policy-Decisions-with-EconML)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Some imports to get us started\n", - "import warnings\n", - "warnings.simplefilter('ignore')\n", - "\n", - "# Utilities\n", - "import os\n", - "import urllib.request\n", - "import numpy as np\n", - "import pandas as pd\n", - "from networkx.drawing.nx_pydot import to_pydot\n", - "from IPython.display import Image, display\n", - "\n", - "# Generic ML imports\n", - "import lightgbm as lgb\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "\n", - "# DoWhy imports \n", - "import dowhy\n", - "from dowhy import CausalModel\n", - "\n", - "# EconML imports\n", - "from econml.iv.dr import LinearIntentToTreatDRIV\n", - "from econml.cate_interpreter import SingleTreeCateInterpreter, \\\n", - " SingleTreePolicyInterpreter\n", - "\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Data\n", - "\n", - "The data* is comprised of:\n", - " * Features collected in the 28 days prior to the experiment (denoted by the suffix `_pre`)\n", - " * Experiment variables (whether the use was exposed to the easier signup -> the instrument, and whether the user became a member -> the treatment)\n", - " * Variables collected in the 28 days after the experiment (denoted by the suffix `_post`).\n", - "\n", - "Feature Name | Details \n", - ":--- |: --- \n", - "**days_visited_exp_pre** |#days a user visits the attractions pages \n", - "**days_visited_free_pre** | #days a user visits the website through free channels (e.g. domain direct) \n", - "**days_visited_fs_pre** | #days a user visits the flights pages \n", - "**days_visited_hs_pre** | #days a user visits the hotels pages \n", - "**days_visited_rs_pre** | #days a user visits the restaurants pages \n", - "**days_visited_vrs_pre** | #days a user visits the vacation rental pages \n", - "**locale_en_US** | whether the user access the website from the US \n", - "**os_type** | user's operating system (windows, osx, other) \n", - "**revenue_pre** | how much the user spent on the website in the pre-period \n", - "**easier_signup** | whether the user was exposed to the easier signup process \n", - "**became_member** | whether the user became a member \n", - "**days_visited_post** | #days a user visits the website in the 28 days after the experiment \n", - "\n", - "\n", - "**To protect the privacy of the travel company's users, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Import the sample AB data\n", - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/RecommendationAB/ab_sample.csv\" \n", - "ab_data = pd.read_csv(file_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " days_visited_exp_pre days_visited_free_pre days_visited_fs_pre \\\n", - "0 1 9 7 \n", - "1 10 25 27 \n", - "2 18 14 8 \n", - "3 17 0 23 \n", - "4 24 9 22 \n", - "\n", - " days_visited_hs_pre days_visited_rs_pre days_visited_vrs_pre \\\n", - "0 25 6 3 \n", - "1 10 27 27 \n", - "2 4 5 2 \n", - "3 2 3 1 \n", - "4 2 3 18 \n", - "\n", - " locale_en_US revenue_pre os_type_osx os_type_windows easier_signup \\\n", - "0 1 0.01 0 1 0 \n", - "1 0 2.26 0 0 0 \n", - "2 1 0.03 0 1 0 \n", - "3 1 418.77 0 1 0 \n", - "4 1 1.54 0 0 0 \n", - "\n", - " became_member days_visited_post \n", - "0 0 1 \n", - "1 0 15 \n", - "2 0 17 \n", - "3 0 6 \n", - "4 0 12 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Data sample\n", - "ab_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Define estimator inputs\n", - "Z = ab_data['easier_signup'] # nudge, or instrument\n", - "T = ab_data['became_member'] # intervention, or treatment\n", - "Y = ab_data['days_visited_post'] # outcome of interest\n", - "X_data = ab_data.drop(columns=['easier_signup', 'became_member', 'days_visited_post']) # features" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The data was generated using the following undelying treatment effect function:\n", - "\n", - "$$\n", - "\\text{treatment_effect} = 0.2 + 0.3 \\cdot \\text{days_visited_free_pre} - 0.2 \\cdot \\text{days_visited_hs_pre} + \\text{os_type_osx}\n", - "$$\n", - "\n", - "The interpretation of this is that users who visited the website before the experiment and/or who use an iPhone tend to benefit from the membership program, whereas users who visited the hotels pages tend to be harmed by membership. **This is the relationship we seek to learn from the data.**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Define underlying treatment effect function \n", - "TE_fn = lambda X: (0.2 + 0.3 * X['days_visited_free_pre'] - 0.2 * X['days_visited_hs_pre'] + X['os_type_osx']).values\n", - "true_TE = TE_fn(X_data)\n", - "\n", - "# Define the true coefficients to compare with\n", - "true_coefs = np.zeros(X_data.shape[1])\n", - "true_coefs[[1, 3, -2]] = [0.3, -0.2, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create Causal Model and Identify Causal Effect with DoWhy\n", - "\n", - "We define the causal assumptions of the intent-to-treat setting with DoWhy. For example, we can include features we believe are instruments and features we think will influence the heterogeneity of the effect. With these assumptions defined, DoWhy can identify the causal effect for us." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = X_data.columns.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Define nuissance estimators\n", - "lgb_T_XZ_params = {\n", - " 'objective' : 'binary',\n", - " 'metric' : 'auc',\n", - " 'learning_rate': 0.1,\n", - " 'num_leaves' : 30,\n", - " 'max_depth' : 5\n", - "}\n", - "\n", - "lgb_Y_X_params = {\n", - " 'metric' : 'rmse',\n", - " 'learning_rate': 0.1,\n", - " 'num_leaves' : 30,\n", - " 'max_depth' : 5\n", - "}\n", - "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\n", - "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\n", - "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# initiate an EconML cate estimator\n", - "est = LinearIntentToTreatDRIV(model_t_xwz=model_T_XZ, model_y_xw=model_Y_X,\n", - " flexible_model_effect=flexible_model_effect,\n", - " featurizer=PolynomialFeatures(degree=1, include_bias=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# fit through dowhy\n", - "test_customers = X_data.iloc[:1000]\n", - "est_dw=est.dowhy.fit(Y, T, Z=Z, X=X_data, outcome_names=[\"days_visited_post\"], treatment_names=[\"became_member\"],\n", - " feature_names=feature_names, instrument_names=[\"easier_signup\"], target_units=test_customers,\n", - " inference=\"statsmodels\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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e/kBvM8vYSiISiRRH9PBESoy64dUjxZTMErYbClpU0nGSritqFt7D6Jsi51AThOqcx4FZgPVzGTuSFsZzQnYzs8kVmGIkC6Gf1PHAR7gxv7yZHd+AsTML3sqhN27YRmMnEikx0eBpZlTAELgZz9dpDG3xrtNnmtleJZhT3RGqc14C3gC2M7O/c6zfAvcEXWAWE1SriaTWkg4APgG6A6ub2QFm9l0D6ycaibYENjSznyo320ik+RANnkgJUQ88QTkfXZBstARWDNUnzY5QnfMScJWZHZFJYycDB+BehPPLOrlIg0hqIWlHXB15K2BzM9vBzBrsSi6pG/AyMBLYPpdhG4lEiicaPHWCpGUkPSdpQpCE3zK831fS+5J+lzRO0pFZxpgNeAzokiIj30XSXyHPILHeSpJ+DE+qgyW9JOkySb9J+lDSBinrzinpeknfzTUXLx9/PLNOy3F7XnhheD2k0956K0jw3nv++vrrYeutAWiz665cL+mWsJ9FJJmk3SR9JemnEC5IzGNWSUMl/SrpfWDVPM/fouG9FuH1tZLGp2x3s6TDwv8HS/o8nOsvyiHBH6pzHgMOzF6KPMM2S+DVPoPTE2Aj5ScklW8CvAYcjldWbWJmb+TYbjXgReBSMzvKzBrbIysSiWQhGjx1gKTWuNDcE7hmx8HArZKWAq4H9jWz2YHlceXdjAS13c2Ab82sfVi+xatGtktZdRBwh5lNCa974Zo58wInAfcFNzx4vs5UYPH33mPck0+i63Jk3fTpA8895/8fMQK6dYPnn0++7tMHgFYdO7JIhs17A0sBGwAnSlomvH8SrvGzGN5XarfEBtnOn5l9AUzExdsA1gH+SBm3DzAiGIuX4KXeswNr4qXfJSOlOmdzM3sgz21a4n+D0xrooxUpI5J6AU/j340zgF759CyTtBUwHNjPzC4v7ywjkQhEg6deWB1oD5xtZpPDBfURXIp+CrCspDnM7NdcT5UNcBOwC/x7A90Rz8VJMB5X9J1iZnfiSZj9JHUC+gKHmdmfXbqwyOGHwx13ZN9Znz5u2AC88AIce2zydYrBQ7t2zJ1h81PM7G8zewt4C1ghvL8dcIaZ/WJmX+M3oATZzh/ACKCPpM7h9T3h9aLAHGE/ANOB5SXNambfmdl72Y80P0Io5BzgMLw6Z1QBmx+BfwcuLcVcIvkhaWlJ9+K9zG4HljOzezOpXmfY9iDgSqCvmT1U5qlGIpFANHjqgy7A12ku77HAAsA2uNExVtIIuXproTyIG02LAhsBv5nZqymfj0u7kI8Nc1oYV3X9TtKEDh1os+++MH48WenTxw2d776DadNgu+3gpZfgyy/ht9+gZ09fr0WLjLlA36f8/y/ckCHM5+u0OZL6WQPnD9zgWRf37jyPe7z6hOUFM5sevGPb44rR30kaLmnp7Eeam5TqnLUosDpH0nLAf4HdYzikMkhaUNK1uI7OKGAJM7s2n1BiMGzPBw4C1jKz0WWebiQSSSEaPPXBt0DXRJ5JYCHcEBltZlvhoZoHgLtyjDXTE2hoPXAX7uUZxIzeHYAFJClt39/iBsYkYF4z6zBhApMnTkzm4zTE4otDu3Zw6aWwzjowxxzQuTNccw307g0twlFOn04+yboJvgO6ps0xQYPnL/x/BLA2bvSMwPMq1iKEsxIbmNnjZrYRrnPzIR5+KpqU6pwWeHXOzwVs2xr3zB0fwnKRMiJpbknn4t6+n3EtnXPzTTKWNCtwJ7AabtjGv1kkUmGiwVMfjMK9Gf8NicTrAlvguTQ7S5oz5NtMxMMu2fgBmEfSnGnvDwMGA1sys8HTETgk7HsgsAzwaCizfQK4QNIc06bx2WefJcNT2ejTBy67LBm+WnfdGV8D/PUXv+Qe6V/uAo6VNJekBfE8nQQNnb87AMzsE7xx4y7ACDObiJ+nbQgGj6ROkrYKuTyTgD/Ifa4bRDNW5+xQRL+rY4GfaKTRFcmOpHaSjsXDuHMCPczsGDP7tYAx5gWewnPdNjazQr7XkUikRESDpw4IInJb4AnHPwFXALviXoZBwJeSJuLhlqyVQ2b2IZ5z8HmoTuoS3n8Jv4G/YWZj0zYbBSwR9n0GsG2KN2JXvBno++3bs9i223qoKhd9+sDvv7uHJ9NrYOr48XyZe6R/OQUPU32BG2H/Gm0Nnb9wLhKMAH4O+T+J18J1cMB/K0fg3qJfcO/P/gXM719SqnMuKaY6R94t/SBgr3xyRiKFEwzjfXEtnRXx3Kp9zWxcjk3Tx1kMN2yfB3aOjVwjkeoRm4dG/kXSM8BtZnZdynuD8Rtr7zxG6AG8guvBNJa/gNWbWpfnUJ1zHbCHmT1cxPZt8fLnc80s3RMXaSQh7LktcDrwFXBssbk2klYH7gdONrOrSzfLSCRSDLGXVgQASavi3Zu3Kn4Uexv0Jl7G3hjxwWnAmCZo7BwEHIeXtr9W5DAnA58Ct5RqXhFH0oZ4aw6AA8zsqUaM1R+4BtdGGl6K+UUikcYRQ1pNEHkPqj8yLI81sP5NeI7BYWb2eyN3PwiYtN9+0L79zMt+++U1xqQwTpMgpTrnQLw6pyhjJ3gMdse1W6JrtkRIWkXSk3ip+LnAao00dg4DLgM2jcZOJFI7xJBWpAxooBk3SUU1EP0b2K2pdEsP1TnD8MTv/sUmrEpqB4zBq7LuKeEUmy2SlsRDV2sBpwI3pIhtFjNeS+ACYGPci5eeCxeJRKpI9PBEyoDdff753P7330w3y7u0fBqet9OUjJ1SVuecCbwejZ3GI2+ncjWeTDwG19K5upHGTjvgblwIc61o7EQitUfM4YmUnPDkvOWkSWw0ZAin/vMPa7RpgzUgJDgVmIzfeAZB09AnCdU5j+FKvMc3RhgwlNEPxDtvR4pE0lzA0cDeeEuWJUtRIi5pPrx1yafAjmY2qbFjRiKR0hNDWpGSEtz6LwC3m9mlkuZbYQU+HzWKW9u2ZW1gcVydeQp+g3gWuLopJSiXsjpH0uzA28DBZvZIKebX3AhhxYOBo3BV8ZPN7JsSjb0k8Ciu6XRCzK2KRGqXaPBESoqk/wKb4srB0yXtgeczDKzy1CqCpAHA1cBuZvZoCca7GmhlZns2enLNDEmtcDHNk4BXcU/bh1k3Kmz8NYH7gCGpUg6RSKQ2iSGtSMmQtDz+FL1qSghnAHBr9WZVOUJ1zlHAJkU2cU0fbxPceOzR2LGaE6ENygBcJPM7XCizkIas+exjIC5gOcjM/q+UY0cikfIQPTyRkhB6O40Crkg87YZwzDigq5n9Vs35lZOU6pyN8A7YjU5YDfkmb+ONQYsukW5uSFof19JpDRwDPFHKMFMwpo4ADgc2N7M3SzV2JBIpL9HDEykVx+GdzK9Pea8v8GITN3ba4SKAc+HVORNKNPTFwIPR2MkPSSsBZ+E5YkOAO0vdQT4YthcB6+ENQL8q5fiRSKS8RIMn0mgkrQwcAKyY9jQ9AE/ebZKUqzpH0tbAmniJcyQLkhbHtXTWCf9eF3qnlXo/swG3Ae3xvloTSr2PSCRSXqIOT6RRhN5ONwFHmNm3Ke/PAmyCV8U0OUJ1ziu4zs6gEho78+GKv4PN7M9SjNkUkTS/pCvwbvPv4CXmV5TJ2OmEVxNOwBPwJ5R6H5FIpPxEgyfSWE4BPsKfflPZEHjbzMZXfkrlRdJaePfrs81sSKlyREJ+yBXALWb2YinGbGpImlPSGcC7uCr30mZ2hpn9Uab9LYUbto/iRmjJDapIJFIZYkgrUjShLHc3YIUMN/3+eMlukyJU51yOe3UeL/Hw2wPL0YT6iJWK4DE8EBcOfAQPn5Y1h0bS2sA9wDFmdmM59xWJRMpP9PBEiiIk6w4FDkz34gT9ky2BByo/s/Ig5z/AhXibiJIaO5LmxxOVdzOzf0o5dj0jqVXQcvoYWBtYz8z2qICxsz2ukr1LNHYiM6LuoMtB74MmgSz8+354Pyqi1yixLD1SFJIuBuYxs10yfLYucIGZrVzpeZWDlOqcdfGy869LPL7w5Oc3zOzEUo5dr4RzsjWupfMT7mV5uUL7/S/uTdrczN4u9z4j9YK6ATcDPYG2kLNVzq5gn1dsepGcxJBWpGAkrQdsQ8O9nQbQRMJZKdU5s+HVOeUosR8MLICft2aPpD64lk474EjgsUq0bAieyUvxCrk1S9V+ItIU0EDco92QoZOgVVhWB94BDW4qzZCbAtHgiRSEpDmAG4G9zezXDJ8Lz9/ZpNJzKzWhOudh4H1gYJkqgBYGzgU2aO4JsZJ64lo6SwEn4P3YSqqlk2Xf7fF+WG2Atc1sYiX2G6kHNBCvRJ21gI1a4gb7TSCi0VMbxByeSKGcDzxpZo818PkqwJ/AB5WbUulJq87ZvUzGTgtcqPHC5hw6kdRN0q3A/+Hne2kzu7WCxk5nYATwA9AvGjuRJOqGe3YKMXZSmdW316KlmlGkeKLBE8kbSZsBGwP/ybJaf+D+eu4aHapzRgCnmdnJZTyW/YDZgfPKNH5NI6mTpEvxxp4fAUuY2aWV9HRJWhY3bB8A9jKzKZXad6QwJH0pacMyjb2upEwhzJvxMFZjaBvGiVSZaPBE8iL0droW93ZkfAIO4axtqOP8HUk7UIHqnKAQfCpelTW1XPupRSTNIelUPFQ4DVjGzE41s98rPI8+uKDgSWZ2Wj0b6ZFyoB54gnK2nJ18aAmsWEz1lqShkk5Pe28RSRZyzpDUW9LLkn6T9IuklySt2sg5N0miwRPJl0uA+8zs2SzrLIPHrV+rzJRKRyg7PxrPp9mwnD2sQtXXUOB0M/uwXPupNSS1lXQ48AmwMLCymR1mZj9WYS47AXcDO5nZsErvP1IX7EvjvTsJ2oTxSkrIqXwET7afGy9+OAUoifJ7UyMaPJGcSBoA9MK7T2ejLsNZ4UnpCmAnvDqn3Pk0h+GejUvKvJ+aQFJLSbvhYav1cINyNzP7sgpzkaTj8OTo9c3s6UrPIdI4guF8kaRvw3JRaHGT+HwrSW9KmijpM0mbhvd3l/SBpN8lfS6pQQNEUpfNNmPQfPPRctFF4ZI8fqnTp8PZZ8Nii8E888B228Evv/hnX35JK4kDJe0m6StJP0k6vrHnAlgSwMxuN7NpZva3mT3RnHMCsxENnkhWQm+ny3FZ/b9yrF53zUJDdc4DQDe8OqespcghZ+RYPDRYkaTcahGMiy2Bt4C98TDhlmb2TpXm0wq4GhgIrGFm71ZjHpFGczxe9t0Tb7C7GjAEQNJqwDDgKKAD3lT2y7DdeGBzYA5gd+B/klZKHzwUEzzcqxftxo2Dp5+Giy6Cx3NIjV56KTzwAIwYAd9+C3PNBQceONNqvfEqxA2AEyUtU8BxZ+JjYJqkmyRtFlIPIg1hZnGJS8YFr6e8Fzgnj3UXBn4EWlV73gUcX2fgdbxSqnUF9tcKGA3sW+1jr8Cx9gZexBt7bkEQOa3ifGYHHgvL7NU+P3Ep6m/4Jd6j7zNcADTx/ibAl+H/VwP/y3O8B4BDw//XBb4J/+8FfJW6+plnYoMHZx9y6aWxp55Kvv72W6xVK2zKFOyLLzDAgAVT9v8qsEOOOQ7FQ9+p7y0SxmoVXi8T1vsGFz58COhU7b9XLS5RhyeSjR3xp5Gd81i3P/CQ1UkCbvC0DAduwC8olQjDHQP8AlxTgX1VBUnd8XDR8sCJwK1mNq3Kc+qC/61HAwfUy3c00iBdgLEpr8eG9wC64tIGMxGqTE/Cw0At8HzDTN7GhYEuHTok35g2DdZeO/ukxo6F/v2hRUrcpGVL+OGHGVb7PuX/fwHts4/KVKB12nutgelhwcw+wMVLkbQ0cAuuDL9jjrGbHTGkFclIuElcRP69nfpTJ+Gs0PriWeBEq1B1ThDVOwQvfa6rHKd8CJUjw4CngCeBpcxsWA0YO8vjZed34Z61aOzUP9/iRkmChcJ7AF8Di6VvEHJ87sV1xDqZWQfcMFKG8b8Bvv3oI36aMAEmTIDff4dHM5pRSbp2hcce8/UTyz//wAIL5H9gGfgK9+iksijwtWUIiZsXQQzFHzgiaUSDJzITobz8WuAKM3s9j/U74rH0slU2lQpJO+M3vx3NrCLaGOFiOww40krch6vaSOoo76v2OvAFrqVzsZlVvUpE0vrAM8CxZnZWUzQ0mym3A0MkzSdpXtyTeEv47Hpgd0kbSGohaYHg9WiDV1z9CExN0RRL0A6YVdINuKxGx8MOY/KffzJt2jR4910YPTr7pPbbD44/3j09AD/+CA8+6P83o1hD+16gn6SNQ/J/Fzxf6Q5wj46k/0haMLzuint2Rha5vyZNNHgimdgDmB9v3JgPWwKP5+kJqgop1Tln4B23n6ng7k/EjYEmIz4maXZJJ+OK2gKWNbOTrEZUiiUNwm+M25nZbdWeT6SknI5LX7yNh6TeCO9hZq8SEpKB33AB0YXNNZ4OwR92fsUrMp8Fekl6GrgH75f3JrAW0O3JJ/mhWzdazjsv7LUX/Jaji96hh8KWW8LGG8Pss8Pqq8OoUf6ZGUUJWprZe7gBcxYeDn8FGIWXngP8juccjZL0J27ovEt2cdhmS+yWHpkBeW+n13CjIK8qFkmPAjeZ2Z1lnVyRpJSdr4J3wP42xyal3Hcv4EGgp5l9n2v9Wid4q/YFjsM9eiea1U5H6OCdHIIb7f3M7P0qTylSI4Tv7jpAv7C0w3O7hgNPm9kfYb318Oa1bX/8kdbzzMNSUqPEB6cBI8F6N+oAIo0mGjyRfwnlmE8CT5jZOXluMyceN1+wVp7uU5E0O/5UB/60XzE1X0mzAmOAE8zqu3lgEEvcETgNV0g+zszequ6sZkRSa+AqvFy5X1MwMCONI4SA+uIGzvr4dzdh5LyZGuaUtCLuSVkCb157hxmL4F6kdo2Yxl/A8mBfNGKMSAmIIa1IKgfgP+zzC9imL/B8jRo7XYDncYNsi0oaO4Ez8Itq3Ro7IRTYDzfcDsST2PvVoLEzB34T6wT0icZO8yTk7fSSdKqkN/DwzoZ4Xs7iZraGmZ1uZmMSxo6kxSXdjicxP4y3OrnNk4Ltc7wC6m+AzTaD9u1nXs48s8Ep/e3bN2zsSHpM0h8ZluNKclIi/xI9PBEAJC0BvAysZWYfF7Dd3cD/mdn1ZZtcEYTqnOH4E//ZlU5Ylfdpuh3obmY/V3LfpULSmrhrfx48hPVQLSb+hoTN4cBLwCGxEqt5IakDnoDcD9gMT0wejrdceLmh74OkzrgnZ3vgYly/548G9jIQr35qS369tabh7R0GQ/0+8DQ1osETSYQrngfuNLO82x2EkM33+JNTxfshNYSkDXBj47BqJKwG9ea3cVGzhyu9/8YiaTngTGBFXLek6uXlDSGpB35juww4rxYNskhpCXlaS+Oqyf2AlYEXCKEqy9GyJIThjwL2B24CzjSzn/LYcze82nJFvOork47dVGAy7hEdFMNYtUUUHowAHIH/SC8rcLuNgDdqzNjZFTgPGGhmI6o0jfOAEfVm7ISE9VPwMOXZwPY1Xnm3EXArcHCtJsxHSoOkWXA15ETCcWvc0D0feMZyt71JjHEAcDQevlrJzMZm3yoV+xzoHbqe74v3hVs8zGUK8Cle+XU1VKd9SiQ70eBp5oSn+f8Cq2YSsspBzYgNhqe+E/CS1HWD+mg15rEJfkHuXo39F0PQMjkO2A2vZlvCzHIU4VYXSbvjRtk2ZvZCtecTKT0hVJkwcNbFvabDga2Bd/L15gUP9q64MT8Gbxr7XvEzs3eAg4rfPlItosHTjAlVLTfhFTdfFrhtK7xH0ollmFpBhOO4GuiBN4WsSsJqyCW4Dm8MWtMGA/wbejscOBS4E1iu1pN9g2F7En4D6xOUZSNNgGCY9CIZqloQ+D9cZG/3QnPhwndlSzw8+wsuNvpSSScdqSuiwdO8ORZP8LuuiG3XAb6otnJwqM65B08QXLfhpMOKcBHwsJnVtOK0pDZ49/IhuAt+dTP7tLqzyk2Y9zXAsrhh+0OOTSI1jqS58eaf/YBN8RYRw/HQ06hiE9AlrYN7ANvjHuxHY35XJBo8zRRJK+Fu2RWLvBAMwEs9q0YtVedI2gpYG2+xUZMEnaUdcC2dT/CO02OqO6v8CImm9wJ/4qKYf1Z5SpEiCF6X5UmGqlbA1ZCH457mrxo5/gq4ls4yeIj79lpNuI9UnmjwNEOC4uhNwBFmNq6I7Vvg+TsblHpuBcyhZqpzQg7MlXiSbzU9TBkJN5lN8RvBJLyB6bPVnVX+hP5AjwLP4ZV38QZWR0hqhyf4Joyc6biBcybwnJn9XYJ9dANOxTV3zgT610I/t0htEQ2e5snJ+BP+rUVuvyrwW7XyJyRtjDcLrJXqnMvxJ8maS56VtDru2u+MJybfX0+ufXmX+Yfx3kj/q6e5N2dCxV/CwFkbTxYejuvkfFCqv6OkTnhodifgEmD/KgiMRuqEaPA0M8INcDCwQiMuOgOoUnVWqM45ixqpzpG0PZ4sPbjKU5kBScvgT7qr4AbuTfUmyCdpU1z35AAzu6fa84k0TChiWAM3cDbHFa8fw/9+u5jZryXe3xx4g8yD8IefZcxsfCn3EWl6RIOnGRFcyzcBBxV7cQjhkQF4LkjFCPs9GdgFr875qJL7z0RQar0Eb1vRaLd8KQjhn5Px6pRzgZ1qZW6FIGkvvAN2/1hZU5uEUO6muJGzMfAVHmbeCxhdjtBjCMfvjxdcPA6sXGiFaaT5Eg2e5sWZwGtmdm8jxlgOVxl9ozRTyk2ozrkWT0Rcsxaqc4IBdi1wrZm9WgPzmQc4Bu8SfjWupTOhqpMqgnBeT8MN6nUKaXMSKS/hb7MCyVDVcniV33DgyGLyAQvYd0tgZzxP5x1gIzN7u1z7izRNosHTTJC0LrAtHn5pDP2pYB5IqM65D/iD2qrO2Q1YCNimmpOQNBuuo3MEXp7f3cy+reaciiUYttfj6rVr1JKCd3MlfL82wA2cvrgi+yO4F3FEuRODg5G1Of6wNhEYVAuh7Eh9Eg2eZoCk2YEbgX3M7JdGDjcAv8GWnVqtzgnzOg/Y0MwmV2kOrfHQwQl4H6E1zOyTasylFEiaCzdsf8WVcOsuDNdUCBVPCS/OWsBo3IvzP+CjCj7s9MYT7jvgCfcPx6T1SGOIzUObAZKuBlqa2V6NHGdRYBQwf7mNj1qtzglPnE/g5bRnVGH/LYCBeH7LF8CxZvZ6pedRSkJFz2N4TsaRtWLYNheC8bwWyYTjufEHjeHAk5VWDZfUHffo9MCV3G+J34lIKYgeniZOqHTZhMaHssDDWQ9WwNip5eqc/YA5gXMqudNgaG2EV6hNB/Yzs6crOYdyIGll4CHgXDO7uNrzaS5I6oiXiPfDv1ef4QbOrsDrRfTVK8WcFsH7XW2Ke3YG1nLz2kj9EQ2eJkwIE1wL7GZmE0sw5AD8yatsSNobT1rd2sxeLue+CkXSYvjceleyxFvSqvgNoCtwPHBPrXi8GoOkvnjV4D5mVhNNaJsqwTO4IslQ1VLA07iRc6iZfVfFuc2Hf68H4ZpWS5ToehWJzEAMaTVhJA3DBQIPLsFYnYEPgM7lSFQMHozTge2BzWotHyVUiTwLPGBmF1Zon0sBZ+D6JqcAN5rZlErsu9xI2hdPfO1vZiOrPJ0mScjd25BkwvHvuIEzHHihWvlnCcL8jgAOAW4DTq+FCsxI0yV6eIpG3fHwxnrAYnip9mTcNfwscBXYO1WbnbQ1fqPsWaIhtwIeK5Ox0xavzlmM2q3OSSRqlz3sImkBvCN4f+ACYFcz+6vc+60EwdNwBl7dtnY9NC2tJyQtQdKLszowEjdwzqmVh4hQjbcv7tV5GljVzD6v7qwizYFo8BSMugE344ZEW6BlyodtcK2YJYDBoDHArlDZH3NwEV8BbFvCMu7+uFFSUuqhOieoFh8H9Cpn/lI4F8fg1VfXAUuVoKquZgiG7VC8nH9NM/upujOqf4LxsA5JI6c9nnB8BTCgltosBGN3J1xL50NgUzN7s6qTijQrYkirIDQQv2CnGzoNMQ1v1jgY7O7yzStJCA3dBXxhZv8t0ZgdcBXVLqVsjlkP1TlBMv9lPJx0ZZn20Q536/8Hb9lxqpl9U459VQtJc+PHNh73WNWcYVsvhPByX9zA2QD4CNfGGQ68WY2E42yEa9JmeML938DRZjaiurOKNEeihydvNBBPsJy1gI1aAqGdg6iQ0bM9sCyeAFgq+uFl2KU0duqlOudoYAJwVakHDuXAu+OltyPxEE9VGrKWkyBn8Ch+Uz661m7ItU7wjKyMl4z3w0O/T+K/n/1ruYeUpDXwhPv5cC/pg00h4T5SnzRbD4+kq4BxZnZaHmt3w+XM2xW7v9NOY8rFF3PfTz9Z2XpQSZofeBPoW0ptFkn34qJfQ0s0Xj9cCHE/M7uvFGOWA0krAE8BK5nZ1yUcV7jq9enAN8AxZja6VOPXEqHC7EHgTDO7rNrzqReCwvhGuIGzGfALyYTjl2o9eV3SsnhF50p4cvqwemteG2l6NFuDpzD0EtCL/MJYDTENGAnWuzRzmpFwE30Y19A4qYTjtgO+A7qZ2c8lGG8/QkJuLVfnhNyI0bjo4dASjrsB/sTbEs/XebKpPvFK2hLP+9rTzB6q9nxqmfD7XYpkLs6qwEsEI6deknolLYRXFPbDtaquiOHLSK0QQ1o5UQ88QbklwNSp0Kq4s9YSWNGru8pSvTUY6IJr5ZSSjfGGo40ydoJb/kx8fr3N7LNSTK6MnAiMxcOYjSaE8M4CFgWGAHc35dCOpAPxKpx+tdBctRYJSdx9SIaq2uIGzkXA0zXUNy4noXP6sfh16CpgyXpsXhtp2rSo9gTyRVIXSfdK+lHSF5IOCe+vJukVSRMkfSfpsvB0jpz/SRovaaKkdyQtHz4bKun0lPE3l/RmGOdlSQll4n0XWYR255wDPXrAbLO50dMQ55wDCywAs88OSy0FTwct3JNPhp13Zla8HBNJu0oaK+lnSSdI+lLShuGzkyXdJWmYpN8lvSdplZS5mqTFU17fDVyGN7RcU9I3ko6T9FMYd+dGnPoBeLJp0YQL+63A2nh1Tk0bO5JWA/bGBfEa5X2RtISkO3Hv2/3AsmZ2Z1M1diS1kHQecDBu2EZjJwVJC0jaW9IDeAL3ycAP+O+sq5nta2YP1YuxI2k2SUPwqqtZgeXN7Pho7ERqkboweIJ34GHgLWABvDLhMEmb4KGiw4F5cd2ZDYADwqYb4yWbS+LtALYDZvJUSFoRuAE3RuYBrgYeCjfq9QBuvx2GD4cJExr28Hz0EVx2GYweDb//Do8/DosskrofBKwX4ttXADsD84e5LZA23JbAHXjjvIdwg6ahc7MWMMrsX89R53A+FsCNoGuCiF1BhKTafsADhW6bMsbceIJlK7zZZk2XIkuaFffqHGJm3zdinC4hT+wV/Hu7hJldWeu5F41B0iz4d7YXbtjWRRimnEhqKWkNSadLGoPnAq6Pd7ZfzMzWNLMzzOytegptSmoj6QDgE2A5YHUzO6Cais2RSC7qwuDB49nzmdmpZjY5XEivBXYws9fNbKSZTTWzL3FjpU/YbgowO7A0nq/0QQM/yH2Aq81slJlNM7Ob8HLy1fGKCA45BLp2hVmz1Gi1bAmTJsH778OUKW7sLLbYTKstjiesPmxmLwa10xOB9Ivdi2b2aCjVvhlYoYHd7ocbE6+kvX+CmU0K5Z/DcWOvUNYFPim2RDpU57yMNxzdvk5i+acDb5vZncVsLKmDpDPxG9sfuJbOmfXyxF4skubBE7ynARs3Jf2gQgnfge3lSuffA9fgv9FDgI5mtqOZ3VLrxn8mggdvR1x1fStg83A8UUAyUvPUSw7PwkAXSRNS3msJvCBpSeBCYBW8iqoV8DqAmT0j6TK8P8vCku7D9V7S+7QsDOwmKbUFQxs8J6YNuLGTi8UXh4su8vDVe+/BJpvAhRdCly4zrNY6jPtv1Y+Z/SUp3fOU6l34C5hFUqvUSocQ1joFeIEZDaZf026wY8M+C6U/RYaz6rE6R9LawI4U0Wg1eIYOAo7CvZE9S1nZVcvIe4w9in9Xjmuq4bqGCAnHy5JMOF4R/00Oxx88xlZxeiUhHOPGeB7aVGBvM3umurOKRAqjXjw8X+NCeh1SltnNrC9wJR4/XsLM5sC1HpTY0MwuMbOV8QvSkvgNKdP4Z6SN387MbsfbRSBl2CoDO+0EL74IY8f6NkcfPdMqU/CqpwUTb4Sb5Tz57QFwA6g9LoJ4OjOXy88labaU1wsB3xYwfiJUtjVFGDyhOmc4XnZeL8ZO4nzuV8iTt6RWkvYEPsZDquua2Z7NyNjpBbyIV7Md01yMHUmzStpM0uXAF7jBtwhwLt5vrp+ZXdFEjJ1ewDN4W5UzcMXxaOxE6o56MXheBX6XdHS40LSUtHzwIswOTAT+kLQ0sH9iI0mrSuoVclH+BP4BMl2QrwX2C+sqJOL1kze3yzvB9qOP4JlnPKw1yywe/mox8xn+FI/fbyFpzZBgfTIpRloevIn3WJqK32j7ZFjnlBBnXxuvAilU9LAX8IuZfVzIRvLqnKvw6px6KkU+F2+omNecw/dkAB66GgQMNLMBZvZ+OSdZS8j7tT2CP+2XXJix1pDUVdJ+kh7GE42PwxXINwcWCTksw63p9D1bOnjF78WLDpY3s3vrKdcoEkmlLgyekMeyOV4e/gXwE95raE7gSLw/y++44ZKaezFHeO9XPKzzM3BehvFfw6tyLgvrfoqXV4I3As2LSZPgmGNg3nmhc2cYPx7OOit1PxjwrJm9h1ex3IF7e/7AKzbybcx5EW7krIIf+wNpn38fjuNb/EK1nxWu4DsA73GVFyG2fz5+XGvVk5CepI3w79dhea6/Hq6MfCLe7Xm9WtYUKgfyKsnL8c72j1R7PuUgeO96SzpL0tvAGKA33tl7ETNb28zOMbN3m5IRIGlBSdfhYbmRuPf8OovCgZE6JwoP5kQ98ITgolWWU/gLWD1dhyeEUybgF5Yvss7Gezu9AlxrZtdk+Hxd4BYzWzD9s3wJ8fpPcK/FmDzWnwUYhleHbV1PCatyRdt3gL3M7Ikc666I5zAsAZwA3NFcQjgJQqjzfGBTXNH7y+rOqLSEqsJN8VycTfFwd0LheJTVYL+3UhGO/VhgDzzR+lwz+7W6s4pESke9JC1XEXsb9CalUVoekzB2JG0BPI2Hss7Hb7pf5jHOMbin6tpGzCUX3fFjfTPXiqE650H8xrCxmf1TxnmVg4twJdsGjZ2QlHs6XrV2Om5sTq7I7GqIkGt2CzA37sWr+5thMO67k0w47gE8hxs4xzSHXCy5mvqhePPae4HuZlZQzl8kUg9Egyc/BhF6aX31FSy7bOaV3n8fFlqowTEmMWNDz63wcnMBr+El9lndbZJ64qWtK5bZhT4AuD+P+SyGdzu/jzqszgnJ1evQQMm/vCv1CXhD1ovxXJWSNVCtJyTNhxu2XwCbmlm+4deaI9zg18fDmH3xXLjhuDH7XB0a7UURchv3xL/jL+HaSQXl7EUi9UQMaeVNUd3SE/wN7NaYbulBBHE0cJ6Z3VzsOHnu6y3gQDN7Mcs6q+MVXKfUY8Jq8Ey9gxuaz6d9Nidezbc//jc/y8x+rPwsa4Mgf/AYnh93Qj3mq0hahKQXpzcuXZEIVX1Yj8dULCEsmWhe+xXuyXqturOKRMpP9PDkjd0dCqmG4j1v8glvTcM9O4MbY+wETsQrxm5p5DhZCV6bTswsZJi6Tn88xj/YzIaXcz5l5HI8B+dfYyfkIh2Ahw0fxbuk131ZcWOQtCYe5jjRzMoZRi0pwXuxJkkjZz7caBsK7GTNtPVBSNBPlFIcYGZPVXM+kUgliQZPQdjdoNeBYWasNHUqs7ZunXHFqbh+zxhgEGRPRM5F0MHYE1ihAk+i/YEHG0rOlHQo8F88rPF6medSFiRth1f87R5etwR2xeUB3gTWN7N3qzS9mkHSNrjO1W5m9li155OLEHZLJBxvjIffhuNJuKPrLeRaSuS9+M7GNbmGAPc05/MRaZ7EkFaRDBqkIVttxeBtt2US3i6iNS4q+Cleyn51Kbqih0TRMcAQM7unsePlsb+X8TDV42nvt8STqzfBS5Hr0vMR8nLewnuVvRr+PRP4BXftv1TF6dUEIZH3MDyJdYt8KvWqQZhnT5JenGVxgbzhwKMx8RaCEv0ZuLfrVOAGa8L93CKRbESDp0gkPQTcZWblDjFdiCu37lTO/YR9zQ+8F/Y3OeX91OqcAfVanRNukA8A7wKP40+87fFS3EebUx5HQwTD9n94Um9fM/uqylOagSDhsCFu4PTFpR4SuTjP13MydSmRtAAeBt8GFym9uKkIIkYixRJDWkUQLrrr4mGQcu6nD970s+DeTkWyNX7jTzV25sO7tX9OnVfn4H+vpfH+aDvhN4TbmrK2SiGE6qXbcPXy3rWS5xLyyhJenDVxz9wjeAJ/rCpKQdJcwNG4kOr1wJL1pIsViZSTaPAUx2bAy+W8IQSj6kZg3wpesPrj3eYTc1gCT96t2+qcBCH59mpc1fpyXCCxno23kiKpI9709ENgu2rqDIV2K71JGjkd8O/hNbgYZnrz32ZP8MIejFcXPoDn+31T1UlFIjVGNHiKo+gu4gVwHt6GoiJVUEFldXX82BIGwn24oVM31TnphBv5EGA/vMnlVmb2e3VnVVtIWgo3KG4BTq6GYSupE/4g0Q/YCO8RNxzYBXgjJthmJiiv7w6cBIwC1i6ijUwk0iyIBk+BBD2czfAeSuXaxyZhH5UKZYGLsD1jZn9K2havzhlkZv9XwTmUDElz4Em3B+FJyu/iStCxH1AKknrjzWyPM7MbKrjfFsBKJL04SwJP4UbOwWb2faXmUo+EfLRt8ITkcXhu3avVnVUkUttEg6dwNgDeK9cFWVIHvG3E7hV23fcH7pN0BG7MbWRmb1Zw/yUhGKT744nIjxOOC3/yjcZOCqE8/zJgl1x9xEq0vzlw700/3KD/jdDCAXixObbrKAZJ6+MJ961x5fUn6jncHIlUimjwFE7iBlouLgIeMrOny7iPGZA0G27I/Ygnha5Za9U5uQjVRTvjpbfv4jfWd/G+SGeZ2QfVm11tEbwDR+I3y43M7K0y7mtJkl6cXsDLuJFzhpl9Vq79NkUkrYSLBi6Gh2nviqG+SCR/osFTAOGmuhWu21KO8bcC1sK1RSrJFsA/+IW0Zqpz8iHcvDfH/yYT8TDcC+Gzw3F57IuqNsEaI+R8XIwnBa9R6sTW4GFbh6SR0w7PD7oMTxRvlr3IGkNo7XE60Ac4DbguesMikcKJBk9hrAWMM2uccnImJM2L580MNLM/Sz1+lv12xG9Gn+CCgnVzIZW0FnAOXsVzHPBwwrUvaWngeKBXLDt3gifvDrw1ytqlCpkG/aa+uOG5PvA+7sXZDngzhluKI5zXE4GBuDbSXtFgjESKJxo8hTGA8lVnXQ7cWkml35TqnNmAberF2JHUHU/WXAG/IdySatQEL8ZNeP+nGDbhX4Xph/GGqfs2Rm03JByvStKL0w14Av9t7NOcG62WgpDHdxReWTgUWNrMfqrmnCKRpkA0ePIkhE764xf4Uo+9PdAd2K3UY2fZ59p4dc4teGij5mX4JS2M5+hsiidtbmdm/2RY9b94eKvuuriXA0nL4B6XG4HTi/G4hA7yG+NenM3wfK/heIL7y7FdQeMJzWsPwr+/jwAr1lsuXSRSy0SDJ39Wwjufv1fKQYPb+hKgXwM375ITDKxL8STfbSi/plCjCGrPxwODcE/YEg2FYyT1AA4HVo4JnSBpHeBu4L9mdlMB2wlXpU54cVbGdYyG41o9JQ/rNleCRzLRvPZ1YD0zK+l1JhKJRIOnEPoD95cyHyHcVK4Brjaz10o1bo79HYU/RW6IG2834wmsNYek2XHj5RA892RZM/shy/ptgGH4zb3ZPxlL2hFPUN4xn6q/4GFYl6SR0xo3cC4kaDSVb7bNj/B73BoPz/4IbG9mr1R1UpFIEyYaPPkzAFc0LSW7AQviXpayEp4iL8ETr9c0s29C0u94M/u03PsvhGC47It7dZ4GVjOzz/PY9ATgazzvodkSbqRH43pEG5jZO1nWXRBPOO4HrIfn+DyC34jfiQnH5SH0yTsbr2I7EngsnutIpLxEgycPQsXPnMDoEo65EN4+YoNyJwtnqc4ZQHk1hQoiJMPuiJfefog3K30zz21XBfYBejbnG0cwbC8HViNDblaQVuhF0ovTFRdovAvYw8x+ruyMmxeSeuJaOkvhBvrtMfQaiVSGaPDkRyKcVZILU3gCvx640MzeLsWYWfbVGX9if5uU6pwwhwG4rlBVCXPZDL8R/I2rTI8oYPtZ8aqsQ83su/LMsvYJDWfvBFoA6yR6hoU+aZvgBs6mwLd4qOogYGRUoC4/oeP7aXjZ/ul4T7e6qIqMRJoKLao9gTqh1OXo+wFz4B6eshGqc14BHgT2TKukWQGYjocwqoakNXA15AvwpM01CjF2Aqfhqsp3lnRydURIfh+BGzNbAgtLOlrS88CXwE540vFKZtbDzI41sxejsVNeJHWSdBnwKu61XMLMLovGTiRSeaKHJwch9LQo8HyJxlsML60ua2+nkCNwF3CUmQ3LsMoA4L5qhX8kLYurI6+EGzrDijkfofnlTkCP5hrKkrQcrqf0HDAF+BQw3ItzNvCsmf1dtQk2Q0LfsKOAA/BE+qWjPlEkUl2iwZObrXEF30brjIT8iaF4H6EPGztelv3shLdTyFad0x9PDK4owYA8BQ+vnIPPsaibcQjhDAUOaI7CbOFcHo7fVKcBi+BGTl/g/eZqAFaTUOm2P94Q9f9weYQvqzqpSCQCRIMnH/rjsu6l4FA8jHRJicabgZALcwweMlvfzN5tYL0lgHmBkeWYRwP7nAdv/zAYFwRcsgQ9u84BXjKzBxo5Tl0QEpLXIJlwvDBeOn42cJGZ/VrF6TVrwsPMINyYfwsvRsj4+4tEItUhGjxZCIJ3KwFPlmCsZYBj8d5OJa/KCDfDK3DJ/1zKyf2BBypRHRIqxA7DPRF3AcuXIrFY0oZ4rkr3xo5Vy4Qea5viBs7GwFe4F+dloD0uWPl+9WbYvAkPGVvg4dkJwM5m9mJVJxWJFIS64w/J6+ENpNsAk4HPgGeBq6BhaYt6Iho82dkCeKKx+Q8pvZ2G5KknU+j4s+MJuyKlOicLA/CS2LIhqTWwNzAEz39avVR6P6HNwfV4M8UJpRizVgg30B4kvTjL4xed4bhey3i8yeyKuGH7fZWm2uwJ7VnOxgsQjgGGxzBipH5QN1x4ticuWdIy5cM2wDLAEsBg0BhgVyj9/auSKP4+G0bSI3hDz9sbOc7xwDq4rkxJT7ikLnjZ+et4LkvWXCNJC+CVWZ3K0f8oaOlsj1dOfQYca2ZvlHgfNwCTzWy/Uo5bLYIXbAPcwOmLP10Nx/+uI8xsUlhvDtxLNhXYIXbOrg6hfcmZuDF6In6NmJZ9q0ikltBAPP8x3dBpiGl4a6XBYHeXb17lJXp4GiDcXNbB+001ZpyeeO7OymUwdpbHb4rXAGflOf7WwCOlNnaCZ2JjXEtnKt41+5lS7iPsZ3O8/cEKpR67kkhaFDdwNsfVr0fjRs7/gI/S/5bBUB2OywwcHMvJK0/4m52Kf8/PBLZJGKORSP2ggXjEYdYCNmqJq4Lf5IGE+jR6og5Pw2wGvGhmvxU7QGiRcBNwpJl9XbKZ+djrA88Ax5nZmQUYUyVXV5bUK8zlErwvUK8yGTvzAFfjwoS5wnY1haTWktaVdJ6k9/GE8ZWB64AFzWx9M7vAzD7MYOx0x3N2bsO9eNHYqSCSOkq6GHgN+BzX0rk4GjvND0lDJZ1e7XkkkLSQpD9C0ny29XaW9EQIYw2lMGMnlVmBofPMo3tq6TzkSzR4GqYUYoMn4qJvNzd6NilIGgTcDmxnZrcVsN08wCrAEyWax9KS7gXuxW/Gy5nZvWXMY7gMuLsIYcKqEG6Uu0m6C8+9OR/4C69Um9/Mdjeze7IZ1ZI2wPuJHW1m58YckcohaXZJJwMf4I+1y5jZSSmtWSKRqmJmX5lZ+1whVTO71cw2xu9FbSX4tPiMyrbrrMPaRW9dRWJIKwNBS2MT4OBGjNEL2IsS9nYKYaMhwB7AekVU52wBPGVmfzVyHgsCJ+HhsfOAXcotbCdpIF4x17Oc+2kM4e+zIsmE46VxY2U4RbS9kLQbcC6wrZmVRPgykhtJbXGNquPwCs1Vy1FsEIlUFvXAr5/55Oxko+XsszP34oszX+Pn5EhqVQnPdfTwZGZD4C0zG1/Mxim9nQ4uVRVNqHq6Djcy1iiyFLk/jfBaSZpb0rm4zsjPuJbOuRUwdjoBlwK71ZpicPAC9Jd0HTAOb9I6F97pvaOZbWNmNxRi7Mg5CTcq+0RjpzJIahm8px/iDzybmNmgaOw0XyStKOkNSb9LuhOYJbw/l6RHJP0o6dfw/wXDZwMlvZ42zhGSHgz/7yvp/TDmOElH5pjDByF3MfG6VdjvSpIWkWShEhhJgyV9Hsb+QtLOifeXWor/w70zAKywArRvD3eGhjyPPAI9e0KHDrDmmvB2SpfHMWNgpZVg9tlh++1h0iRarroqK5Vg3ntK+gp4RtIskm6R9LOkCZJGh2t/tn08J+ksSa9Kmpg4xw1iZnFJW/CS58Masf0FwB0lnM8ceEfrR4D2RY7RHpgIdChi23a4htCPeA7NAhX8Wwh4ADiz2t+LlDktjmsLPQn8Hv49DM/taOzYbYAb8d5Lnap9rM1hCd+xzfEGuy/j0g5Vn1dcqv69aAOMxTXEWgPb4q1bTgfmAbYJ18bZgbtxbTPwyqdf8BBoYqwxeJI7wHd4ayHwh6OVcswjUQmYeN0P+CD8fxG8jUwrYLZwjV8qfDY/nmYAMHjllfkrMSxgn3yS3M0bb2DzzYeNHIlNnYoNHYotvDD2zz/YpEnYQgthF16ITZ6M3X031qoVduih/FiCeQ8L854V96o+HM5pSzzHcY4c+3gOf9BcPoxzb9b1q/2lqrUlfHF+BBYucvt1wh9gnhLNZ0Hco3Il0KoR42wLPF7gNq1xQapxeDn0UlX4ewwKN6K2VfxOtMHLxi8EPsIbdF6He8xmL+F+5gzG00PAbNU63ua04BVyLwDvAVsRpDriEpdwLf829TsRDOLTM6zbE/g15fWVeAshgOWAXxPXMFw8dN9cN/OUsRbHH6zahde3AieG/6cbPBNwQ2zWtDEGr7km0xNvpRs8++2HDRky466XXBJ77jlsxAhs/vmx6dOTn62xBnbssUwtwby7pay/Rzi/PQr4Gz0HnJ3yetls68eQ1sz0Br4ys7GFbijv7XQjsJ+Z/dzYiQS9j5fxL0pjq3Pyrs6S1ELSdvhNYFtgazPbzsw+asT+Cya4iC/AQ1kVrYiR1FnSHiEpezxJJd0d8aqqvczsfitRtVg41hdwg6q/mf1ZinEjmZG0vKSH8GT76/GL7IMWrpqRCNAFGJf2nRgLIKmdpKsljZU0ERdX7ZBSLXUTsFPI6xsE3JVyDdsG19saK2mEpDWyTcJcsPUDYAtJ7XCF+ZmKVcI1Y3u8t94Pkl6SdKCk/YCtJdTQPsaOhQsu8HBWYvn6a/j2W18WWACUsvXCC0OLFtlzgfKcd2r18s14JOMOSd9KOjekcuQidYys9+2YtDwzjanOOhd43swebuwkJG2EGzoHm9mdjRyrLV5mf0Qe626Iq8eCG1lPNWbfxRIuFNcDl5rZmArsrwXuQk0kHC9O0tuyvxWZz5XnvlfAw5UXARfGm275kLQw3u+qL/49387M/qnurCI1ynfAApKU8ptcCBdU/Q+wFC7B8b1cb20MHh7FzEZKmgysDewUFsJno4Gtws38INx73jXHXO7AqzsXwT3ua0naBlgyfP50yHfpjIeHxgPdcN2oe32/DQ/etSscf7wv6YwYAePG+fYJo+err2DRRclHbPN2/CGxBd7QOL027N9ZmWvDnQKcImkR4FH8AfD6HPtIPXcL4UZWRqLBk0K4yfbHExYL3XZj/EbZowTz2B2/GG9jZi80djxgfeA9y5JALWmVsM+F8YTbe6wCvbaysDceJz8714rFIheXTPzdNsPdzo8AR+FNSUuuRJ1hDhsDtwAHmtWnmFc9IO9JdjywK95zbglrhMZWpFnwCi6ieoikK/Aq19XwVi+zA38DEyTNjRcYpDMMl9KYYqG/mlybbSB+nZmI5wS1kNQHN1YaWubF79drA9/gIfbv8ZAbuLL9JPyG/0CY20m4SOtlwAn//MN0QqFSp07w+eew+OK+8d57Q//+sOGGsNpq8Ndf8NxzsM46sMYa0KoVXHIJHHAAPPwwvPoqrLoq+TQrvgPXZpubDF6pVCStB/wEvJ9ybvK5B+0iaRguAXMqfn4zU674Zz0ueOPNDykwjg90wOOyGzVy/wJOxsXNli7hcV0LHNHAZ0viTxjj8Lhy6xr4O3QLX/zlSjyu8KeyI3ChxN+B/8PlB7qVcl95zmcP/KK1VrXPeVNd8GT9E8L36XKgc7XnFJf6WXDdsjHhWnFnWE7Hw13PAX8AH4drZyKXpi1ueGwRbtgP4wm8V+DRg1/xVg0W/v0aD2ffjYu3HheuDX1xKY4ueD7l07gB1jllfouk7Hd+YATwW5jXONwg+gJ4aokl+Hn6dKaYYVdeiXXujM05J3bnnX64jz2GrbKKv9e5M7btttjEif7Z6NFYz55Y+/bYdtthAwcyfccdGZ3nOcw675T3dsQ9On8CP4RzkTVvNfwNzsKLPCYCD2dbP/bSSkHSWQBmdmyB290I/G1mBzRi323wFhHLAluY2Q/FjpU2bkv8S7+6mX2R8n4X/AlgGzxP5mJrpD5PKQihpWfw9hfnl2C8tkAfkqGqWXBdnOHA01aFXJngSTwV/4H3NbOPKz2Hpk74Pe2De3WeBU4ws8+qO6tIvRKuS/OS3QuTWNrjN+wf8GTme3Gj6Pu05QcrUT+8EAJKXOPWxnsrJq5zH5iZBR2eV/AqqMbyF7B6tbuoS3oOuMXMrstn/RjSCoSb0ABglwK32xLP5i+6t5O8+/e9+JdovRLfhNcCvk0YO5LmAo7GQ0bX41o6v5Rwf43lYPx7+b9iB5D3neqL//jXw5Ovh+PG3dtWRSs/3Iivwz1ra5jZj9WaS1Mk3Jh2wF38H+MGZdlzwCL1R7jmtyc/I6Yj7jlJN1rG4cZF6nu/mtl0SUcAE81s+zLMvTWwJkkjpyOe8zIU2MnMJsy8lb0NehPoRePEB6cBY6pt7BRDNHiSLINbvq/lu4G8VcNVwPbFWuqSuuJf1Odw7Z9Sd13uD9wvF0M8GM9PeRBYwcy+KfG+GoWkpfDww+qFnIfgxVqV5I9/ETzb/x5gLzP7qfSzLRxJHfBKuQnA+rXgUWsqhJvXprh7exKwp5k9V9VJRapCeKhIJPDmWsCTk9MNmVfSXo83s8kFzOFLPIS+dZ7rH4eHstJ5wcw2C+vMi+ca9sNzD7/EH+T2BEbnec0cBLxD47w8k8I4ec27sUhq6N5a8PgxpBWQdDwu9HZIAdvcgZct/qfIffbE47v/A/5Xas9DuAl8iXty9sbjnMeb2Yel3E8pkCuFvgjcbGaX57F+Bzy5vB9+o/uBpAv3FauxBpuSFsIN26eA/5TBsG22SFodT27vjF9876+mFy9SeoLnbm5yGzDz40Kt45nZiPmeNOOmVCGlchCu3z1JPsgti4f7hwOPmtm3DW+ddeRiuqUn+BvYrV67pUcPT5IBeKlhXkjaHg9j7V7MziRtimfxH2Bm9xQzRo7xhR/P/HhYZ1szG1Xq/ZSQI/FktSszfRiOZxn8h7853rPqBfzHf4IVoZtUKSStiBu255vZRVWeTpNB0jK4PtIqeLL/TbVm6EayE7TL8g0p/U5mI+attNc/W3UrTIsmnI8NSBo5f+PXuBNxyZMS6JHZ3aF6fiieYJ1PeGsa7tkZXK/GDkQPD/CvNsdreAfrnBdMSZ3xH9kWZvZqEfvbC8/038bMXip0+zzGXx9/4l0IF8TavpafeCV1x59cVkk1XEIYbl2SP/4WJL04z9ZDSEjSZvjT1H5mlpfwYyQ7IQx8Cm74ngtcbjXWY605E/JLOpKfIdOKzCGl9PfGl+ZmX3tIWozkNW5N3BOfuM59XL5rt7rhD90r4mrymRwgU4HJeKXaIEgWvtQj0cPj9AceytPYEd5P6tpCjZ2w7Wl4UuU6pa7OkbQSnsOwON5VfQg1LmQX4u3DgGPMbGy4mSV+/H1ww/IRvMTzvVo+lnQk7YPfmLcys1eqPZ96J+TMHYt7Va/GE+4nVHVSzYRw7conpNQZl+n4kZmNmE9wr2zqe7/X02+6FIRrXm+S17kOeLj7GmCgmU2szEzsc5+HuuNl9evh947WuAbOp3iF49X1mKCciWjwOAPwJ8V82BVPim1Y3CgD4Ut+PbAEJa7OkbQ47jFaJ/x7Ha5l0wF/WqhlhuBu28UkvQUsgGvj3Ia3lKilCrK8CPkGp+NtOda2mdVFIwUgaTa8OevhuFZJ9+LzFyKpBMn/fIyYTngVaaaQ0rvM6JH5OeaozUhQQU4kHG+IGxPD8argN6obgrN3cMXnJk+zN3jCF7EHnkyaa92uwPnAhgVm7M+FV+f8SgmrcyTNj1c1bYcnPu+VSMKT1B9P3qy5WHZQJt0Uz/TfBFfWNGB/YFQ9XyyD7s+NuGL1mrVSIVaPhNDIXvh3/AX8QeGT6s6q9gkFAPmGlNqQOaSUXmr9g8UWHHkTHnpWIunFWRK/xwzH2wV9X8XpNVuavcGDNzR7PNePObh0rwcuMrO38h085Ac9hpdJH1mKm3nQ7fkv3sl8KK7KnH5j7Y+7/6tOOHfLk0w47oHnFq0AHGJml1VxeiUjGLb346q+G8a8kuIIN4vtcC/Z53iu3OvVnVV1Cb+hDuRnxMwN/MzMRszneDPiVANnYnMLKZULSbMDG+HXub648u8jwDHAi4U8JEfKQzR43DC4KY/19sUvOOfkO7CklfHmk+ea2cVFzW7G8WYBDsSFAx8BVjSzrzKs1xUPaT3f2H0WS3CVr0/yCWcq/nRzOq45dCrwDy73X/co2ezuUeC/tehZq3XCTX0jPOF+GrCvmT1d3VmVl5CY3wmvpswVUvqHzCGlD9Je/xSr1SqDpCVJXuN64fo9w4GzYii79mjWVVrBU/I1sGC2RDFJ3fBcmLXNrMFOrGnb9MUNqX3MrNju64mxWuG5QycDb+BaOu9lWf9gYGUzG9yY/RZKmrx5b2aUN/8w8SQpaS1cFLBHU1AaljdefRA428wurfZ86hFJq+GGzgJ4O4h769XzEIQw5yM/b8ysZDZi0quVfogew+oTcjHXwT3V/YDZSF7jnqplXZ9I9PD0xbUNshk7LfCw0ZkFGDv74sbJFmY2stjJhSferfFusz/i5eX5VPsMoBGtGfIlGGLp8uaPkUXePCSgDsX1h5qCsbM5cAOwt5k9WO351BuSlsa9fmvgFW03WgW61BdK+C3OQW5PTGdgHuAXZjZexgKj0t6bUK+GXXMh5EomWtVsgHvUhgPbA2Pi369+aO4GzwA8mTgbh4Z/c4akgnF0Bt6zqVHVOZL64E+87XBRvsfy+WEF+fGVgCeL3Xce46fKm3/BjPLmuUI5ZwMjG+v1qgUk7Y8n1G5ejB5Tc0be7+xk3KA/H9i1GrpKIUycbxuCKWT2wqQ2hvwO+DGGlOqXcB1PbVXTDXgCeAAPs9b9g1pzpdmGtELs/Htg8Ya+wOHp80Wgl+XotByqc4biYn9bFVudE9pNnAksjd9Mby8kH0TSHsBmZlZQ2XyW8YQnFydcuAl580dwIyzv8mBJG+DnqIeZ/VqK+VWDcEE8G09472tmn1d5SnVDqNA7Gq++ug4PA5b0uxBCSvOQbDWQzYhph7clyRZWSoSUStnUN1JDhPSGjUkmHP9IMlT1ci16HSOF05w9PBvh+gcNGTut8BycE/IwdubGq3PGU2R1TsgTOg13mZ4BbF1kVv8A4NYitkudS6q8eV9KIG8uaQ5CT686N3Zmwb8XXfCy87rTCaoGIYn9ELzdyf240TuugO0FzE5+npj58Aat6UbLN7iiempuzK8xJNH8CN+npUl6cVbBH26HA6eY1beicCQzzdnDcyMef72kgc+Pw5UnN852QZS0KJ638jBwdKHVOUEHaAiwI3AJ3kT090LGSBlrdmAc0NXMfitw23R581GEJ5xSKEJLug6Ybmb7NHasahGUfh/Az/HgqEuSm6ClsztuLL8CDDGzj1I+T+9snc0jM43cnpjv8TYE8Yk8MgPhYWVdkte51iS9OM9ED17Tp1kaPOEi/B1e1v11hs9XwEWiVs5U9p2y3qp4dc6ZhWrJBI/HkXiZ+c3AGY2NDcsbmu5mZn3zWLc1M8qbz4WXVA8HniylvLmkfsBl+FN9UcZctQkeuMdwg+fYWHbeMCHkNw9eWXgYrkfyFC5NkG7EzE7Dna1nWGIFTKRQJC1IMuF4PeAdPBw/HHgneveaF801pLUO8EUDxk4bPGRxVA5jZ0s8RLOnmT2U745Drs8BuBjV/+FG1ZeFTb9B+uPhgob2nS5v/gn+wx9EmeTNQ7jvamBQHRs7q+GGzmlmlrGbe3NAmTtbZ/LIdAybTMUrWt4nmfA7Jvz7A/7Q8Us0HiOlIuRv9SL5INcVF329C9jDzH6u4vQiVaa5enguA8aZ2VkZPjsN6Als2ZD1L+lAXCtk63yrc8IPcRe89PZtXEunZA3Zgrv2e7yh4vjwXgu8E25C4Tghb55IOP6hVPvPMq9bcSG0Q3OuXINI2gq4Fr9YPlLt+ZSaYODn24agBZnbECSWDsDeeH7T8cDd0ZiJlJugcL4Jfo3bFPiWZKhqZKyYiyRodh6eYAT0x5Ny0z9bFdgH6JnJ2AnbnoN37u6dT3VOSI7bAq+8mgDsYmYvNuYYGmAD3JD6W9IAkgnHv+E//GOosLy5pG3w8s6eldpnKZF0EN6eo6+ZvVbt+eRL+M411Nk63SMzJ16Rkm7IfASMSHvvjwZ+F0vgifa9cQXt62MOTaRchO/3ciS9OD1xVfnhwHHZPPOR5k2zM3jwG/BvZvZh6puhTP0mvLfTd+kbBQ/KMPwmkVd1jqTeePnynLjBMbwcMeMgb34CnoczjqS8+Zm5KszKhaSOeNuI/tXQV2kMwbA9Dw//rVXCkGOjCKKN+XhiOgF/kNkL807a66I7W0vqgicjbwtcCOweEz8j5SBcn1Nb1Rh+jTsbeDaqUEfyoTkaPAPInOdyGvCumd2Z/kGoznkQb0Oxca7qHEndcY9Od/yGcGuxN5UGxk/Imyd+/LPhT/SH4Lo9VU3uDE9gVwND81SGrhnChfVmYF7c2ClrCb1yd7ZO9ci0YuaWA98Do5nRiPmhGOmAAubcAdfS2QdXmV4q5kZESo2khUhe49YB3sTD8X2B92PCcaRQmpXBE27EA4Ad0t5fG9gJ7+Kdvs1ieHXOfbi7tMGchNBL6lQ8nnwmsG2pbjxp8ubrM6O8+ZzABWZ2bSn2VQJ2BpYg7TzXOkFF+iFcPXqTYv924Xs2F/l5Y+bCu6une2I+A15Ke6+qna2DMXgQ8F/8PPXMlPgfiRRDMP7XIGnkdMYLO27Bix7qVr8rUhs0C4NH0hBcjGw00AZvwJn4rD1wI7BfujqypF54dc4pZnZVlvHnw7V0dsHLr5dobFl3CKusQlLhuBtebXA/afLmki4hd4uMihBaBlwIbFpOL0OpkbQ4XpZ/Ny42OZNhG274uQyY+fGQ0t9k9sa8x4xGzE+l9P6Vg3AjGgychP+G1rE8+8pFItkI3vNN8WvcJsBX+IPcPsCrtf7biNQXzaJKS9I9uGcnkUg5Ag8z3STpcqC9me2Wts3WeHXO7g1V5wShvyOAg4HbcC2doiuf0uTNN8Of/LPKmwdvwle4R+L9YvddCsJcHsUrI06p5lzyJdzMN8WN3ttwt3lDxkxb8hO+axKdrcPfsz+ekPwDcIw1ohluJBK+Uz1IenG6A8/i17hHzeybKk4v0sRpFh4e3KOzFe7dAW8rsaG803Uv0kJZkg7BcxQ2y1SdE7R09gWOw8u8Vyumn1IGefOVScqbn5xnsuwqwJ94iKva7IXno5xZzUmE8zon+YWU5sXLrb/E/xYdcI/MF3jyd6oh81tzyRuQtB6eENoWOBx4vLkce6S0hGT71FY1k/Fr3KnAiKhYHqkUzcXg+QD4B2gf/m0Vlm2An/GOzUNDGOl8/Il/puqcoKWzI57g/D7uVXmrkImEaq8+JI2cNviP/0KKkzfvD9xf7ZtRyF86E1i3XCXJ4dzlY8R0BiaR2fvyUcr/t8Bd51ua2RtEkLQicBaeg3UCcEfU0okUSmi5k7jG9cZ7mA3HHzY/qvb1KtI8aS4hrWXwUEVL4C28+3fLtNV2ALbDJfH7pybIBY9BX/xG8Afu2n++gP0vQPLHvy5eGpwIVTVK3lzSh3hC3+hix2gswVB8GhczPLfAbVviXpZ8jJiGOluna8j8kK0UPuzzQlxtuq+ZjS1kzk2RkMN0Gv79PB24tpKaTZH6JrSqWYvkdW4evNhjOPCEFdjbLxIpB03Q4FF3YD+8b8piQBszJn/4IW1eeYWJ337LPiecwI3ArLgXoG3YcBqefzIwNdlW0pq4a38ePIT1UC4DJdxQVyOpcNwVrzYYjocGSlLCGwy5J4CFqly9cwhuMK5tZtOCgZjobJ2tGWQipPQr+eXG/NrY45R37b4VD3kNMLMJjRmv3pHUGffkbA9cBFxUbVmDSH0QtLYSrWo2wisLEw9yr0XPYKTWaEIGj7rh+ik9cSMm3YODGVOnTWP6yJG02W03Jn/+Oe/i+TutcCErA7qZ2VhJy+EhmhXx6pRh2SoGUuTN++Ehse8os7y5pOOBTmZ2SKnHbmB/bZmxs3VnXPF0H1zpdPaU97N1tk71yPxYKVXecIF+CPgY2Ks5ezBCgvxRwP644OaZ6VWKkUgq4UEm0aqmH7AM7tlNJBzPJNgaidQSTcTg0UBgKA0YOulMnYq1aME/O+1Eyzvv/DeROcFbePirL+7ZuSJTUl348S9Lsmy8J179lfjxl13eXNLrwJFm9mwjxkh0tk4tq27IG9OeGUNKP+DG3cvAncwYUqopL0FQo34Ur8Q6qbnmEIQ8qAPxpPxEcnyzD+lFMhMqUTckmXD8B8kHuRfqSXoiEmkCScsaiD+hzprvFq1aIWDWG24AM6bddRdT8eRh4fk9b+FaOjPEnYMOy3okn3DAlT/PAp6rZCmypIWBhYAXMnwm3DjJJy9mPmAiM3thvsWr21I9Mr+muqklHY3rA+1Qy+5rSWsB9+LCkTdUez7VIIRZd8Wb144B1jOz96o7q0gtEvK5Ete4NYBR+HXuHDP7pJpzi0QaQ517eNQNTwBuV+wIkyczdemlsS++oHXK25+b2WKQUd58DMknnIrKm2vGztb74C7lJ8hsyED2ztaJZXwxoR1Jy+P6GavWSq+pTEgaiPf0GmRmj1d7PpUmGL9b4Vo6P+MJ9y9Xd1aRWiJcV9YmeZ2bA/eGDgeeNLPfqzi9SKRk1LvB8xKuo5MzjNUQU6fCqFFY795MgRnCW4ny9M7MWG1QkLy5pC/xfJGnGvi8BQ13tk5f5gTG44bKIsDrzNxH6Xvgu3KGlEJFxig83HddufbTGMKN/j/AocAWZvZmdWdUeSStg4dl2+Nd3x9trqG8yIyEZPVEq5oNcLmGxIPcmFr22EYixVLHBo964MJwRXt3EkyaxLRVV2X6O+/M4OV5AjiZIuXNUzpbv4g30vyRzDkyHYHfya9K6Wczmx6Sbz8GOldDtEvSyYQqtFq8gYbwzcWEBqvNrd+TpBXwMOsyeAXW7VGiv3kTHqxWJunFWRwXTR2Oy0kUrRAfidQL9ZzDsy/JkvJG0aIFLffbjxYHHjjD220trdN38Gxk62ydurTCQ0pz4U9Q7+BGy6sU0dlaUquUp64t8fL2ahg7KwMH4I0ja9HYmQ24Hc/pWrs56X9I6oar126IVxj2j0mlzRdJczBjq5pfcQPnv8CLlaqOjERqhRbZPpT0q6QbQ2UHkjaX9KakCZJeltQjZd2uku6T9KOknyVdFt5fTNIz4b2fJN0qqUPKdl9KOkrS25L+lHS9pE6SHpP0u6SnQsl3Yv3VJb08xxzst8IKtHzuudwHue66MGQIrLkmtG8PW2wBP/8MO+8Mc8zh7y+5JIaXUifoLWlSmNNXkn4E/gI+wTV1bgSuwjV/vsc9TT3DOd0MaGdm3cJnj+KCbofjSYBPmNmroZJroyzn9EtJR0t6G/hT3vcJvC9YxZuFhu/BTcDhZvZtpfefC0mdgOfwXJW+zcXYCb+XS/Hw5id4wv0l0dhpXshZStIRkp4BxuHtXt4A1jSzZczsSDN7Nho7kWaJmTW44LklL+HKqyvi+SOJnJnd8P5DiVLwt4D/AbMBswC9wxiL46JUbfGKoOdxcbPEPr4ERuL6LguEfbwR9jcL8AxeRkz4/Geg79SpTHriCWzuubHx47MehvXpgy22GPbpp9iECdgyy2BLLIE9+SQ2ZQo2aJAvMMMyBdgd92b8iif1tcTL33/C3cOJ+X2BV8C0DOfq2bTjexcXH/z3fIbPGjynKdu+GbadNbw3J15VNUfWgy7DApyDVzup0vvOY25LA58DJ9bi/Mp0zHPgVVc/4yG8jtWeU1wq/h1oi3txLgY+xY2ca/BE9fbVnl9c4lJLS+4VPLHtM+BK4LS0zz7C+0KtgeeotMpjvK3xpLjE6y+BnVNe3wtcmfL6YOCB8P+jgZv9Mx9y442xoUOzH0afPtjppydfH3EEtummydcPPYStsAIGTE8xeP5OmcPVKUbXUFx2P3V+H6S87g5MSDu+/dLPZ/h/g+c0Zds90j7fEXik4l8UWBMP0dXcTRXP1fkB2K3ac6nQ8bYFDgvHPAxYpNpziktF//5dcM/NA8BvuA7W8biHuVkY+3GJSzFLPjk8Y8MPbGFgN0kHp3zWJnw2DRhrGdSEQ5jhYtxDMjse8kmvdEpNmPs7w+v24f8LAwMlbTHnnP7GlCmw3nq5D6JTp+T/Z5115te/e+GlUjaZRdJk3AgS8KKkTfCcnM8ltTRPBM023wSpSbOJ85k4nobOaaZtoQrhrJAXMxQ40MzGV3LfuZC0A3AJsJM1UAnXVAjJ2DvjeTrvABuZ2dvVnVWk3IS/+6okE44XwYsq7sErQKNCdiSSB/kYPAvhInRfA2eY2RnpK0haA1goJNamGz1n4h6T7mb2i6StgcuKnO/XuIdnb9D7eBVKSZjiEW0jafSMx3NuUquqjgRWwZNCDwp5PVOA9pJuwPN1puLh9HXCa+EhqQSJ85k4noznNIV/E4OD8OHGeJitkpwFjDaziucNNUQoO/8vrhq8gZm9U+UplY1wrJvjv6WJuKbQTIKTkaZDyHNMbVUzHk84Pgx4OdPDZSQSyU5Wg0fS3Lir9E7cq3C/pKfwSqN2eCLu8+H1d8DZkk7CPT4rm9lLuFfnN+A3edfwoxox31uA0ZI2mTSJ56ZNY4lRo2i1+OKw4ILFDzp1KvztGsmpHp6vgbvD/3sCf5jZB5KGAt/guRMd8aTlrfDcnM7AUmGcM8LrrsAVko7FPUHdgLGSTsRzgQ6T9C1eIvo73nH4ecss9rUR8IaZ/Vj80RaGpPVwr1KPXOtWipC8fRmwOrCGmY2r8pTKhqTeuJZOB7x57cNmVnPVcZHGEYzaZUh6cVbGr63DgROthsU9I5F6IZeH53PgQTzJ9i9Je+M3miXw0M2L+M15mqQt8NDCV7hX4jbcCDgFzzP4DU+quxn3nBSMmX0taSvg3Hbt6DnXXLRabTW48spiRksyfTrTJ06cqWJtaWAC7rF5HTgibS5TgHGSxgK/mNn18K8s+05mtnZ4/SVe0bULfkF7DT+n8+KJ2p/j53RW3FCajIfMvg3r7CtpRdxbtAswUtJ8BE2exh15dkJZ643APmb2Szn3lS+S2uMGeEtgHTObWOUplQVJ3XGPTg88EfsWi1o6TYpQ9ZjaqqYlbuCcDzxjZn9VcXqRSJOjjoUHoVRKyyNHwtprzxDOMtybsil+IZqHGdWWy1LunEN1eX5gIG4gzYtX6PxIHh3JrUjVZUnXAC3MbK/ij6p0SJof7+nzBnCANcHSWkmL4A8Jm+KhxKusCnpLkfIgqStJA6cPXt2aUDh+N3rvIpHyUe8GTyl6aU0JvbRS20o8Z2b/pkJLWpTkRao37qVJXKQ+rMRFStL6ePO+VcPr1L5a6YZR+uvp5Kfk/G9fLUmb4VVkPWrBiyJpWVzP6FrgzKZ2Ywheu+OBQXjvr/Nr4bxHGkdIOF6d5PVjAVzHazguHloTntNIpDlQ5wYPJLqlt2+fuVv6Y4/B2mtn3vKvv2D33Zl61120wL07wvOP3gGOylT1EyqW1id5AZtM0vgZUa6n8SDkOM7Mzipwu0I6p3fEQ4/j8VyjF/An0Bk8RmH5pVJGh6R18TDWf8zslkrss1JImh0Plx6Ch4FPtyjzX9eE3MeEd3gTXBsncY0YGUOTkUh1aAIGDwSjZyhJEcSsTPX6hsm77cZft91Gh7SP78GTlc/AdXCOMbPXM+7VjYkeJI2f5XGl3+HA8FIl04ZQ19d4NdKHpRgzy37mAW7ADb97adhz1B5Pws7pOTKzPxsxp52BC4EdzOzZYsepNSS1xTveHw88DZxgZp9Xd1aRYgjXgeXxa8Dm+DXhOfw68Kg1s15ukUit0kQMHgjhrWG4enEbMiRkmzF1+nSmjRxJ20GDmPzFF/yC6+q0xcM+PwILmtlUed+sPfHmiy/iN6SPs85AmocZn+y+xnNOhlNkE9Iwbi/gRjNbtpjtC9zXAFxRuWc2QyXcsDuR22s0P5743WCOETOG1KaG8YVXJe2NNwB9r9THWg2CUbkTrqXzIXCcNcNO7vWOpHbM6Omdhv/OH8FD4jHvKhKpMZqQwZNA3fHGouvhbS1aA1O++opJ333Hqx9/zLm77soDMEMIzPAwzu/AZmb26b+jeQjrEDzscC9wquXRRyqUTidi95vjN//U2H26+GK2sc4BppjZkHy3KYaQR/I2sI2ZvVyiMYVLE2QziBL/nxcXpfweT96eFbgDV/pON45+rac8nnAeNsMTkf8GjjazEdWdVaQQQkJ5wsBZG6/eTISqPqin72Mk0hxpggZPZiTtAmwH9Af+IekBSnhduuMtCk4GBtjMndLnAY4B9sB71ZxjZhMK2P9CJC+W6+A9shJPhO83dLEMN8qP8ZBOxtBaKQj7uQdve/Hfcu0nxxxa4urTN+OG6nW4By6TodSOGUNqmTxG3+Pd6Kta3huEOc/BDbrjgAfjzbH2CQ8ta5L83XbCE+cT1ZoTqje7SCRSKM3J4OmAawR1wfWAOuFhrNeBrRNeG0l98Y7g+2ZSFg5lpScBWwLnAZeZ2d8FzmVWZtTfgOST4rOp40laPry/SDlvkpJ2wvNJVq6WO15SF/xYRwEHZVOTDRom2UJqqZ6jSeRXpfZjKRVsQ2XZmcBKuCE9LCrk1jaS5sU9cf1wVfMvSf42R8eE40ikfmk2Bg+ApMdxr8EhuHjiobjo33pm9m7KeisBDwPnmdlFDYy1DJ7YvCqumzK0mJtZ8Kwsi4e9+uGqzgmF1eF4B/V5zOywQscuYA5dcI/TZuX0IuWYQ8KwuwI4t1TGXTi/c5Jfldo8wC9k9xgllt+yeOUWwr8T/XDPzuUxp6M2Cd+PFUg+fCwHPEMy4Thn+DoSidQHzc3g2RcX+zoKv2H9IWkvvDdVr1QhO0kL4+7rJ/Fy6IxPdiGh+Gz8hnk8cH9jbtaS5mLGHjrt8bYeV+IlrSX1EIQL/iPAa2Z2UinHLmAOGwC3A4ea2e3VmEOYRys87JQrCbsznuiebgRNxJPme+H5XhcCn0Rjp7YIat0b4L+xvniIO1VaYlIVpxeJRMpEczN4OgMfAJ0TF7Vwwx8OjDKzU9LW74AbGxOAXRrKBQljbIInpE7GS9kbXUItaTFgNG7s9MUbjz4e5vt/ZvZzCfaxJ96Ac/WE6GAlkbQrHhocaGbPV3r/xRKqdBIhtYXxyqsNgS/w6rzU3KO/ye0x+h74KYZMykP4LSW8OGvi/f8SRs7HMacqEmn6NCuDB0DSi3iH8sdS3lsAGANsamZvpK3fBrgeD4FtaWbjs4zdAtgeOB34BDjWzMY0Yq5HAMuY2d7h9YK44dMPzwF6h+RF++1CL9rBi/UasL5VuNt4MBJPAHYH+prZB5XcfykI3429gCHACFy64NO0dYQ3/synfH8uvKFsPvlGE+NNumGCrERvkkbOXCQTjp+MKtaRSPOjORo8MxgRKe/vDByLJ+1OSvtMuG7KjvjNOZceTxtcP2YILkA2040wz7m+iLdReDTDZ7Pg4bnEBb0NyQv607nE/oJx9iR+8T+70Lk1hnAzugavjNvczL6v5P4bS5ph+ylu2L6Rfau8xm0NzEd++UatyM8w+qG5hGgkdSKZcLwh/rdJPBC8bmVuthuJRGqb5mjwLAqMBLqkhg+CUXMv7t4+poFt98QTlbcxs5fy2Fd74LCw3Amclu/NPVP4Lcu6ApYimfi8Ci6WmFB8/iLDNgcBOwNrV7JySN6B/R68cmqHxqgwV5q00OUUPHT5TJXmMhv5CT92Av4kt2H0HfBzPRkFwfBckaTW1ZLAU/j3/rF6M6QjkUh5aXYGD4CkMXiC7PNp73fEhff6p+vwpKyzCa4Tc6CZ3Z3n/hL6K7vh+TjnWY6O64kEazPbKZ99pG07J15S2w9/4v2Z5JPuS8AiwCvAmrm8VaUkhOSG48bYofVUop2SnD4/npx+Xz2ElIJR0JCWUfoyJ642no/n6I9qHL+899hGJBOOJ5L8br9QjTy0SCRSHzRXg+dEYO5Mpd6htcLZeGuFhpKUe+Jl6xcBF+Z74S+kXFnS/wHX52tUZdlnC9zjkwh9LYa3engcONzMfmzM+AXMYwW8Guxi4IJ6MBYAJC2Na+mshmvpFCU/UA+EkFpH8ss3Erk9Rt/j7UIaZYRIWpLk97cXbqwnvJcFh4ojkUjzpLkaPFnF/CTdilfMHJpljK54zsxzwGGFVNdIWg4Pja2EixgOSwuvdSCIJJrZH/mOm+e+T8UVp9/HS3M/IPmEPKYchoikjYFbcDHBu0o9fjkI3qiTga3wKrJLCxWYbMqEcG0+XqOOuBcmH6/RL2Y2PeTArUPSyGlP8jv6tJn9XpmjjEQiTYnmavBkbdcgaW48tDUoW3l5CB3di+dI7FRoPoqkNXFv0jx4mORBM7OQQL29mW1ZyHh57G853EBbzcy+kDcAXZsZbyyP4p6Yp0phbEnaHc952dbMXmzseOUm/O2PJdlC5FwroO9ZZEaCh3FuMnuJOqW9nhPPjWqN97X7DBfEfJO0sv56yv2KRCK1QbM0eICcDTlDi4nLgR7ZnijD0+g1uFryFmb2Q4HzSG0q+Sfer+tQ4GEzG1rIWDn20xoPBVxjZtc0sM4SJBNAGxU6CMd1Cp4Y3dfMPmrE9MtO0NU5FPgPbsSeElV2y0dKqDWRaN8NTzh+Cfc+tiV7SG0a+XmNxqcKikYikeZLczZ4egE3mtmyWda5DphuZvvkGEvAiXhScl8z+7CI+bTAxetOBxYANiilEF/IW1oTbx+R84/emOTQYAReh1eObZFNu6jaBENwT1wT6CVgSCUTuZsTDSTTP4J/p17O1zAJv7dcIbWEIvZ8uHBoQzlGqcuv9ZJbFolECqc5GzwtcEXcDRoyUEIJ9dvA/qlChVnGHIwnIxetGixpWzy/Z078ifdEM/u8mLFSxlwJ+D9gRTMbV8T2qeW//XBD5mn8ZjVD+W/IP7oXD0ns1FDid7UJx7QtbmB+hZeYv1bdWTUtgmGyNMnvTU65hDLMoSUeMs4n32g24Afy8BzV6vc6Eok0TLM1eAAkXQaMM7OzsqyzPt49vUc+uRySNgRuAw4xszuKmNMwvFv4MOAIvNHpbcDphYbLwnhtcTXlc83s5kK3b2DMhgTeXsNzkp4GjigkkbuSSNoIDyGCGzpPVXM+TYkGBDETnsFnajn3JvxWMmkbzZ/hvSnk5zX6salW9UUi9UZzN3g2AM42s1VzrHcp0MHMBuU5bg/c+3E5BXT+DuGV73Hjalx4bz48oXkQ3tn9AitAFl/SWfhT9oAyVWAlJPx3x5Wo/8Y9PMOBJwqZa7mRtCpu6CyEq2DfU09Ce7WKvDVLwsBpdMuTWid4ruYgP6/RvMAv5JdvNKGpnatIpJZo7gZPa/ypbCUz+yrLerPhlSL/NbP78xw7IbL3MnBwPk95wfNwmpmtnuGzRfD2Fgml3yvzUGBeHXgAWKEY71C+SNoM94Ltj/ckS9z81qIGmjQGHZcz8BymU4EbYiJr8YQwUS+Sf+eulLipbVMhnKt5ye4tSiyzMHNILZPX6IcokRCJFE6zNngAJA0F3jCzS3KstybuueiRr1hfyAG6G3d/75CrzFvSFcBYMzsnyzrdcSG87nii9K2ZQkeh6mgMcLyZ3ZPPfItB0t64ETHA0tSpg1bLBiRvjH+TNH5G5DLYSjC3BfBztA1wAXBxzL0oDklz4cZ2P2BT/MabSDgeGcM2jUfSrOTXLqQz8A/5eY1+rNXQciRSaaLBI22JKw6vl8e65+JKxdsWGKa6CuiJN8r8roH1WgDfAOvmUyUkqTeeID0H3rbikdQ5SboI6GhFtKbIh+DWPx0XMexrZp/ksf4KJI2f5YBn8Zvmo6UsAQ8356PxBq7X42HLX0o1fnMg/L2WJVk23hN4nmTCcYMe0Uh5CX+bOcnuLUosc+PVcNk8RollYgypRZoy0eDxp6rvgcVzeW5CQubrwBlmdlsB+xCeh7MXbhy8n2GdNYBrzWz5AsfdHA9xTcATcF+UtC5wK9C9HDf6kNx5A66dsmW+Hq+0MebFPQWb46XKX5L0/owu5qk0/C0PBo7CQ3mnmNk3hY7TXAnnbz2SRikk/ybPxjBK/SGpFV6an8swmh8XfMzHa/SDNdASJxKpZZq9wQMg6W68vPqGPNZdGVcjXrFQr4SkQcD5uIryc2mfnQf8bWYnFjJm2LYlsAseWnoP6AHsa2bDCx0rj33NBdyPPzXuUoqbYLgor0nyRtsRL6MfDjxuZhPy2H53vE3HKDyM92Fj59UckPd3S5z3dfBcteG45+39+MTffAi5ivmE1DoBf5E71+h74OcYUovUCtHgASTtCOxsZpvnuf4pBJXYQm8Iocz9DjyMdmt4T3hp97ZmNqagyc84dls8TLQicBdwkpl9Wex4GcZfBDf2/g84qlwXsrCfviRvwq+T9DR8kDjn4bxtgyckj8M9XK+WY05NhWAcrk4yVNWZGY3L2EYjkpXwu5uL3KKPnYEOwI/k5zn6PRrYkXISDR7+VYD9GljA8mhMGJSER+Ldzq8vYn/L4TeYa/BwVHfgQaBbY37wkjbF84XWAvYBDgJuxkNwjeqKHjxbDwHn5ErwLiUh+Xp9kl6Iafi5+wY3dlrh7TieiBfLzEiaBw8f9sMTj78iaUC+Gp/AI+Ui5DB2JL9E7Jbkl2v0g2VReo8UiroD++Hh7MVw7azJeC+7Z4GrwN6p3vxKRzR4ApIeBW4yszvzXL878AywipmNLWJ/XfCwwWv4j3g2M/tPoeOkjDcXrgo92MyeDu91wvVmdgIuAS7Mx6DLMHY/4EZgHzN7oNg5NpbwZDkQT5buChgucphIpP26WnOrJcJ56kHSSOyOX7iG4wniMa8pUnOEqs70kFqmxOyOuJJ7Pl6jn6PWVkOoG/5A3BPvXdcyw0pTceNnDLArNE71v9pEgycQyqs3NLPtC9jmGLzf1EbF/Kjk/aruwj0y25jZk4WOkTLWMLzK4qAMn3UDTsNLxM/AG4jmVRIuaX+8tHtrMxtV7Pwai6TFcUOnD34s1+H9lFJLpceR9FyMbE6ei5B/keoJm8yMEgAxyTTSJAgVrXOTn9doDjykls1j9D3eLiSrbEjTQgOBoTRs6KQzDZgEDAa7u3zzKi/R4AlI6gh8DHTO9+YQ8iFeAG4xs8uL3O+SeKLoR0C/YsqzJW2NJ0OvYFmk+yX1xDV8lsaNmNsaMtTCReUsYGu8suyzQudVCiTNj891IPA/XEtnpgtTSNxeneQNfwFmFMNrcmXpkhYleby9gdEkjZyPYogv0twJ6QcdyV3CPz8wnfy8RuMTIbWg4/aNmQ2p2EFlIRQhvA/M2fADnwbefDO33HwzbZ54ouBd/A3slm701Np5aIho8KQg6Xm8FcQjBWyzJN5le81cWjQNbH8ksATwBa5U3M/M3i1g+/nwUNZAM3sxz2364D2v2gHH4hVqqRo+s+DW/4LAVtVQzpU3IT0Kjy0PBc4ys58K2L4rycTndfFzlBDKe7cejYGQD7EmyYTjeYDHSLbx+K2K04tE6pYQBm5Pfl6jjsBvuPEzN/AT/nCVyTj6pbauNeqGt35pN8O7gk8+gcUXz2uQv4DlIdn8Nxo8dYikw3Dtmj0L3O5QXIBvnULDKJJexvViHpe0E3ARsGMiDyfHtsJDYl+a2VEF7lfAVrjH5yfgaDN7RdLceAL1d8CulQ6FBGPrIOC/uIFycmNF7sKY65I0FFoyY0PLmlVfDgZtolHrRsDnJOf+WsxPiEQqS/B+J9qFnI97PV4is3HUnpnbhTQUUqtAY129hLeFmSGMVaDBMw0YCdY7uX3hBo+kVlZphXYzi0tYgEXweG+rArdrATwHHFngdl2AX4E2Ke/1wX8gu+ax/Q64+3KWRhxzQsPmK+AJ/IZ6HtCiwue+FbBHmMf9wHJl2k9CQfio8Df7HS+1PxBYpAa+gwJWAk7AKwF/A+4L52b+as8vLnFpzgsu+fFGuG7ciUuMnI6X6T8S7h+/hv8viOfI7A98AGyJV8+eiBtI3wKv4AbPdLwIY0q4Bt+FF5ocF377fcN1oUsYa/OUObUK+10p3MMscQ8DBofxfm/Thm9uuol/zLAbb8TWWssPa+21McDatcNmmw274w5//+GHsRVWwOacE1tjDeytt5Kn4rXX+LtdO95PPw85zt26eHXt0eGYb8YNx0dw4dxf8BSRrPceXKT22HDv+xUvqJklsY+s21b7C1RrC675kvWkNbDdouFLl/eNOvwQbsnw/jJ4iOtEghcuwzrz44bRqiU67rXCzfX38AVaqELnW0D/8OUdAaxR4b93B9w7d1P4+72Lt+xYhwIN30bMoT2eK3VduAh+jOcrbQi0reT5iEtc4pJ5wcu1xwKH46rU2wYD5XQ8vLwNHiqaHe+h+EDYrm24mS+TMtYYvFAF3Ju+Nt4uZFVckX974FA8j/JGPHQ9Jqw7De+l9jb+kPo0LgR7BHAIbvB0xw2uicBSZsYHH3DjO+8w1WxGg8fMDZ5PPkm+fuMNbL75sJEjsalTsaFDsYUXxv75B5s0CVtoIezAA3kh/TzkOH/r4lVf54RzMms4vqvCOK3Dech4z0sZ58twne6KhxRfSuw7GjyFf6mPBy4pctt98DLz1nmu/2TiS5/hs85hrBvSxwtGwiPAqSU65i2B8cAW4Ud3evgBXQDMW8Zz3Qd/wnkLf4LJ+kWvwN8+kfh8Gv4U9wv+5DKo1OcBWDxc0J7AjcwngcOAJap5DuISl7hkXvCHoG9Tr1PAy5lu9Hip968pr6/E9dDA+wj+SniYwb3a+wJz5DmPJYE/8NDUJmEOT+DpEA8Hg+dD3Gti+IPcq2PH8ntimFwGz377YUOGzLjrJZfEnnsOGzECm39+bNo03st1HtLmvS5ePTpLynun4ikUixfwd/gS2C/ldV/gs8Q+sm3bgkg69wP9Q45LoVyL58Mck2vFkCvTC1e5nQkz+x7/gswHDJd3Xk8wGK9COr2IOabP4yD8x9jXzB42s9/M47DL4xb4h5KGBI2MkiCpp6TH8CeXy/A2HY9a+MZWCzObZmYjzewEM1sJPwdPAQOAzyS9Es5Fz0K/H5LaSNpA0oWSPsJdtz3wp5suZraRmV1kRSS+RyKRitAFGJd2nRoLLpAq6WpJYyVNxBvtdgjVo+Ae5J3CdWMQcJclpUG2wW/aYyWNkPdVbBDz5tLv4+GrF3BvzgFmdhjeSxBgeTPrgD/Mfggss+++zPbhh/kd6NixcMEF0KFDcvn6a/j2W18WWABatCA14ydfLbofbca80PPwLgNPSPo8SL3kQ6rm2lj8b5OTaPDMzAfAn3jriIIIP4Q9gYMlrZhj9c2Bpy1Lopp5+XV//AvxgqQFJS0MnAvsZo1QG5XUQtL5eO5KbzN7LW3f35nZAbjHYzngE0kHhjLPYve5mKTbSLYyWNrMbrUaTbw1s2/N7Doz649XZpyIG6D3AF9LukbSVg0Zg5I6S9pd0j24B+1M/KlrJ1zVe08zu8+KEIOMRCIV5ztggbSHnYXCv/8BlgJ6mdkcuDcI3BuPmY3EvRtr47//mxMDmNloM9sKv8Y8gOfv5OJ2YEe88OQD4O+ghr9e+Pw4SZcBu4U5jF92WbT33vkdaNeucPzxMGFCcvnrL9hxR5h/fhg3DqZPp3WG85CLGR5qzex3M/uPmXXDjbMjJG2QzxTT9p2XnEs0eNIIRsv9+FN9MduPw7/8w+S9rRqif9hPrvGm4kbJLXj4505cMfntYuYH/3bFvhOPF69lliwvzLD/T81sR7xKaAvgfUk7hkqFfPfXKfz4XsWfNpYws8saY7BVGjObZGZPmtmhuIzABvixHAJ8J+lxSYdK2lrSyZJG4xeizXA385Jm1svMTjWz12vVyItEIg3yCp6Dcoik1pIGAKuFz2bHq7UmBO/9SRm2H4Z7tKdYkBAJnt+dJc1pZlPwnJvpcuaRtFzwDO8s6T+SzpN0M57ztyXuOVoZ1+C6Ftg17GtW3ED7HA8b9W3XjmktGrhqd+oEn6doKO+9N1x1FYwaBWbw558wfDj8/jussQa0agUXX8z0DOehICRtLmnxYET+hucn5XNtPDA4AObG01Dy6pBQ9bhoLS64IfARReaU4Bb1/cCZDXw+G/7FnrvAca/Dk8M2bcSxzYsned1OEQmx+BPEKDyBbtNs5whXOT0Nzwf6HzBftf+2ZfiuzIF3qn8G16eYGo73TjzpOK98rrjEJS61v+Ce/zEkq5PuxFMLuuBVn3/gRQf7klItFbZdCL+ZX4zraQ3AQ1Cf4irGU/Dowg+4N+gXPHT1DHAbcCEu17ErsHG4Dk/FQ+KJfSyS2C9e2DICNyQm9OrFn++954eSnsNz5ZVY585ekXXnnf7eY49hq6zi73XujG27LTZxon82ejS2zDL8nX4ecpy7dfHS9dT3Dsdzcv7EK7hOyONv8CXJKq0JuNHXLrGPbNtGHZ4MBGvzK2ATM3u/yDE64cm4W5u7M1M/GwDsb2YbFTDe4niZ8qF4MvGxZnZjgXNaDM/2vxc43or0MoTzMwBvU/Ed3qV8VMrns+AVaMfg4auTrIRd26tJOPYlSSocr4YbkAltnC/x8tCE5s/ieB7QcFzg8YfKzzoSiZQLueJ+vg1SZ8dv7N+QXZfnByu5BpouxwtrWpVgsKnA1TBzK6NyI+lLYC8ze6rgbaPBkxlJl+BfujMaMca2uFGwoqWI20m6BXjJzK7Mc5yWuKV+j5ldJGkpXDvmFlyYL+cfUdLquNfpZDO7uvCjyThmKzyB+mQ8XHUC7h07BTf2jjer/y67ITTZh6SRMwt+/ofjeVgN9uCR1JmkcOCG+NPfcLzKbkyxRmckEikf4cGmAw23oUh9PRderJJLXHAbvF/j+hU8lBTUAw/Ltcu1Zh78BaxejS7q0eApA5LWBS4ws5UbOc5tuOF0eHjdBndZLmd59s2S9B88Xrte4gYZPEgP43kie1uWfBhJ/YGr8U7qjzbmeBoYf1bgCrz64Ce8YuC+Uu+nksi72SdaU6wPvEfSi/NWPkZmhjHb4D2v+uEeoDlIGk5PWkxejkTKSrhW5eOJ6Yzn5OTTW+tHy6GwH27Swj3+Y/KY53G46GA6L5jZZrm2zzJyRqXlAplJafnf0UswbyX7gWViWbwCLho8pSR4L74DVjGzfEvuMo0zN967ZGcze07SJniIZ808t0/8gVczs8/TPpsNj+22BwZYhl5K8nYZRwJbmtkbxR5HlvmtjfflmgOPZffE3aY34P2v6qJpZ/CirUrSi7MIMzYfzbuPVwH7XDxlf2vgMfnhwHDz0tNIJJKD8Nudj+yNQRP/nwU3UnJ1T//BzP6u6IFUhMy9tApkpl5a9UI0eLIg6XrgHTO7qJHjbA5ciuuunA98ambn5bFdK9wFeV1DYajwY/8f7oXoa6HvVHj/Arz/Ut/GGG0N7LcHXma9PF6ufWviKSd4R07EXbgX4kKOFegTUxjyBqUb496WTfHS8YQX52WrYJ+XUNq+IUkD6E+SzU6fz+bBi0SaGiGkNCf5eWLmwRN88/HGTCjGO9u00EA80XfWIjbO2C29XogGTxaCofJfM1sn58q5x7oeT/TaCte9+TSPbYbgug2bZvuRhovDYXg5/BZ4hdkteGy5v5lNaOz8U/a1KF7muDFu8FxlSQGt9HWXxL0+a+HVWtebl15WhXCeliFpVKyEC3cNBx6tlcTqMM8VSc5zGVw+PjHP76o4vUikaEJBQyfy88ZMJmmoZPPI/FjJh5OmgQYCQ/EWD/mEt6bhlWSD69XYgWjwZCX8OL/He5E0qrpG0py4IfKXuchSrvV74nLhK5vZ1zlWT2yzLa7c+yPeE2zPhoyRQpHUEdc72AXXkrjAzCbmue0qeM+UhYEhePJ1RZJ1w99wPZLGQ6JT+iPAs1bDndITaMaO6RszY8f00THxOVJNgjc50T0819KO/LqH/1CLXuGmhbrh2kAr4n3CMlVvTcUNzzHAoHoMY6USDZ4cSLoDr8S5tgRj3Y3fsBbO5nUJVUGjgfPNbFgB4y+JazZ0AA4v0Zxnxz1HBwO34loL44sca0M83we8rP7Jxs6vgf10JZlwvC5eMZYwEN6tZ5e2pNa4hkfCgJsXlxoYDjyRKY8rEimU4GWcndwGzPz4d3ACMxosDXlkfq3n31/TRN1x3aD1cBmN1rgm0KfAs3j5ed1X20I0eHIiaXu8jUPfRo4j/Ml8JDDZzHbLsu4ZeG7M1vleHCStCdyHe1BG4DfBO4EhRVYUtcV/BMfhjS1PSk+aLga5QvM2eLn+V7jhM7qRYyaafiaMgAVItq94vF4Sp4shhBgTx90bbzibMO4+jDeXSCrhd50tpJS6TCO/vJjx1QxVRyL5Eg2eHAQPxziga2OenuW9te7BE5ffAv5jZg9mWK8X3j22p3kD0XzG3pZQFm5mj4f35gMeAj6jgNBWMB52wvN03geOM7O38tm2EIKnYg88ufll3DD7qIDt58YTjfvhHYPHkbzRj8xVJtoUCVV765M0gKaQDN+NKL2QWaQWCA8R85C/8N14ZjZa0j0yP2TTl4pE6pFo8OSBpEeA28zstkaMcSowq5kdJak33iCuR2q5c9CIGIPLa+dMDAteoyPwhOUtzOzNtM9nxcNQc+Fl67/mGKsfnoj8B66e/HxBB1kEktrh/aj+gwsjnmLejyzT/JYneTNfAZdyTyTy5pXn1FwI56s7yfPVnRnP1zfVm10kH0LlXrrIXaZlPrxVTT7emF9izlekuRINnjyQtAewmZkNbMQY7wL7mNnL4fV5uNbLdomwg6QL8b4oO+QxXkvgIjxHpW9DN/yw3vm4F2SzTOXpIRx2DjA3HsJ6qNKhkOCxOQbvNn9tmM8kkh6LvngfmkSp9nPRY5E/kuZhRo/Y1yQ9YqOao0esGgTxyXzbELQgcy5M+nvjo2xBJJKbaPDkQQgPfQp0LkaMKiQTjwAWSFFKngV4AzjVzO6Q1Adv6NndzH7OMV5CcHA2YJt8Qm2SDsUbz21pZq+H95bHc2l64t19b672jS+E9C7ARQANb1nxMH5j/iDmpDSeoO+UmvM0PzPmPDXoCYzMTPCmzU3mtgPpy5xkDillWv6I3/dIpHREgydPJD0L/M/MHipi26PxyqwD0t5fFfdYrIknBh9qZg/nGCvRUuJ93GOU95NdaDFxDXAUwTOEV01dUS1vSbj5plYddcQTrsfg3p2V8N5cN0atjfIQpNz74gKM6wBvkvT+vNdcb7rhwSIfT0wnPAycyWhJ98b8HENKkUh1iAZPnkg6GNfEGVzEtiPxvJyZyrBDbs+uuCbM7jnGWQo3BobhuS4F/fEkzYsrPm8fxtmpGmXMYR6pujJfkkyufS3VyyRpNdwoWwDXAbq3ud6AK0HI+0rVLYJkv69nivFw1hIhWT61DUE2j0yivUwuT8wPpdK7ikQi5SMaPHkStF3GAPMXUoIpaQHgbTwcNtN2kvrhybqHmdkVWcZZG6/yOsbMbixw7u2Bw4FD8VL1YcDNeBn7ceV+4gwu/xVI3kSXw/WCEgm0WZuohu03xsULp+Ln4Jlyzjny73lflmSz0554X7dEv6+vqje7JGGec5GfNyZXZ+tUA+f3aFxHIk2HaPAUgKRXcd2YpwvY5kCgl5ntmuGzDngjt1Px6qiVM91EghbQpXgD0rzF+kKC5D64Z+RZ3Mv0WfhsXrz8/Su8i3pJn1BDOCDRG6ov8A8z9oYqeH+h/HY7vF3F57jhU/KGqJHMSJoLT3juhydAf08y9PVKqUOOoYIvk2ZMulemE97QMJ+8mJ+qnacWiUSqQzR4CkDSscCCZnZgAds8DVxqZg9k+Gwo3mrigDD2+sAmKYnNwhONDwQ2N7O389xnC2AHvH/VJ7iRNibDerPi3p5OuMhhowT6JHUj6cVZC084TtwQPy7V03Iw5PYETsA9DieY2SelGDuSH6H6bzWSf++FmLG7fMbE+5Czla2zderSlrTQETOHmL7DQ0qxYi8SiWQlGjwFEHJonsFFCHOGgUIp8Od4GOyvtM+2wjuJr2Bmf4QbwUvATWZ2RXh9KSGhNx/dlGAgbYqHfibhHpBnc2zTAi8B3wIvW8+7V0rIh+hN8qY3F8l8jyctz15bxRK8SIfh4bq78Yq32FizwoTv3TLAtnjocSXcGPka+A03XBJGzNzAz+TnjfkthpQikUipiAZPgUh6H9jDzEbmse5gXBBwm7T358XzerY3sxdS3l8a7969Pm60tAYG5mM4SFodT+7tjGvp3F/IzSKE3o4HtrIsrR7kTUQ3w3M6NsTL9RMJx29UowIlGJbHArvjzVPPsxJ2iG+uBOmEfDwxnXEDO2GojMcTfjsCSwDCZRkexr+XZTWEI5FIJBPR4CkQSacDrc3s6DzWfQi4y8xuSXv/TuAbM/tPhm1OwMNYdwP75kqQlrQMnv+zCnAy7iEqKpdC0pbA9XgriofCey3wbroJL85SwFO4kfOY5dn+ohKE8uqTcWPsXODyeq8qKjXK3dk6NT9mVpKdrbNVK/2Q7sFM2Z/w70zi+7Mq8CLJxOe67r4ciUTqh2jwFIiklfBKpyWzeVBCZdS3wEKp3oaQgHwysFL6zVjSsviNAOAyM7sgy/hdcX2akt7cgzbQQ/jTeEs84fg3krk4L9a6qms4j2dQAiOwHghGxRzk54mZF/iV/EJKv5Y6pCRpTjzs1Q/3FP5M8rv1UiEVkJFIJFII0eApkHBz+QIYDCwNmJldnfL5iXjeTktgRzPbNOWzznjj0C3M7NW0cfvg/bWOwp+ARwHrmNkHaeulhm+uBs4tRfhG0hIkn8LXwMMQo3AvU10mBEtaAw/zzYeH6x6op5wQNa6zdUMemR9rxagI3sNVSH7vFsMFOBPew/FVnF4kEmliRIOnACQtCOwIHA10wHs7vWhm66es8234rC0uqPcQ3vPqq/D/t8xsSNq4OwP/A3ZI6MtI2hfYC1jDzKaGBN1D8WahdwOn5dKvyXEsbXBV3cTNZjaSCcdPAW2AB/Cb5K71WgWTlsj9D57I/VwV55PobJ1PU8j2JENK2ZYm0dla0vwk88M2AD4g6f0ZU0/GaiQSqT2iwVMAku7AK1FahrcMuNDMjkxZ51m8bUMqfwKX4zfeVRMhoXAzPhbXyulnZu+ljCO8v9FLwI94CfYLwJBiPS7hhtIXN3DWBz4kqY3zZvoNJSSt3gh0xZOZs/b4qmWCobEjXqr/EV6q/2aJxhZunOTjiemIhwgzGS7pXplfq5EEXgsE79baJA3y9rhB/gjwVFMw8CKRSGWJBk8BhPyDl4HFcQ/IX8CBZjY0ZZ0L8DJpAX/jnp4W4eOngb3M7MtQdn4F7tLfPN1bE27Q+wMXA6OBgyw0/SxgvplCBk+QDBn8mOcYZwID8LL1zwqZQ62RIsY4BJcYOKGhY9KMna1zeWQgvzYEsbN1EaSFXFcHXiGZ+PxpNecWiUTqg2jwFEhQmx2FGw+TgfVSS9Ql7Y436DS8FcVSeIfkBFOBRYDrwuvtzOz3lO0FbITnnkzDjaTNSPEM5ZhfelLoTyTDAi8Xm78haT+8o3r/fErya5VgwM0NdAMOALbB86reZubE31ydrf81cKLHoXJImh3/jSRUvCeS/I6/EA3KSCSSiWjwFIGkeXv25IO99mLefffl41atWAT3+Ez+80++GzqUhYcN4+1XX+U84FpgFtw4aoMbPP8AdwD7p1YPKUOjzPDRA8C7ZnZ8hrmklv1ujnt0ylL2G/p+DcW7tN9fqnFLgWbubN2QR6Yj8DspYSNgUTwB/Uk8hPc5sbN1XdCAbMLTeOirpmQTIpFIdYkGT8GoG3Dz9OmsaMYsLVui9DWmTsVatuSfTz/l1002ocsXXzAJ9xR0DaskKmq6mpkFBecz8OqoU4AbUz0xkjrhXoitzGxUyK3pQ/Ii34bkE+4zZvZnmQ4eSSvjydfnmtnF5dpP2FdrkiGlXEs+na2/w0NKM/XxkrQIXsK+GW50XlmvidrNmRRhzH64FyghjDkceD0asJFI8yUaPAWhgbiHoy3JxOUGmTYNmzwZe+IJTtx6a07EDZNULsTDKFsD5+M9txoScNsXvyG/hldXvUPyQv5OJStYJC0MPIb3TjrSCmjGGDxS2Tpbp3pmOuAhuXxyY0rS2VrS8njO0gp4CO/mQo4vUjs00PrkMfw380RUfI5EmhfR4MkbDQRuwtVnC2LSJKbtsgst77mHybgnIpHEPB0vWT/dzH6dYW+ZmzP+AbyLl4lXtWIq5DLdB/wC7IInaefjiamLztaSeuOeng54q46HY1l0fVOp5raRSKQ2qSmDJ3QP/yZdp6ZahFYF748fT8/55uMtoF2m9W69FW66CZ54ouGxJk9myiqr8PQ777AB3iMrwWVmdnDY31zAJnh4qzPwGckL8kg8ifZtXNDw+cYeXy7y6Gw9P9ATPy9TyS169z111Nk6eKP64Ro+E3ENnxeybxWpB4IS+gYkDaC/Sf7WRmQKe0YikfomGjx5oZeAXuQRxgKQ4JNPYPHFk+9Nn870l1+mxdprMw337CSMnr+AU/GLbk/geVyYbrSZHTLz2NoC9wqtUExlULiJd6BhI2ZXXJ9nbvLvbL0nHpbrW6+qzNkI3rb/b+/M4+Wer///fEUWIrtGBInUvkUWS4i9LSoJWkQsDdFWxVIUbVW01K5F1dcSosTexPIrEpRaa0koISqWVIiIEEKIkE3O74/znsxkMvfeuevcO/c8H4/P487M5/N5f858Zu5nzue8z3mdw3ENn/8CZ5rZ1NJaFdQV6X+iD1nnZyvgCTzx+cHaCHwGQdB4CIenSrQNrvlRMLpTcI8CDg94lGe33Xht8mR6s3KU51a8ausJM/umqvMg6SbgGzM7Pue1NSi+DUFuZ+v8iMxNwP649s+nxfagknQM7hAcaGbPFbNPUyOJ4Y3Ep7geAf4QzS/LD0nfwUVCh+ASD++Rjf68GDldQdA0aVH1JvWHpH6SXpa0IHUQXz293lnSBEmfSPo8PV4/rRsq6aW8cU6VdF96PEjStDTmbEmnr3Lglfd9Q9KQnOct03H7S+ol8erSpbQBGDsWNtwQ2reH737Xp7Iyr++yiz/ebTf/26cPtGsH48b58wkTYPvtaTV1Kv1Z2dkBmA+cD8zNOw+rSeomqa+kH0oaIekMPPx+tKQlydbleKToP7hQ4dF4ROrHwBHAQLzK63t41GZb3Lnpi4f1u5vZxcBPkz1/x6tbDqrs3OViZmPw/mL3STq42P2aEma2OFWmbYJPN/5H0pWpMigoE8zsUzO7zcwOxasET8ELDm4A5ki6RdIwSZ1KaGYQBNXFzEqy4BeQmbgqcSu8ZcNS/Id/LfzHti3QHu8d9Y+0Xxs8UXaLnLGmAAelx3OAXdPjznhX8srs+ANwe87zwcAb6XEvwJYuxb76CmvfHnvzTd/1ww+x//7XH990E7bzztlhAZs+Pfv85Zexrl2xSZOwWbP4FHdYLGdZgvevGgu8nF5bkM7HXDxv5xHgFrwz+qnAbWm7O/DIzu54C4vNku23APel89cLeBv4WVp3J67z0wJ3rnbJef8GbFyLz7Uv8EGyUaX6fjXQd3ht4Ep82u8coH2pbYql3j/zXrhg5cT0P/ok3vB3y3L/vscSS1NfShnh2RF3dK4ws6Vmdjc+jYKZzTOze8zsa3MV4gvwH3TMkwnH4ZVBSNoKvwhNSOMuBbaU1MHMPjezl6uw4w5gf0mZKavDcYdgFVq0gP/+F775Brp3h622Ku6NXn89HHssDBgAXbuyFimCk8NqeJuDp/GWB1OBvwFtzWxtM9vGzPY2syPN7Ddmdjl+t2nAcjP72Myewi/Ch6Sck0PxflELzOw94DJgeM452gBY18wWmdkzxb2TqjHvTzUQjxZdmWwpS8xsrnme1fa48vZ0SSenqa+gDDGz98zsGjMbjN9oXIqrdj8MzJB0laR9k1ZWEASNiFI6POsCs80sN4loJoCktpKukzRT0pe4I9Ap58fzZuDwlGw4HBhv2aqKg3C5+ZmSnpK0U2VGmPfheQPYLzk9++NO0EqsuaZPT40e7c7O4MHw5pvFvdGZM+Gyy6BTJ+jWzQ+bt8kSM7vQzG40sweBacBXVnUbiHnATpL2zxwKP6/fwZ3Jmblm4ArOAL/By8hfkPS6pJ9Sh5jZ+7j+yZbAPTnOZFliZjPMbDheYbc38Kak4eXs7AWQbsgmmNlx+A3E/nh080x8evoBSSMl9ah0oCAIGoRSOjxzgPWS05KhZ/p7Gi4RP8DMOuBCe+A/0pj3clqCd1M+HE/6Ja170cwOwKcb/gGML8KWO/FO2gcA06yCZoT77AOPPgpz5sDmm8MxxxT1PunRA0aNgvnz4eOPs+8jh9aSzpV0vKQD8SqRTkU4Cp3xJNrRKdGyJ/AhLtaXieJk6AnMBjCzj8zsGDNbFzgWuEZSXop17TCz+bji7RfAE80hz8XMXk13/kfhjV+nSBqS9x0PyhBzXjOzi81sVzzqfAfu+E+R9KqkCyXtHI5wEJSGUjo8z+PaLSdJapV+6HdI69rjeS7zJXXBFW/zuQW4CliamZKR1FrSEZI6pujIl3gJeFX8Hb8zP44C0R1wR+W++2DhQmjTxhOSW1Rw9rp1gxkzss+POcYjQ5Mnw6ef8lmyKzfK8y3eA6sPHn3ZGp+y+0zSl5LelvS0pPEpSfZMvIokE+GagDt2Q4C7zKtIxgMXSGovV0bO5P1kEr/XT8f+PNmSOU8f4yH6WmPexHEEHu5/Xt5Co+wx10jaGfg9cAnwtKSdS2tV0JCY2WdmdqeZ/QSf+spUVF4DfCzpdkmHp+tbEAQNQSkTiPAf+Sl48t+4tJyPT8s8iSsLv41HIQxombNvT/xH+o85r7XGf1w/x52KF8lJyK3ClsdwB2ydnNd6AbZkCUs//BDbbTesQwesY0ds992x11/33fOTlq+9FltnHd9u3Dh/7aGHsG23xdZYY6Vk5cxyZQXnQbjY4GbAHnhezim4AvDDeBPSOek8GJ60/Cre8uFO4PV0Dufh5eZb4lGhP+HRnq/waqNf5LznkWnM+Xgn97r6rH+KV4YV9XmUy4LnZ43ApxTvB7YutU2xlPw70SNd0+5P16l/A2cAvYnE51hiqbelUenwVIekOzMXr8KqR7G76uvwVMSSJSzbfXdemTSJPqxcmj4+LY9akf19JO0B3GZmmXL9nfAL6KFp7Mp0eFbHIzkVafHkqiJ/U/N3vIrNe+NRpl+a2bi6GrcpkJJYj8d/2B4EzjazmZXvFZQ76XuxBx6dHYw7yLmNgAv21guCoPo0ZYfnVGCImX2vAY5WLaXlQuQpLX9LtpHoQjzJcTBe3fQCPkU10czertCiPIcnvXY+sA3eVb3CDzZPpDC3WWeh5RuK63v1iRUhyCZpm/T+rgL+XJmd5YikjsDpuPNzC3CBmX1aWquCxkDK9dqCrOJzf+AZkgNkXm0ZBEENaZIOj6T38OmeH5nZlCK2PxN3KvL5t5ntW8QRN8S7k9c4yrNkCUu3355Hp05lL1aO7vzVzE5Jdmb6+wzBK83a4N3UM07SCrvx3JB8h6c1Po13uZndXFNbc8arqg1F7pLfhqKyflrt8Yv4s8BJVqSaczkhaR1cguBQXCzyL1aDViFB+ZKEDffGrwf74lHZTPTnueb4fxMEtaFJOjyloU66pS/CIzuZdOdvgdHAuWY2d6WjubPRl+zd3pa4Vk+mv8+cglZKfYBHgW3NbFZ1ba0pWrXRaGWRo1b4xbsTHkGagJfzFppSW9RQ76EUSNoIb8mxJ643db15sncQrCBVdm1P9nrQCxcjnQg8FFHCIKiacHiqhYbiashtKG5661tg8ZQpnN6/P1eQncbKcAEewTkC+D/gMnOhxVWPLHXFK7MG43ovM1i5v8/ynG1H4UKN+zTGKSNJa+JTauvhCsUbA3fjkZ9cx6gb3jKjqojRR3jfr2Iq8holkvriXdk3w6u77mzK7yeoXySti0eBB+MtY14nez14tTH+3wdBqQmHp9poQzz3oh/uwLQssNEyXCdoCjAc7F1JT+DJieCO0PtmtiGApA3xjuk/AC4ErrOskOKqFkit8HyfzN1eV+Ah/GL3CF599Rxwo5mNrs27rW9SJOssvIprkJm9kbeuMys7QRVFjjoBn1B1rtEcXNSxUX7xU27WJbhT/Tvg4cZqa9A4SMreu5O9HqyOJ8ZPBB6LqdIgcMLhqTHqjZeW7olHKFrhYn//A54ArgN7bcXW0iC83Lwl3q38Aby31ZKcbfrgDs+WeI+vO4pMBO5F9mK3K95E9AXgGGB7M3unlm+23pF0JPBnvBT+qRrs3woXmywm36gFxSVif1yK6aXk6P0I/y58DJxhLrYZBJWSvjubkr0e7IDf/GQSnxv9tSAI6otweBqINAc/G49C7In3ymqHNz2dn7ftbvhd/pr4Xf6Dxd7lp+mi7+EXu2H43d4N+AXvycacEyPp+7h+0ClmVlAAso6O045slVplkaO1cW2kqiJGHwGf1fUUVMqLOgqf9vsPcGZuBCwIqkJSB2Av/HowCNcoy0x9PWNVt68JgrIhHJ4GRNLWwIdm9llygK7AnZ9B5v2ncrcV3pvnQrw7/G/N7LlqHm81PNIzB5/y2QYXdMwkPn9Qm/dTH6RzNBFP5r64lNM5klrg1WfFRI064LpQVUaOqjvFkGQETsBVuCfgGj4NlpAelAfp+7wt2ejPxsC/yCY+f1xC84Kg3gmHp4Qkp+ZXeNuH/QqV2Cen5Ujgj3hO0Jlm9no1jrERMAnvRzYXT3gejCdAzyJ7tze5mOmzhiAlZE7ES+yPbwrlt0kSIH9KrVDUqDuujF1VxOgjYG7uHXgqU/4NPpV6E3CRmc2r/3cXlCNJGmFf/HrwA1zVfgL+vzclkuaDciMcnkaApIOBa4EjzeyhCrZZnRoq9Uo6DjgaGJhxHtJ0yQCyCq/r4u0qJgL/NLPPavWmaomk9rj6tAHDKqpea2okJ7cdxUWNuuLNV/OdooV4U8rt8arBS4EPIrk5qCnJYd+VbPSnA9nE50fL5f8vaN6Ew9NIkDQQuBf4vZmNqWS7jsCv8UanRSn1ph/Zf+I5PBdWsE1PsmWuuwOvkI3+vF6KH9OUiHwNHoYfYmYfNrQNpSRNQaxF4SjROsB38QT39njlX2XRotwptWhXEFSKpI3JOj87AZPJKsDXYyufIKg/wuFpREjaBC8vHwecVUV7iHVwvZZhFKHUK6kH8BKwl5m9WoUda+Al9JkLXgtW7u9TZ/21qiI5a7/Dp3EGVWc6r7kgaVu8oewmwBjgv6yalJ3rLC2luCq1uU1hOjGoX1KS/w/IXg++Ins9eDqEMoOmQjg8jYwkMHg/Liz408r0eNL2G+NKvXvgHdbHVHQBkjQCzxnavtiLVE5/n8zUVz+8tUXmbu/9SnavMyQdAVwOHGZmjzfEMZsaqcrtYlwU8wx8KsLythGrCjxWlHP0Hbyqp9KIUVrmx5Ra+ZO+P/3IOj9bAI/hzk+FCvBB0BgIh6cRkiIst+MVQj82s8+L2KcfrtS7CR75+Xt+0mG6WN2HK7H+voa2dcb7+wzGEx4/Inu393x9RgSSKN844HQzu7W+jtOUSZ/xQbiK92xcw+eFGo61Gu70VJVr1B2XP/iY4qrUGixCGNQvktYmqwC/N/AO2evBfyLxOWhMhMPTSEk/NpfiVVWDiu2ULGlP/C6/oFJvmgp7Bdi/pj+EeTZm+vsMAXqS7e/zcH3095G0ZRr/RuD8iCoUJiWlHw2cjVfpnWVmb9bj8Van8DRaoUq1xRTXLuSTxlI5GFRNyrnbmWz0Zy1yFODN7IsSmhcE4fA0diSdjJci729mLxW5j4Af4xo+H5Gn1CtpGC5m178u77YlrUc28XlPPJckc7c3ta6ck+S0TcQdt5EhnlYxktoCvwROB/4BnGNms0toj4COFFelthYwj+Lyjb4I57dxIem7ZJ2fXXDxzMz14M34vIKGJhyeJoCkHwPXAyPMbGI19qtQqVfSOGCWmZ1e9xav0t9nCN53LLe/z8Jajt8O+Dve0mOomX1ZO4vLmzQV+Vu83cgNwCWllh6oivT9rWhKLT9y1Jri24UsatA3EuQrwA/Gew1mnJ+n4jMJGoJweJoIknYE/h/wx+o2BE05QSfikaL7cQfoG2AqrnHz77q1dpXjC+8CnrnYbQ88Szbx+d0ajtsS7zI/EJ/2K1nkoqmQonBn4xHAy4Ary6FMPUWyKqpMy1++pjjn6NOYUqt70vVgG7LXg61xBfhMv6/4Pw7qhXB4mhBJNfkhXK/nzOomBOYp9d6ITwmdC/SpbruD2pC0hHL7+8wje7f3bHWmqNLF89e4QzfYLNuwNagYSZvhVX0D8e/Ajc1hajB9XzpRXK5RZ+BTqs41+ghYEFM0NUPSWmQTn/fBFeAzis8vhNMZ1BXh8DQxJH0Hr7R6H5/iqrRsvYIx1sW7sR8MzMSrKY6tU0OLt6VQf59Hyfb3mVvkOMPwaM/hZvavejK37JC0PZ7k3gMYBdwdP9xOSsLtSnFRo5YUP6VW7f/Z5kKK2u5IVgZjHVZWgK+yYjUIKiIcniZImqK6BQ/h/6imuRiSNsV/7PYHrsbLvUt6ly+pO9n+Pt8H3iQb/ZlShRjjrsDdeKPVsfVvbXmQoh4/wL8Lhie5h9NYDVKOSu6UWqGI0TppmwUU5xzNa+5l3ZI2IFsIsRtZBfgJwLRwzoPqEA5PEyVFRi4B9gP2rWkeTBrrBLwE/kPgTOCuxnChLdDfpz3ZxOd/FervI2nztM0teL5TfMGLJH2nhuJTXe/hjk9RlYFBcaRz3JniokYdgU+o2jGa05BT0qUi3ejtSbYQwsjeDD3RcPpO6g2MTLZshCfML8E1iJ4ARkNMrTdGwuFp4kg6EXdSDjCzF2sxzrVAL7wqpgWu4bOKUm8pyenvMwQPez9PNtHxfznbdQMeAKYBvwjp++qRpnJ+hk97PoNr+LxdWquaH+lzWJvKI0aZdUbx7UKa/P9DikpuRfZmqC/wNNnrQT0owGtD4NZ0rDa4onk+y3DnZwpwJNiMurcjqCnh8JQBkvYH/gb8zMzur+EY7YBXgVNw1dwL8OTBM2rjSNUXqZt6pr/PIHyaIHO392+8XP1OoC1wUIieVZ80TXMScCpwD3Buc2vg2lRI/7/FRI3WBr6kiohR+vtZY7rhqYwku7APfj34IW5/JvF5Uu0V4DUUGEvFjk4+3+ICmyPA7qrdsYO6IhyeMkHSDriw3AVmdnUNx9gN17bpjV8UM0q9zwOjzOyturG2bknTBH3J3u1tjvf3eRCvQtoBL1ufVSobmzKpiuYM4Ke4HtQlZja/pEYFNSL9r3Sh8ohRZmkHzKW4diG10tWqS5IC/A5kE597AP8kqwA/r5ojDgVuBtaogTnfAEeF09M4CIenjEjKpg/hdza/qUkejqTLgXXN7ND0vC1+l38aWR2gRq2Tkfr7ZBKf98KjPx2B44E7G0N+UlNEUg9cw2l/4E/AVdEXq3xJ4qGZKbXKlu54RKMyp+hYvCHyrxu6MELS+qysAP8aPlX7S6B9ZWXv/fvrlC5duPRf/yoqqlMRXwNbg70raSzwgZmdVYm9ewC3mdn6tThmUIBweMoMSV3wSM9HwJHVVTBNiYFTgD+Y2fi8cX8L/BxX6r24KZSI5vT3+RV+wfsSL+vP9PcJheZqImkLfMpze+CPwNj6bBobNG5SPk1mSq2iyNEAPLm3DTCf4vKNPq/rKbXU8y1XAb4V2anwx1eNVOnZZPtqvj9Mnw4bb1ytw34LTALbJRye0hIOTxmS/qlvwpt57l/dEK6kAbgicx8z+yhv3fp4MmuTU+qVtDPuDD6C92naGXiR7Fz/200lZ6ExkL4nF+M/cqOAe+P8BYXI/NDjU+RrUVy+0ZrAx1Sda/RxTa5ByVHbnOxU+HZ45CclPtMen85vm92nRg4PeJRnR4nTaOQOj6TVylXssUWpDQjqnhTVOQJP3n0+KTRXZ//JeBTnunRRyF33gZn9Ai8X3w54W9IvkmBYo8bMnsWdnAF4b7F1gCvwthePAdMl/VXS3imcH1RC+p58D090/z0wSdKeJTUqaBRI6ifpZUkLUt++1dOqDrjK+2PAOOAQ4A4zOxWfMu9oZluaWRd8GvpmPA/mb3gS8Ej8f/Z2vCjhcWCepC8kvSXpKUnj0v/xXElXSNo32bO+pE8k9ZfUC1gOTDezS9NxPgX2AC4CXhk7lidvvJE1dtnFDd9tN//bpw+0awfjxvnzCROgb1/o1AkGDoSpU7PnYcoU6N8f2ren7c47c2/OeSjmHJ6W3sMcSUfnvD5I0rR0bmdLqrQfoqQ9JH0g6UxJn0p6T9IROevHSrpW0oOSFgJ7SlpX0j3pfL0r6aRi7W7UmFksZbzgF4g5wIBq7tcGr9o6sortdsAvOm/hys0q9Xsu4r11BSbhJaat02sC+uAl/s8CX+DRoJ/jOU0lt7sxL/jN02G4FsnDQL9S2xRLyb4LrXEF91/hU0YHA0txfae1gIPwqEl74C7gH2m/NsBnwBY5Y03BqyxJ17Fd0+POQP/0WLhztBk+XTUMOBkvU38Hz2ucksY2PGo0LT2+BbgcWJTs3QO/mdtp4ULevekmbOeds28PsOnTs89ffhnr2hWbNAlbtgwbOxbbYANs0SJs8WKsZ0/s8suxJUuwsWOZlTkPVZy/PfDy9nPT+RuER4g6V3Yeihjv8nSOdwcWApul9WPT9W7n9H/cFngJj+S3BjbE86/2KfV3q9bfzVIbEEsDfMg+V/0Jrspcnf364lUaParYTsDewMv4FNH3S/2ei3hvbfGeZI8DnQqs/w7wE/wucl56b+fh+j+rldr+xrqkC+QJ6aJ8J7BxqW2KpcG/A7vhIqbKee25Qj/06Rrzec7za/FKU3Cdnc+BNun5+3jyc4ci7dgYL1hom57fjk+prZOcCMMrD3+PR4+eSU7Sm8D8RYuwqhyekSOxs85a+dCbboo9+ST21FNY9+7Y8uUr1i2u6Dzk2b0HHtVqmfPaXGDHGp6HjMOzZs5r44Hfp8djgVty1g0A3s8b43fATaX+btV2iSmtZoCZTcC1Ka6WdHI19nsFuBK4IX9qK287M7NH8Cmuy/CpsEckbVs7y+sP8zn/oXjH+GeThH3u+k/N7DYzOwxvB3Ayfrc1Bpgj6RZJw+QNWYOEmS0xl0XYBHgdn+a6WtI6JTYtaDjWBWZb+qVMzASv+pR0naSZkr7EHYxOqZQcfGrp8HS9GQ6Mt2zvsYNwR2VmmrraqTIjzMVI3wD2S9Wm+wO3m+clTkub3WJm56V13+Bd3N8BdmxTxKT2zJlw2WU+nZVZZs2CDz/0Zb31PO8n0SpzHopgnq1cCPA1nhgO1TwPic9t5YTsmfjnlCFXsmMDYF1J8zMLHvnuVqTtjZZweJoJ5i0CBgLHSvpLzgWmKi7Gw6a/KOIYy83s78AW+Hz8A2k+fZOa2l2fmNm3ZnYKri3zrKT+FWy3zMz+bWZnmFlvvDrpefyC/L6kJyX9WtKWlTmGzQkz+8rMzseTQhcDr0s6X1LHEpsW1D9zgPXy/hd6pr+n4VNPA8ysAx4NAo8SY2aTcKXiXYHD8Wln0roXzewAvFT+H3iUoiruxKdaD8B7b/2v0EZm9k8z2wtPwH8Tv7GpUpG6Rw8YNQrmz88uX38Nhx0G3bvD7NmQ4/YtzTkPNaaG56GzXEg0Q088Crdi2JzHs4B3zaxTztLezAbV1vZSEw5PM8LMZuLztH2B8akEvap9lgFHARdI2rDI4yw1s2vxu/xX8cTp0fLGoI0OM/srrsnxsKQq/6nNbKaZXWtmQ/Dw+J+BjAbSDElXpUTJohMUy5UUKTsV6AeshyeGnxbnpqx5Hp9COUlSK0kH4rl+4Hk73wDz5VIXZxfY/xbgKmCpmT0D3ldP0hGSOprr+HyJJx1Xxd/x6fbjgDsKbSCpm6QDkkOwGPgqjf1O/rbdusGMnGYRxxwDo0fD5Mnu2CxcCBMnwoIFsNNO0LIlXHklLF0Kt97KxznnoUbU4jwA/DHtvyue5lCRGOILwAJJv5W0hqTVJG0tafva2N4YCIenmWGunfNDPEnvcUldi9jnDbxy4Sa5Umuxx1poZhfid3RfAf+VdGFjnAYys/+Hh7X/JunYauz3tZlNNLPj8V5k++F3SL8D5kp6QNJIuWhfs8XM3jezo3Hht13x6r6jqxFpDJoI5r26DgRG4InCw/B8OfAKqzXwiqhJeIJ7PrcCWwO35b0+HHgvTYWNxCtRq7JlDu6ADcSrwgrRAm+f8mGyd3fcQXrCjJXKs885B446yqeuxo+H7baDMWPgxBOhc2cvVx871rdt3Rruvdefd+kCo0ezOOc81IZqnweSrlF6j7cDI83szUIbmpekD8FvjN/FP6sb8MTwJk3o8DRTUrj5PPxiNMjMplex/WrAk8A9ZnZFDY/ZE7+j2w+PijQ6pV55g9IH8d5Ro6wWqszpDja3v89ssiJnk6xMtS6KQdJAfLp0LVzD5z6Li1HACvHTuXj1UaXXpXq2ZBvydHhqwdfAjqXooq4QMlxBODzNHEnH4OWPB5nZc1VsuxEwGdiloruDIo+5Ja7Uux3equBma0RKvZK+g6sxvw+MyEmarM2Yq+HVDxmRs/VZub/PZ7U9RlMjOd374tHDhXij2qdLa1VQaiSdCgwxs++V2pZ8peUaskJpuW5sqh7h8GSJKa1mjpmNwZuE/kPSQVVs+w6uzXCzaiE0aGbTzOzHeJXUcOA1SQc2loRfM/sU78TeEngkRWpqO+a3ZvacmY0ys754uPgpPML2rqR/SzpDUu/Gch7qm1Td9yCe33MN/r16UFKfEpsWlAhJ7+EVkacVuf2Zkr4qsDxURyYNx/N6asPiNM4K6truBjgPZUFEeAIAJPUFHgD+AvyloumFlMPzT7zvzEV1cFzh0z0X4ReGM8zsidqOWxek93oJPp89yMzerafjrI5rZWSiPy1Zub9Pk2jdUVvk6ta/wKe4/oX3c5tR+V5BUN9Et/RyIRyeYAUpx+ZB4AnglIpyTNJ2L+ECg1MLbVODY7cADsXVWN8GfmdmU+pi7Noi6URch2J/M/tPPR8rv7/PtqzU38feq8/jNwYktceTSE/CK2vON7OPS2tV0LzRUFygrw3FTW99i9/AjQhnp/EQDk+wEqmC6h5cofTwiqIL8t4uJwM7pKqMujp+a7J3+U/gaqCrlIc2NJL2xysVfmZmDzTgcTviZbVD8HyXuWSjP881ptynuiZVEI7CpwOuAi6z6G4flAxtiJfM98MVxQtN6y/D9XumAMOhfqLCQc0IhydYheR0jMEjDfuZ2dwC2wjvqD7FzP5QDza0w3vbnIyXk55neZ3bGxpJO+BCX+eb2TUlOH4LXPQwE/35Lt75fSLwUMo9KjvkjR7PxSveLgKurYtE8iCoGeqNt3bYE29f0QoXFfwffpN2XSmqsYKqCYcnKEhyaM7G764HmdlbBbbpDryCV1S8WE92dMWnk47EE1svNbMv6uNYRdqzIT7t9wDw29qUrdeBLeviUZ/BwPfxVg6Z6M+r5VbmLak3cCHQG0+ev705l/YHQVA9wuEJKiVNXV0EHJxRPc1bfyj+49PfzBbVox0bAH/Ef+AvAa6pz+NVYUsXvGx9Dt5NviR25JISfncjG/1ZA3fMJgKPmdlXJTSvTpG0C/4d6IA7wxPKzbkLgqDuCYcnqBJJe+HqnCea2fi8dcKnnGaa2a8bwJatcQ2ffngE6pZS3OWnyqqxuJ7OAWY2r6FtqAxJm5J1fnbABdQyic8lz4mqLel7NwR3xufj1X2rOORBEAQZwuEJikLSNsAE4P/waSXLWfcdvOv4IQ31oyNpZ1yptwt+l39/Q9/lp5yaC4Ef49N+jdKRkNQB1xUagndZno9/lhOBZ1JPniZJEnT8CZ7jMxU40yzyJ4IgWJVweIKikbQ+/iP5LHBSboWQpB8BlwJ9zGxhA9kjPIJxEV5VVhKlXkkj8Wm9H5vZ5IY+fnVITlp/stGfTXDNm0zic5Ms/05TesfhPcz+iWv4vFdSo4IgaFSEwxNUixQtuBvXmDg017mRdAvwpZmd2MA2rQYcjt/lT8M1fOpEH6gaNgwBbgSOMbP7GvLYtUFSN7KJz3vhGkgT8QjQlFImZdeE9P08DTgRb0J5gZl9UlqrgiBoDITDE1QbSa2A0UAfvELro/R6Z3xa4Wgz+1cJ7GqDdw8+Ey/X/kN9qSNXcPzt8GTmS8zsyoY6bl2R5Ah2IRv96Ug28flRM1tQQvOqRXLkzsId4SuBy5uS/UEQ1D3h8AQ1Ik0nnQX8FM9feSO9vg9wPbBNqcrHk1LvacAv8WTr8wtpCdXTsXvhTsLDwOlNLUKSS2oWm3F+BuKNYzOJz2+X0rZiSTIC5+I5TBcA14eGTxA0T8LhCWqFpCOBP+MJy0+l164DWprZz9JWvfHIy57ARrhK6RLgHVyoa3R9CHVJWhtX6v0JDajUmyJd/w+YB/zEzL6p72PWN0kI8vtkE5+/Jpv4/HRdqm3XB6kh6UW4mOYfgDuasjMaBEH1CYcnqDWSvg/cifffuiNFWKYecgjnjxvHT/HO4BX1oMmVYj8S6r5ZpKTv4ho+e+M/eqPr+y4/Ta/diKshH1BOeSQputeXbPRnC+Bx3Pl50MzmlM66ypG0O17d1xZPcH4oNHyCoHkQDk9QJyR9nInAtcAld93FHwYN4uw11mC51Dia7aXS+guBrWkApd7kGJwHDAP2NbP/1dexSklSw/4h7vzsA8wgq/j8YmOLpKTP5QD8u/Aprpj9fGmtCoKgvgmHJ6gzUquDCaefzrw//YmdJdaowTDfAEfVZ4dhSbviSr3t8bv8ifV5ly/pGDyP5MBy/2FNCe0DyUZ/vgM8hDs/j5SyLUg+qbrvSDz69zIwysxeL61VQRDUF+HwNBIkjQU+MLOzSm0LgKSeeIl3x8qiIJKOAI4ys70BnnxSvXfckSmrr15UVGcVRoyA7t1ZetFFbFafnYbTXf5++F3+57iGz7P1eLx9gZuB48zsnvo6TmMjJXFnnJ9dgJfIRn/ebAzTSUk1+wTgt7hdZ5vZ+6W1KgiCuqZFqQ0IGidm9r6ZtatqysfMbs84OwB77snU92v5U9GiBS1xDZV6w5z78dL6vwF3SLo/Tc3Vx/Eewqd7/irpV8nhKnvM7D0zu9rMBgHdgcvxDtOPAO9I+j9J+ySno1Q2LjKzy3ARxtnAFEmXJQXxIAjKhHB4gjpE2wC0aFGz6M6KUYSAfqm6q14xs2/NbCywGV4x9pikm1Oz0mojp+D/lZlNwad7foY7PrU6T00NM1toZg+Y2UigJ/Aj4EPg98DHku6T9Iuk6F0K+75IEdat8earb0o6K1WoBUHQxAmHp0RI6ifpZUkLJI0DVk+vd5Y0QdInkj5Pj9dP64ZKeilvnFMl3ZceD5I0LY05W9LpVdjwRlIIzjxvmY7bX1IvSSapZVo3QtKMNPa7aSor8/ozAJtvzsMAffpAu3YwbpyPO2EC9O0LnTrBwIEwNUcDecoU6N8f2reHYcNgUbbveGvg2EpsHyLpFUnzJT2XEpKRNCzZ1yE931fSRymxlvSeTkrv5VNJf5bUIt3l/wW/y58JvCzpCkld03t8VtJVkr6Q9GaqTMvY8qSkCyQ9i5drbyhpc0mPSvpM0luSDgGPnOFTO1sB90hqW9lnVK6kCNtUM7vIzHYBNsSb0O4OvJo+2wskDWxox9DM5pjZ8cCO+Oc0XdIJcmHGIAiaKmYWSwMv+I/5TOBXQCvgYGApcD6wFnAQXjbbHrgL+Efarw3wGbBFzlhTgIPS4znArulxZ6B/FXZkKpUyzwcDb6THvQADWgJrAl8Cm6V13YGt0uMReANKzJgG2PTp2cO8/DLWtSs2aRK2bBk2diy2wQbYokXY4sVYz57Y5ZdjS5Zgd92FtWyJjRq1Yv/XK7C7HzAXGICXuh8FvAe0SetvxzuZr4VHEIbk7Gt4JKcLHmV4G/h5gWN0w7V75gH34uXzmc9rGPAF0CVt+yTwPv7j2BJXKJ4FHJ2e98OrgbbM+w7cjIv5rV3q72RjWtI52wWXEJgKfIJPcR4KdC6BPf1wIcn/AYcBLUp9jmKJJZbqLyU3oDkuwG7ph1g5rz2HKwLnb9sX+Dzn+bV4fyDSD+znOT/07+NRkQ5F2rEx3nSzbXp+O96OoZDDMx93xNbIGyPX4Vmc7/CMHImdddbKh950U+zJJ7GnnsK6d8eWL8+u22mnlRyexRXYfS1wXt5rbwG7p8ed0rl4DbgubzsDfpjz/HjgsUrO0UbA83jZ/IlA6/T6C8Dw9PhJ4NycfYYB/84b5zo8GTb3NeEVQu+QnMlYCn4GPXHhygdwx/tpPMF469z/oQawY8/koE7By/Ab7NixxBJL7ZeY0ioN6wKzzSy3QmUmgKS2kq6TNFNS5uLeKSesfzNweEp6HQ6Mt6yI3kG4Cu5MSU9J2qkyI8x1Yd4A9ktTK/sDdxTYbiH+Iz4SmCNpoqTNCwy5Ssh/5ky47DKfzsoss2bBhx/6st56kJu+u8HKmTOtKjB9A+C0NJ01X9J8oAd+XjGz+XhkbGvgsgL7z8o1MbNfIczsHdxZeQOPgL2ZpvPy98sdcwNgQJ59RwDr5I1tZnY23vLgKUm7VGRHc8Y8gX60me2HR94uwj/vCcC7kq6RNFhSTWQQqmPHE/g013nAFcDjkgbU5zGDIKg7wuEpDXOA9ZLTkqFn+nsankA7wMw64NEg8GgAZjYJVybeFW+MuKKaycxeNLMDgLWBfwDji7DlTjxMfwAwzSoQxzOzf5rZXvh01pvAmAKbrdJeoEcPGDUK5s/PLl9/DYcdBt27w+zZkOv25VV4La3A5ll4lKtTztLWzO4EkNQX7/F1J944chWzch73xKNtVdEFdyZ/ivfoGgR0zfkMc53XWcBTefa1M7PjCg1sZjfiejD3ZnJ9gsKY2Tdm9pCZnYirWA/GpzN/gyc+T5B0nFxWoT6Ob2Z2L+5M347nYd1bwQ1AEASNiHB4SsPzeE7ISZJaSToQ2CGta4+L782X1AU4u8D+t+D5JUvN7BnwTteSjpDU0cyW4qH/YhRu/463XDiOAtGdNHY3SQdIWhNXQ/6qgrHf6dYNZuQ0hzjmGBg9GiZPdsdm4UKYOBEWLICddoKWLeHKK2HpUrj3XnjhhZXGq0iZeAwwUtIAOWumO/z28vLm2/CO6UfjjuXxefv/Wp4c3gM4GU+WrYq1gZOAZ/GokfCI2JNAh7xtJwCbShqePt9WkraXtEVFg5vZI8BewGWSfp3nDAcFSM7H62b2JzPbHY+s3QrsBLwk6TVJF0vaNZN8X4fHXmZmN+BJ7s8DT0u6QSWqMAuCoAhKPafWXBdgOzwXYAH+gzsOT1peF/8R/QpPqD2WlEuTs29P3OH4Y85rrfHEys9xZ+dFYJcibXkMd8DWyXmtF9kcnu7AU3ii7vxk35ZpuxFkc3iuvuYavl1nHaxjR2zcOD/EQw9h223nr62zDnbwwdiXX/q6F1/E+vbF2rXDDjnEl5TDs9SMqyqx+YfpPc7HI2Z34c7iX/D+SJnt+uCJ3puk54Y7LjPwhOTLgNWqOD8jcEfnqnQO3sadxJZ4xGdR+ixzk5I3w0XsPknHeRzoW8RnsT7wKnB17mceS7X/v1bDHZ/z02czD4/4/QT4Tj0crxM+1TYPb6bbpdTnIJZYYll5CaXlJkjKVZiLV2FNL7U9WbQNfrdbF6XWXwM71nUXdUmGOz9F97WSNAKv5CqYY5M+jxPwaZUJeHLyrELbFnm8DsDdeDTtUPMcqqAWSFoPn4YcjCcf/5es4vNUq6MLoby9yh/wfLrLgSvj8wuCxkFMaTVNjsObMjYiZwfApgKv4BVNteFbYEpdOzv1hXleyaXApni06RVJl0paq4bjfYn/MH8CPClpnSp2CarAzGab2Rgz+xE+PflHPIn8XuD9VCiwf5q2rc1xPjQXVtwZr7B8W9JIeY+xIAhKSDg8TQxJ7+F5J6cVuf2Zkr4qsDxUTyYOxyMTtWFxr14835B2SxpdwfFGFzuGmc03s1F4Qmtb4C1Jo2ryI2qeh/Uz4H7gucryf4LqYWaLzewRMzsJl2bYC5+mPAX4SNLDkk6U9N1aHONtMxuGFwMcBLwu6RBVoMIdBEH9E1NaQT2goXj5fKPslt5QSNoYzyHZLf0dkxyZ6o5zFPAnYKiZPV23Vga5SOqIO0CD8SmweWSnvp6tyeeXxv0BcHF6+jsze7QOzA2CoBqEwxPUExqKqx23gaJ6a32LR4ZGlIOzk4ukbfGu7BvhfaPGmVkxFXS5Y3wfT7o92VL5fVC/pGjMdmS7vW8EPIo7Pw+Z2dwajHcQrrv0Pu74vFinRgdBUCHh8AT1iDbES+j74VVkhUqDl+H6PVOA4WDvNpx9DYuk7+F3+S2B3wGPVCdZVlJvPCn6WuCSukq0DYpDUndgX9z5+T6uR5WJ/kwp9vNI+Tw/xZObnwNGmdnb9WJ0EAQrCIcnaADUGy+v3xPPmWiFiwr+D+9rdV1TSVCuLUlf50A84jMbv8ufXI3918V/YF8ATjCzZfViaFAp8kaiuwJDcAeoHfAg/tn8y8wWFDFGW1wi4TQ8efpcM5tdb0YHQTMnHJ4gKAFJCO9oXFhyMn6X/2aR+7bHVbSXA8PM7Kt6MzQoCkmbkJ362hGYhDs/E6qSQEgCo78Ffo6Lal5iZp9X4+i98bYve+LTbq3xqOk7+A3F6OZyQxEElREOTxCUkKTh80vg13g7kD+a2QdF7NcKuAboj3eDn1OfdgbFkxzSH5B1gL4kO/X1bzNbpQVL2m993AH+EXAp8H9m9nUlR9oQV5buS8W5crlTxkeCzSiwTRA0C8LhCYJGgKTO+F3+McDfgIvN7LMq9hGeC/QLYLCZvV7vhgbVIiUq9yPr/GyGK5tPBB40s48K7LM5XtW3I64XdNOqU5dRFBAE1SU0IYKgEWBmn5vZGcA2QEdcw+d3Kc+jon3MzC4ERuGdu/dsIHPLBkljJZ1fX+Ob2XIze8nMzjWzAbg45f14a5Q3JL0o6ZzUa61Fanr6H2AYnut1GK7hc3C2v5qG3nort+29N20pztkhbdcWuDk5SytR7HmQN2b9OGlU1UhYMwhKRUR4gqARImkz4Dxcsfdc4MbKNGAk7YH3YzvNzG5rCBvLAUljgQ/M7KwSHLsV/vlmEp87Aw/h0Z9HzOzL5OTshVf3LTvqKP46dizXk9e+RYLp02HjjYs69NfA1rkVkcWch2Tvl8COZvZq0W80CBoJEeEJgkaImb1lZofg+RxDqUKp18yexJNWz5N0VnRbb/yY2VIze9LMTjezLYCBwMt48vJsSY8DpwIzcT2gy088kRuWL6dNLQ/dBs/9qS7dgNWBBpk6resO90EQDk8QNGLM7EUz+wHenPS3wAtJtbfQttPwDuE/BsZE/6ZVkdRP0suSFkgah/+AI6mzpAmSPpH0eXq8flo3VNJLeeOcKum+9HiQpGlpzNmSTq/ChjckDcl53lLSJ3jH9QeAfYD1gCvw6M/rwLLWrbnh9ddZrUULVhs7FnZJrWx3283/9ukD7drBuHH+fMIE6NsXOnWCgQNh6tQVJqz20ktsu+aaK2xecR4qsXlT4K30dL6kx+X8RdJcSV9Kek3S1lWMMza1cXk0HfspSRvkrDdJJ0iaDkxPrw2R9Iqk+ZKek7RNZccIggopZav2WGKJpfgFv0E5BP8heBTYroLt2uEChQ8DHUptd2NZ8HLtmcCvcC2og3E9qPOBtXAV5LZAe+Au4B9pvzbAZ8AWOWNNAQ5Kj+cAu6bHnYH+VdjxB+D2nOeDgTfS416A4eKUa+JTSJsBfe64g5emTuVbM+ymm7Cdd84OC9j06dnnL7+Mde2KTZqELVuGjR2LbbABtmgRtngx1rMndsIJ/Dv/PFRh9wrb0vN9gJdwR03AFkD3KsYYCyzA2620Af4KPJOz3tJ3uwvemqYfMBcYgOchHQW8B7Qp9fcplqa3RIQnCJoI5gmw44EtgbuB+ySNT3ffudt9hU+FvQs8LWm9Bje2cbIj/gN/hfl00t3AiwBmNs/M7jGzr81FAy8Adk/rFuP5UT8BkLQV/uM/IY27FNhSUgfz5POXq7DjDmD/nIT0w/G2IYVYjjejffuww1ijd+/irtnXXw/HHgsDBsBqq8FRR0GbNjBpki9Ll8KVV9Il/zxUk6W4c7g5ng/6hhUnjzDRzJ5O53UUsJOkHjnrLzKzz8zsG7wC8Tozm2xm35rZzXi12Y41sDdo5oTDEwRNjPQjdR2wCR5peE7SdUmFObPNMuB4/Mf1udSWormzLjDbzHIrNWaCqx6nczhT0pfA00AnSZkqqJuBw1Nu1HBgfPrBBo8MDQJmpimanSozwlyI8A1gv+T07I9/TvnbLcSrtUYCcwYNYrM3i5KmhJkz4bLLfDors8yaBR9+6Mt660GLFuSmOM8sbuSV7HscuAq4Gpgr6XpJHYrYdVbOGF/h0bN1C60HNgBOS9NZ8yXNB3rkbR8ERREOTxA0UVI04iK81PkL4DVJFyVNH8z5E57785i8AWkB1Bt0NWgaaDHI0t9p6fVycZbmAOvlJXT3TH9Pw6eOBphZB3zKBXyqBjObhAv47YpHZFYk/ZrnWR0ArI2LR44vwpY78ZLzA4BpVoEas5n908z2ArpvsQUtjjmmmLcJPXrAqFEwf352+fprOOww6N4dZs+G5cvJzfHqWXikyjGzK81sWzzquCkuoFmleZkHktrh01cf5g6b83gWcIGZdcpZ2lo00A1qQDg8QdDESeH/3wB9gK7A25J+I1dxxsz+judp3C7pqOye2hD0LN4G4Vg8B6N1Wtk6Pf+Fr9czSdm3KfM8rjx8kqRWkg4Edkjr2gPf4Am5XXDF43xuwSMaS83sGfCeWpKOkNTRXDbgS3waqir+DuwNHEeB6E4au5ukAyStCSxu25ZvW1Rwxe7WDWbkaCgfcwyMHg2TJ4MZLFwIEyfCggWw007QsiX89a8sL3AeikauHTQgJccvBBZR3HsfJGkXeT+y84BJZjargm3HACPTcSRpTUmD5WrWQVAtwuEJgjLBzD4ws5/jUYgBuOPzc0ktzexpYA/gbElnm2ko8FrarjIBu5Zp/Y6+/aqidU0F85YOBwIj8GmUYXjTTvCKqDWAT3EH8OECQ9yK59Pk6xwNB95LU2EjgSOKsGUO7oANxPODCtECL0v/EPjs0UdZfO21hTc85xzP0+nUCcaPh+22gzFj4MQToXNn1+cZO9a3bd0a7r0XxoxhKaueh+rQAXdIPsenxOYBfy5ivztwh/IzYFtSblQhzOw/uPr4Vek4/8M/vyCoNiE8GARliqQBuGBddzw59F5g7Z/9jGeuvprvtmlTtEpvLt8ARzXH9gQpYjYXr8KaXgILrsYjbnWhT7MMuA7sxDoYq2hUQqHHIIgITxAUgeq5BUF1kdRTLu9fodNiZpOBG/DphrOAyUcdxQ/GjGHdGjo7jBjBGr/7HbeDvluFfeXYguA44MXSODsAXIfnEdUFS9J4QdBsCIcnCJogZva+mbUzs2+r2O72lFS6LfCXm2/mtrfeYo3aHLtFC1pSiVJvyum4HNg72TivNsdrDEh6DzgZT24uZvszk7OXvzxUcytsKvAK3gi0NnwLTAF7LX9FXdgt6fUKxqhyqi8I6pOQ7g6CZoCZLQe9LkHLltSq7YSEgH5evbXqjyZVtCBIOUXLCq1rKKprg5n1qs745k1dL6yuXUUwHM+9qrCpbBEsTuOsQl3YbWZbVbL69tqMHQS1ISI8QVAANeIWBJL6S+oll+FvmdaNkDQjjf1u5m46vf4MwOabeyJuNVoQMGUK9O8P7dvDsGGwaNGKVa3xyq58m1dpQZBer1bLAEnrSronvd93JZ1U2blK+5wj6W5J49J5eFlSn5z170n6raSpwMJ0PndMx54v6VV5E9ZGjM3Ak3a/qeEA3/j+2cahQdBsKLXUcyyxNLaFJtyCIK3rDmyVHo9ghXQ/06hBC4LLL8eWLMHuugtr2RIbNWrF/q9XYPcK23JeK7plAH4j9lJ6/62BDYEZwD5VnK9z0ud0cPrcTsfVplul9e/hU0I9kg3r4ZVFg9Ix90rPu5b6O1j1wlAzFpqxrMhdlqXth5be9lhiKc0SEZ4gWJUm24JA0hpmNsfMCk0nbZT/QjEtCE45BVq1goMPhu23X2n3jfPHq4JiWwZsjzsd55rZEjObgZc/H1rEMV4ys7vNNXEuxyNzuW0IrjSzWcmGnwAPmtmD5m07HgX+gztAjRy7C+iNl9B/jVddFWJZWj8J2Lo5VtcFQYZweIJgVZp0CwJJEyVtXmDI1vkvFNOCIFeXeIMNVtq9ut3Yi20ZsAGwbt66M/HcoKKPYWbLgQ+ovG3B0Lzj7IJHyJoANgNsF9yhuw6YhldfWfo7Lb2+o28X01hB8yaSloNgVVa0IMhxenoC77ByC4KPJPXFp61WtCCQlNuC4PDMoGb2InBAqmI6EW9BkNs0sRCZFgQtqKIFAfBPuVbM+XhEZNe8zZaQ5/RkWhCMGrXqmE895S0IzLJOz/vvw0bZONHSKmxfxcycx5mWARfkb5QcwXfNbJNqjg8rty1oAaxP5W0LbjWzIhs2NFbsNfz7FARBJUSEJwhWpUm3IAC+qmDsd2rSguDKK31q69574YUXVhqvoPNVJJW1DHgBWJASjNeQtJqkrSVtX8WYANtKOjAlc5+Cn49JFWx7Gx492ycdY3VJe2SS0IMgKC/C4QmCPKyJtyDAc4qOK7DdE2efzfLqtiAYOxa6dPGqrgMPXDHWMuCJquyv5H1V2DLAXFtoCNAXTzr+FBdQ7FjE0Pfhn9fn+Pk+MDmYhWyYhTfvPBP4BI/4/Jq4LgZBWRKtJYKgjlHJWxBUhLbBnafaaLhk+BrPDSmkw1MSJJ0DbGxmFfZmCoKg+RJ3MkFQ95S6BUEF1L9SbxAEQWMlHJ4gqEPUKFoQVMpwPK+lNizu1YvnG9huACQ9VMFxz6zP4wZB0PSJKa0gaHZoKF4+X5OeWs22W3oQBE2bKEsPgmaH3ZWq6MfiysbFdE7/Fo8MjQhnJwiCpkhEeIKg2aIN8RL6frg+T6EboGW4fs8UYHiI1wVB0FQJhycImj3qjTcC3RNvF9EKFxX8H156fl0kKAdB0NQJhycIgiAIgrInqrSCIAiCICh7wuEJgiAIgqDsCYcnCIIgCIKyJxyeIAiCIAjKnnB4giAIgiAoe8LhCYIgCIKg7AmHJwiCIAiCsiccniAIgiAIyp5weIIgCIIgKHvC4QmCIAiCoOwJhycIgiAIgrInHJ4gCIIgCMqecHiCIAiCICh7wuEJgiAIgqDsCYcnCIIgCIKyJxyeIAiCIAjKnnB4giAIgiAoe8LhCYIgCIKg7AmHJwiCIAiCsiccniAIgiAIyp5weIIgCIIgKHvC4QmCIAiCoOwJhycIgiAIgrInHJ4gCIIgCMqecHiCIAiCICh7wuEJgiAIgqDsCYcnCIIgCIKyJxyeIAiCIAjKnnB4giAIgiAoe8LhCYIgCIKg7AmHJwiCIAiCsiccniAIgiAIyp5weIIgCIIgKHv+P3ECkX3K7u+dAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize causal graph\n", - "plt.figure(figsize=(10,8))\n", - "est_dw.view_model() " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimand type: nonparametric-ate\n", - "\n", - "### Estimand : 1\n", - "Estimand name: backdoor\n", - "Estimand expression:\n", - " d \n", - "────────────────(Expectation(days_visited_post|days_visited_vrs_pre,days_visit\n", - "d[became_member] \n", - "\n", - " \n", - "ed_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_fr\n", - " \n", - "\n", - " \n", - "ee_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US))\n", - " \n", - "Estimand assumption 1, Unconfoundedness: If U→{became_member} and U→days_visited_post then P(days_visited_post|became_member,days_visited_vrs_pre,days_visited_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_free_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US,U) = P(days_visited_post|became_member,days_visited_vrs_pre,days_visited_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_free_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US)\n", - "\n", - "### Estimand : 2\n", - "Estimand name: iv\n", - "Estimand expression:\n", - "Expectation(Derivative(days_visited_post, [easier_signup])*Derivative([became_\n", - "member], [easier_signup])**(-1))\n", - "Estimand assumption 1, As-if-random: If U→→days_visited_post then ¬(U →→{easier_signup})\n", - "Estimand assumption 2, Exclusion: If we remove {easier_signup}→{became_member}, then ¬({easier_signup}→days_visited_post)\n", - "\n", - "### Estimand : 3\n", - "Estimand name: frontdoor\n", - "No such variable found!\n", - "\n" - ] - } - ], - "source": [ - "identified_estimand = est_dw.identified_estimand_\n", - "print(identified_estimand)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Get Causal Effects with EconML\n", - "\n", - "To learn a linear projection of the treatment effect, we use the `LinearIntentToTreatDRIV` EconML estimator. For a more flexible treatment effect function, use the `IntentToTreatDRIV` estimator instead. \n", - "\n", - "The model requires to define some nuissance models (i.e. models we don't really care about but that matter for the analysis): the model for how the outcome $Y$ depends on the features $X$ (`model_Y_X`) and the model for how the treatment $T$ depends on the instrument $Z$ and features $X$ (`model_T_XZ`). Since we don't have any priors on these models, we use generic boosted tree estimators to learn them. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** Causal Estimate ***\n", - "\n", - "## Identified estimand\n", - "Estimand type: nonparametric-ate\n", - "\n", - "### Estimand : 1\n", - "Estimand name: backdoor\n", - "Estimand expression:\n", - " d \n", - "────────────────(Expectation(days_visited_post|days_visited_vrs_pre,days_visit\n", - "d[became_member] \n", - "\n", - " \n", - "ed_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_fr\n", - " \n", - "\n", - " \n", - "ee_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US))\n", - " \n", - "Estimand assumption 1, Unconfoundedness: If U→{became_member} and U→days_visited_post then P(days_visited_post|became_member,days_visited_vrs_pre,days_visited_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_free_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US,U) = P(days_visited_post|became_member,days_visited_vrs_pre,days_visited_exp_pre,days_visited_hs_pre,days_visited_rs_pre,revenue_pre,days_visited_free_pre,os_type_windows,days_visited_fs_pre,os_type_osx,locale_en_US)\n", - "\n", - "## Realized estimand\n", - "b: days_visited_post~became_member+days_visited_vrs_pre+days_visited_exp_pre+days_visited_hs_pre+days_visited_rs_pre+revenue_pre+days_visited_free_pre+os_type_windows+days_visited_fs_pre+os_type_osx+locale_en_US | days_visited_exp_pre,days_visited_free_pre,days_visited_fs_pre,days_visited_hs_pre,days_visited_rs_pre,days_visited_vrs_pre,locale_en_US,revenue_pre,os_type_osx,os_type_windows\n", - "Target units: Data subset provided as a data frame\n", - "\n", - "## Estimate\n", - "Mean value: 2.074836810507913\n", - "Effect estimates: [-1.74309260e+00 5.53465593e+00 3.67174621e+00 -8.69405764e-02\n", - " 2.49205011e+00 6.53811401e+00 -9.27156048e-01 -2.03494478e+00\n", - " -1.58974243e+00 6.60355284e-01 -2.35253192e+00 -9.72266006e-01\n", - " 2.58512829e+00 -4.28449527e+00 3.53941978e+00 2.01005015e+00\n", - " 8.55532638e+00 -3.89445854e+00 6.49937040e+00 5.97849997e+00\n", - " 6.01222833e-01 8.69152554e+00 8.49341505e-01 -4.38862542e-01\n", - " 2.69974073e+00 -1.07172634e+00 3.83412079e+00 1.34634769e+00\n", - " 3.97833032e+00 5.42847073e+00 -1.19288892e+00 2.10402278e+00\n", - " 2.08726067e+00 1.56419260e+00 3.63830156e+00 -1.01578151e+00\n", - " -3.02499000e+00 2.18876746e+00 -1.43604094e+00 5.89519266e+00\n", - " -4.44503657e-01 3.55396415e+00 -4.28027219e+00 6.12424113e+00\n", - " -1.94476700e+00 1.08536153e-01 -3.36965727e+00 6.33474174e+00\n", - " 5.89486409e+00 -1.38630934e+00 6.73856605e-01 1.85980353e+00\n", - " 3.69212314e+00 4.24501313e+00 4.70614279e+00 3.30653739e+00\n", - " 6.10412585e+00 4.09982762e+00 1.62321176e+00 4.20534679e+00\n", - " -2.13333488e-03 -7.97476772e-01 4.24218332e+00 1.83376652e+00\n", - " 7.02336492e+00 1.45619212e+00 5.73407744e+00 2.44412507e+00\n", - " 2.26878288e+00 2.13634127e+00 3.23433510e+00 3.95106838e+00\n", - " 1.60719511e+00 -1.85150207e+00 1.17771791e+00 -1.67746289e+00\n", - " 3.45943014e+00 -1.28928124e+00 6.34299463e+00 -2.14774544e+00\n", - " -1.17201152e+00 8.09765933e+00 1.83124154e+00 3.95896807e+00\n", - " 2.16741726e+00 1.82690318e+00 3.59654887e+00 3.21166170e+00\n", - " 3.12268300e+00 -1.25797987e-02 3.52929338e+00 2.26757870e+00\n", - " -4.71185895e-01 8.27256803e-01 4.13499096e+00 1.62878679e+00\n", - " -1.10086813e+00 -9.70798823e-01 2.02758406e-01 1.28683768e+00\n", - " 4.51709657e+00 1.05840050e+00 6.87684925e+00 4.43733264e+00\n", - " 1.06629793e+00 4.19063540e+00 6.52544128e+00 5.72281224e+00\n", - " 3.22966990e+00 -2.81420032e+00 4.99916055e+00 8.05158036e+00\n", - " 7.67863330e+00 -3.46375509e-02 1.30546854e+00 1.05531219e+00\n", - " -1.26010025e+00 7.91691211e-01 -2.36538221e+00 6.73795194e+00\n", - " 4.89705680e+00 1.93764755e+00 3.50080228e+00 -3.95106313e+00\n", - " 7.78051059e+00 2.81160250e+00 5.55741787e+00 2.50008057e+00\n", - " 5.88765140e+00 5.76020139e+00 2.15759457e-01 3.83114064e+00\n", - " 3.35538095e+00 4.57464892e+00 1.73651262e+00 2.20478312e-01\n", - " 3.59258894e+00 6.52812764e+00 -9.18804011e-01 9.72567765e-01\n", - " 4.56181090e+00 4.45009046e+00 9.87813083e-01 -1.32559925e+00\n", - " 7.56776255e+00 -1.95094181e+00 6.53097736e+00 -1.10594109e+00\n", - " 6.21188278e-01 3.70136780e+00 -1.23360221e+00 5.20526542e+00\n", - " 3.12794715e+00 6.54476985e+00 7.75956636e-01 2.38163644e+00\n", - " 3.36417138e+00 4.26756508e+00 -3.04866742e+00 1.96070583e+00\n", - " 7.35077271e+00 3.08845773e-01 7.23580460e+00 1.34716185e+00\n", - " 2.18156021e+00 4.83274103e+00 -2.34998026e+00 3.35537618e+00\n", - " 5.55369276e+00 2.74221800e-01 6.18384351e+00 5.94164635e+00\n", - " 4.03024538e+00 -3.74984445e-01 3.84850361e+00 9.82501156e-01\n", - " 8.77008493e+00 5.56976699e+00 6.25675975e+00 -1.02349503e+00\n", - " 6.05053434e+00 -3.38384566e-01 -1.50995724e+00 -4.45531012e+00\n", - " 4.79286266e+00 3.62620644e+00 5.05075984e+00 -3.81138355e+00\n", - " -4.39662442e+00 4.74418953e+00 -1.11632755e+00 2.22967461e+00\n", - " 4.22320361e+00 8.89930991e-01 5.50984790e+00 5.28657271e+00\n", - " 1.93668179e+00 5.16460815e+00 1.48803005e+00 3.16128421e+00\n", - " -2.85838153e+00 3.45563312e+00 1.91304882e+00 6.87287368e-01\n", - " 1.21567485e+00 5.13325916e+00 2.30208699e+00 2.60595527e+00\n", - " -4.66029855e-01 6.72281877e+00 3.17388410e+00 9.80905414e-01\n", - " 1.70294587e+00 3.93991122e+00 1.35473574e+00 4.93767675e+00\n", - " 7.31608033e+00 -5.30408826e-02 4.48173291e+00 4.61592086e+00\n", - " 6.57130629e-01 7.06942280e+00 2.08104238e+00 1.96786944e+00\n", - " 4.77461409e+00 -2.62570989e+00 2.15465388e+00 6.18481952e-01\n", - " 2.41600923e+00 5.68683208e+00 3.57537757e+00 3.50248852e+00\n", - " -1.28753691e+00 -2.80029980e+00 3.99450642e+00 9.92538123e-01\n", - " 1.01213435e-01 7.81927442e+00 1.25738051e+00 4.16983403e+00\n", - " -6.81940715e-01 -4.42587106e+00 5.74988975e+00 1.68593438e+00\n", - " 6.69688320e+00 5.21616476e+00 -2.27260936e+00 1.13831168e+00\n", - " 5.36413952e+00 2.83253418e-01 4.88560931e+00 -1.88996135e+00\n", - " 3.18349182e+00 -1.02463348e+00 6.06684750e+00 4.20634750e+00\n", - " 1.62840431e+00 5.63158806e+00 4.99040472e+00 -1.10327523e+00\n", - " 3.00969651e+00 3.03817784e-01 3.87607353e+00 1.08792523e+00\n", - " 4.92775808e-01 7.49203732e+00 -3.02149323e-01 -2.02578596e+00\n", - " -2.22250377e+00 1.89698906e+00 -1.09787805e+00 2.31600416e+00\n", - " -3.65366226e+00 2.66605037e+00 -1.46510115e-01 1.21847490e+00\n", - " -2.88910050e+00 4.06739838e+00 9.15531012e-01 7.04984898e+00\n", - " -3.51882824e-01 3.66044140e+00 -5.98493435e-01 6.82966626e+00\n", - " -2.69842544e+00 1.07744986e+00 1.79752059e+00 7.79385014e+00\n", - " -4.56509202e+00 5.80269337e+00 -6.56688906e-01 5.88851860e+00\n", - " 3.93658994e+00 -1.69590535e+00 5.20126161e+00 6.82804500e+00\n", - " 7.16098922e+00 6.51613242e+00 1.73272127e+00 8.89407848e-01\n", - " -2.04866376e-01 4.56717995e+00 -1.05419237e+00 -7.86606424e-01\n", - " 3.83260536e+00 2.12661458e+00 4.01653937e+00 5.44296894e+00\n", - " 2.72182931e+00 2.75573638e+00 2.90579391e+00 4.43073324e+00\n", - " -1.66402361e+00 -1.97191416e+00 -1.28059779e+00 2.88480188e+00\n", - " 2.51965402e+00 -1.36302697e-01 -7.80022805e-01 -1.49424695e+00\n", - " -1.34282981e+00 3.44975636e+00 -3.03072104e-01 -2.45459527e+00\n", - " -3.00629481e-01 2.29090581e+00 1.56543593e+00 3.35750472e+00\n", - " 2.33223505e+00 -3.82989563e+00 4.88021332e+00 7.56731917e-01\n", - " 4.25582832e+00 5.98434505e+00 3.01986691e+00 -6.77442677e-01\n", - " -1.64953867e+00 5.66357995e+00 5.64428003e-01 5.10242590e+00\n", - " 8.26971905e+00 4.97946101e+00 1.29196275e+00 7.76284555e-01\n", - " 3.33445074e+00 1.30853736e+00 -1.53525283e+00 5.36833802e+00\n", - " 1.15992695e+00 2.81928116e+00 -4.07206472e+00 -2.50815511e+00\n", - " 8.78792298e+00 6.50547578e+00 5.01930927e+00 5.41797922e-01\n", - " -9.09773180e-01 -2.39454791e-01 4.96199723e+00 -4.35634019e-01\n", - " 3.80466959e+00 3.86120839e+00 2.03636643e+00 2.81371356e+00\n", - " 3.36389949e+00 6.24676032e+00 2.21810552e+00 -9.70461750e-01\n", - " 4.39036661e+00 8.75240453e+00 1.16978502e+00 7.93325547e-01\n", - " 1.95900018e+00 2.11067965e+00 4.67378254e+00 -9.81738213e-01\n", - " 5.62730702e+00 2.28988281e+00 6.39554347e-01 3.56780655e+00\n", - " 2.55972508e+00 7.74507056e+00 -1.70026139e+00 4.79636075e+00\n", - " 4.58045725e+00 3.92461050e+00 -4.50476384e-01 3.64823178e-01\n", - " 5.25977278e+00 2.71290477e+00 -2.73514000e+00 7.35033429e-01\n", - " -8.93039089e-01 4.47635864e+00 -4.00819110e-01 9.84948255e-01\n", - " -1.25187326e-02 7.43522358e+00 2.30060347e+00 1.38459891e+00\n", - " 1.77678620e+00 2.99902550e+00 2.88012576e+00 1.60279207e+00\n", - " -2.17076889e+00 4.02771144e+00 1.76082775e+00 4.28513483e+00\n", - " 4.28349578e+00 5.65144489e+00 1.82377346e+00 3.54960094e-01\n", - " 2.81727581e+00 -4.52598385e-01 -6.03200625e-01 2.60658700e+00\n", - " 3.08520007e+00 2.15407701e+00 -1.42112638e+00 3.21798527e+00\n", - " 5.61414647e+00 -5.47711654e-01 4.36176805e+00 1.63973179e+00\n", - " 1.58738126e+00 5.88689122e+00 8.97612240e-01 5.72790582e+00\n", - " -2.08776274e+00 8.15370684e+00 -1.11019576e+00 4.31710187e+00\n", - " 4.79763806e+00 3.53119218e+00 2.80709783e+00 3.73105939e+00\n", - " 1.45150046e+00 -2.01269415e+00 1.57131277e+00 6.72753596e+00\n", - " 2.32073341e+00 1.17117638e+00 1.73436699e+00 4.16817944e-01\n", - " 2.05029100e+00 -3.51544594e+00 3.54557735e+00 -4.34142231e+00\n", - " 4.06513401e+00 5.99370062e+00 -3.70340315e+00 3.05069363e-01\n", - " 3.62131945e+00 5.91110995e+00 3.60378283e+00 7.90079958e+00\n", - " -9.82519600e-01 -1.47391313e+00 -3.70396976e+00 -1.75277144e+00\n", - " 8.22746049e+00 6.25546949e+00 -3.27053389e+00 5.80854238e+00\n", - " -5.38139284e-01 7.33055362e+00 3.45144595e+00 1.25728783e+00\n", - " -2.25877957e+00 2.94036380e+00 7.16332361e+00 -1.89523849e+00\n", - " 4.59896257e+00 3.24588494e+00 -8.27676346e-01 -1.64001795e+00\n", - " -3.39468343e-01 3.16084694e+00 -4.02755628e+00 2.68044647e+00\n", - " -3.52309942e+00 5.44565273e+00 2.64071028e-01 3.48066706e+00\n", - " 2.71503355e+00 6.64645821e+00 6.03453338e+00 -4.10295028e-01\n", - " 2.19121161e+00 -2.57672250e+00 5.39241076e-02 5.10824345e+00\n", - " 3.26779962e+00 -1.25823539e-01 -2.69624903e-01 -1.02591192e+00\n", - " -1.65936525e-01 -3.68833403e+00 2.60254511e+00 2.33586105e+00\n", - " -1.98761269e-01 7.92978882e-02 1.35104743e+00 -1.47352587e+00\n", - " 8.19287501e+00 4.36825745e+00 5.62348538e+00 -3.02310464e+00\n", - " -1.24952469e+00 3.60869767e+00 -7.14943836e-01 1.72827688e+00\n", - " -1.08401619e+00 9.71797607e-01 -3.67172373e+00 2.87376017e+00\n", - " 4.02261559e+00 4.40185980e+00 6.08622771e+00 2.52161723e+00\n", - " 5.02390682e+00 2.05971964e+00 6.63443395e+00 7.86739483e-01\n", - " 4.05075295e+00 7.14878181e+00 -1.06458487e+00 2.25387880e+00\n", - " 3.78587974e+00 5.86594420e+00 5.28191092e+00 1.56829712e+00\n", - " 1.11992377e+00 6.25970542e+00 4.59518376e+00 -4.73532804e-02\n", - " 9.91925106e-01 1.78780084e+00 -1.67704486e+00 2.57846392e+00\n", - " 5.01363080e+00 3.62578165e+00 2.98383041e+00 -3.54004556e+00\n", - " 1.35146165e+00 1.84404206e+00 3.48719456e-01 5.49275762e+00\n", - " -1.24477657e-01 5.71975488e+00 -7.58653139e-01 5.63162877e+00\n", - " 1.26831897e+00 4.86859077e+00 -1.13386422e+00 1.06421474e-01\n", - " 8.53634114e-01 3.72871105e+00 2.95248652e+00 1.55466009e-01\n", - " 4.68613518e+00 3.15019073e+00 4.34200117e+00 -1.30058519e+00\n", - " 2.00072905e+00 2.88969282e+00 -1.06451735e+00 -1.82511376e+00\n", - " 3.84745924e+00 7.22350673e-01 -2.19343278e+00 3.08092161e-01\n", - " 3.71253968e+00 -1.39914348e+00 1.54079973e+00 7.60225010e+00\n", - " 6.67954964e+00 7.14655416e+00 -4.19495837e+00 4.88257788e+00\n", - " -2.51365816e+00 1.77547611e+00 3.46728670e+00 3.31741550e+00\n", - " 8.14876747e-02 8.11447031e+00 6.09301853e+00 1.24339832e+00\n", - " 2.38910718e+00 6.81992598e+00 -7.40858729e-01 6.05346401e+00\n", - " 7.75580654e-01 3.12830439e+00 6.51307396e+00 -3.09787098e+00\n", - " 5.52236505e+00 2.12292849e+00 -1.58532072e+00 -7.10785546e-01\n", - " 2.18485629e+00 2.46401673e+00 8.20218100e+00 -2.86368324e-01\n", - " 1.96456943e-01 -3.27360049e-01 -1.76152094e+00 -4.13507498e+00\n", - " 2.77048361e+00 3.40322662e+00 2.44251620e+00 2.67490436e+00\n", - " -1.99694213e+00 3.99706804e+00 4.26671123e+00 4.76257264e+00\n", - " -6.93027569e-01 -3.05010862e+00 3.05740918e+00 2.13801727e+00\n", - " 4.34430442e+00 1.89650356e+00 2.38504437e+00 4.95921476e+00\n", - " 3.17285628e+00 3.12211963e+00 5.46801748e+00 2.73860789e+00\n", - " 1.76167846e+00 -1.51200597e+00 -7.96905771e-01 1.35456411e+00\n", - " 9.29340211e-01 3.49435329e+00 4.23603904e+00 6.69448510e+00\n", - " 5.10603232e+00 -2.36369230e+00 8.37297836e+00 1.22940278e-01\n", - " 2.19571073e+00 3.69819816e+00 6.74460063e+00 -3.74331119e+00\n", - " 8.92857533e-01 4.59329727e+00 3.48895907e+00 4.37231542e+00\n", - " 2.93079746e+00 -2.97465690e+00 1.61162351e+00 1.99255762e+00\n", - " 5.40951089e+00 -9.98206039e-01 1.43407717e+00 3.83693457e+00\n", - " 4.19456558e-01 4.94522100e-01 6.07062543e-01 1.04986790e+00\n", - " 3.09447525e+00 1.97291718e+00 3.46325689e-01 2.44618465e+00\n", - " 1.64297716e+00 8.07359838e-01 7.74146481e-02 3.04319821e-01\n", - " 2.17399157e+00 -3.76197035e+00 1.31547331e+00 2.77034800e+00\n", - " 2.82394908e+00 2.03193459e-01 5.17072376e-01 4.97024954e+00\n", - " -2.38117209e+00 -1.31368064e+00 -3.74253696e+00 -1.33734460e+00\n", - " 7.50410766e+00 -2.29343424e+00 -8.28129164e-01 1.02559807e+00\n", - " 4.72529662e+00 7.49061575e+00 -2.16372322e+00 3.00583132e+00\n", - " 3.47987760e+00 -6.46683474e-02 -9.11975347e-01 4.28485556e+00\n", - " 1.75228884e+00 -1.40813589e+00 3.63239066e+00 5.11789487e-01\n", - " 1.41743073e+00 3.88809859e+00 -3.54327130e-01 3.74289817e+00\n", - " 6.25828786e+00 5.13827132e+00 3.64615060e+00 4.38494964e+00\n", - " 3.34938791e+00 -1.03516982e+00 1.74261092e+00 1.62080770e+00\n", - " 6.44783537e-01 3.84808165e-01 6.66597093e+00 6.58042866e+00\n", - " 1.65316580e+00 4.36179208e+00 4.96272829e+00 -1.32003578e+00\n", - " -3.37267784e-01 1.17435888e+00 6.32191868e+00 -2.19374986e+00\n", - " 6.98513892e+00 -2.41678836e+00 7.36416922e+00 4.11466970e+00\n", - " 2.00458399e+00 6.91096947e+00 -2.81991921e+00 5.10144395e+00\n", - " 3.61381266e+00 4.63366500e+00 -1.03542313e+00 -1.49851299e+00\n", - " -1.59144759e-01 3.90583528e+00 6.22492672e+00 1.20704979e-01\n", - " 4.61311085e+00 2.31444040e+00 -1.48449492e+00 1.20596862e-01\n", - " 8.08987223e+00 6.60641742e+00 -2.00176137e+00 -2.34127540e+00\n", - " 5.35472054e+00 -6.95845533e-01 7.35014465e+00 -1.99873366e+00\n", - " 2.02638264e+00 5.62287844e+00 9.01788090e-01 3.05411553e+00\n", - " 4.88057954e+00 5.98858828e+00 -3.14881269e+00 1.36720169e-01\n", - " -1.03873249e+00 -1.55201233e+00 -5.88124947e-01 7.88855367e+00\n", - " -9.11510636e-01 6.97647326e+00 7.99263959e+00 -2.19542887e+00\n", - " 7.62333863e-01 4.71366269e+00 5.26937330e+00 7.48340869e-01\n", - " 6.25446602e+00 -9.29526957e-01 1.67602852e+00 3.53900851e+00\n", - " -9.89236796e-01 5.72960088e-01 -4.96209121e+00 1.41808646e-01\n", - " 4.38174259e+00 3.23980166e+00 1.57245128e+00 -2.09363206e-01\n", - " 1.23547521e+00 3.79249825e+00 1.37442484e+00 6.56595315e+00\n", - " 4.19975970e-01 4.76295795e+00 8.03273165e+00 3.85206502e+00\n", - " -3.14809072e+00 -1.42281734e+00 4.53843319e+00 -3.09966441e-01\n", - " 2.73278787e+00 -3.23098446e+00 -2.07087082e+00 -1.06979572e-01\n", - " 1.60618511e+00 -2.95772871e+00 3.81466244e+00 9.21786010e-01\n", - " -1.35387289e+00 -7.03129229e-01 3.79559591e-01 5.77799097e+00\n", - " 2.94330351e+00 -2.38336168e-01 2.40932306e-01 7.94506684e+00\n", - " 3.17267188e+00 -1.49882027e+00 3.55261410e+00 -1.50667458e+00\n", - " 2.99165436e+00 3.75253621e+00 -1.12006173e+00 -8.35947935e-01\n", - " -9.02295356e-01 6.26190996e-01 -3.41116453e-01 -3.20073873e+00\n", - " 6.66413349e+00 1.81441271e+00 -1.39927597e-02 -6.30747430e-01\n", - " 4.67113793e+00 7.21881314e-01 -2.10520878e+00 7.04350250e-01\n", - " 3.09285720e+00 2.92249368e+00 7.56353546e+00 -1.13274244e+00\n", - " 5.17376347e+00 9.24948945e+00 -3.09396390e+00 2.26610772e+00\n", - " 4.74759828e+00 4.91781123e+00 1.23358925e+00 -2.02618829e+00\n", - " 1.75889103e+00 4.17112041e+00 6.97209339e+00 3.09761268e+00\n", - " 5.89551465e+00 4.45768950e+00 1.64791986e+00 1.44191054e+00\n", - " 5.76985139e-01 1.42627610e+00 9.66448598e-01 7.53556981e+00\n", - " 4.10629551e+00 -1.39509766e+00 -3.29797971e-01 1.40107822e+00\n", - " 5.15825742e+00 -2.75657454e+00 7.20215059e-01 -2.07694148e+00\n", - " 1.59247633e+00 2.59882260e+00 2.15560971e+00 6.77307070e+00\n", - " 4.47166256e+00 -2.53766184e+00 4.40442171e+00 3.52260241e+00\n", - " -2.40405287e-01 2.78356222e+00 1.79254816e+00 3.22573393e+00\n", - " 2.80867711e+00 2.66260613e+00 1.87084207e+00 6.39594723e-01\n", - " -2.55020546e+00 -1.97359748e+00 4.63770438e+00 -3.79032496e+00\n", - " 5.34371491e+00 6.78652025e-01 2.05061234e+00 3.16840712e+00\n", - " 1.89895555e+00 5.90313510e+00 4.92421932e+00 4.23968004e+00\n", - " -2.40522898e+00 5.75356577e+00 3.31254451e-01 4.47200032e+00\n", - " 5.07581093e+00 4.51219872e+00 1.56077006e+00 -3.81885967e+00\n", - " 3.47850715e+00 -2.16399475e+00 4.44585883e+00 -1.06202980e+00\n", - " 4.82758393e+00 -3.06705558e-01 2.22457446e+00 1.89227309e+00\n", - " -6.93047420e-01 -2.74082774e+00 4.48112366e+00 3.24373615e+00\n", - " 2.40937313e+00 2.72945617e+00 4.06023806e+00 7.34159840e+00\n", - " 4.35356041e+00 7.37977906e+00 -1.60024165e+00 3.72050168e+00\n", - " 2.91322179e+00 -1.44928107e+00 -7.32480014e-01 -7.98501972e-01\n", - " 8.05410850e-01 6.16302382e+00 1.49803296e-01 -3.57438812e+00\n", - " 4.41876630e+00 6.57121478e+00 2.35263834e+00 -1.62730164e-01\n", - " 6.34172715e+00 5.32343220e+00 6.21613634e+00 6.47585479e+00\n", - " 7.58301854e+00 5.06271128e+00 -2.23422979e-01 -3.77726474e-01\n", - " -2.21815777e+00 6.62474901e+00 -2.12185513e+00 -4.03188972e+00\n", - " 3.93918428e-01 4.70401667e+00 -1.12479614e+00 -7.64482146e-01\n", - " 5.46199850e-01 -1.17406273e+00 1.98813303e+00 4.22495335e+00\n", - " -3.46998475e+00 5.23831859e+00 3.80043337e+00 -3.94604969e+00\n", - " 2.71623267e+00 8.39105247e+00 6.27411909e+00 5.49172457e+00\n", - " 1.59829426e-01 -3.69921183e-01 5.74470090e+00 -3.31019029e+00\n", - " -2.32851148e+00 9.70311108e-01 5.19089524e+00 1.37619180e+00\n", - " 2.07126559e+00 2.44072904e+00 -1.57609213e-01 3.46134772e+00\n", - " 3.36619971e+00 2.55965265e+00 -4.18502036e+00 3.32595277e-01\n", - " 6.23600406e-01 2.99559994e+00 4.56222143e+00 2.12829124e+00\n", - " 7.32010757e-01 -4.55998526e-01 -2.04518168e+00 -2.78017056e+00\n", - " 8.94433533e-01 -1.54750416e+00 3.46372078e+00 -4.87762972e+00\n", - " 4.01784829e+00 4.65737498e-01 3.56823217e+00 2.98560714e+00\n", - " 6.37814649e-02 1.53870397e+00 -2.93768797e+00 5.95941996e+00\n", - " 2.62252390e+00 3.15463337e+00 -2.34714284e+00 -1.72444634e+00\n", - " 2.58803802e+00 2.29550703e+00 8.93617057e-02 4.57914480e+00]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "lineardml_estimate = est_dw.estimate_\n", - "print(lineardml_estimate)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True ATE on test data: 1.9633999999999998\n" - ] - } - ], - "source": [ - "true_customer_TE = TE_fn(test_customers)\n", - "print(\"True ATE on test data: \", true_customer_TE.mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare learned coefficients with true model coefficients\\\n", - "econml_coefs = est_dw.coef_.flatten()\n", - "coef_indices = np.arange(econml_coefs.shape[0])\n", - "# Calculate error bars\n", - "coef_error = np.asarray(est_dw.coef__interval()).reshape(2, coef_indices.shape[0]) # 95% confidence interval for coefficients\n", - "coef_error[0, :] = econml_coefs - coef_error[0, :]\n", - "coef_error[1, :] = coef_error[1, :] - econml_coefs" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.errorbar(coef_indices, econml_coefs, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 95% confidence interval\")\n", - "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\n", - "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "We notice that the coefficients estimates are pretty close to the true coefficients for the linear treatment effect function. \n", - "\n", - "We can also use the `model.summary` function to get point estimates, p-values and confidence intervals. From the table below, we notice that only the **days_visited_free_pre**, **days_visited_hs_pre** and **os_type_osx** features are statistically significant (the confidence interval doesn't contain $0$, p-value < 0.05) for the treatment effect. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
days_visited_exp_pre 0.001 0.007 0.133 0.894 -0.01 0.012
days_visited_free_pre 0.286 0.007 38.483 0.0 0.274 0.298
days_visited_fs_pre -0.008 0.007 -1.206 0.228 -0.019 0.003
days_visited_hs_pre -0.189 0.007 -28.043 0.0 -0.201 -0.178
days_visited_rs_pre 0.001 0.007 0.13 0.897 -0.01 0.012
days_visited_vrs_pre -0.0 0.007 -0.054 0.957 -0.011 0.011
locale_en_US -0.021 0.113 -0.185 0.853 -0.207 0.165
revenue_pre -0.0 0.0 -1.255 0.209 -0.0 0.0
os_type_osx 0.961 0.139 6.931 0.0 0.733 1.189
os_type_windows 0.013 0.138 0.091 0.927 -0.215 0.24
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.482 0.27 1.786 0.074 0.038 0.926


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "============================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "----------------------------------------------------------------------------\n", - "days_visited_exp_pre 0.001 0.007 0.133 0.894 -0.01 0.012\n", - "days_visited_free_pre 0.286 0.007 38.483 0.0 0.274 0.298\n", - "days_visited_fs_pre -0.008 0.007 -1.206 0.228 -0.019 0.003\n", - "days_visited_hs_pre -0.189 0.007 -28.043 0.0 -0.201 -0.178\n", - "days_visited_rs_pre 0.001 0.007 0.13 0.897 -0.01 0.012\n", - "days_visited_vrs_pre -0.0 0.007 -0.054 0.957 -0.011 0.011\n", - "locale_en_US -0.021 0.113 -0.185 0.853 -0.207 0.165\n", - "revenue_pre -0.0 0.0 -1.255 0.209 -0.0 0.0\n", - "os_type_osx 0.961 0.139 6.931 0.0 0.733 1.189\n", - "os_type_windows 0.013 0.138 0.091 0.927 -0.215 0.24\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.482 0.27 1.786 0.074 0.038 0.926\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est_dw.summary(feature_names=X_data.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# How close are the predicted treatment effect to the true treatment effects for 1000 users?\n", - "plt.scatter(true_customer_TE, est_dw.effect(test_customers), label=\"Predicted vs True treatment effect\")\n", - "plt.xlabel(\"True treatment effect\")\n", - "plt.ylabel(\"Predicted treatment effect\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Estimate Robustness with DoWhy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add Random Common Cause\n", - "\n", - "How robust are our estimates to adding another confounder? We use DoWhy to test this!" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Refute: Add a Random Common Cause\n", - "Estimated effect:2.074836810507913\n", - "New effect:2.0655457813564473\n", - "\n" - ] - } - ], - "source": [ - "res_random = est_dw.refute_estimate(method_name=\"random_common_cause\", num_simulations=10)\n", - "print(res_random)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add Unobserved Common Cause\n", - "\n", - "How robust are our estimates to unobserved confounders? Since we assume we have a valid instrument, adding an unobserved confounder should not affect the estimates much. We use DoWhy to test this!" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:2.074836810507913\n", - "New effect:2.285224693713481\n", - "\n" - ] - } - ], - "source": [ - "res_unobserved = est_dw.refute_estimate(method_name=\"add_unobserved_common_cause\",\n", - " confounders_effect_on_treatment=\"binary_flip\", confounders_effect_on_outcome=\"linear\",\n", - " effect_strength_on_treatment=0.05, effect_strength_on_outcome=0.5)\n", - "print(res_unobserved)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Replace Treatment with a Random (Placebo) Variable\n", - "\n", - "What happens our estimates if we replace the treatment variable with noise? Ideally, the average effect would be $0$. We use DoWhy to investigate!" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Refute: Use a Placebo Treatment\n", - "Estimated effect:2.074836810507913\n", - "New effect:-112.27353652608205\n", - "p value:0.045937003218721184\n", - "\n" - ] - } - ], - "source": [ - "res_placebo = est_dw.refute_estimate(method_name=\"placebo_treatment_refuter\", placebo_type=\"permute\", \n", - " num_simulations=2)\n", - "print(res_placebo)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the \"New effect\" is not zero, the p-value is greater than 0.05 which means that we cannot reject the null hypothesis that $0$ is under the average treatment effect distribution. Increasing `num_simulations` should produce a \"New effect\" closer to $0$. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove a Random Subset of the Data\n", - "\n", - "Do we recover similar estimates on subsets of the data? This speaks to the ability of our chosen estimator to generalize well. We use DoWhy to investigate this!" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Refute: Use a subset of data\n", - "Estimated effect:2.074836810507913\n", - "New effect:2.0718850819612706\n", - "p value:0.3507294961332309\n", - "\n" - ] - } - ], - "source": [ - "# Removing a random subset of the data\n", - "res_subset = est_dw.refute_estimate(method_name=\"data_subset_refuter\", subset_fraction=0.8, \n", - " num_simulations=2)\n", - "print(res_subset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"New effect\" is close to the estimated effect from the original dataset and the p-value is greater than 0.05. Thus, we cannot reject the null hypothesis that the estimated effect is under the average treatment effect distribution. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Understand Treatment Effects with EconML\n", - "\n", - "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", - "\n", - "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users who respond negatively to the membership program and in dark green users who respond positively." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", - "intrp.interpret(est_dw, test_customers)\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=X_data.columns, fontsize=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Make Policy Decisions with EconML\n", - "\n", - "Interventions usually have a cost: incetivizing a user to become a member can be costly (e.g by offering a discount). Thus, we would like to know what customers to target to maximize the profit from their increased engagement. This is the **treatment policy**. \n", - "\n", - "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=10)\n", - "intrp.interpret(est_dw, test_customers, sample_treatment_costs=0.2)\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=X_data.columns, fontsize=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Conclusions\n", - "\n", - "In this notebook, we have demonstrated the power of using EconML and DoWhy to:\n", - "\n", - "* Get valid causal insights in seemingly impossible scenarios\n", - "* Test causal assumptions and investigate the robustness of the resulting estimates\n", - "* Intepret individual-level treatment effects\n", - "* Build policies around the learned effects\n", - "\n", - "To learn more about what EconML can do for you, visit the [website](https://aka.ms/econml), [GitHub page](https://github.com/microsoft/EconML) or [docummentation](https://econml.azurewebsites.net/).\n", - "\n", - "To learn more about what DoWhy can do for you, visit the [GitHub page](https://github.com/microsoft/dowhy) or [documentation](https://microsoft.github.io/dowhy/index.html)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb deleted file mode 100644 index ddbcd66b1..000000000 --- a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb +++ /dev/null @@ -1,776 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "# Recommendation A/B Testing: Experimentation with Imperfect Compliance\n", - "\n", - "An online business would like to test a new feature or offering of their website and learn its effect on downstream revenue. Furthermore, they would like to know which kind of users respond best to the new version. We call the user-specfic effect a **heterogeneous treatment effect**. \n", - "\n", - "Ideally, the business would run an A/B tests between the old and new versions of the website. However, a direct A/B test might not work because the business cannot force the customers to take the new offering. Measuring the effect in this way will be misleading since not every customer exposed to the new offering will take it.\n", - "\n", - "The business also cannot look directly at existing data as it will be biased: the users who use the latest website features are most likely the ones who are very engaged on the website and hence spend more on the company's products to begin with. Estimating the effect this way would be overly optimistic." - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "In this customer scenario walkthough, we show how tools from the [EconML](https://aka.ms/econml) library can still use a direct A/B test and mitigate these shortcomings.\n", - "\n", - "### Summary\n", - "\n", - "1. [Background](#Background)\n", - "2. [Data](#Data)\n", - "3. [Get Causal Effects with EconML](#Get-Causal-Effects-with-EconML)\n", - "4. [Understand Treatment Effects with EconML](#Understand-Treatment-Effects-with-EconML)\n", - "5. [Make Policy Decisions with EconML](#Make-Policy-Decisions-with-EconML)\n", - "6. [Conclusions](#Conclusions)" - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Background\n", - "\n", - "\n", - "\n", - "In this scenario, a travel website would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. \n", - "\n", - "A direct A/B test is infeasible because the website cannot force users to become members. Likewise, the travel company can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. \n", - "\n", - "**Solution:** The company had run an earlier experiment to test the value of a new, faster sign-up process. EconML's IV estimators can exploit this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. This is known as an **intent-to-treat** setting: the intention is to give a random group of user the \"treatment\" (access to the easier sign-up process), but not not all users will actually take it. \n", - "\n", - "EconML's `IntentToTreatDRIV` estimator model takes advantage of the fact that not every customer who was offered the easier sign-up became a member to learn the effect of membership rather than the effect of receiving the quick sign-up." - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "# Some imports to get us started\r\n", - "# Utilities\r\n", - "import os\r\n", - "import urllib.request\r\n", - "import numpy as np\r\n", - "import pandas as pd\r\n", - "\r\n", - "# Generic ML imports\r\n", - "import lightgbm as lgb\r\n", - "from sklearn.preprocessing import PolynomialFeatures\r\n", - "\r\n", - "# EconML imports\r\n", - "from econml.iv.dr import LinearIntentToTreatDRIV\r\n", - "from econml.cate_interpreter import SingleTreeCateInterpreter, \\\r\n", - " SingleTreePolicyInterpreter\r\n", - "\r\n", - "import matplotlib.pyplot as plt\r\n", - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Data\n", - "\n", - "The data* is comprised of:\n", - " * Features collected in the 28 days prior to the experiment (denoted by the suffix `_pre`)\n", - " * Experiment variables (whether the use was exposed to the easier signup -> the instrument, and whether the user became a member -> the treatment)\n", - " * Variables collected in the 28 days after the experiment (denoted by the suffix `_post`).\n", - "\n", - "Feature Name | Type | Details \n", - ":--- |:--- |:--- \n", - "**days_visited_exp_pre** | X | #days a user visits the attractions pages \n", - "**days_visited_free_pre** | X | #days a user visits the website through free channels (e.g. domain direct) \n", - "**days_visited_fs_pre** | X | #days a user visits the flights pages \n", - "**days_visited_hs_pre** | X | #days a user visits the hotels pages \n", - "**days_visited_rs_pre** | X | #days a user visits the restaurants pages \n", - "**days_visited_vrs_pre** | X |#days a user visits the vacation rental pages \n", - "**locale_en_US** | X | whether the user access the website from the US \n", - "**os_type** | X | user's operating system (windows, osx, other) \n", - "**revenue_pre** | X | how much the user spent on the website in the pre-period \n", - "**easier_signup** | Z | whether the user was exposed to the easier signup process \n", - "**became_member** | T | whether the user became a member \n", - "**days_visited_post** | Y | #days a user visits the website in the 28 days after the experiment \n", - "\n", - "\n", - "**To protect the privacy of the travel company's users, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*" - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "# Import the sample AB data\n", - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/RecommendationAB/ab_sample.csv\" \n", - "ab_data = pd.read_csv(file_url)" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "# Data sample\n", - "ab_data.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " days_visited_exp_pre days_visited_free_pre days_visited_fs_pre \\\n", - "0 1 9 7 \n", - "1 10 25 27 \n", - "2 18 14 8 \n", - "3 17 0 23 \n", - "4 24 9 22 \n", - "\n", - " days_visited_hs_pre days_visited_rs_pre days_visited_vrs_pre \\\n", - "0 25 6 3 \n", - "1 10 27 27 \n", - "2 4 5 2 \n", - "3 2 3 1 \n", - "4 2 3 18 \n", - "\n", - " locale_en_US revenue_pre os_type_osx os_type_windows easier_signup \\\n", - "0 1 0.01 0 1 0 \n", - "1 0 2.26 0 0 0 \n", - "2 1 0.03 0 1 0 \n", - "3 1 418.77 0 1 0 \n", - "4 1 1.54 0 0 0 \n", - "\n", - " became_member days_visited_post \n", - "0 0 1 \n", - "1 0 15 \n", - "2 0 17 \n", - "3 0 6 \n", - "4 0 12 " - ], - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 3 - } - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# Define estimator inputs\n", - "Z = ab_data['easier_signup'] # nudge, or instrument\n", - "T = ab_data['became_member'] # intervention, or treatment\n", - "Y = ab_data['days_visited_post'] # outcome of interest\n", - "X_data = ab_data.drop(columns=['easier_signup', 'became_member', 'days_visited_post']) # features" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "The data was generated using the following undelying treatment effect function:\n", - "\n", - "$$\n", - "\\text{treatment_effect} = 0.2 + 0.3 \\cdot \\text{days_visited_free_pre} - 0.2 \\cdot \\text{days_visited_hs_pre} + \\text{os_type_osx}\n", - "$$\n", - "\n", - "The interpretation of this is that users who visited the website before the experiment and/or who use an iPhone tend to benefit from the membership program, whereas users who visited the hotels pages tend to be harmed by membership. **This is the relationship we seek to learn from the data.**" - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "# Define underlying treatment effect function \n", - "TE_fn = lambda X: (0.2 + 0.3 * X['days_visited_free_pre'] - 0.2 * X['days_visited_hs_pre'] + X['os_type_osx']).values\n", - "true_TE = TE_fn(X_data)\n", - "\n", - "# Define the true coefficients to compare with\n", - "true_coefs = np.zeros(X_data.shape[1])\n", - "true_coefs[[1, 3, -2]] = [0.3, -0.2, 1]" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Get Causal Effects with EconML\n", - "\n", - "To learn a linear projection of the treatment effect, we use the `LinearIntentToTreatDRIV` EconML estimator. For a more flexible treatment effect function, use the `IntentToTreatDRIV` estimator instead. \n", - "\n", - "The model requires to define some nuissance models (i.e. models we don't really care about but that matter for the analysis): the model for how the outcome $Y$ depends on the features $X$ (`model_Y_X`) and the model for how the treatment $T$ depends on the instrument $Z$ and features $X$ (`model_T_XZ`). Since we don't have any priors on these models, we use generic boosted tree estimators to learn them. " - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "source": [ - "# Define nuissance estimators\r\n", - "lgb_T_XZ_params = {\r\n", - " 'objective' : 'binary',\r\n", - " 'metric' : 'auc',\r\n", - " 'learning_rate': 0.1,\r\n", - " 'num_leaves' : 30,\r\n", - " 'max_depth' : 5\r\n", - "}\r\n", - "\r\n", - "lgb_Y_X_params = {\r\n", - " 'metric' : 'rmse',\r\n", - " 'learning_rate': 0.1,\r\n", - " 'num_leaves' : 30,\r\n", - " 'max_depth' : 5\r\n", - "}\r\n", - "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\r\n", - "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\r\n", - "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# Train EconML model\r\n", - "model = LinearIntentToTreatDRIV(\r\n", - " model_y_xw = model_Y_X,\r\n", - " model_t_xwz = model_T_XZ,\r\n", - " flexible_model_effect = flexible_model_effect,\r\n", - " featurizer = PolynomialFeatures(degree=1, include_bias=False)\r\n", - ")\r\n", - "model.fit(Y, T, Z=Z, X=X_data, inference=\"statsmodels\")" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 7 - } - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "# Compare learned coefficients with true model coefficients\r\n", - "coef_indices = np.arange(model.coef_.shape[0])\r\n", - "# Calculate error bars\r\n", - "coef_error = np.asarray(model.coef__interval()) # 95% confidence interval for coefficients\r\n", - "coef_error[0, :] = model.coef_ - coef_error[0, :]\r\n", - "coef_error[1, :] = coef_error[1, :] - model.coef_" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "plt.errorbar(coef_indices, model.coef_, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 95% confidence interval\")\r\n", - "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\r\n", - "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\r\n", - "plt.legend()\r\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "We notice that the coefficients estimates are pretty close to the true coefficients for the linear treatment effect function. \n", - "\n", - "We can also use the `model.summary` function to get point estimates, p-values and confidence intervals. From the table below, we notice that only the **days_visited_free_pre**, **days_visited_hs_pre** and **os_type_osx** features are statistically significant (the confidence interval doesn't contain $0$, p-value < 0.05) for the treatment effect. " - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "model.summary()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "============================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "----------------------------------------------------------------------------\n", - "days_visited_exp_pre 0.0 0.007 0.055 0.956 -0.011 0.012\n", - "days_visited_free_pre 0.284 0.007 38.206 0.0 0.272 0.296\n", - "days_visited_fs_pre -0.011 0.007 -1.582 0.114 -0.022 0.0\n", - "days_visited_hs_pre -0.19 0.007 -27.999 0.0 -0.201 -0.178\n", - "days_visited_rs_pre 0.001 0.007 0.092 0.927 -0.01 0.012\n", - "days_visited_vrs_pre 0.0 0.007 0.028 0.978 -0.011 0.011\n", - "locale_en_US -0.013 0.113 -0.111 0.911 -0.199 0.174\n", - "revenue_pre -0.0 0.0 -1.231 0.218 -0.0 0.0\n", - "os_type_osx 0.961 0.139 6.922 0.0 0.732 1.189\n", - "os_type_windows 0.041 0.138 0.298 0.766 -0.186 0.269\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.537 0.27 1.989 0.047 0.093 0.981\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
days_visited_exp_pre 0.0 0.007 0.055 0.956 -0.011 0.012
days_visited_free_pre 0.284 0.007 38.206 0.0 0.272 0.296
days_visited_fs_pre -0.011 0.007 -1.582 0.114 -0.022 0.0
days_visited_hs_pre -0.19 0.007 -27.999 0.0 -0.201 -0.178
days_visited_rs_pre 0.001 0.007 0.092 0.927 -0.01 0.012
days_visited_vrs_pre 0.0 0.007 0.028 0.978 -0.011 0.011
locale_en_US -0.013 0.113 -0.111 0.911 -0.199 0.174
revenue_pre -0.0 0.0 -1.231 0.218 -0.0 0.0
os_type_osx 0.961 0.139 6.922 0.0 0.732 1.189
os_type_windows 0.041 0.138 0.298 0.766 -0.186 0.269
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.537 0.27 1.989 0.047 0.093 0.981


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ] - }, - "metadata": {}, - "execution_count": 10 - } - ], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "source": [ - "test_customers = X_data.iloc[:1000]\r\n", - "true_customer_TE = TE_fn(test_customers)\r\n", - "model_customer_TE = model.effect(test_customers)" - ], - "outputs": [], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "source": [ - "# How close are the predicted treatment effect to the true treatment effects for 1000 users?\n", - "plt.scatter(true_customer_TE, model.effect(test_customers), label=\"Predicted vs True treatment effect\")\n", - "plt.xlabel(\"True treatment effect\")\n", - "plt.ylabel(\"Predicted treatment effect\")\n", - "plt.legend()\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "slideshow": { - "slide_type": "skip" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Understand Treatment Effects with EconML\n", - "\n", - "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", - "\n", - "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users who respond negatively to the membership program and in dark green users who respond positively." - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", - "intrp.interpret(model, test_customers)\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=X_data.columns, fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Make Policy Decisions with EconML\n", - "\n", - "Interventions usually have a cost: incetivizing a user to become a member can be costly (e.g by offering a discount). Thus, we would like to know what customers to target to maximize the profit from their increased engagement. This is the **treatment policy**. \n", - "\n", - "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. " - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "source": [ - "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=10)\n", - "intrp.interpret(model, test_customers, sample_treatment_costs=0.2)\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=X_data.columns, fontsize=12)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": { - "needs_background": "light" - } - } - ], - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "# Conclusions\n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* Get valid causal insights in seemingly impossible scenarios\n", - "* Intepret the resulting individual-level treatment effects\n", - "* Build policies around the learned effects\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ], - "metadata": { - "slideshow": { - "slide_type": "slide" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb b/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb deleted file mode 100644 index af0628d1e..000000000 --- a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb +++ /dev/null @@ -1,1151 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
\n", - "\n", - "This notebook applies EconML's `LinearDML` and `LinearDRLearner` methodologies to the data from National Supported Work (NSW) Demonstration that have been previously analyzed by LaLonde(1986) and others. We first replicate the results from LaLonde's paper comparing estimates of the Average Treatment Effect (ATE) of a worker training program estimated using an experimental data sample and using alternative observational data sets as controls. Then we generate new estimated ATEs using **Double Machine Learning** and **Doubly Robust** estimation on these same datasets. These new causal machine learning techniques have a moderately better performance than the traditional nonexperimental evalutaion strategies from the original paper in some samples. We further improve the point estimate by estimating heterogeneous treatment effects and using reweighting techniques to make the observational control sets more closely resemble the experimental control set. Finally, we prove that our package provides reasonable effect heterogeneity estimates from observational data that are comparable with what we can learn from the experiment. \n", - "\n", - "These results reinforce the value of Causal ML techniques like DML and DR Learners when attempting to estimate causal effects from non-experimental data. The reweighting techniques demonstrated here are also generally useful when trying to use treatment effects estimated from one, potentially non-representative, experimental sample to forecast average treatment effects in a new population.\n", - "\n", - "### Summary \n", - "\n", - "1. [Background](#background)\n", - "2. [Data](#data)\n", - "3. [Compare EconML Solution with LaLonde OLS Results](#comparison)\n", - "4. [Further Improve EconML Result with Reweighting Trick](#improvement)\n", - "7. [Heterogeneous Treatment Effect with EconML](#hte)\n", - "8. [Conclusions](#conclusion)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background \n", - "\n", - "\n", - "The National Supported Work Demonstration (NSW) was a temporary employment program designed to help a population of disadvantaged workers in the U.S. move into long-term jobs by offering job training. The training program was targeted at populations of male and female workers who met certain conditions of disadvantage (for example, were receiving federal welfare payments or had previously been incarcerated). Participation in the program was also voluntary. Participation in this program is therefore not random, and likely to correlate with other confounding worker characteristics in difficult-to-identify ways.\n", - "\n", - "However, once workers opted into the program, the NSW randomly assigned them to actually receive the training or remain as a control sample to facilitate later program evaluation. This set-up creates a good environment for testing estimation techniques that are designed to capture causal effects from non-experimental data. Following LaLonde, we first estimate the effect of the training by comparing the treated and control workers within the NSW program. Because these workers were randomly assigned across groups, we're not worried about any confounders, so these estimates serve as a benchmark for the true causal effect. We then re-estimate the effects of training by comparing the trained workers in the NSW sample to workers in other U.S. datasets, the Panel Study of Income Dynamics (PSID) and the Current Population Survey (CPS). These alternative control samples should differ from the workers who were targeted for the NSW program in complex ways that mirror the usual difficulties of estimating causal effects from observational data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import statsmodels.api as sm\n", - "import seaborn as sns\n", - "\n", - "from econml.dml import LinearDML\n", - "from econml.dr import LinearDRLearner\n", - "from econml.cate_interpreter import SingleTreeCateInterpreter\n", - "from econml.dml import CausalForestDML,NonParamDML\n", - "\n", - "from sklearn.linear_model import LogisticRegressionCV, LinearRegression,LogisticRegression,Lasso,LassoCV\n", - "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor,GradientBoostingClassifier\n", - "import lightgbm as lgb\n", - "from sklearn.preprocessing import StandardScaler\n", - "from tqdm import tqdm\n", - "from econml.sklearn_extensions.model_selection import GridSearchCVList\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.transforms import ScaledTranslation\n", - "import seaborn as sns\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# helper functions\n", - "# preprocess data\n", - "def preprocessing(df,outcome_name,column_names):\n", - " # add indicator of zero earnings\n", - " df[\"re74_dummy\"]=(df[\"re74\"]>0)*1\n", - " df[\"re75_dummy\"]=(df[\"re75\"]>0)*1\n", - " # get growth of pre training earning\n", - " df[\"re_diff_pre\"]=df[\"re75\"]-df[\"re74\"]\n", - " # add age square\n", - " df[\"age_2\"]=df[\"age\"]**2\n", - " # select columns\n", - " df=df[column_names+[outcome_name]]\n", - " return df\n", - "\n", - "# linear regression wrapper\n", - "def ols_reg_wrapper(reg_data, x_columns, y_column, print_summary=False):\n", - " X = reg_data[x_columns].values\n", - " X = pd.DataFrame(sm.add_constant(X,has_constant='add'),columns=[\"intercept\"]+x_columns)\n", - " Y = reg_data[y_column]\n", - " model = sm.OLS(Y, X, hasconst=True)\n", - " results = model.fit()\n", - " # save results for summary table\n", - " effect = int(round(results.params['treated']))\n", - " se = int(round(results.bse['treated']))\n", - " lb=int(round(results.conf_int().loc[\"treated\"][0]))\n", - " ub=int(round(results.conf_int().loc[\"treated\"][1]))\n", - " if print_summary:\n", - " print(results.summary())\n", - " return effect, se, lb, ub\n", - "\n", - "# nuisance regression model auto tunning wrapper\n", - "def first_stage_reg(X, y, *, automl=True):\n", - " if automl:\n", - " model = GridSearchCVList([LassoCV(),\n", - " RandomForestRegressor(n_estimators=100),\n", - " lgb.LGBMRegressor()],\n", - " param_grid_list=[{},\n", - " {'max_depth': [5,10,20],'min_samples_leaf': [5, 10]},\n", - " {'learning_rate': [0.02,0.05,0.08], 'max_depth': [3, 5]}],\n", - " cv=3,\n", - " scoring='neg_mean_squared_error')\n", - " best_est = model.fit(X, y).best_estimator_\n", - " if isinstance(best_est, LassoCV):\n", - " return Lasso(alpha=best_est.alpha_)\n", - " return best_est\n", - " else:\n", - " model = LassoCV(cv=5).fit(X, y)\n", - " return Lasso(alpha=model.alpha_)\n", - "\n", - "# nuisance classification model auto tunning wrapper\n", - "def first_stage_clf(X, y, *, make_regressor=False, automl=True):\n", - " if automl:\n", - " model = GridSearchCVList([LogisticRegressionCV(max_iter=1000),\n", - " RandomForestClassifier(n_estimators=100),\n", - " lgb.LGBMClassifier()],\n", - " param_grid_list=[{},\n", - " {'max_depth': [5,10,20],\n", - " 'min_samples_leaf': [5, 10]},\n", - " {'learning_rate':[0.01,0.05,0.1],\n", - " 'max_depth': [3,5]}],\n", - " cv=3,\n", - " scoring='neg_log_loss')\n", - " est = model.fit(X, y).best_estimator_\n", - " if isinstance(est,LogisticRegressionCV):\n", - " return LogisticRegression(C=est.C_[0])\n", - " else:\n", - " model = LogisticRegressionCV(cv=5, max_iter=1000).fit(X, y)\n", - " est = LogisticRegression(C=model.C_[0])\n", - " if make_regressor:\n", - " return _RegressionWrapper(est)\n", - " else:\n", - " return est\n", - "\n", - "# econml dml/dr wrapper\n", - "def econml_homo_model_wrapper(reg_data, control_names,outcome_name,model_type,*,cols_to_scale, print_summary=False):\n", - " # get variables\n", - " X = None # no heterogeneous treatment\n", - " W = reg_data[control_names].values\n", - " # scale W\n", - " scaler = StandardScaler()\n", - " W = np.hstack([scaler.fit_transform(W[:, :cols_to_scale]).astype(np.float32), W[:, cols_to_scale:]]) \n", - " T = reg_data[\"treated\"]\n", - " y = reg_data[outcome_name]\n", - " \n", - " # select the best nuisances model out of econml estimator\n", - " model_y=first_stage_reg(W, y)\n", - " model_t=first_stage_clf(W, T)\n", - " \n", - " if model_type=='dml':\n", - " est = LinearDML(model_y=model_y,\n", - " model_t=model_t, \n", - " discrete_treatment=True, mc_iters=5,cv=5)\n", - " elif model_type=='dr':\n", - " est = LinearDRLearner(model_regression=model_y,\n", - " model_propensity=model_t,\n", - " mc_iters=5,cv=5)\n", - " else:\n", - " raise ValueError('invalid model type %s' % model_type)\n", - " try:\n", - " est.fit(y, T, X=X, W=W, inference=\"statsmodels\")\n", - " except np.linalg.LinAlgError as e:\n", - " est.fit(y, T, X=X, W=W, inference=\"statsmodels\")\n", - "\n", - " # Get the final coefficient and intercept summary\n", - " if model_type==\"dml\":\n", - " inf=est.intercept__inference()\n", - " else:\n", - " inf=est.intercept__inference(T=1)\n", - " effect=int(round(inf.point_estimate))\n", - " se=int(round(inf.stderr))\n", - " lb,ub=inf.conf_int(alpha=0.05)\n", - " if print_summary:\n", - " if model_type=='dml':\n", - " print(est.summary(alpha=0.05))\n", - " else:\n", - " print(est.summary(T=1,alpha=0.05))\n", - " return effect, se, int(round(lb)), int(round(ub))\n", - "\n", - "# summary table helper function\n", - "def get_summ_table(dfs,treat_df,*,df_names,basic_ols_controls,complete_ols_controls,econml_controls,outcome_name,cols_to_scale):\n", - " summ_dic={\"control_name\":[],\"# of obs\":[],\"earning_growth\":[],\"OLS\":[],\"OLS full controls\":[],\"DML full controls\":[],\"DR full controls\":[]}\n", - " summ_dic1={\"control_name\":[],\"method\":[],\"point_estimate\":[],\"stderr\":[],\"lower_bound\":[],\"upper_bound\":[]}\n", - " for df, name in tqdm(zip(dfs, df_names)):\n", - " summ_dic[\"control_name\"].append(name)\n", - " summ_dic[\"# of obs\"].append(df.shape[0])\n", - " summ_dic1[\"control_name\"]+=[name]*4\n", - " # get table 5 col 1\n", - " growth=int(np.round((df[outcome_name]-df[\"re75\"]).mean(),0))\n", - " summ_dic[\"earning_growth\"].append(growth)\n", - " # get table 5 col 5\n", - " summ_dic1[\"method\"].append(\"OLS\")\n", - " df_all=pd.concat([treat_df,df],axis=0,ignore_index=True)\n", - " effect,se,lb,ub=ols_reg_wrapper(df_all,basic_ols_controls,outcome_name,print_summary=False)\n", - " summ_dic[\"OLS\"].append([effect,se])\n", - " summ_dic1[\"point_estimate\"].append(effect)\n", - " summ_dic1[\"stderr\"].append(se)\n", - " summ_dic1[\"lower_bound\"].append(lb)\n", - " summ_dic1[\"upper_bound\"].append(ub)\n", - " # get table 5 col 10\n", - " summ_dic1[\"method\"].append(\"OLS full controls\")\n", - " effect,se,lb,ub=ols_reg_wrapper(df_all,complete_ols_controls,outcome_name,print_summary=False)\n", - " summ_dic[\"OLS full controls\"].append([effect,se])\n", - " summ_dic1[\"point_estimate\"].append(effect)\n", - " summ_dic1[\"stderr\"].append(se)\n", - " summ_dic1[\"lower_bound\"].append(lb)\n", - " summ_dic1[\"upper_bound\"].append(ub)\n", - " # dml\n", - " summ_dic1[\"method\"].append(\"DML full controls\")\n", - " effect,se,lb,ub=econml_homo_model_wrapper(df_all, econml_controls,outcome_name,\"dml\",cols_to_scale=cols_to_scale, print_summary=False) \n", - " summ_dic[\"DML full controls\"].append([effect,se])\n", - " summ_dic1[\"point_estimate\"].append(effect)\n", - " summ_dic1[\"stderr\"].append(se)\n", - " summ_dic1[\"lower_bound\"].append(lb)\n", - " summ_dic1[\"upper_bound\"].append(ub)\n", - " # dr\n", - " summ_dic1[\"method\"].append(\"DR full controls\")\n", - " effect,se,lb,ub=econml_homo_model_wrapper(df_all, econml_controls,outcome_name,\"dr\",cols_to_scale=cols_to_scale, print_summary=False) \n", - " summ_dic[\"DR full controls\"].append([effect,se])\n", - " summ_dic1[\"point_estimate\"].append(effect)\n", - " summ_dic1[\"stderr\"].append(se)\n", - " summ_dic1[\"lower_bound\"].append(lb)\n", - " summ_dic1[\"upper_bound\"].append(ub)\n", - " \n", - " return summ_dic,summ_dic1\n", - "\n", - "# error bar helper function\n", - "def plot_errorbar(df,df_names):\n", - " fig, ax = plt.subplots(figsize=(10,6))\n", - " for ind,control_name in enumerate(df_names):\n", - " sub_df=df[df[\"control_name\"]==control_name]\n", - " method_name=sub_df[\"method\"].values\n", - " point=sub_df[\"point_estimate\"].values\n", - " yerr=np.zeros((2,point.shape[0]))\n", - " yerr[0,:]=point-sub_df[\"lower_bound\"].values\n", - " yerr[1,:]=sub_df[\"upper_bound\"].values-point\n", - " trans = ax.transData + ScaledTranslation((-10+ind*5)/72, 0, fig.dpi_scale_trans)\n", - " plt.errorbar(method_name,point,yerr,fmt=\"o\",capsize=5,elinewidth=2,label=control_name,alpha=0.7,transform=trans)\n", - " plt.axhline(y=0, color='black', linestyle='--',alpha=0.5)\n", - " plt.legend()\n", - " plt.xlabel(\"Methodology\")\n", - " plt.ylabel(\"ATE with CI\")\n", - " plt.title(\"Error bar of each method for each dataset\")\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data \n", - "We use an experimental dataset from the NSW (which includes both a control sample of workers and treated sample). We also consider several sets of control workers from two large cross-sectional survey sof U.S. workers, the PSID and CPS. For the PSID and CPS, we consider somewhat broader samples of workers (CPS1 and PSID1 for men, PSID1 for women) and smaller samples chosen to better match the characteristics of the NSW population (CSP3 and PSID3 for men, PSID2 for women). The data for men are provided by [Dehejia and Wahba](https://users.nber.org/~rdehejia/data/.nswdata2.html) from their 1999 follow-up to LaLonde's paper. The data for women are provided by [Calonico and Smith](https://www.journals.uchicago.edu/doi/10.1086/692397) from their 2017 follow-up paper. More details on both sources are available through the linked references. \n", - "\n", - "All data sets have the same set of variables:\n", - "\n", - "Feature Name|Type|Data Type|Details \n", - ":--- |:---|:--- |:--- \n", - "**treated** |T| Boolean| whether this person had undergone NSW job training treatment\n", - "**age** |W/X| Integer| user's age\n", - "**educ** |W/X| Integer| years of education this person has completed\n", - "**nodegree** |W/X| Boolean| whether this person has NO high school degree\n", - "**black** |W/X| Boolean| whether this person is Black\n", - "**hisp** |W/X| Boolean| whether this person is Hispanic\n", - "**married** |W/X| Boolean| whether this person is married\n", - "**haschild**|W|Boolean|whether this person has child for female\n", - "**nchildren75**|W|Integer|number of child in 1975 for female\n", - "**afdc75**|W|Boolean|AFDC status for female in 1975\n", - "**re74** |W| Float| user's real earnings in 1974 (See Smith & Todd for a discussion)\n", - "**re75** |W| Float| user's real earnings in 1975 (before treatment)\n", - "**re78** |Y| Float| user's real earnings in 1978 (after treatment for males)\n", - "**re79** |Y| Float| user's real earnings in 1979 (after treatment for females)\n", - "\n", - "Below we also show the mean and standard error of each feature from different experimental and control sets for men and women:\n", - "\n", - "- Summary Statistics for Men:\n", - " \n", - "Feature Name|exp treatment|exp control|cps1|cps3|psid1|psid3\n", - ":--- |:--- |:--- |:---|:---|:--- |:--- \n", - "**age** |24.62 (6.69) |24.45 (6.59) |33.23 (11.05)|28.03 (10.79)|34.85 (10.44)|38.26 (12.89) \n", - "**educ** |10.38 (1.82) |10.19 (1.62) |12.03 (2.87) |10.24 (2.86)|12.12 (3.08) |10.30 (3.18)\n", - "**nodegree** |0.73 (0.44) |0.81 (0.39) |0.30 (0.46) |0.60 (0.49) |0.31 (0.46) |0.51 (0.50)\n", - "**black** |0.80 (0.40) |0.80 (0.40) |0.07 (0.26) |0.20 (0.40) |0.25 (0.43) |0.45 (0.50)\n", - "**hisp** |0.09 (0.29) |0.11 (0.32) |0.07 (0.26) |0.14 (0.35) |0.03 (0.18) |0.12 (0.32)\n", - "**married** |0.17 (0.37) |0.16 (0.36) |0.71 (0.45) |0.51 (0.50) |0.87 (0.34) |0.70 (0.46)\n", - "**re74** |3571 (5773) |3672 (6522) |14017 (9570) |5619 (6789) |19429 (13407) |5567 (7255)\n", - "**re75** |3066 (4875) |3026 (5201) |13651 (9270) |2466 (3292) |19063 (13597) |2611 (5572)\n", - "**re78** |5976 (6924) |5090 (5718) |14847 (9647) |6984 (7294) |21553 (15555) |5279 (7762)\n", - "**# of obs** |297 |425 |15992 |429 |2490 |128\n", - "\n", - "- Summary Statistics for Women:\n", - "\n", - "Feature Name|exp treatment|exp control|psid1|psid2\n", - ":--- |:--- |:--- |:---|:---\n", - "**age** |33.76 (7.39) |33.74 (7.15) |37.07 (10.57)|34.54 (9.34)\n", - "**educ** |10.29 (1.93) |10.26 (2.03) |11.30 (2.77) |10.49 (2.13)\n", - "**nodegree** |0.70 (0.46) |0.68 (0.47) |0.45 (0.50) |0.59 (0.49)\n", - "**black** |0.84 (0.37) |0.82 (0.39) |0.65 (0.48) |0.86 (0.35)\n", - "**hisp** |0.11 (0.32) |0.13 (0.33) |0.02 (0.12) |0.02 (0.15)\n", - "**married** |0.02 (0.15) |0.04 (0.19) |0.02 (0.14) |0.01 (0.10)\n", - "**haschild** |0.97 (0.16) |0.98 (0.14) |0.67 (0.47) |0.97 (0.16)\n", - "**nchildren75** |2.18 (1.29) |2.23 (1.34) |1.71 (1.78) |2.97 (1.79) \n", - "**afdc75** |1.00 (0.00) |1.00 (0.00) |0.28 (0.45) |1.00 (0.00)\n", - "**re74** |913 (2149) |962 (2376) |7509 (7296) |2726 (4414)\n", - "**re75** |861 (2004) |879 (2195) |7510 (7541) |2211 (3568)\n", - "**re79** |4665 (5554) |3833 (5039) |8827 (8762) |4623 (6921)\n", - "**# of obs** |601 |585 |648 |182\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Data\n", - "## female\n", - "### read in and slice data\n", - "female_data = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/calonico_smith_all.csv')\n", - "female_data[\"haschild\"]=(female_data[\"nchildren75\"]>0)*1\n", - "female_data = female_data[pd.notnull(female_data.re75) & pd.notnull(female_data.re79)]\n", - "female_treatment = female_data[female_data.treated==1.].copy()\n", - "female_control = female_data[female_data.treated==0.].copy()\n", - "female_psid1 = female_data[female_data['psid1']==1].copy()\n", - "female_psid2 = female_data[female_data['psid2']==1].copy()\n", - "### some preprocessing\n", - "female_psid1.loc[:, 'treated'] = 0\n", - "female_psid2.loc[:, 'treated'] = 0\n", - "\n", - "## male\n", - "### read in and slice data\n", - "male_data = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/smith_todd.csv')\n", - "male_treatment = male_data[male_data.treated==1.].copy()\n", - "male_control = male_data[male_data.treated==0.].copy()\n", - "male_cps1 = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/cps_controls.csv')\n", - "male_psid1 = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/psid_controls.csv')\n", - "male_cps3 = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/cps_controls3.csv')\n", - "male_psid3 = pd.read_csv('https://msalicedatapublic.blob.core.windows.net/datasets/Lalonde/psid_controls3.csv')\n", - "### some preprocessing\n", - "for df in [male_psid1,male_psid3,male_cps1,male_cps3]:\n", - " df.rename(columns={'treat':'treated', 'education':'educ', 'hispanic':'hisp'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# outcome\n", - "outcome_name_male=\"re78\"\n", - "outcome_name_female=\"re79\"\n", - "# ols controls\n", - "basic_ols_columns = ['treated', 'age', 'age_2', 'educ', 'nodegree', 'black', 'hisp']\n", - "complete_ols_columns_male = basic_ols_columns+[\"married\",\"re75\",\"re75_dummy\",\"re74\",\"re74_dummy\",\"re_diff_pre\"]\n", - "complete_ols_columns_female=basic_ols_columns+[\"married\",\"re75\",\"re75_dummy\",\"re74\",\"re74_dummy\",\"re_diff_pre\",\"afdc75\",\"nchildren75\",\"haschild\"]\n", - "# econml controls (exclude treatment)\n", - "econml_controls_male= ['age', 'age_2', 'educ', 're75','re74','re_diff_pre','nodegree', 'black', 'hisp', 'married','re75_dummy','re74_dummy']\n", - "econml_controls_female= ['age', 'age_2', 'educ','nchildren75', 're75','re74','re_diff_pre','nodegree', 'black', 'hisp', 'married','re75_dummy','re74_dummy','afdc75','haschild']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# preprocessing data\n", - "male_control, male_treatment, male_psid1, male_psid3, male_cps1, male_cps3 = [preprocessing(df,outcome_name_male,complete_ols_columns_male) for df in (male_control, male_treatment, male_psid1, male_psid3, male_cps1, male_cps3)]\n", - "female_control, female_treatment, female_psid1, female_psid2 =[preprocessing(df,outcome_name_female,complete_ols_columns_female) for df in (female_control, female_treatment, female_psid1, female_psid2)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compare EconML Solution with LaLonde OLS Results \n", - "We first repilcate key results from Table 5 (for men) and Table 4 (for women) from [Lalonde (1986)](http://public.econ.duke.edu/~hf14/teaching/povertydisc/readings/lalonde1986.pdf). These tables calculate the benchmark ATE using only the experimental sample and attempt to match that benchmark by estimating treatment effects using OLS and various alternative observational untreated samples, first with a basic set of control features and then with an expanded set. We then train both `LinearDML` and `LinearDRLearner` models on each dataset, using the expanded control set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Treatment Effect Comparison for Men" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "5it [04:27, 53.44s/it]\n" - ] - } - ], - "source": [ - "#male\n", - "control_dfs_male=[male_control,male_psid1,male_psid3,male_cps1,male_cps3]\n", - "df_names_male=[\"exp controls\",\"psid1\",\"psid3\",\"cps1\",\"cps3\"]\n", - "summ_male,summplot_male=get_summ_table(control_dfs_male,male_treatment,df_names=df_names_male,\n", - " basic_ols_controls=basic_ols_columns,\n", - " complete_ols_controls=complete_ols_columns_male,\n", - " econml_controls=econml_controls_male,\n", - " outcome_name=outcome_name_male,cols_to_scale=6\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numbers under each method represent [point estimate, standard error]. \n" - ] - }, - { - "data": { - "text/html": [ - "
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control_name# of obsearning_growthOLSOLS full controlsDML full controlsDR full controls
0exp controls4252063[798, 472][817, 469][851, 487][868, 486]
1psid124902491[-8067, 990][-1827, 825][-1966, 697][-1576, 401]
2psid31282669[-509, 967][-239, 1029][133, 976][92, 669]
3cps1159921196[-4416, 577][-867, 445][-784, 542][-2540, 1022]
4cps34294518[-1, 681][210, 683][480, 609][310, 448]
\n", - "
" - ], - "text/plain": [ - " control_name # of obs earning_growth OLS OLS full controls \\\n", - "0 exp controls 425 2063 [798, 472] [817, 469] \n", - "1 psid1 2490 2491 [-8067, 990] [-1827, 825] \n", - "2 psid3 128 2669 [-509, 967] [-239, 1029] \n", - "3 cps1 15992 1196 [-4416, 577] [-867, 445] \n", - "4 cps3 429 4518 [-1, 681] [210, 683] \n", - "\n", - " DML full controls DR full controls \n", - "0 [851, 487] [868, 486] \n", - "1 [-1966, 697] [-1576, 401] \n", - "2 [133, 976] [92, 669] \n", - "3 [-784, 542] [-2540, 1022] \n", - "4 [480, 609] [310, 448] " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summ_male_df=pd.DataFrame(summ_male)\n", - "print(\"Numbers under each method represent [point estimate, standard error]. \")\n", - "summ_male_df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "summplot_male_df=pd.DataFrame(summplot_male)\n", - "plot_errorbar(summplot_male_df,df_names_male)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Treatment Effect Comparison for Women" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "3it [01:25, 28.42s/it]\n" - ] - } - ], - "source": [ - "# female\n", - "control_dfs_female=[female_control,female_psid1,female_psid2]\n", - "df_names_female=[\"exp controls\",\"psid1\",\"psid2\"]\n", - "summ_female,summplot_female=get_summ_table(control_dfs_female,female_treatment,df_names=df_names_female,\n", - " basic_ols_controls=basic_ols_columns,\n", - " complete_ols_controls=complete_ols_columns_female,\n", - " econml_controls=econml_controls_female,\n", - " outcome_name=outcome_name_female,cols_to_scale=7\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numbers under each method represent [point estimate, standard error]. \n" - ] - }, - { - "data": { - "text/html": [ - "
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control_name# of obsearning_growthOLSOLS full controlsDML full controlsDR full controls
0exp controls5852954[858, 307][880, 307][830, 307][834, 307]
1psid16481316[-2730, 441][1068, 529][1012, 682][762, 610]
2psid21822412[-90, 514][510, 560][790, 711][777, 848]
\n", - "
" - ], - "text/plain": [ - " control_name # of obs earning_growth OLS OLS full controls \\\n", - "0 exp controls 585 2954 [858, 307] [880, 307] \n", - "1 psid1 648 1316 [-2730, 441] [1068, 529] \n", - "2 psid2 182 2412 [-90, 514] [510, 560] \n", - "\n", - " DML full controls DR full controls \n", - "0 [830, 307] [834, 307] \n", - "1 [1012, 682] [762, 610] \n", - "2 [790, 711] [777, 848] " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summ_female_df=pd.DataFrame(summ_female)\n", - "print(\"Numbers under each method represent [point estimate, standard error]. \")\n", - "summ_female_df" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "summplot_female_df=pd.DataFrame(summplot_female)\n", - "plot_errorbar(summplot_female_df,df_names_female)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Improved Performance with Sample Reweighting \n", - "\n", - "An important difficulty in the above exercise is that all of the alternative control samples are somewhat different than the experimental NSW sample (for example, workers in the experimental sample are younger on average and far more likely to be Black or Hispanic). Even with a perfect tool for estimating causal effects from observational data, estimating the *average* treatment effect across these separate samples is unlikely to yield identical results. \n", - "\n", - "We now address this limitation by reestimating our `LinearDML` model after reweighting the observational samples to better align with the experimental sample. For this exercise we focus on the CPS3 sample for men, which was most similar to the experimental sample to begin with. We estimate a classification model between the full NSW sample and observational sample (which combines treated workers from NSW with the CPS3 control sample) to estimate the likelihood that a worker with a given set of features originated from each sample. We then reweight the CPS3 sample to give higher weight to workers who are more likely to have come from the experimental set: $$weight=P(obs\\_from\\_experimental\\_distribution)/(1-P(obs\\_from\\_experimental\\_distribution)) $$\n", - "Because the data samples in this exercise are so small, the estimated treatment effects from both the weighted and unweighted models are unstable. We therefore run this analysis 100 times and learn the distribution of ATE from each approach and their significance level (p value). " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# experimental data male\n", - "df=pd.concat([male_treatment,male_control])\n", - "# cps3 data male\n", - "df_cps=pd.concat([male_treatment,male_cps3])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df[\"label\"]=1\n", - "df_cps[\"label\"]=0\n", - "male_cls=pd.concat([df,df_cps]).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "X=male_cls[econml_controls_male].values\n", - "y=male_cls[\"label\"].values\n", - "# scale numeric features\n", - "cols_to_scale=6\n", - "scaler = StandardScaler()\n", - "X=np.hstack([scaler.fit_transform(X[:, :cols_to_scale]).astype(np.float32), X[:, cols_to_scale:]]) " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# train a classification model to learn the weight\n", - "cls=first_stage_clf(X,y)\n", - "cls.fit(X,y)\n", - "male_cls[\"prob\"]=cls.predict_proba(X)[:,1]/cls.predict_proba(X)[:,0]\n", - "weight=male_cls[male_cls[\"label\"]==0][\"prob\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "X = None\n", - "W = df_cps[econml_controls_male].values\n", - "# scale W\n", - "W = np.hstack([scaler.fit_transform(W[:, :cols_to_scale]).astype(np.float32), W[:, cols_to_scale:]]) \n", - "T = df_cps[\"treated\"]\n", - "y = df_cps[outcome_name_male]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "model_y=first_stage_reg(W, y)\n", - "model_t=first_stage_clf(W, T)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# train dml with sample weight 100 times\n", - "p_value_with_weight=[]\n", - "point_estimate_with_weight=[]\n", - "for _ in range(100): \n", - " est=LinearDML(model_t=model_t,model_y=model_y,discrete_treatment=True,mc_iters=10,cv=3)\n", - " est.fit(y, T, X=None, W=W, sample_weight=weight,inference=\"statsmodels\")\n", - " point_estimate_with_weight.append(est.intercept_)\n", - " p_value_with_weight.append(est.effect_inference().pvalue()[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# train dml without sample weight 100 times\n", - "p_value_without_weight=[]\n", - "point_estimate_without_weight=[]\n", - "for _ in range(100): \n", - " est1=LinearDML(model_t=model_t,model_y=model_y,discrete_treatment=True,mc_iters=10,cv=3)\n", - " est1.fit(y, T, X=None,W=W,inference=\"statsmodels\")\n", - " point_estimate_without_weight.append(est1.intercept_)\n", - " p_value_without_weight.append(est1.effect_inference().pvalue()[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'frequency')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ranges=(min(point_estimate_with_weight+point_estimate_without_weight),max(point_estimate_with_weight+point_estimate_without_weight))\n", - "plt.hist(point_estimate_with_weight,label=\"with sample weight\",bins=20,range=ranges,alpha=0.8)\n", - "plt.hist(point_estimate_without_weight,label=\"without sample weight\",bins=20,range=ranges,alpha=0.8)\n", - "plt.legend()\n", - "plt.title(\"Comparison of point estimates between LinearDML with or without reweighting trick (n=100)\")\n", - "plt.xlabel(\"point estimate\")\n", - "plt.ylabel(\"frequency\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'frequency')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ranges=(min(p_value_with_weight+p_value_without_weight),max(p_value_with_weight+p_value_without_weight))\n", - "plt.hist(p_value_with_weight,label=\"with sample weight\",range=ranges,alpha=0.8)\n", - "plt.hist(p_value_without_weight,label=\"without sample weight\",range=ranges,alpha=0.8)\n", - "plt.legend()\n", - "plt.title(\"Comparison of p-value between LinearDML with or without reweighting trick (n=100)\")\n", - "plt.xlabel(\"p value\")\n", - "plt.ylabel(\"frequency\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Overall, reweighting the sample substantially improves the ability of the DML model to approximate the experimental average treatment effect using observational data. Recall the benchmark ATE for men is around 817. The average ATE estimated by the unweighted DML model is 471 , while the average ATE from the weighted model is 584. However, adding sample weights increases the variance of the estimate. As shown in the second plot, the estimated treatment effects from the weighted model are somewhat less likely to be significantly different from zero." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Heterogeneous Treatment Effect with EconML \n", - "Finally, we want to learn whether we could get heterogeneous treatment effect insight using EconML, which could better tell us what kind of people are more or less responsive to this training program. We will start with using the unbiased experimental dataset, and then we are also interested to learn whether the observational dataset could recover the same findings from the \n", - "experiment.\n", - "\n", - "We train a `CasualForestDML` to learn non-parametric heterogeneous treatment effect by fitting a Casual Forest as the final stage model. EconML also supports interpretability tools such as `SingleTreeCateInterpreter` to further segment the users with different responsiveness to the treatment.\n", - "\n", - "## Experimental Data" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# experimental data male\n", - "X = None\n", - "W = df[econml_controls_male].values\n", - "# scale W\n", - "cols_to_scale=6\n", - "scaler = StandardScaler()\n", - "W = np.hstack([scaler.fit_transform(W[:, :cols_to_scale]).astype(np.float32), W[:, cols_to_scale:]]) \n", - "T = df[\"treated\"]\n", - "y = df[outcome_name_male]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "model_y=first_stage_reg(W, y)\n", - "model_t=first_stage_clf(W, T)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est=CausalForestDML(model_y=model_y,model_t=model_t,discrete_treatment=True, mc_iters=5,cv=5)\n", - "est.fit(y,T,X=W,W=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", - "# We interpret the CATE models behavior on the distribution of heterogeneity features\n", - "intrp.interpret(est, W)\n", - "# plot\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=econml_controls_male, fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Observational Control - CPS3" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "X = None\n", - "W = df_cps[econml_controls_male].values\n", - "# scale W\n", - "cols_to_scale=6\n", - "scaler = StandardScaler()\n", - "W = np.hstack([scaler.fit_transform(W[:, :cols_to_scale]).astype(np.float32), W[:, cols_to_scale:]]) \n", - "T = df_cps[\"treated\"]\n", - "y = df_cps[outcome_name_male]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "model_y=first_stage_reg(W, y)\n", - "model_t=first_stage_clf(W, T)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est=CausalForestDML(model_y=model_y,model_t=model_t,discrete_treatment=True, mc_iters=5,cv=5)\n", - "est.fit(y,T,X=W,W=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", - "# We interpret the CATE models behavior on the distribution of heterogeneity features\n", - "intrp.interpret(est, W)\n", - "# plot\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=econml_controls_male, fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the experimental tree plot we see this training program has higher positive effects for older workers with lower income before the training. Workers with relatively higher income in the pre-period experience a small and imprecise effect of training. While the exact splits are somewhat different in the observational sample, the main message is the same: the training is most effective for workers with lower pre-period earnings and, within that set, for older workers. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusion \n", - "\n", - "In this notebook, we have demonstrated the power of using EconML to:\n", - "\n", - "* Outperform the traditional OLS method on estimating ATE under some settings.\n", - "* Substantially improve performance when reweighting samples with analytical confidence intervals.\n", - "* Learn treatment effect heterogeneity and recover the same insight from using observational dataset.\n", - "\n", - "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/Deep IV Examples.ipynb b/notebooks/Deep IV Examples.ipynb deleted file mode 100644 index f45ca81e4..000000000 --- a/notebooks/Deep IV Examples.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deep IV: Use Case and Examples\n", - "\n", - "Deep IV uses deep neural networks in a two-stage instrumental variable (IV) estimation of causal effects, as described in [this ICML publication](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf) or in the `econml` [specification](https://econml.azurewebsites.net/spec/estimation/deepiv.html). In the EconML SDK, we have implemented Deep IV estimation on top of the Keras framework for building and training neural networks. In this notebook, we'll demonstrate how to use the SDK to apply Deep IV to synthetic data.\n", - "\n", - "### Data\n", - "\n", - "Deep IV works in settings where we have several different types of observations:\n", - "* Covariates, which we will denote with `X`\n", - "* Instruments, which we will denote with `Z`\n", - "* Treatments, which we will denote with `T`\n", - "* Responses, which we will denote with `Y`\n", - "\n", - "The main requirement is that `Z` is a set of valid instruments; in particular `Z` should affect the responses `Y` only through the treatments `T`. We assume that `Y` is an arbitrary function of `T` and `X`, plus an additive error term, and that `T` is an arbitrary function of `Z` and `X`. Deep IV then allows us to estimate `Y` given `T` and `X`.\n", - "\n", - "### Estimation\n", - "\n", - "To do this, the Deep IV estimator uses a two-stage approach that involves solving two subproblems:\n", - "1. It estimates the *distribution* of the treatment `T` given `Z` and `X`, using a mixture density network.\n", - "2. It estimates the dependence of the response `Y` on `T` and `X`.\n", - "\n", - "Both of these estimates are performed using neural networks. See the paper for a more complete description of the setup and estimation approach.\n", - "\n", - "### Using the SDK\n", - "\n", - "In the `econml` package, our Deep IV estimator is built on top of the Keras framework; we support either the Tensorflow or the Theano backends. There are three steps to using the `DeepIV`:\n", - "\n", - "1. Construct an instance. \n", - " * The `m` and `h` arguments to the initializer specify deep neural network models for estimating `T` and `Y` as described above. They are each *functions* that take two Keras inputs and return a Keras model (the inputs are `z` and `x` in the case of `m` and the output's shape should match `t`'s; the inputs are `t` and `x` in the case of `h` and the output's shape should match `y`'s). Note that the `h` function will be called multiple times, but should reuse the same weights - see below for a concrete example of how to achieve this using the Keras API.\n", - " * The `n_samples`, `use_upper_bound_loss`, and `n_gradient_samples` arguments together determine how the loss for the response model will be computed.\n", - " * If `use_upper_bound_loss` is `False` and `n_gradient_samples` is zero, then `n_samples` samples will be averaged to approximate the response - this will provide an unbiased estimate of the correct loss only in the limit as the number of samples goes to infinity.\n", - " * If `use_upper_bound_loss` is `False` and `n_gradient_samples` is nonzero, then we will average `n_samples` samples to approximate the response a first time and average `n_gradient_samples` samples to approximate it a second time - combining these allows us to provide an unbiased estimate of the true loss.\n", - " * If `use_upper_bound_loss` is `True`, then `n_gradient_samples` must be `0`; `n_samples` samples will be used to get an unbiased estimate of an upper bound of the true loss - this is equivalent to adding a regularization term penalizing the variance of the response model (see the `econml` specification linked above for a derivation of this fact).\n", - "2. Call `fit` with training samples of `Y`, `T`, `X`, and `Z`; this will train both sub-models.\n", - "3. Call `effect` or `predict` depending on what output you want. `effect` calculates the difference in outcomes based on the features and two different treatments, while `predict` predicts the outcome based on a single treatment.\n", - "\n", - "The remainder of this notebook will walk through a concete example." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from econml.iv.nnet import DeepIV\n", - "import keras\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Synthetic data\n", - "\n", - "To demonstrate the Deep IV approach, we'll construct a synthetic dataset obeying the requirements set out above. In this case, we'll take `X`, `Z`, `T` to come from the following distribution: " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n = 5000\n", - "\n", - "# Initialize exogenous variables; normal errors, uniformly distributed covariates and instruments\n", - "e = np.random.normal(size=(n,))\n", - "x = np.random.uniform(low=0.0, high=10.0, size=(n,))\n", - "z = np.random.uniform(low=0.0, high=10.0, size=(n,))\n", - "\n", - "# Initialize treatment variable\n", - "t = np.sqrt((x+2) * z) + e\n", - "\n", - "# Show the marginal distribution of t\n", - "plt.hist(t)\n", - "plt.xlabel(\"t\")\n", - "plt.show()\n", - "\n", - "plt.scatter(z[x < 1], t[x < 1], label='low X')\n", - "plt.scatter(z[(x > 4.5) * (x < 5.5)], t[(x > 4.5) * (x < 5.5)], label='moderate X')\n", - "plt.scatter(z[x > 9], t[x > 9], label='high X')\n", - "plt.legend()\n", - "plt.xlabel(\"z\")\n", - "plt.ylabel(\"t\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we'll imagine that `Z` and `X` are causally affecting `T`; as you can see in the plot above, low or high values of `Z` drive moderate values of `T` and moderate values of `Z` cause `T` to have a bi-modal distribution when `X` is high, but a unimodal distribution centered on 0 when `X` is low. The instrument is positively correlated with the treatment and treatments tend to be bigger at high values of x. The instrument has higher power at higher values of x " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Outcome equation \n", - "y = t*t / 10 - x*t / 10 + e\n", - "\n", - "# The endogeneity problem is clear, the latent error enters both treatment and outcome equally\n", - "plt.scatter(t,z, label ='raw data')\n", - "tticks = np.arange(-2,12)\n", - "yticks2 = tticks*tticks/10 - 0.2 * tticks\n", - "yticks5 = tticks*tticks/10 - 0.5 * tticks\n", - "yticks8 = tticks*tticks/10 - 0.8 * tticks\n", - "plt.plot(tticks,yticks2, 'r--', label = 'truth, x=2')\n", - "plt.plot(tticks,yticks5, 'g--', label = 'truth, x=5')\n", - "plt.plot(tticks,yticks8, 'y--', label = 'truth, x=8')\n", - "plt.xlabel(\"t\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Y` is a non-linear function of `T` and `X` with no direct dependence on `Z` plus additive noise (as required). We want to estimate the effect of particular `T` and `X` values on `Y`.\n", - "\n", - "The plot makes it clear that looking at the raw data is highly misleading as to the treatment effect. Moreover the treatment effects are both non-linear and heterogeneous in x, so this is a hard problem!\n", - "\n", - "## Defining the neural network models\n", - "\n", - "Now we'll define simple treatment and response models using the Keras `Sequential` model built up of a series of layers. Each model will have an `input_shape` of 2 (to match the sums of the dimensions of `X` plus `Z` in the treatment case and `T` plus `X` in the response case)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),\n", - " keras.layers.Dropout(0.17),\n", - " keras.layers.Dense(64, activation='relu'),\n", - " keras.layers.Dropout(0.17),\n", - " keras.layers.Dense(32, activation='relu'),\n", - " keras.layers.Dropout(0.17)])\n", - "\n", - "response_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),\n", - " keras.layers.Dropout(0.17),\n", - " keras.layers.Dense(64, activation='relu'),\n", - " keras.layers.Dropout(0.17),\n", - " keras.layers.Dense(32, activation='relu'),\n", - " keras.layers.Dropout(0.17),\n", - " keras.layers.Dense(1)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll instantiate the `DeepIV` class using these models. Defining the response model *outside* of the lambda passed into constructor is important, because (depending on the settings for the loss) it can be used multiple times in the second stage and we want the same weights to be used every time." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "keras_fit_options = { \"epochs\": 30,\n", - " \"validation_split\": 0.1,\n", - " \"callbacks\": [keras.callbacks.EarlyStopping(patience=2, restore_best_weights=True)]}\n", - "\n", - "deepIvEst = DeepIV(n_components = 10, # number of gaussians in our mixture density network\n", - " m = lambda z, x : treatment_model(keras.layers.concatenate([z,x])), # treatment model\n", - " h = lambda t, x : response_model(keras.layers.concatenate([t,x])), # response model\n", - " n_samples = 1, # number of samples to use to estimate the response\n", - " use_upper_bound_loss = False, # whether to use an approximation to the true loss\n", - " n_gradient_samples = 1, # number of samples to use in second estimate of the response (to make loss estimate unbiased)\n", - " optimizer='adam', # Keras optimizer to use for training - see https://keras.io/optimizers/ \n", - " first_stage_options = keras_fit_options, # options for training treatment model\n", - " second_stage_options = keras_fit_options) # options for training response model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting and predicting using the model\n", - "Now we can fit our model to the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 4500 samples, validate on 500 samples\n", - "Epoch 1/30\n", - "4500/4500 [==============================] - 2s 380us/step - loss: 1.6199 - val_loss: 0.8568\n", - "Epoch 2/30\n", - "4500/4500 [==============================] - 1s 116us/step - loss: 1.1357 - val_loss: 0.6315\n", - "Epoch 3/30\n", - "4500/4500 [==============================] - 1s 117us/step - loss: 0.9836 - val_loss: 0.7512\n", - "Epoch 4/30\n", - "4500/4500 [==============================] - 1s 118us/step - loss: 0.8963 - val_loss: 0.7189\n", - "Train on 4500 samples, validate on 500 samples\n", - "Epoch 1/30\n", - "4500/4500 [==============================] - 3s 774us/step - loss: 4.8558 - val_loss: 3.0255\n", - "Epoch 2/30\n", - "4500/4500 [==============================] - 1s 183us/step - loss: 5.1271 - val_loss: 2.9335\n", - "Epoch 3/30\n", - "4500/4500 [==============================] - 1s 187us/step - loss: 5.0416 - val_loss: 3.1960\n", - "Epoch 4/30\n", - "4500/4500 [==============================] - 1s 198us/step - loss: 5.3328 - val_loss: 3.0213\n" - ] - } - ], - "source": [ - "deepIvEst.fit(Y=y,T=t,X=x,Z=z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now we can create a new set of data and see whether our predicted effect matches the true effect `T*T-X*X`:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_test = 500\n", - "for i, x in enumerate([2, 5, 8]):\n", - " t = np.linspace(0,10,num = 100)\n", - " y_true = t*t / 10 - x*t/10\n", - " y_pred = deepIvEst.predict(t, np.full_like(t, x))\n", - " plt.plot(t, y_true, label='true y, x={0}'.format(x),color='C'+str(i))\n", - " plt.plot(t, y_pred, label='pred y, x={0}'.format(x),color='C'+str(i),ls='--')\n", - "plt.xlabel('t')\n", - "plt.ylabel('y')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that despite the fact that the response surface varies with x, our model was able to fit the data reasonably well. Where is does worst is where the instrument has the least power, which is in the low x case. There it fits a straight line rather than a quadratic, which suggests that the regularization at least is perfoming well. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Double Machine Learning Examples.ipynb b/notebooks/Double Machine Learning Examples.ipynb deleted file mode 100644 index 99d6f55f0..000000000 --- a/notebooks/Double Machine Learning Examples.ipynb +++ /dev/null @@ -1,2476 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double Machine Learning: Use Cases and Examples\n", - "\n", - "Double Machine Learning (DML) is an algorithm that applies arbitrary machine learning methods\n", - "to fit the treatment and response, then uses a linear model to predict the response residuals\n", - "from the treatment residuals.\n", - "\n", - "The EconML SDK implements the following DML classes:\n", - "* LinearDML: suitable for estimating heterogeneous treatment effects.\n", - "* SparseLinearDML: suitable for the case when $W$ is high dimensional vector and both the first stage and second stage estimate are linear.\n", - "\n", - "In ths notebook, we show the performance of the DML on both synthetic data and observational data.\n", - "\n", - "**Notebook contents:**\n", - "\n", - "1. Example usage with single continuous treatment synthetic data\n", - "2. Example usage with single binary treatment synthetic data\n", - "3. Example usage with multiple continuous treatment synthetic data\n", - "4. Example usage with single continuous treatment observational data\n", - "5. Example usage with multiple continuous treatment, multiple outcome observational data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import econml" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "## Ignore warnings\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Main imports\n", - "from econml.dml import DML, LinearDML, SparseLinearDML, CausalForestDML\n", - "\n", - "# Helper imports\n", - "import numpy as np\n", - "from itertools import product\n", - "from sklearn.linear_model import (Lasso, LassoCV, LogisticRegression,\n", - " LogisticRegressionCV,LinearRegression,\n", - " MultiTaskElasticNet,MultiTaskElasticNetCV)\n", - "from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Example Usage with Single Continuous Treatment Synthetic Data and Model Selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1. DGP \n", - "We use the data generating process (DGP) from [here](https://arxiv.org/abs/1806.03467). The DGP is described by the following equations:\n", - "\n", - "\\begin{align}\n", - "T =& \\langle W, \\beta\\rangle + \\eta, & \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y =& T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, &\\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim& \\text{Normal}(0,\\, I_{n_w})\\\\\n", - "X \\sim& \\text{Uniform}(0,1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders and $\\beta, \\gamma$ have high sparsity.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta(x) = \\exp(2\\cdot x_1).\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Treatment effect function\n", - "def exp_te(x):\n", - " return np.exp(2*x[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# DGP constants\n", - "np.random.seed(123)\n", - "n = 2000\n", - "n_w = 30\n", - "support_size = 5\n", - "n_x = 1\n", - "# Outcome support\n", - "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\n", - "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n", - "# Treatment support\n", - "support_T = support_Y\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n)\n", - "\n", - "# Generate controls, covariates, treatments and outcomes\n", - "W = np.random.normal(0, 1, size=(n, n_w))\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\n", - "# Heterogeneous treatment effects\n", - "TE = np.array([exp_te(x_i) for x_i in X])\n", - "T = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n", - "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n", - "\n", - "Y_train, Y_val, T_train, T_val, X_train, X_val, W_train, W_val = train_test_split(Y, T, X, W, test_size=.2)\n", - "# Generate test data\n", - "X_test = np.array(list(product(np.arange(0, 1, 0.01), repeat=n_x)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2. Train Estimator\n", - "We train models in three different ways, and compare their performance.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.2.1. Default Setting" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "est = LinearDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestRegressor(),\n", - " random_state=123)\n", - "est.fit(Y_train, T_train, X=X_train, W=W_train)\n", - "te_pred = est.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.2.2. Polynomial Features for Heterogeneity" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The number of features in the final model (< 5) is too small for a sparse model. We recommend using the LinearDML estimator for this low-dimensional setting.\n" - ] - } - ], - "source": [ - "est1 = SparseLinearDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestRegressor(),\n", - " featurizer=PolynomialFeatures(degree=3),\n", - " random_state=123)\n", - "est1.fit(Y_train, T_train, X=X_train, W=W_train)\n", - "te_pred1 = est1.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.2.3. Polynomial Features with regularization " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "est2 = DML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestRegressor(),\n", - " model_final=Lasso(alpha=0.1, fit_intercept=False),\n", - " featurizer=PolynomialFeatures(degree=10),\n", - " random_state=123)\n", - "est2.fit(Y_train, T_train, X=X_train, W=W_train)\n", - "te_pred2 = est2.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.2.4 Non-Parametric Heterogeneity with Causal Forest" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "est3 = CausalForestDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestRegressor(),\n", - " criterion='mse', n_estimators=1000,\n", - " min_impurity_decrease=0.001,\n", - " random_state=123)\n", - "est3.tune(Y_train, T_train, X=X_train, W=W_train)\n", - "est3.fit(Y_train, T_train, X=X_train, W=W_train)\n", - "te_pred3 = est3.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "plt.plot(X_test, te_pred, label='DML default')\n", - "plt.plot(X_test, te_pred1, label='DML polynomial degree=3')\n", - "plt.plot(X_test, te_pred2, label='DML polynomial degree=10 with Lasso')\n", - "plt.plot(X_test, te_pred3, label='ForestDML')\n", - "expected_te = np.array([exp_te(x_i) for x_i in X_test])\n", - "plt.plot(X_test, expected_te, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.4. Model selection\n", - "\n", - "For the three different models above, we can use score function to estimate the final model performance. The score is the MSE of the final stage Y residual, which can be seen as a proxy of the MSE of treatment effect." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'DML default': 1.815769478666336,\n", - " 'DML polynomial degree=3': 1.6913528143752934,\n", - " 'DML polynomial degree=10 with Lasso': 2.222578476206349,\n", - " 'ForestDML': 1.9002757666765648}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "score={}\n", - "score[\"DML default\"] = est.score(Y_val, T_val, X_val, W_val)\n", - "score[\"DML polynomial degree=3\"] = est1.score(Y_val, T_val, X_val, W_val)\n", - "score[\"DML polynomial degree=10 with Lasso\"] = est2.score(Y_val, T_val, X_val, W_val)\n", - "score[\"ForestDML\"] = est3.score(Y_val, T_val, X_val, W_val)\n", - "score" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best model selected by score: DML polynomial degree=3\n" - ] - } - ], - "source": [ - "print(\"best model selected by score: \",min(score,key=lambda x: score.get(x)))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'DML default': 0.3565984526892961,\n", - " 'DML polynomial degree=3': 0.257796113358552,\n", - " 'DML polynomial degree=10 with Lasso': 0.26248739267870214,\n", - " 'ForestDML': 0.42857317657348887}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mse_te={}\n", - "mse_te[\"DML default\"] = ((expected_te - te_pred)**2).mean()\n", - "mse_te[\"DML polynomial degree=3\"] = ((expected_te - te_pred1)**2).mean()\n", - "mse_te[\"DML polynomial degree=10 with Lasso\"] = ((expected_te - te_pred2)**2).mean()\n", - "mse_te[\"ForestDML\"] = ((expected_te - te_pred3)**2).mean()\n", - "mse_te" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best model selected by MSE of TE: DML polynomial degree=3\n" - ] - } - ], - "source": [ - "print(\"best model selected by MSE of TE: \", min(mse_te, key=lambda x: mse_te.get(x)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.5 Changing only Final Stage Model Specification and Refitting\n", - "\n", - "It is also feasible to change the parameters of the estimator and only fit the final stage model using the existing first stage residual calculation. To enable this feature, the `fit` method should be called with the flag `cache_values=True`, so that the original fit data and their corresponding first stage estimates be stored in the model. This can be done for any estimator in the dml module. We portray an example below, using the `DML` estimator." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Final model doesn't have a `coef_stderr_` and `intercept_stderr_` attributes, only point estimates will be available.\n", - "Final model doesn't have a `coef_stderr_` and `intercept_stderr_` attributes, only point estimates will be available.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate
X0 5.953
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CATE Intercept Results
point_estimate
cate_intercept 0.5


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - "Coefficient Results\n", - "=================\n", - " point_estimate\n", - "-----------------\n", - "X0 5.953\n", - " CATE Intercept Results \n", - "=============================\n", - " point_estimate\n", - "-----------------------------\n", - "cate_intercept 0.5\n", - "-----------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est = DML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestRegressor(),\n", - " model_final=Lasso(alpha=0.1, fit_intercept=False),\n", - " featurizer=PolynomialFeatures(degree=1, include_bias=False),\n", - " random_state=123)\n", - "est.fit(Y_train, T_train, X=X_train, W=W_train, cache_values=True)\n", - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 -0.054 0.685 -0.079 0.937 -1.18 1.072
X0^2 7.253 0.695 10.437 0.0 6.11 8.396
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 1.289 0.154 8.374 0.0 1.036 1.542


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "==========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "----------------------------------------------------------\n", - "X0 -0.054 0.685 -0.079 0.937 -1.18 1.072\n", - "X0^2 7.253 0.695 10.437 0.0 6.11 8.396\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 1.289 0.154 8.374 0.0 1.036 1.542\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import DebiasedLasso\n", - "est.featurizer = PolynomialFeatures(degree=2, include_bias=False)\n", - "est.model_final = DebiasedLasso(fit_intercept=False)\n", - "est.refit_final()\n", - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 -0.068 0.759 -0.089 0.929 -1.316 1.18
X0^2 7.26 0.744 9.761 0.0 6.037 8.484
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 1.29 0.159 8.116 0.0 1.029 1.552


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "==========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "----------------------------------------------------------\n", - "X0 -0.068 0.759 -0.089 0.929 -1.316 1.18\n", - "X0^2 7.26 0.744 9.761 0.0 6.037 8.484\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 1.29 0.159 8.116 0.0 1.029 1.552\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression\n", - "est.model_final = StatsModelsLinearRegression(fit_intercept=False)\n", - "est.refit_final()\n", - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CATE Intercept Results: No intercept was fitted!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 5.566 0.347 16.029 0.0 4.995 6.137
X0^2 2.243 0.451 4.975 0.0 1.501 2.984


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "==========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "----------------------------------------------------------\n", - "X0 5.566 0.347 16.029 0.0 4.995 6.137\n", - "X0^2 2.243 0.451 4.975 0.0 1.501 2.984\n", - "----------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import StatsModelsRLM\n", - "est.model_final = StatsModelsRLM(fit_intercept=False)\n", - "est.fit_cate_intercept = False\n", - "est.refit_final()\n", - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Final model doesn't have a `prediction_stderr` method, only point estimates will be returned.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point
0.697
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
0.398 0.075 1.319
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.model_final = Lasso(fit_intercept=False)\n", - "est.refit_final()\n", - "est.effect_inference(X).population_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Example Usage with Single Binary Treatment Synthetic Data and Confidence Intervals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1. DGP \n", - "We use the following DGP:\n", - "\n", - "\\begin{align}\n", - "T \\sim & \\text{Bernoulli}\\left(f(W)\\right), &\\; f(W)=\\sigma(\\langle W, \\beta\\rangle + \\eta), \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y = & T\\cdot \\theta(X) + \\langle W, \\gamma\\rangle + \\epsilon, & \\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim & \\text{Normal}(0,\\, I_{n_w}) & \\\\\n", - "X \\sim & \\text{Uniform}(0,\\, 1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders, $\\beta, \\gamma$ have high sparsity and $\\sigma$ is the sigmoid function.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta(x) = \\exp( 2\\cdot x_1 ).\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Treatment effect function\n", - "def exp_te(x):\n", - " return np.exp(2 * x[0])# DGP constants\n", - "\n", - "np.random.seed(123)\n", - "n = 1000\n", - "n_w = 30\n", - "support_size = 5\n", - "n_x = 4\n", - "# Outcome support\n", - "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\n", - "epsilon_sample = lambda n:np.random.uniform(-1, 1, size=n)\n", - "# Treatment support\n", - "support_T = support_Y\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n", - "\n", - "# Generate controls, covariates, treatments and outcomes\n", - "W = np.random.normal(0, 1, size=(n, n_w))\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\n", - "# Heterogeneous treatment effects\n", - "TE = np.array([exp_te(x_i) for x_i in X])\n", - "# Define treatment\n", - "log_odds = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n", - "T_sigmoid = 1/(1 + np.exp(-log_odds))\n", - "T = np.array([np.random.binomial(1, p) for p in T_sigmoid])\n", - "# Define the outcome\n", - "Y = TE * T + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n", - "\n", - "# get testing data\n", - "X_test = np.random.uniform(0, 1, size=(n, n_x))\n", - "X_test[:, 0] = np.linspace(0, 1, n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "est = LinearDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestClassifier(min_samples_leaf=10),\n", - " discrete_treatment=True,\n", - " linear_first_stages=False,\n", - " cv=6)\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred = est.effect(X_test)\n", - "lb, ub = est.effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "est2 = SparseLinearDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestClassifier(min_samples_leaf=10),\n", - " discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=2),\n", - " linear_first_stages=False,\n", - " cv=6)\n", - "est2.fit(Y, T, X=X, W=W)\n", - "te_pred2 = est2.effect(X_test)\n", - "lb2, ub2 = est2.effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "est3 = CausalForestDML(model_y=RandomForestRegressor(),\n", - " model_t=RandomForestClassifier(min_samples_leaf=10),\n", - " discrete_treatment=True,\n", - " n_estimators=1000,\n", - " min_impurity_decrease=0.001,\n", - " verbose=0,\n", - " cv=6)\n", - "est3.tune(Y, T, X=X, W=W)\n", - "est3.fit(Y, T, X=X, W=W)\n", - "te_pred3 = est3.effect(X_test)\n", - "lb3, ub3 = est3.effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.99117512, 0.00264324, 0.00244716, 0.00373448])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est3.feature_importances_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "expected_te=np.array([exp_te(x_i) for x_i in X_test])\n", - "plt.figure(figsize=(16,6))\n", - "plt.subplot(1, 3, 1)\n", - "plt.plot(X_test[:, 0], te_pred, label='LinearDML', alpha=.6)\n", - "plt.fill_between(X_test[:, 0], lb, ub, alpha=.4)\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.subplot(1, 3, 2)\n", - "plt.plot(X_test[:, 0], te_pred2, label='SparseLinearDML', alpha=.6)\n", - "plt.fill_between(X_test[:, 0], lb2, ub2, alpha=.4)\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.subplot(1, 3, 3)\n", - "plt.plot(X_test[:, 0], te_pred3, label='ForestDML', alpha=.6)\n", - "plt.fill_between(X_test[:, 0], lb3, ub3, alpha=.4)\n", - "plt.plot(X_test[:, 0], expected_te, 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Interpretability with SHAP Values\n", - "\n", - "Explain the hetergoeneity model for the constant marginal effect of the treatment using SHAP values." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import shap" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap_values = est.shap_values(X)\n", - "shap.plots.beeswarm(shap_values['Y0']['T0_1'])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap_values2 = est2.shap_values(X)\n", - "shap.plots.beeswarm(shap_values2['Y0']['T0_1'])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 98%|===================| 983/1000 [00:54<00:00] " - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap_values3 = est3.shap_values(X)\n", - "shap.plots.beeswarm(shap_values3['Y0']['T0_1'])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.5. Other Inferences\n", - "#### 2.5.1 Effect Inferences\n", - "Other than confidence interval, we could also output other statistical inferences of the effect include standard error, z-test score and p value given each sample $X[i]$." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
00.3720.1462.5400.0110.1310.613
10.4160.1632.5530.0110.1480.683
20.2910.1701.7090.0870.0110.571
30.4100.1502.7330.0060.1630.656
40.5840.1523.8340.0000.3330.834
50.5530.1393.9740.0000.3240.782
60.4300.1243.4720.0010.2260.634
70.6050.1543.9210.0000.3510.859
80.4390.1273.4710.0010.2310.647
90.4940.1623.0480.0020.2270.760
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 0.372 0.146 2.540 0.011 0.131 0.613\n", - "1 0.416 0.163 2.553 0.011 0.148 0.683\n", - "2 0.291 0.170 1.709 0.087 0.011 0.571\n", - "3 0.410 0.150 2.733 0.006 0.163 0.656\n", - "4 0.584 0.152 3.834 0.000 0.333 0.834\n", - "5 0.553 0.139 3.974 0.000 0.324 0.782\n", - "6 0.430 0.124 3.472 0.001 0.226 0.634\n", - "7 0.605 0.154 3.921 0.000 0.351 0.859\n", - "8 0.439 0.127 3.471 0.001 0.231 0.647\n", - "9 0.494 0.162 3.048 0.002 0.227 0.760" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.effect_inference(X_test[:10,]).summary_frame(alpha=0.1, value=0, decimals=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could also get the population inferences given sample $X$." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
3.368 0.061 54.777 0.0 3.267 3.469
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.729 0.683 6.074
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.73 0.675 6.067
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point_estimatestderrzstatpvalueci_lowerci_upper
X
X05.9690.22926.0530.0005.5926.346
X1-0.0600.216-0.2770.782-0.4150.295
X2-0.3630.219-1.6600.097-0.722-0.003
X30.2710.2121.2800.201-0.0770.620
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "X0 5.969 0.229 26.053 0.000 5.592 6.346\n", - "X1 -0.060 0.216 -0.277 0.782 -0.415 0.295\n", - "X2 -0.363 0.219 -1.660 0.097 -0.722 -0.003\n", - "X3 0.271 0.212 1.280 0.201 -0.077 0.620" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.coef__inference().summary_frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
cate_intercept0.4560.2182.090.0370.0970.815
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "cate_intercept 0.456 0.218 2.09 0.037 0.097 0.815" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.intercept__inference().summary_frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 5.969 0.229 26.053 0.0 5.592 6.346
X1 -0.06 0.216 -0.277 0.782 -0.415 0.295
X2 -0.363 0.219 -1.66 0.097 -0.722 -0.003
X3 0.271 0.212 1.28 0.201 -0.077 0.62
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.456 0.218 2.09 0.037 0.097 0.815


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 5.969 0.229 26.053 0.0 5.592 6.346\n", - "X1 -0.06 0.216 -0.277 0.782 -0.415 0.295\n", - "X2 -0.363 0.219 -1.66 0.097 -0.722 -0.003\n", - "X3 0.271 0.212 1.28 0.201 -0.077 0.62\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.456 0.218 2.09 0.037 0.097 0.815\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.5.3 Doubly Robust Average Treatment Effect Inference\n", - "\n", - "For the case of `CausalForestDML`, the estimator also fits a doubly robust average treatment effect at fit time. This inference result can be accessed as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3.53511397])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est3.ate_" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
ATE3.5350.07944.6710.03.4053.665
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "ATE 3.535 0.079 44.671 0.0 3.405 3.665" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est3.ate__inference().summary_frame()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Example Usage with Multiple Continuous Treatment Synthetic Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1. DGP \n", - "We use the data generating process (DGP) from [here](https://arxiv.org/abs/1806.03467), and modify the treatment to generate multiple treatments. The DGP is described by the following equations:\n", - "\n", - "\\begin{align}\n", - "T =& \\langle W, \\beta\\rangle + \\eta, & \\;\\eta \\sim \\text{Uniform}(-1, 1)\\\\\n", - "Y =& T\\cdot \\theta_{1}(X) + T^{2}\\cdot \\theta_{2}(X) + \\langle W, \\gamma\\rangle + \\epsilon, &\\; \\epsilon \\sim \\text{Uniform}(-1, 1)\\\\\n", - "W \\sim& \\text{Normal}(0,\\, I_{n_w})\\\\\n", - "X \\sim& \\text{Uniform}(0,1)^{n_x}\n", - "\\end{align}\n", - "\n", - "where $W$ is a matrix of high-dimensional confounders and $\\beta, \\gamma$ have high sparsity.\n", - "\n", - "For this DGP, \n", - "\\begin{align}\n", - "\\theta_{1}(x) = \\exp(2\\cdot x_1)\\\\\n", - "\\theta_{2}(x) = x_1^{2}\\\\\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# DGP constants\n", - "np.random.seed(123)\n", - "n = 6000\n", - "n_w = 30\n", - "support_size = 5\n", - "n_x = 5\n", - "# Outcome support\n", - "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n", - "coefs_Y = np.random.uniform(0, 1, size=support_size)\n", - "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n", - "# Treatment support\n", - "support_T = support_Y\n", - "coefs_T = np.random.uniform(0, 1, size=support_size)\n", - "eta_sample = lambda n: np.random.uniform(-1, 1, size=n)\n", - "\n", - "# Generate controls, covariates, treatments and outcomes\n", - "W = np.random.normal(0, 1, size=(n, n_w))\n", - "X = np.random.uniform(0, 1, size=(n, n_x))\n", - "# Heterogeneous treatment effects\n", - "TE1 = np.array([x_i[0] for x_i in X])\n", - "TE2 = np.array([x_i[0]**2 for x_i in X]).flatten()\n", - "T = np.dot(W[:, support_T], coefs_T) + eta_sample(n)\n", - "Y = TE1 * T + TE2 * T**2 + np.dot(W[:, support_Y], coefs_Y) + epsilon_sample(n)\n", - "# Generate test data\n", - "X_test = np.random.uniform(0, 1, size=(100, n_x))\n", - "X_test[:, 0] = np.linspace(0, 1, 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2. Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import GradientBoostingRegressor\n", - "from sklearn.multioutput import MultiOutputRegressor\n", - "est = LinearDML(model_y=GradientBoostingRegressor(n_estimators=100, max_depth=3, min_samples_leaf=20),\n", - " model_t=MultiOutputRegressor(GradientBoostingRegressor(n_estimators=100,\n", - " max_depth=3,\n", - " min_samples_leaf=20)),\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", - " linear_first_stages=False,\n", - " cv=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "T = T.reshape(-1,1)\n", - "est.fit(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "te_pred = est.const_marginal_effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "lb, ub = est.const_marginal_effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import GradientBoostingRegressor\n", - "from sklearn.multioutput import MultiOutputRegressor\n", - "est2 = CausalForestDML(model_y=GradientBoostingRegressor(n_estimators=100, max_depth=3, min_samples_leaf=20),\n", - " model_t=MultiOutputRegressor(GradientBoostingRegressor(n_estimators=100,\n", - " max_depth=3,\n", - " min_samples_leaf=20)),\n", - " cv=5,\n", - " criterion='mse', n_estimators=1000,\n", - " min_samples_leaf=10,\n", - " min_impurity_decrease=0.001,\n", - " random_state=123)\n", - "T = T.reshape(-1,1)\n", - "est2.tune(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)\n", - "est2.fit(Y, np.concatenate((T, T**2), axis=1), X=X, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "te_pred2 = est2.const_marginal_effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "lb2, ub2 = est2.const_marginal_effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"LinearDML with 2d-Poly Features\")\n", - "plt.plot(X_test[:, 0], te_pred[:, 0], label='DML estimate1')\n", - "plt.fill_between(X_test[:, 0], lb[:, 0], ub[:, 0], alpha=.4)\n", - "plt.plot(X_test[:, 0], te_pred[:, 1], label='DML estimate2')\n", - "plt.fill_between(X_test[:, 0], lb[:, 1], ub[:, 1], alpha=.4)\n", - "expected_te1 = np.array([x_i[0] for x_i in X_test])\n", - "expected_te2=np.array([x_i[0]**2 for x_i in X_test]).flatten()\n", - "plt.plot(X_test[:, 0], expected_te1, '--', label='True effect1')\n", - "plt.plot(X_test[:, 0], expected_te2, '--', label='True effect2')\n", - "plt.ylabel(\"Treatment Effect\")\n", - "plt.xlabel(\"x\")\n", - "plt.legend()\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"CausalForestDML\")\n", - "plt.plot(X_test[:, 0], te_pred2[:, 0], label='DML estimate1')\n", - "plt.fill_between(X_test[:, 0], lb2[:, 0], ub2[:, 0], alpha=.4)\n", - "plt.plot(X_test[:, 0], te_pred2[:, 1], label='DML estimate2')\n", - "plt.fill_between(X_test[:, 0], lb2[:, 1], ub2[:, 1], alpha=.4)\n", - "expected_te1 = np.array([x_i[0] for x_i in X_test])\n", - "expected_te2=np.array([x_i[0]**2 for x_i in X_test]).flatten()\n", - "plt.plot(X_test[:, 0], expected_te1, '--', label='True effect1')\n", - "plt.plot(X_test[:, 0], expected_te2, '--', label='True effect2')\n", - "plt.ylabel(\"Treatment Effect\")\n", - "plt.xlabel(\"x\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4 Tree Interpreter\n", - "\n", - "Interpreting heterogeneity via a tree based rule." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.cate_interpreter import SingleTreeCateInterpreter\n", - "\n", - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2)\n", - "intrp.interpret(est, X)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "intrp.plot()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Example Usage with Single Continuous Treatment Observational Data\n", - "\n", - "We applied our technique to Dominick’s dataset, a popular historical dataset of store-level orange juice prices and sales provided by University of Chicago Booth School of Business. \n", - "\n", - "The dataset is comprised of a large number of covariates $W$, but researchers might only be interested in learning the elasticity of demand as a function of a few variables $x$ such\n", - "as income or education. \n", - "\n", - "We applied the `LinearDML` to estimate orange juice price elasticity\n", - "as a function of income, and our results, unveil the natural phenomenon that lower income consumers are more price-sensitive." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.1. Data" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# A few more imports\n", - "import os\n", - "import pandas as pd\n", - "import urllib.request\n", - "from sklearn.preprocessing import StandardScaler" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the data\n", - "file_name = \"oj_large.csv\"\n", - "\n", - "if not os.path.isfile(file_name):\n", - " print(\"Downloading file (this might take a few seconds)...\")\n", - " urllib.request.urlretrieve(\"https://msalicedatapublic.blob.core.windows.net/datasets/OrangeJuice/oj_large.csv\", file_name)\n", - "oj_data = pd.read_csv(file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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storebrandweeklogmovefeatpriceAGE60EDUCETHNICINCOMEHHLARGEWORKWOMHVAL150SSTRDISTSSTRVOLCPDIST5CPWVOL5
02tropicana409.01869503.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
12tropicana468.72323103.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
22tropicana478.25322803.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
32tropicana488.98719703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
42tropicana509.09335703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
\n", - "
" - ], - "text/plain": [ - " store brand week logmove feat price AGE60 EDUC ETHNIC \\\n", - "0 2 tropicana 40 9.018695 0 3.87 0.232865 0.248935 0.11428 \n", - "1 2 tropicana 46 8.723231 0 3.87 0.232865 0.248935 0.11428 \n", - "2 2 tropicana 47 8.253228 0 3.87 0.232865 0.248935 0.11428 \n", - "3 2 tropicana 48 8.987197 0 3.87 0.232865 0.248935 0.11428 \n", - "4 2 tropicana 50 9.093357 0 3.87 0.232865 0.248935 0.11428 \n", - "\n", - " INCOME HHLARGE WORKWOM HVAL150 SSTRDIST SSTRVOL CPDIST5 \\\n", - "0 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "1 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "2 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "3 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "4 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", - "\n", - " CPWVOL5 \n", - "0 0.376927 \n", - "1 0.376927 \n", - "2 0.376927 \n", - "3 0.376927 \n", - "4 0.376927 " - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oj_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare data\n", - "Y = oj_data['logmove'].values\n", - "T = np.log(oj_data[\"price\"]).values\n", - "scaler = StandardScaler()\n", - "W1 = scaler.fit_transform(oj_data[[c for c in oj_data.columns if c not in ['price', 'logmove', 'brand', 'week', 'store','INCOME']]].values)\n", - "W2 = pd.get_dummies(oj_data[['brand']]).values\n", - "W = np.concatenate([W1, W2], axis=1)\n", - "X=scaler.fit_transform(oj_data[['INCOME']].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "## Generate test data\n", - "min_income = -1\n", - "max_income = 1\n", - "delta = (1 - (-1)) / 100\n", - "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1,1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2. Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "est = LinearDML(model_y=RandomForestRegressor(),model_t=RandomForestRegressor())\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred=est.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot Oranje Juice elasticity as a function of income\n", - "plt.figure(figsize=(10,6))\n", - "plt.plot(X_test, te_pred, label=\"OJ Elasticity\")\n", - "plt.xlabel(r'Scale(Income)')\n", - "plt.ylabel('Orange Juice Elasticity')\n", - "plt.legend()\n", - "plt.title(\"Orange Juice Elasticity vs Income\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4. Confidence Intervals\n", - "\n", - "We can also get confidence intervals around our predictions by passing an additional `inference` argument to `fit`. All estimators support bootstrap intervals, which involves refitting the same estimator repeatedly on subsamples of the original data, but `LinearDML` also supports a more efficient approach which can be achieved by leaving inference set to the default of `'auto'` or by explicitly passing `inference='statsmodels'`." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "est.fit(Y, T, X=X, W=W)\n", - "te_pred=est.effect(X_test)\n", - "te_pred_interval = est.const_marginal_effect_interval(X_test, alpha=0.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot Oranje Juice elasticity as a function of income\n", - "plt.figure(figsize=(10,6))\n", - "plt.plot(X_test.flatten(), te_pred, label=\"OJ Elasticity\")\n", - "plt.fill_between(X_test.flatten(), te_pred_interval[0], te_pred_interval[1], alpha=.5, label=\"1-99% CI\")\n", - "plt.xlabel(r'Scale(Income)')\n", - "plt.ylabel('Orange Juice Elasticity')\n", - "plt.title(\"Orange Juice Elasticity vs Income\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Example Usage with Multiple Continuous Treatment, Multiple Outcome Observational Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the same data, but in this case, we want to fit the demand of multiple brand as a function of the price of each one of them, i.e. fit the matrix of cross price elasticities. It can be done, by simply setting as $Y$ to be the vector of demands and $T$ to be the vector of prices. Then we can obtain the matrix of cross price elasticities.\n", - "\n", - "\\begin{align}\n", - "Y=[Logmove_{tropicana},Logmove_{minute.maid},Logmove_{dominicks}] \\\\\n", - "T=[Logprice_{tropicana},Logprice_{minute.maid},Logprice_{dominicks}] \\\\\n", - "\\end{align}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.1. Data" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the data\n", - "oj_data = pd.read_csv(file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare data\n", - "oj_data['price'] = np.log(oj_data[\"price\"])\n", - "# Transform dataset. \n", - "# For each store in each week, get a vector of logmove and a vector of logprice for each brand. \n", - "# Other features are store specific, will be the same for all brands.\n", - "groupbylist = [\"store\", \"week\", \"AGE60\", \"EDUC\", \"ETHNIC\", \"INCOME\",\n", - " \"HHLARGE\", \"WORKWOM\", \"HVAL150\",\n", - " \"SSTRDIST\", \"SSTRVOL\", \"CPDIST5\", \"CPWVOL5\"]\n", - "oj_data1 = pd.pivot_table(oj_data,index=groupbylist,\n", - " columns=oj_data.groupby(groupbylist).cumcount(),\n", - " values=['logmove', 'price'],\n", - " aggfunc='sum').reset_index()\n", - "oj_data1.columns = oj_data1.columns.map('{0[0]}{0[1]}'.format) \n", - "oj_data1 = oj_data1.rename(index=str,\n", - " columns={\"logmove0\": \"logmove_T\",\n", - " \"logmove1\": \"logmove_M\",\n", - " \"logmove2\":\"logmove_D\",\n", - " \"price0\":\"price_T\",\n", - " \"price1\":\"price_M\",\n", - " \"price2\":\"price_D\"})\n", - "\n", - "# Define Y,T,X,W\n", - "Y = oj_data1[['logmove_T', \"logmove_M\", \"logmove_D\"]].values\n", - "T = oj_data1[['price_T', \"price_M\", \"price_D\"]].values\n", - "scaler = StandardScaler()\n", - "W=scaler.fit_transform(oj_data1[[c for c in groupbylist if c not in ['week', 'store', 'INCOME']]].values)\n", - "X=scaler.fit_transform(oj_data1[['INCOME']].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "## Generate test data\n", - "min_income = -1\n", - "max_income = 1\n", - "delta = (1 - (-1)) / 100\n", - "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2. Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "est = LinearDML(model_y=MultiTaskElasticNetCV(cv=3, tol=1, selection='random'),\n", - " model_t=MultiTaskElasticNetCV(cv=3),\n", - " featurizer=PolynomialFeatures(1),\n", - " linear_first_stages=True)\n", - "est.fit(Y, T, X=X, W=W)\n", - "te_pred = est.const_marginal_effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.3. Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot Oranje Juice elasticity as a function of income\n", - "plt.figure(figsize=(18, 10))\n", - "dic={0:\"Tropicana\", 1:\"Minute.maid\", 2:\"Dominicks\"}\n", - "for i in range(3):\n", - " for j in range(3):\n", - " plt.subplot(3, 3, 3 * i + j + 1)\n", - " plt.plot(X_test, te_pred[:, i, j],\n", - " color=\"C{}\".format(str(3 * i + j)),\n", - " label=\"OJ Elasticity {} to {}\".format(dic[j], dic[i]))\n", - " plt.xlabel(r'Scale(Income)')\n", - " plt.ylabel('Orange Juice Elasticity')\n", - " plt.legend()\n", - "plt.suptitle(\"Orange Juice Elasticity vs Income\", fontsize=16)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Findings**: Look at the diagonal of the matrix, the TE of OJ prices are always negative to the sales across all the brand, but people with higher income are less price-sensitive. By contrast, for the non-diagonal of the matrix, the TE of prices for other brands are always positive to the sales for that brand, the TE is affected by income in different ways for different competitors. In addition, compare to previous plot, the negative TE of OJ prices for each brand are all larger than the TE considering all brand together, which means we would have underestimated the effect of price changes on demand. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.4. Confidence Intervals" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "te_pred_interval = est.const_marginal_effect_interval(X_test, alpha=0.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot Oranje Juice elasticity as a function of income\n", - "plt.figure(figsize=(18, 10))\n", - "dic={0:\"Tropicana\", 1:\"Minute.maid\", 2:\"Dominicks\"}\n", - "for i in range(3):\n", - " for j in range(3):\n", - " plt.subplot(3, 3, 3 * i + j + 1)\n", - " plt.plot(X_test, te_pred[:, i, j],\n", - " color=\"C{}\".format(str(3 * i + j)),\n", - " label=\"OJ Elasticity {} to {}\".format(dic[j], dic[i]))\n", - " plt.fill_between(X_test.flatten(), te_pred_interval[0][:, i, j],te_pred_interval[1][:, i,j], color=\"C{}\".format(str(3*i+j)),alpha=.5, label=\"1-99% CI\")\n", - " plt.xlabel(r'Scale(Income)')\n", - " plt.ylabel('Orange Juice Elasticity')\n", - " plt.legend()\n", - "plt.suptitle(\"Orange Juice Elasticity vs Income\",fontsize=16)\n", - "plt.show()" - ] - } - ], - "metadata": { - "file_extension": ".py", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Dynamic Double Machine Learning Examples.ipynb b/notebooks/Dynamic Double Machine Learning Examples.ipynb deleted file mode 100755 index d0fdebfb0..000000000 --- a/notebooks/Dynamic Double Machine Learning Examples.ipynb +++ /dev/null @@ -1,778 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Dynamic Double Machine Learning: Use Cases and Examples\n", - "\n", - "Dynamic DoubleML is an extension of the Double ML approach for treatments assigned sequentially over time periods. This estimator will account for treatments that can have causal effects on future outcomes. For more details, see [this paper](https://arxiv.org/abs/2002.07285) or the [EconML docummentation](https://econml.azurewebsites.net/).\n", - "\n", - "For example, the Dynamic DoubleML could be useful in estimating the following causal effects:\n", - "* the effect of investments on revenue at companies that receive investments at regular intervals ([see more](https://arxiv.org/abs/2103.08390))\n", - "* the effect of prices on demand in stores where prices of goods change over time\n", - "* the effect of income on health outcomes in people who receive yearly income\n", - "\n", - "The preferred data format is balanced panel data. Each panel corresponds to one entity (e.g. company, store or person) and the different rows in a panel correspond to different time points. Example:\n", - "\n", - "||Company|Year|Features|Investment|Revenue|\n", - "|---|---|---|---|---|---|\n", - "|1|A|2018|...|\\$1,000|\\$10,000|\n", - "|2|A|2019|...|\\$2,000|\\$12,000|\n", - "|3|A|2020|...|\\$3,000|\\$15,000|\n", - "|4|B|2018|...|\\$0|\\$5,000|\n", - "|5|B|2019|...|\\$100|\\$10,000|\n", - "|6|B|2020|...|\\$1,200|\\$7,000|\n", - "|7|C|2018|...|\\$1,000|\\$20,000|\n", - "|8|C|2019|...|\\$1,500|\\$25,000|\n", - "|9|C|2020|...|\\$500|\\$15,000|\n", - "\n", - "(Note: when passing the data to the DynamicDML estimator, the \"Company\" column above corresponds to the `groups` argument at fit time. The \"Year\" column above should not be passed in as it will be inferred from the \"Company\" column)\n", - "\n", - "If group memebers do not appear together, it is assumed that the first instance of a group in the dataset corresponds to the first period of that group, the second instance of the group corresponds to the second period, etc. Example:\n", - "\n", - "||Company|Features|Investment|Revenue|\n", - "|---|---|---|---|---|\n", - "|1|A|...|\\$1,000|\\$10,000|\n", - "|2|B|...|\\$0|\\$5,000\n", - "|3|C|...|\\$1,000|\\$20,000|\n", - "|4|A|...|\\$2,000|\\$12,000|\n", - "|5|B|...|\\$100|\\$10,000|\n", - "|6|C|...|\\$1,500|\\$25,000|\n", - "|7|A|...|\\$3,000|\\$15,000|\n", - "|8|B|...|\\$1,200|\\$7,000|\n", - "|9|C|...|\\$500|\\$15,000|\n", - "\n", - "In this dataset, 1st row corresponds to the first period of group `A`, 4th row corresponds to the second period of group `A`, etc.\n", - "\n", - "In this notebook, we show the performance of the DynamicDML on synthetic and observational data. \n", - "\n", - "## Notebook Contents\n", - "\n", - "1. [Example Usage with Average Treatment Effects](#1.-Example-Usage-with-Average-Treatment-Effects)\n", - "2. [Example Usage with Heterogeneous Treatment Effects](#2.-Example-Usage-with-Heterogeneous-Treatment-Effects)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import econml" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Main imports\n", - "from econml.dynamic.dml import DynamicDML\n", - "from econml.tests.dgp import DynamicPanelDGP, add_vlines\n", - "\n", - "# Helper imports\n", - "import numpy as np\n", - "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV, MultiTaskLassoCV\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Example Usage with Average Treatment Effects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.1 DGP\n", - "\n", - "We consider a data generating process from a markovian treatment model. \n", - "\n", - "In the example bellow, $T_t\\rightarrow$ treatment(s) at time $t$, $Y_t\\rightarrow$outcome at time $t$, $X_t\\rightarrow$ features and controls at time $t$ (the coefficients $e, f$ will pick the features and the controls).\n", - "\\begin{align}\n", - " X_t =& (\\pi'X_{t-1} + 1) \\cdot A\\, T_{t-1} + B X_{t-1} + \\epsilon_t\\\\\n", - " T_t =& \\gamma\\, T_{t-1} + (1-\\gamma) \\cdot D X_t + \\zeta_t\\\\\n", - " Y_t =& (\\sigma' X_{t} + 1) \\cdot e\\, T_{t} + f X_t + \\eta_t\n", - "\\end{align}\n", - "\n", - "with $X_0, T_0 = 0$ and $\\epsilon_t, \\zeta_t, \\eta_t \\sim N(0, \\sigma^2)$. Moreover, $X_t \\in R^{n_x}$, $B[:, 0:s_x] \\neq 0$ and $B[:, s_x:-1] = 0$, $\\gamma\\in [0, 1]$, $D[:, 0:s_x] \\neq 0$, $D[:, s_x:-1]=0$, $f[0:s_x]\\neq 0$, $f[s_x:-1]=0$. We draw a single time series of samples of length $n\\_panels \\cdot n\\_periods$." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Define DGP parameters\n", - "np.random.seed(123)\n", - "n_panels = 5000 # number of panels\n", - "n_periods = 3 # number of time periods in each panel\n", - "n_treatments = 2 # number of treatments in each period\n", - "n_x = 100 # number of features + controls\n", - "s_x = 10 # number of controls (endogeneous variables)\n", - "s_t = 10 # treatment support size" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "dgp = DynamicPanelDGP(n_periods, n_treatments, n_x).create_instance(\n", - " s_x, random_seed=12345)\n", - "Y, T, X, W, groups = dgp.observational_data(n_panels, s_t=s_t, random_seed=12345)\n", - "true_effect = dgp.true_effect" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.2 Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "est = DynamicDML(\n", - " model_y=LassoCV(cv=3, max_iter=1000), \n", - " model_t=MultiTaskLassoCV(cv=3, max_iter=1000), \n", - " cv=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(Y, T, X=None, W=W, groups=groups)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average effect of default policy: 1.40\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "A scalar was specified but there are multiple treatments; the same value will be used for each treatment. Consider specifyingall treatments, or using the const_marginal_effect method.\n" - ] - } - ], - "source": [ - "# Average treatment effect of all periods on last period for unit treatments\n", - "print(f\"Average effect of default policy: {est.ate():0.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effect of target policy over baseline policy: 1.40\n" - ] - } - ], - "source": [ - "# Effect of target policy over baseline policy\n", - "# Must specify a treatment for each period\n", - "baseline_policy = np.zeros((1, n_periods * n_treatments))\n", - "target_policy = np.ones((1, n_periods * n_treatments))\n", - "eff = est.effect(T0=baseline_policy, T1=target_policy)\n", - "print(f\"Effect of target policy over baseline policy: {eff[0]:0.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Marginal effect of a treatments in period 1 on period 3 outcome: [0.04000235 0.0701606 ]\n", - "Marginal effect of a treatments in period 2 on period 3 outcome: [0.31611764 0.23714736]\n", - "Marginal effect of a treatments in period 3 on period 3 outcome: [0.13108411 0.60656886]\n" - ] - } - ], - "source": [ - "# Period treatment effects + interpretation\n", - "for i, theta in enumerate(est.intercept_.reshape(-1, n_treatments)):\n", - " print(f\"Marginal effect of a treatments in period {i+1} on period {n_periods} outcome: {theta}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|(T0)$_0$ 0.04 0.041 0.977 0.328 -0.027 0.107
cate_intercept|(T1)$_0$ 0.07 0.04 1.74 0.082 0.004 0.136
cate_intercept|(T0)$_1$ 0.316 0.036 8.848 0.0 0.257 0.375
cate_intercept|(T1)$_1$ 0.237 0.036 6.608 0.0 0.178 0.296
cate_intercept|(T0)$_2$ 0.131 0.003 45.665 0.0 0.126 0.136
cate_intercept|(T1)$_2$ 0.607 0.003 210.244 0.0 0.602 0.611


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "==============================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "------------------------------------------------------------------------------\n", - "cate_intercept|(T0)$_0$ 0.04 0.041 0.977 0.328 -0.027 0.107\n", - "cate_intercept|(T1)$_0$ 0.07 0.04 1.74 0.082 0.004 0.136\n", - "cate_intercept|(T0)$_1$ 0.316 0.036 8.848 0.0 0.257 0.375\n", - "cate_intercept|(T1)$_1$ 0.237 0.036 6.608 0.0 0.178 0.296\n", - "cate_intercept|(T0)$_2$ 0.131 0.003 45.665 0.0 0.126 0.136\n", - "cate_intercept|(T1)$_2$ 0.607 0.003 210.244 0.0 0.602 0.611\n", - "------------------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Period treatment effects with confidence intervals\n", - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "conf_ints = est.intercept__interval(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.3 Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Some plotting boilerplate code\n", - "plt.figure(figsize=(15, 5))\n", - "plt.errorbar(np.arange(n_periods*n_treatments)-.04, est.intercept_, yerr=(conf_ints[1] - est.intercept_,\n", - " est.intercept_ - conf_ints[0]), fmt='o', label='DynamicDML')\n", - "plt.errorbar(np.arange(n_periods*n_treatments), true_effect.flatten(), fmt='o', alpha=.6, label='Ground truth')\n", - "for t in np.arange(1, n_periods):\n", - " plt.axvline(x=t * n_treatments - .5, linestyle='--', alpha=.4)\n", - "plt.xticks([t * n_treatments - .5 + n_treatments/2 for t in range(n_periods)],\n", - " [\"$\\\\theta_{}$\".format(t) for t in range(n_periods)])\n", - "plt.gca().set_xlim([-.5, n_periods*n_treatments - .5])\n", - "plt.ylabel(\"Effect\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Example Usage with Heterogeneous Treatment Effects on Time-Invariant Unit Characteristics\n", - "\n", - "We can also estimate treatment effect heterogeneity with respect to the value of some subset of features $X$ in the initial period. Heterogeneity is currently only supported with respect to such initial state features. This for instance can support heterogeneity with respect to time-invariant unit characteristics. In that case you can simply pass as $X$ a repetition of some unit features that stay constant in all periods. You can also pass time-varying features, and their time varying component will be used as a time-varying control. However, heterogeneity will only be estimated with respect to the initial state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 DGP" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Define additional DGP parameters\n", - "het_strength = .5\n", - "het_inds = np.arange(n_x - n_treatments, n_x)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate data\n", - "dgp = DynamicPanelDGP(n_periods, n_treatments, n_x).create_instance(\n", - " s_x, hetero_strength=het_strength, hetero_inds=het_inds, random_seed=12)\n", - "Y, T, X, W, groups = dgp.observational_data(n_panels, s_t=s_t, random_seed=1)\n", - "ate_effect = dgp.true_effect\n", - "het_effect = dgp.true_hetero_effect[:, het_inds + 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Train Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "est = DynamicDML(\n", - " model_y=LassoCV(cv=3), \n", - " model_t=MultiTaskLassoCV(cv=3), \n", - " cv=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(Y, T, X=X, W=W, groups=groups, inference=\"auto\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0|(T0)$_0$ 0.009 0.045 0.203 0.839 -0.065 0.083
X0|(T1)$_0$ 0.017 0.042 0.416 0.677 -0.051 0.086
X0|(T0)$_1$ -0.001 0.041 -0.035 0.972 -0.069 0.067
X0|(T1)$_1$ -0.031 0.041 -0.76 0.447 -0.099 0.036
X0|(T0)$_2$ -0.306 0.008 -36.667 0.0 -0.32 -0.292
X0|(T1)$_2$ 0.158 0.008 19.656 0.0 0.145 0.171
X1|(T0)$_0$ 0.017 0.044 0.378 0.706 -0.056 0.09
X1|(T1)$_0$ -0.007 0.045 -0.164 0.87 -0.082 0.067
X1|(T0)$_1$ -0.034 0.042 -0.821 0.412 -0.103 0.034
X1|(T1)$_1$ -0.025 0.042 -0.6 0.549 -0.095 0.044
X1|(T0)$_2$ -0.302 0.008 -35.72 0.0 -0.316 -0.288
X1|(T1)$_2$ 0.156 0.008 18.801 0.0 0.142 0.169
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept|(T0)$_0$ 0.024 0.036 0.653 0.513 -0.036 0.084
cate_intercept|(T1)$_0$ -0.033 0.036 -0.929 0.353 -0.092 0.025
cate_intercept|(T0)$_1$ -0.105 0.034 -3.067 0.002 -0.162 -0.049
cate_intercept|(T1)$_1$ 0.037 0.034 1.079 0.281 -0.019 0.093
cate_intercept|(T0)$_2$ -0.743 0.005 -140.503 0.0 -0.752 -0.734
cate_intercept|(T1)$_2$ 0.48 0.005 91.061 0.0 0.472 0.489


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "==================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "------------------------------------------------------------------\n", - "X0|(T0)$_0$ 0.009 0.045 0.203 0.839 -0.065 0.083\n", - "X0|(T1)$_0$ 0.017 0.042 0.416 0.677 -0.051 0.086\n", - "X0|(T0)$_1$ -0.001 0.041 -0.035 0.972 -0.069 0.067\n", - "X0|(T1)$_1$ -0.031 0.041 -0.76 0.447 -0.099 0.036\n", - "X0|(T0)$_2$ -0.306 0.008 -36.667 0.0 -0.32 -0.292\n", - "X0|(T1)$_2$ 0.158 0.008 19.656 0.0 0.145 0.171\n", - "X1|(T0)$_0$ 0.017 0.044 0.378 0.706 -0.056 0.09\n", - "X1|(T1)$_0$ -0.007 0.045 -0.164 0.87 -0.082 0.067\n", - "X1|(T0)$_1$ -0.034 0.042 -0.821 0.412 -0.103 0.034\n", - "X1|(T1)$_1$ -0.025 0.042 -0.6 0.549 -0.095 0.044\n", - "X1|(T0)$_2$ -0.302 0.008 -35.72 0.0 -0.316 -0.288\n", - "X1|(T1)$_2$ 0.156 0.008 18.801 0.0 0.142 0.169\n", - " CATE Intercept Results \n", - "===============================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------------------\n", - "cate_intercept|(T0)$_0$ 0.024 0.036 0.653 0.513 -0.036 0.084\n", - "cate_intercept|(T1)$_0$ -0.033 0.036 -0.929 0.353 -0.092 0.025\n", - "cate_intercept|(T0)$_1$ -0.105 0.034 -3.067 0.002 -0.162 -0.049\n", - "cate_intercept|(T1)$_1$ 0.037 0.034 1.079 0.281 -0.019 0.093\n", - "cate_intercept|(T0)$_2$ -0.743 0.005 -140.503 0.0 -0.752 -0.734\n", - "cate_intercept|(T1)$_2$ 0.48 0.005 91.061 0.0 0.472 0.489\n", - "-------------------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average effect of default policy:-0.42\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "A scalar was specified but there are multiple treatments; the same value will be used for each treatment. Consider specifyingall treatments, or using the const_marginal_effect method.\n", - "A scalar was specified but there are multiple treatments; the same value will be used for each treatment. Consider specifyingall treatments, or using the const_marginal_effect method.\n" - ] - } - ], - "source": [ - "# Average treatment effect for test points\n", - "X_test = X[np.arange(0, 25, 3)]\n", - "print(f\"Average effect of default policy:{est.ate(X=X_test):0.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effect of target policy over baseline policy for test set:\n", - " [-0.37368525 -0.30896804 -0.43030363 -0.52252401 -0.42849622 -0.48790877\n", - " -0.34417987 -0.51804937 -0.36806744]\n" - ] - } - ], - "source": [ - "# Effect of target policy over baseline policy\n", - "# Must specify a treatment for each period\n", - "baseline_policy = np.zeros((1, n_periods * n_treatments))\n", - "target_policy = np.ones((1, n_periods * n_treatments))\n", - "eff = est.effect(X=X_test, T0=baseline_policy, T1=target_policy)\n", - "print(\"Effect of target policy over baseline policy for test set:\\n\", eff)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 0.02374269, -0.03302781, -0.10526464, 0.03675719, -0.74294675,\n", - " 0.48025068]),\n", - " array([[ 0.00914226, 0.01675409],\n", - " [ 0.01732804, -0.00741467],\n", - " [-0.00143705, -0.03431712],\n", - " [-0.03136295, -0.02536834],\n", - " [-0.30581311, -0.30189654],\n", - " [ 0.15773252, 0.15564665]]))" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Coefficients: intercept is of shape n_treatments*n_periods\n", - "# coef_ is of shape (n_treatments*n_periods, n_hetero_inds).\n", - "# first n_treatment rows are from first period, next n_treatment\n", - "# from second period, etc.\n", - "est.intercept_, est.coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Confidence intervals\n", - "conf_ints_intercept = est.intercept__interval(alpha=0.05)\n", - "conf_ints_coef = est.coef__interval(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.3 Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# parse true parameters in array of shape (n_treatments*n_periods, 1 + n_hetero_inds)\n", - "# first column is the intercept\n", - "true_effect_inds = []\n", - "for t in range(n_treatments):\n", - " true_effect_inds += [t * (1 + n_x)] + (list(t * (1 + n_x) + 1 + het_inds) if len(het_inds)>0 else [])\n", - "true_effect_params = dgp.true_hetero_effect[:, true_effect_inds]\n", - "true_effect_params = true_effect_params.reshape((n_treatments*n_periods, 1 + het_inds.shape[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# concatenating intercept and coef_\n", - "param_hat = np.hstack([est.intercept_.reshape(-1, 1), est.coef_])\n", - "lower = np.hstack([conf_ints_intercept[0].reshape(-1, 1), conf_ints_coef[0]])\n", - "upper = np.hstack([conf_ints_intercept[1].reshape(-1, 1), conf_ints_coef[1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.errorbar(np.arange(n_periods * (len(het_inds) + 1) * n_treatments),\n", - " true_effect_params.flatten(), fmt='*', label='Ground Truth')\n", - "plt.errorbar(np.arange(n_periods * (len(het_inds) + 1) * n_treatments),\n", - " param_hat.flatten(), yerr=((upper - param_hat).flatten(),\n", - " (param_hat - lower).flatten()), fmt='o', label='DynamicDML')\n", - "add_vlines(n_periods, n_treatments, het_inds)\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/ForestLearners Basic Example.ipynb b/notebooks/ForestLearners Basic Example.ipynb deleted file mode 100644 index d9fabee20..000000000 --- a/notebooks/ForestLearners Basic Example.ipynb +++ /dev/null @@ -1,939 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ForestDML, ForestDRLearner, OrthoForest and CausalForest: Basic Example\n", - "\n", - "We depict the performance of our `ForestDML`, `ForestDRLearner`, `OrthoForest` and `CausalForest` estimators on the same data generating process as the one used in the tutorial page of the grf package (see https://github.com/grf-labs/grf#usage-examples). This is mostly for qualitative comparison and verification purposes among our implementation of variants of Causal Forests and the implementation in the grf R package." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper imports\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DGP\n", - "\n", - "We use the following data generating process (DGP) from [here](https://github.com/grf-labs/grf#usage-examples):\n", - "\n", - "\\begin{align}\n", - "X \\sim& \\text{Normal}(0,\\, I_{p})\\\\\n", - "T =& \\text{Binomial}(1, .4 + .2 \\cdot 1\\{X[0] > 0\\})\\\\\n", - "Y =& (X[0] \\cdot 1\\{X[0] > 0\\}) \\cdot T + X[1] + X[2] \\cdot 1\\{X[2] < 0\\} + \\epsilon, &\\; \\epsilon \\sim \\text{Normal}(0, 1)\\\\\n", - "\\end{align}\n", - "\n", - "We use $p=10$ and draw $n=2000$ samples from this DGP." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.special\n", - "np.random.seed(123)\n", - "n = 2000\n", - "p = 10\n", - "X = np.random.normal(size=(n, p))\n", - "true_propensity = lambda x: .4 + .2 * (x[:, 0] > 0)\n", - "true_effect = lambda x: (x[:, 0] * (x[:, 0] > 0))\n", - "true_conf = lambda x: x[:, 1] + np.clip(x[:, 2], - np.inf, 0)\n", - "T = np.random.binomial(1, true_propensity(X))\n", - "Y = true_effect(X) * T + true_conf(X) + np.random.normal(size=(n,))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cross-Validated Forest Nuisance Models\n", - "\n", - "We use forest based estimators (Gradient Boosted Forests or Random Forests) as nuisance models. For the meta-learner versions of our forest based estimators, we also use a generic forest estimator even as a final model. The hyperparameters of the forest models (e.g. number of estimators, max depth, min leaf size) is chosen via cross validation. We also choose among Gradient or Random Forests via cross validation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.sklearn_extensions.model_selection import GridSearchCVList\n", - "from sklearn.linear_model import Lasso, LogisticRegression\n", - "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", - "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", - "from sklearn.base import clone\n", - "from econml.sklearn_extensions.linear_model import WeightedLasso\n", - "\n", - "def first_stage_reg():\n", - " return GridSearchCVList([Lasso(),\n", - " RandomForestRegressor(n_estimators=100, random_state=123),\n", - " GradientBoostingRegressor(random_state=123)],\n", - " param_grid_list=[{'alpha': [.001, .01, .1, 1, 10]},\n", - " {'max_depth': [3, None],\n", - " 'min_samples_leaf': [10, 50]},\n", - " {'n_estimators': [50, 100],\n", - " 'max_depth': [3],\n", - " 'min_samples_leaf': [10, 30]}],\n", - " cv=5,\n", - " scoring='neg_mean_squared_error')\n", - "\n", - "def first_stage_clf():\n", - " return GridSearchCVList([LogisticRegression(),\n", - " RandomForestClassifier(n_estimators=100, random_state=123),\n", - " GradientBoostingClassifier(random_state=123)],\n", - " param_grid_list=[{'C': [0.01, .1, 1, 10, 100]},\n", - " {'max_depth': [3, 5],\n", - " 'min_samples_leaf': [10, 50]},\n", - " {'n_estimators': [50, 100],\n", - " 'max_depth': [3],\n", - " 'min_samples_leaf': [10, 30]}],\n", - " cv=5,\n", - " scoring='neg_mean_squared_error')\n", - "\n", - "def final_stage():\n", - " return GridSearchCVList([WeightedLasso(),\n", - " RandomForestRegressor(n_estimators=100, random_state=123)],\n", - " param_grid_list=[{'alpha': [.001, .01, .1, 1, 10]},\n", - " {'max_depth': [3, 5],\n", - " 'min_samples_leaf': [10, 50]}],\n", - " cv=5,\n", - " scoring='neg_mean_squared_error')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingRegressor(min_samples_leaf=30, n_estimators=50,\n", - " random_state=123)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_y = clone(first_stage_reg().fit(X, Y).best_estimator_)\n", - "model_y" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(max_depth=3, min_samples_leaf=10, random_state=123)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_t = clone(first_stage_clf().fit(X, T).best_estimator_)\n", - "model_t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DML Estimators" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.dml import CausalForestDML\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", - "from sklearn.dummy import DummyRegressor, DummyClassifier\n", - "\n", - "n_samples, n_features = X.shape\n", - "subsample_fr_ = (n_samples/2)**(1-1/(2*n_features+2))/(n_samples/2)\n", - "est = CausalForestDML(model_y=model_y,\n", - " model_t=model_t,\n", - " discrete_treatment=True,\n", - " cv=3,\n", - " n_estimators=4000,\n", - " random_state=123)\n", - "est.tune(Y, T, X=X).fit(Y, T, X=X, cache_values=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.dml import NonParamDML\n", - "est2 = NonParamDML(model_y=model_y,\n", - " model_t=model_t,\n", - " cv=3,\n", - " discrete_treatment=True,\n", - " model_final=final_stage())\n", - "est2.fit(Y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "X_test = np.zeros((100, p))\n", - "X_test[:, 0] = np.linspace(-2, 2, 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pred = est.effect(X_test)\n", - "lb, ub = est.effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pred2 = est2.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(X_test[:, 0], true_effect(X_test), '--')\n", - "plt.plot(X_test[:, 0], pred2, label='nonparamdml')\n", - "plt.plot(X_test[:, 0], pred, label='forestdml (causal forest)')\n", - "plt.fill_between(X_test[:, 0], lb, ub, alpha=.4, label='honestrf_ci')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/latex": [ - "$\\displaystyle 0.02469985268609504$" - ], - "text/plain": [ - "0.02469985268609504" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean((true_effect(X) - est.effect(X))**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/latex": [ - "$\\displaystyle 0.041538512990649396$" - ], - "text/plain": [ - "0.041538512990649396" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean((true_effect(X) - est2.effect(X))**2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Stage Learned Models" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Model T\n", - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('honestrf')\n", - "for mdls in est.models_t:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict_proba(X_test)[:, 1])\n", - "plt.plot(X_test[:, 0], true_propensity(X_test), '--', label='truth')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('rf')\n", - "for mdls in est2.models_t:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict_proba(X_test)[:, 1])\n", - "plt.plot(X_test[:, 0], true_propensity(X_test), '--', label='truth')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Model Y\n", - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('honestrf')\n", - "for mdls in est.models_y:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict(X_test))\n", - "plt.plot(X_test[:, 0], true_effect(X_test) * true_propensity(X_test) + true_conf(X_test), '--', label='truth')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('rf')\n", - "for mdls in est2.models_y:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict(X_test))\n", - "plt.plot(X_test[:, 0], true_effect(X_test) * true_propensity(X_test) + true_conf(X_test), '--', label='truth')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpretability of CATE Model of NonParamDML with SHAP" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 97%|=================== | 97/100 [00:41<00:01] " - ] - } - ], - "source": [ - "import shap\n", - "import string\n", - "\n", - "feature_names = list(string.ascii_lowercase)[:X.shape[1]]\n", - "# explain the model's predictions using SHAP values\n", - "shap_values = est.shap_values(X[:100], feature_names=feature_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)\n", - "shap.force_plot(shap_values[\"Y0\"][\"T0_1\"][0], matplotlib=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap.summary_plot(shap_values[\"Y0\"][\"T0_1\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DRLearner" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingRegressor(min_samples_leaf=30, n_estimators=50,\n", - " random_state=123)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_regression = clone(first_stage_reg().fit(np.hstack([T.reshape(-1, 1), X]), Y).best_estimator_)\n", - "model_regression" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.dr import ForestDRLearner\n", - "from sklearn.dummy import DummyRegressor, DummyClassifier\n", - "\n", - "est = ForestDRLearner(model_regression=model_y,\n", - " model_propensity=model_t,\n", - " cv=3,\n", - " n_estimators=4000,\n", - " min_samples_leaf=10,\n", - " verbose=0,\n", - " min_weight_fraction_leaf=.005)\n", - "est.fit(Y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.dr import DRLearner\n", - "est2 = DRLearner(model_regression=model_y,\n", - " model_propensity=model_t,\n", - " model_final=final_stage(),\n", - " cv=3)\n", - "est2.fit(Y, T.reshape((-1, 1)), X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "X_test = np.zeros((100, p))\n", - "X_test[:, 0] = np.linspace(-2, 2, 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "pred = est.effect(X_test)\n", - "lb, ub = est.effect_interval(X_test, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "pred2 = est2.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(X_test[:, 0], true_effect(X_test), '--')\n", - "plt.plot(X_test[:, 0], pred2, label='nonparamdml')\n", - "plt.plot(X_test[:, 0], pred, label='forestdml (causal forest)')\n", - "plt.fill_between(X_test[:, 0], lb, ub, alpha=.4, label='honestrf_ci')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First stage nuisance models" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Model T\n", - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('honestrf')\n", - "for mdls in est.models_propensity:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict_proba(X_test)[:, 1])\n", - "plt.plot(X_test[:, 0], true_propensity(X_test), '--', label='truth')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('rf')\n", - "for mdls in est2.models_propensity:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict_proba(X_test)[:, 1])\n", - "plt.plot(X_test[:, 0], true_propensity(X_test), '--', label='truth')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Model Y\n", - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('honestrf')\n", - "for mdls in est.models_regression:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict(np.hstack([X_test, np.ones((X_test.shape[0], 1))])))\n", - "plt.plot(X_test[:, 0], true_effect(X_test) + true_conf(X_test), '--', label='truth')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('rf')\n", - "for mdls in est2.models_regression:\n", - " for mdl in mdls:\n", - " plt.plot(X_test[:, 0], mdl.predict(np.hstack([X_test, np.ones((X_test.shape[0], 1))])))\n", - "plt.plot(X_test[:, 0], true_effect(X_test) + true_conf(X_test), '--', label='truth')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpretability of CATE Model of DRLearner with SHAP" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 98%|===================| 98/100 [00:24<00:00] " - ] - } - ], - "source": [ - "# explain the model's predictions using SHAP values\n", - "shap_values = est.shap_values(X[:100], feature_names=feature_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)\n", - "shap.force_plot(shap_values[\"Y0\"][\"T0_1\"][0], matplotlib=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap.summary_plot(shap_values[\"Y0\"][\"T0_1\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OrthoForest" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 45.3s\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 48.1s\n", - "[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 53.0s\n", - "[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 1.0min\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 1.2min\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 1.4min finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 0.9s\n", - "[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 5.6s\n", - "[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 12.9s\n", - "[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 25.0s\n", - "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 44.6s\n", - "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 1.1min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.orf import DROrthoForest\n", - "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV\n", - "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n", - "\n", - "est3 = DROrthoForest(model_Y=Lasso(alpha=0.01),\n", - " propensity_model=LogisticRegression(C=1),\n", - " model_Y_final=WeightedLassoCV(cv=3),\n", - " propensity_model_final=LogisticRegressionCV(cv=3),\n", - " n_trees=1000, min_leaf_size=10)\n", - "est3.fit(Y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 1.2min\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.4min finished\n" - ] - } - ], - "source": [ - "pred3 = est3.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(X_test[:, 0], true_effect(X_test), '--')\n", - "plt.plot(X_test[:, 0], pred, label='forestdr')\n", - "plt.plot(X_test[:, 0], pred2, label='nonparamdr')\n", - "plt.plot(X_test[:, 0], pred3, label='discreteorf')\n", - "plt.fill_between(X_test[:, 0], lb, ub, alpha=.4, label='forest_dr_ci')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Generalized Random Forests.ipynb b/notebooks/Generalized Random Forests.ipynb deleted file mode 100644 index d26595758..000000000 --- a/notebooks/Generalized Random Forests.ipynb +++ /dev/null @@ -1,1677 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generalized Random Forest Module: Use Cases and Examples\n", - "\n", - "Causal Forests and [Generalized Random Forests](https://arxiv.org/pdf/1610.01271.pdf) are a flexible method for estimating treatment effect heterogeneity with Random Forests. The `econml.grf` module implements a high-performance Cython version of the [`grf`](https://github.com/grf-labs/grf) R-package, with support for CausalForests, IVForests and RegressionForests. The module provides estimators that adhere to the scikit-learn fit and predict API, as well as providing methods for uncertainty quantification and confidence intervals.\n", - "\n", - "Within the EconML SDK we use these estimators as final models for CATE estimation, such as in the case of `econml.dml.CausalForestDML`, where we combine a Causal Forest with Double Machine Learning, to residualize the treatment and outcome and call the `econml.grf.CausalForest` on the residuals. Similarly, the `econml.dr.ForestDRLearner` uses an `econml.grf.RegressionForest` as a final stage estimator on the doubly robust targets estimated by the first stage. The estimators here should primarily be used in conjunction with CateEstimators and not as standalone, but we provide here examples of their direct usage functionality.\n", - "\n", - "The EconML SDK implements the following Generalized Random Forest variants:\n", - "\n", - "* `CausalForest`: suitable for many continuous or discrete treatments, when there is no unobserved confounding\n", - "\n", - "* `CausalIVForest`: suitable for many continuous or discrete treatments, when there is unobserved confounding and access to an instrument\n", - "\n", - "* `RegressionForest`: an analogue of `sklearn.ensemble.RandomForestRegressor`, but with support for confidence intervals.\n", - "\n", - "If you also have multiple outcomes, then the class `econml.grf.MultiOutputGRF`, is a wrapper class that wraps any generalized random forest and enables support for multiple outcomes by fitting a separate forest for each target outcome.\n", - "\n", - "Our estimators provide support for the heterogeneity criterion as outlined in [Generalized Random Forests](https://arxiv.org/pdf/1610.01271.pdf), using `criterion='het'`, as well as a mean squared error criterion that penalizes high-variance splits, using `criterion='mse'`.[Generalized Random Forests](https://arxiv.org/pdf/1610.01271.pdf).\n", - "\n", - "Uncertainty quantification and confidence intervals is computed via the Bootstrap-of-Little-Bags approach outlined in [Generalized Random Forests](https://arxiv.org/pdf/1610.01271.pdf).\n", - "\n", - "The tree data structure that is used to store each tree is compatible with `sklearn.tree._tree.Tree` and so scikit-learn functionalities on trees can be applied to our trained trees (e.g. tree plotting).\n", - "\n", - "\n", - "## Notebook Contents\n", - "\n", - "1. [Causal Forest: Heterogeneous causal effects with no unobserved confounders](#1.-Causal-Forest:-Heterogeneous-causal-effects-with-no-unobserved-confounders)\n", - "2. [Causal IV Forest: Heterogeneous causal effects with unobserved confounders](#2.-Causal-IV-Forest:-Heterogeneous-causal-effects-with-unobserved-confounders)\n", - "3. [Regression Forest: Random Forest Regressor with confidence intervals](#3.-Regression-Forest:-Random-Forest-Regressor-with-confidence-intervals)\n", - "4. [Combining with Double Machine Learning](#4.-Combining-with-Double-Machine-Learning)\n", - "6. [Customer Linear Moment Forest](#5.-Custom-Linear-Moment-Forest)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.grf import CausalForest, CausalIVForest, RegressionForest\n", - "from econml.dml import CausalForestDML\n", - "import numpy as np\n", - "import scipy.special\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.tree import plot_tree" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Causal Forest: Heterogeneous causal effects with no unobserved confounders" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(123)\n", - "n_samples = 2000\n", - "n_features = 10\n", - "n_treatments = 1\n", - "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n", - "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n", - "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n", - " np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "for t in range(n_treatments):\n", - " T[:, t] = np.random.binomial(1, scipy.special.expit(X[:, 0]))\n", - "y = np.sum(true_te(X) * T, axis=1, keepdims=True) + np.random.normal(0, .5, size=(n_samples, 1))\n", - "X_test = X[:min(100, n_samples)].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), min(100, n_samples))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "est = CausalForest(criterion='het', n_estimators=400, min_samples_leaf=5, max_depth=None,\n", - " min_var_fraction_leaf=None, min_var_leaf_on_val=True,\n", - " min_impurity_decrease = 0.0, max_samples=0.45, min_balancedness_tol=.45,\n", - " warm_start=False, inference=True, fit_intercept=True, subforest_size=4,\n", - " honest=True, verbose=0, n_jobs=-1, random_state=1235)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CausalForest(criterion='het', min_var_leaf_on_val=True, n_estimators=400,\n", - " random_state=1235)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(X, T, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "point, lb, ub = est.predict(X_test, interval=True, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "point = est.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for t in range(n_treatments):\n", - " plt.plot(X_test[:, 0], point[:, t])\n", - " if est.inference:\n", - " plt.fill_between(X_test[:, 0], lb[:, t], ub[:, t], alpha=.4)\n", - " plt.plot(X_test[:, 0], true_te(X_test)[:, t])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import shap\n", - "explainer = shap.Explainer(est, shap.maskers.Independent(X, max_samples=100))\n", - "shap_values = explainer(X[:200])\n", - "shap.plots.beeswarm(shap_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(<1x47712 sparse matrix of type ''\n", - " \twith 3902 stored elements in Compressed Sparse Row format>,\n", - " array([ 0, 119, 234, 355, 476, 597, 712, 837, 956,\n", - " 1079, 1198, 1321, 1440, 1565, 1686, 1809, 1930, 2055,\n", - " 2166, 2283, 2404, 2519, 2638, 2759, 2880, 3001, 3120,\n", - " 3237, 3358, 3477, 3600, 3719, 3840, 3965, 4076, 4201,\n", - " 4314, 4435, 4562, 4691, 4808, 4931, 5054, 5169, 5288,\n", - " 5409, 5524, 5643, 5756, 5877, 5992, 6115, 6238, 6357,\n", - " 6474, 6593, 6706, 6825, 6942, 7063, 7182, 7299, 7412,\n", - " 7523, 7644, 7763, 7890, 8013, 8136, 8255, 8370, 8485,\n", - " 8606, 8733, 8856, 8979, 9102, 9221, 9334, 9453, 9578,\n", - " 9697, 9820, 9939, 10056, 10173, 10282, 10395, 10516, 10633,\n", - " 10752, 10877, 11004, 11119, 11246, 11367, 11484, 11597, 11716,\n", - " 11837, 11954, 12075, 12186, 12305, 12430, 12545, 12668, 12791,\n", - " 12910, 13029, 13146, 13269, 13390, 13511, 13632, 13749, 13866,\n", - " 13979, 14106, 14219, 14328, 14449, 14566, 14685, 14808, 14937,\n", - " 15052, 15169, 15288, 15409, 15532, 15647, 15762, 15879, 16000,\n", - " 16117, 16240, 16359, 16482, 16605, 16722, 16841, 16962, 17077,\n", - " 17196, 17313, 17426, 17549, 17674, 17797, 17916, 18031, 18144,\n", - " 18257, 18370, 18487, 18598, 18723, 18834, 18953, 19076, 19197,\n", - " 19320, 19439, 19560, 19673, 19790, 19911, 20026, 20151, 20274,\n", - " 20393, 20512, 20631, 20744, 20865, 20980, 21101, 21224, 21335,\n", - " 21458, 21583, 21708, 21831, 21950, 22069, 22188, 22305, 22430,\n", - " 22551, 22666, 22785, 22904, 23031, 23148, 23269, 23388, 23499,\n", - " 23612, 23731, 23842, 23967, 24088, 24205, 24322, 24443, 24568,\n", - " 24679, 24802, 24925, 25042, 25165, 25286, 25405, 25524, 25643,\n", - " 25764, 25885, 26008, 26135, 26266, 26385, 26508, 26623, 26746,\n", - " 26859, 26978, 27091, 27212, 27329, 27450, 27573, 27696, 27819,\n", - " 27930, 28045, 28172, 28293, 28408, 28521, 28636, 28749, 28872,\n", - " 28991, 29112, 29233, 29352, 29471, 29592, 29709, 29826, 29945,\n", - " 30068, 30189, 30306, 30429, 30544, 30653, 30774, 30891, 31014,\n", - " 31131, 31252, 31369, 31490, 31609, 31728, 31851, 31960, 32077,\n", - " 32190, 32309, 32426, 32543, 32662, 32787, 32908, 33023, 33138,\n", - " 33265, 33390, 33509, 33632, 33751, 33878, 34003, 34124, 34241,\n", - " 34364, 34481, 34598, 34719, 34836, 34959, 35076, 35189, 35312,\n", - " 35421, 35540, 35661, 35784, 35897, 36020, 36137, 36258, 36383,\n", - " 36508, 36625, 36750, 36873, 36990, 37119, 37236, 37355, 37472,\n", - " 37593, 37710, 37827, 37936, 38049, 38168, 38287, 38408, 38533,\n", - " 38648, 38759, 38876, 38989, 39108, 39229, 39348, 39467, 39594,\n", - " 39713, 39832, 39957, 40068, 40181, 40300, 40421, 40548, 40669,\n", - " 40786, 40897, 41014, 41137, 41258, 41371, 41490, 41613, 41736,\n", - " 41861, 41982, 42103, 42218, 42341, 42462, 42585, 42698, 42821,\n", - " 42938, 43059, 43180, 43295, 43416, 43543, 43660, 43771, 43892,\n", - " 44019, 44134, 44251, 44370, 44501, 44622, 44745, 44866, 44991,\n", - " 45108, 45233, 45350, 45469, 45586, 45701, 45812, 45933, 46048,\n", - " 46169, 46288, 46411, 46520, 46645, 46764, 46885, 47008, 47129,\n", - " 47252, 47371, 47484, 47595, 47712], dtype=int32))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.decision_path(X_test[:1])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[31, 24, 47, 17, 15, 61, 13, 34, 53, 29, 21, 54, 23, 44, 8, 9,\n", - " 8, 52, 12, 6, 12, 16, 53, 68, 46, 29, 13, 15, 11, 30, 15, 19,\n", - " 36, 58, 20, 9, 52, 80, 33, 75, 46, 14, 19, 37, 64, 32, 58, 29,\n", - " 65, 42, 66, 21, 8, 68, 26, 23, 32, 23, 73, 51, 39, 21, 16, 9,\n", - " 12, 74, 20, 39, 13, 20, 37, 16, 8, 6, 74, 18, 24, 26, 50, 26,\n", - " 30, 23, 64, 34, 18, 73, 34, 45, 51, 50, 15, 31, 56, 20, 89, 55,\n", - " 8, 72, 24, 28, 14, 60, 72, 30, 27, 37, 22, 22, 6, 56, 7, 69,\n", - " 28, 67, 43, 6, 54, 32, 9, 36, 10, 19, 28, 12, 91, 10, 36, 88,\n", - " 13, 27, 24, 46, 11, 29, 17, 46, 27, 54, 17, 9, 45, 10, 12, 58,\n", - " 37, 6, 65, 16, 71, 26, 66, 7, 8, 6, 32, 18, 12, 45, 27, 17,\n", - " 19, 34, 68, 68, 40, 28, 33, 23, 83, 12, 7, 46, 28, 23, 82, 54,\n", - " 11, 44, 20, 36, 29, 47, 26, 9, 10, 49, 40, 54, 8, 45, 24, 43,\n", - " 10, 6, 36, 15, 29, 33, 73, 9, 51, 27, 27, 45, 41, 42, 41, 46,\n", - " 12, 12, 22, 13, 53, 9, 50, 11, 47, 7, 44, 12, 9, 69, 77, 19,\n", - " 71, 59, 29, 53, 45, 18, 19, 33, 75, 16, 10, 73, 33, 10, 29, 46,\n", - " 28, 35, 56, 34, 27, 29, 35, 28, 24, 66, 51, 74, 10, 18, 70, 35,\n", - " 10, 9, 73, 21, 27, 68, 48, 6, 47, 20, 42, 5, 6, 9, 12, 47,\n", - " 54, 17, 60, 34, 18, 53, 21, 40, 25, 45, 31, 45, 10, 17, 20, 23,\n", - " 6, 39, 25, 63, 76, 68, 7, 4, 34, 14, 5, 59, 17, 62, 55, 16,\n", - " 18, 22, 23, 13, 55, 11, 44, 38, 7, 68, 6, 42, 15, 37, 9, 70,\n", - " 17, 19, 8, 64, 42, 50, 8, 8, 8, 12, 44, 53, 4, 45, 45, 12,\n", - " 46, 45, 63, 5, 45, 31, 30, 57, 40, 33, 9, 16, 70, 48, 27, 16,\n", - " 65, 20, 60, 20, 28, 22, 29, 23, 55, 49, 22, 56, 85, 13, 29, 74,\n", - " 52, 45, 52, 22, 44, 48, 68, 23, 35, 44, 56, 65, 51, 39, 28, 10,\n", - " 38, 37, 45, 25, 39, 14, 55, 27, 56, 68, 25, 75, 48, 29, 44, 86]],\n", - " dtype=int64)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.apply(X_test[:1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Causal IV Forest: Heterogeneous causal effects with unobserved confounders" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1227)\n", - "n_samples = 2000\n", - "n_features = 10\n", - "n_treatments = 2\n", - "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n", - "true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n", - "# true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n", - "# np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n", - "Z = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "U = np.random.normal(0, .2, size=(n_samples, 1))\n", - "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "for t in range(n_treatments):\n", - " T[:, t] += U[:, 0] + Z[:, t]\n", - "y = np.sum(true_te(X) * T, axis=1, keepdims=True) + 10 * U[:, [0]]\n", - "X_test = X[:1000].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.grf import CausalIVForest\n", - "est = CausalIVForest(criterion='mse', n_estimators=400, min_samples_leaf=40,\n", - " min_var_fraction_leaf=0.1, min_var_leaf_on_val=True,\n", - " min_impurity_decrease = 0.001, max_samples=.45, max_depth=None,\n", - " warm_start=False, inference=True, subforest_size=4,\n", - " honest=True, verbose=0, n_jobs=-1, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CausalIVForest(min_impurity_decrease=0.001, min_samples_leaf=40,\n", - " min_var_fraction_leaf=0.1, min_var_leaf_on_val=True,\n", - " n_estimators=400, random_state=123)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(X, T, y, Z=Z)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "point, lb, ub = est.predict(X_test, interval=True, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for t in range(n_treatments):\n", - " plt.plot(X_test[:, 0], point[:, t])\n", - " if est.inference:\n", - " plt.fill_between(X_test[:, 0], lb[:, t], ub[:, t], alpha=.4)\n", - " plt.plot(X_test[:, 0], true_te(X_test)[:, t])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,5))\n", - "plt.plot(est.feature_importances(max_depth=4, depth_decay_exponent=2.0))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "explainer = shap.Explainer(est, shap.maskers.Independent(X, max_samples=100))\n", - "shap_values = explainer(X[:200])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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oHFfM4FMr8eZ7yU/WMcWDHloKvHhL/eSNKMSTkqEUJGJ4sDSTRwtBLB78WHYyHAcPMYIk2P0FxEFhi4LYvADe/zke74kHd/kz6W652NPQlZqG9bi1Bmd0sPxc4BLgVGC5McbRWht2p1tOB9s1AakVVJV7WKf9tutxz354LK3I3XqKL7Wbdyi76yyy0q8+6+NXn/Vx0d9jPLjE/QjGlMPOhhi1cWgI+rBNyQ/YWrxA2Ouh2eul3uehNuCjsilM2O+nKOHgtZawEyAYi3HwtGJO/MZIKie1/QcaPKGYSx+bkXaMY04YyJgT/nN895z7jtzj+kdcOpYjLt3zKNL02WOYPntM2q/d2wRLAnzhT0d3uHz8mcMYf+bea0iyzbCjB3Luv05Oa92RM4dwwVt73n0MmFrOOQtP687QutVBs4Zz0KyunfZ36Hcnceh3//NU55KRRXzp2VO61Ha6Bk7vz1fe6GgX3rGx54+hpbqFmvdqGX76UMae7/6fHpmS+KZKNMf5cO4SGteEGH3hWIZ+bhgvjX8SpWhNHPJpQSWTAvdsiQAFOEQppIkK8tnJNoopoAUAH/UEt/0cCuQaMwdSV5KGB4FrtNazgflAFLc48WBjzLO4Xf1xoBrwaK0vxu1peDq5fTXuwX8ckHrKpQEu0lq/jJswXJ1GLL8AbtJar8I98OfhFmfWGGM+2cP6TwG3a62/D/wK9wyKs3BrMrLen2b5OH6EQ32L5aklUT5tcBOIsEdRXRSktDFCjd9HWMGUaIyiWIyIJ0CLx8vOQIDKcDOtHdIeBRX5nPvzyag0uhiFEEJ5FJ+5Zkra63vzfRxy6/Q280Z/ZyLL/8/gjzt4nQQFiRiWPXfiN1LCBka5bdFMGdspvnx6L0gYcm+fut+FkMaYrbhDAF/EHS7YiXswPii5ygPAYmA1UAVMAl5L2b4ZuAF4ODmkcH1y0RXAWKAWeBS4P41Y7gFuB+5LxrEh2fYeb+1ojKkDPgf8N25twz3AN7P5dMtUXo/iG4d6+c4RXhat3V1Ih2PZ5PHwTlE+a/MCFEVilEVjlMTijGwMsz0/yJohZUw8Y1Cb9o47c4AkDEKIA2r0FRM55mfjGeCpJ5hIQGs3PoDFTyw57eAhTIQCIhRQzVC453L43TczGH16cnF4Qtm9FL2JHtGtH/hxvw2zaJ2bOJQELKGYe8U1gFnbdjC6OdK67rjDS7jiWre7+51/17Ds9TqGji3g5POG7PO8bJGbFixYAMCsWbMyHInoq2oWbGDZmS+QR5R8ojjueRcUEEFhGcX7bGcE25I9DQAjfn0M/a/o1pPdemQHGFHfarO/D9rf9vodrVxGupd7ZnY+9yyOEfTB4o0OD33oQNwtbFteXMCoZvfiKQmguGJ3x8vhp/Xn8NP6ZyZoIYRI6j9rBFOfO43ND33Kxqc2EGyJkhePEyafAprZwjjyCOElRgI/fiIU+/Z8dlG2yZXehVSSNPRyxUHF1ce7FQqJt+I89FEM8gPgWGaMgIb6JvLiCVq8Hk44LdsvuSqE6IsqThlKxSlDOWh9Ix+f8k8iq+oJECeIQxMVNFHBSJYDAQI4+Cd/PtMhp0mSBpHFvn2El60hy9tVDmdN8vL1qfn8sQI2b49zxomFjBoR2HcjQgiRIQUji6g4fThbVtXjaXeSXAOVlBWFCdz+OTzHjctQhJ0jPQ0iq3k9iltPaVv7+Z3zyzIUjRBCdN6oO2dQMKWcyMe1ND+wFKe2BU9ZkIJvzaTwexpvRd+7R002kaRBCCFE1lBeD4MunQhA/LpDiSyvJW9af7xleRmObH9IT4MQQghxQPgGFOA7oSDTYey3XBye2O/rNAghhBCib5GeBiGEEKIH5GJPgyQNQgghRI+QpEEIIYQQacjFngapaRBCCCFEWiRpEKKPeGVdglG/aGHIz5p56uPEvjcQQnRJLt6wSpIGIfqIb/w9xvo6y9ZGuPDJKI4jN6sTQnSO1DQI0UfUNyTY9T0hEpOEQYieliu9C6mkp0GIPuKwrXV4HQdlLcN3NrHxd5+Qd34twXNqWX7lYpyoDFkI0b1Uu0fvJ0mDEH3AxjqHnQlIhGPQFGWNP8Dv51VBA9gWxaZff8zK694l0RjdvZG10NSSuaCF6OVysaZBhieE6APm/qWerflBztxZj9/C8vw8asvy+Xh6JbGgj/7bGyi8832a71xM4dhiRv/xGPzn3w6bdsBFJ8F9V4DKjZ2eEAdKriQKqSRpECLH1TQ4PLsqweTGZooiMfqFGikLBhlhY8SCPrCWgkiUfE+YQc567OogO2ZXM3jTDreBB16GMw+Hs2Zk9o0IITJun0mD1nohcBQQS5n9iDHmkq68sNZ6FLAWGG6M2dSVtrrw2mFgV0VYnTFm2IGMQ4gDQllGh5qpCDdz6LqNBOJu7UKLzwt4mLyiioG1ISDIFsYxkbeIr2nCwYMHB4D4xb/Dd/R4GFyeufchRK/Td3sa5hpjbunRSPaT1tpvjInte809Gn+gExYhDrRPNiZwlCISjfHUhDEMaGrm+HWbyIvE8EdjhDx+nIIiSiPN5CdiNFFOGY2EGUyYQkqpxh+K4Vx2L55/fC/Tb0eIXiMXz1Hq0vCE1noKcAcwHfdb+1+AG3cdxLXW9wEnA2XARuAWY8z85OZLkj9XaK0t8FNjzNzk9HHGmEXJNk4EXjDG+JLPFwIfAKOAmcCtwG1a60uBq4DhwBrgWmPMc115f0Lkgv99oJ4K67B0yAAssK2okPJwM4dv2ExFTTMF4TgWD3WBAopaaqi3A+lHiAKaSOCngcH0YzMs+BC7tho1ekCm35IQvUIu1jTs99kTWuuBwCvAk0Al7hDGKcB1KastAg7BTRrmAPdrrScll01L/hxvjCkyxsztxMvPBuYBpcA8rfVlwLXAeUA5cD3wpNZ67D7aWay1rtZaL0wmJz0uFArJtEwf0OloxFIX8FPiWEoci9+xbC/IQwHehNO6LkpRSRUDqW79huQhgYN31wqwvSFr3pdMy3R3T3e/3DvlUlm79w6U5Df7I4FIyuzPAscCnzPGzExZ98u4PQZ7PFhrrQ3wJ2PM3R3VNKTZ07DGGDM7ZZtlwO3GmAdT5i0AFu9pWEVrXQRMAd4D/LhJyO3AkcaYpXv9QLouF3usRBb76VONPPZ8U+vzODBx6zZO/GQt/pjDgC1NeCz0T+xgamSFm0zgnnpZx0DyCVFACDugAM/W36A8cqa2yDk9ckTfoX7QZn/fz97a6zOHdIcnftz+4Ku1vgA4RmtdlzLb3d+4yz3ATcDZwGDcg2Uh0B19m+vaPR8N3KW1npcyzwfssV7BGNMIvJV8GgV+rbU+E/hvoKeTBiEOqKvOKOTRF5pQyd1XOM9HnuOgHFAJh9p+QfyJBCdUrWzdc4aKBtPcCF4cIvgI/vp8fN/+L5ScdilE2nJxeKIrNQ3rcXsAzuhg+bnAJcCpwHJjjJPsadj1KTodbNeEm1zsUrmHddpvux74kTHmsbQi3zOHXOk/EiJFXkAxmjCrPAWEfT6qAn7+NnYkRU0RplRtw5NwGLmlliZKCNJCvLSU8k+uQV39PIkN9RT87wz8X56Y6bchhMgCXUkaHgSu0VrPBubjfmMfBRxsjHkWKMHtCa0GPFrri3HrGJ5Obl+Ne6AeR9seAQNcpLV+GTdhuDqNWH4B3KS1XoVbYJmHW5xZY4z5pP3KWusZQCPwCe5ncCFwAvCDNN+7EL3K2f9VxOt/3MBdh4wn7vHQ7PPxz7Ej+MLLy/ElLBY/m7yVlKkIlU+fg3dwMeXzz8p02EKILLPfg5PGmK3AScAXcYcLdgJPAQclV3kAWAysBqqAScBrKds3AzcAD2ut67TW1ycXXQGMBWqBR4H704jlHtyahPuScWxItu3vYJPRwN+A+mRsFwCzjDHv7vONC9EL/fdZA3hyygjiKcMLEa+H7QMLqe5fQOOYQsbedRzD3r6A/GPlciVCdIdcvIz0PgshRbeTD1xkxNg7w3y6Y/ef35yBdYz65RvggxN+czQjThuaweiEyKgeOaJvVze02d8PtHN7feYgl5EWoo84cpiHT3e4V4Msz4Orv1XJiyOCoJCEQYge0etzhP8gSYMQfcRvzwzSvyDK9ibL9471UxhQKF/u7dSEyBa52K0sSYMQfURJnuJXnw9mOgwh+oxcqWNIJVdpEUIIIURapKdBCCGE6AG52NMgSYMQQgjRIyRpEEIIIUQapKdBCCGEEGmRsyeEEEIIkRbpaRBCCCFEmnIvaZBTLoUQQgiRFulpEKIPqb/yXzTf/wHKOviJMPK0Aay/aBShHVH+OncVNRtaOOz0AZx66YhMhypEr5eLwxPS0yBEHxF5eS3hX7+NDUVxGuPEGx2GP7GJolUhFv65iqpPmoiEE7z5xFY2Lg9lOlwhej3b7pELJGkQoo9wVmzFQ4x8qmkhQS0lRPERa7B8tKCKkEfx/qBythYVsHFFU6bDFaLXy8VbY0vSIEQfYDfugG8/RD9WESZANZU0UMImKvnonwOoLSrBEwgyfmcT/ZpaeOaeKjYsb8x02EL0apI0CCF6pw/Wg5PAR4wQFa2zE3jxJnavZj0eokqhrGXDx5I0CNE1qt2j95OkQYhc1xIlPvteEvixeCmlpnWRowAnAdYdcfXG4hTGYwTicao3t2QoYCFyQy7WNOzz7Amt9ULgKCCWMvsRY8wlXXlhrfUoYC0w3BizqStt7cdrnwf8vt3sfOCfxpgzD2QsQvQ0+4cXaKnxkkczcUrw4lDITkKUg4Whm2tRjmXx2BGMSCTwJvduyxZs5Qv/MxKPNze+IQkhui7dUy7nGmNu6dFI9pPW2m+Mie17zd2MMX8B/pLSRimwGfhzN4cnROYVBvHQgiVOmFJilODzKOKOoiXfT3MwwKoB/dhYVsLmigIa84NM27qTYQ1NKOmLFGK/5UodQ6ouXadBaz0FuAOYDoRxD8Q37jqIa63vA04GyoCNwC3GmPnJzZckf67QWlvgp8aYucnp44wxi5JtnAi8YIzxJZ8vBD4ARgEzgVuB27TWlwJXAcOBNcC1xpjn0nwrFwAh4Kn9+BiEyGqJexfhBxzygAAFRChwIiS8ivcrRrFw6jhWDB/ExvIC1vcrBOCDYf25wKzmww1Rpo4MZjR+IXqrXEwa9vt7hNZ6IPAK8CRQiTuEcQpwXcpqi4BDcJOGOcD9WutJyWXTkj/HG2OKjDFzO/Hys4F5QCkwT2t9GXAtcB5QDlwPPKm1Hptme5cDf+psj8X+CIVCMi3TB3Z6+SbAg8VDajFWkdOCQlFbUgBAzAPehONOe73sLMzDrIxlPn6ZlukDNN3dcvHsCWXt3sszkt/sjwQiKbM/CxwLfM4YMzNl3S/j9hjs8WCttTa4B+e7O6ppSLOnYY0xZnbKNsuA240xD6bMWwAs3tewitb6GOBVYKwxZu1eP4zukSv1MKKXiH//YezPn8chQCNleLFYYJ13MMsHV7LkoGG8Oekgd12P4qMhpaAsZy5bxy//MJ7+Jd7MvgEhel6PHNFXqZ+12d+Ps9/v9ZlDusMTP25/8NVaXwAco7WuS5mtAG9yuQe4CTgbGIx7sCwEBnQxZoB17Z6PBu7SWs9LmecD0imwvBx47gAlDEIccL6fnUv9axtpWFxHLYPJp4U4Pkj4GLlzO/MOnkFg17qO5fANNZTHoniOHCQJgxBd0utzhP/QlZqG9bg9AGd0sPxc4BLgVGC5McZJ9jTs+hSdDrZrwk0udqncwzrtt10P/MgY81hakSdprSuA/wbO6cx2QvQ2gTOn0bj4Qxw8hHGHI3zEmBZex8ohFUyubmz9xyyPxHB8inGDJWEQoityZUgiVVeShgeBa7TWs4H5QBS3OPFgY8yzQAkQB6oBj9b6Ytw6hqeT21fjHvzH0bZHwAAXaa1fxk0Yrk4jll8AN2mtV+EWWObhFmfWGGM+2ct2FwE1KTEJkZPyf3AapfcuI7EmSowACksp9Tj4uPy1xTxyhGZwYwtl0SgJj2VYXSNnDSvNdNhCiCyz34WQxpitwEnAF3GHC3binn1wUHKVB4DFwGqgCpgEvJayfTNwA/Cw1rpOa319ctEVwFigFngUuD+NWO4BbgfuS8axIdm2fx+bXgb80RiT2H0znacAACAASURBVMd6QvR6+d8/mWJaqKCBckJ4cbv9/EMsQ0MteC2E/AGavAH6hyPUb4tmOmQherU+WQgpup184CIj4lsb2Dj0bjyOA1gKCdE8IZ8nrzma5xaNoy4/D6xl0o5aDrIR/ue3kykdIKdbij6hR47on6g72+zvJ9ire33m0KXrNAgheg/vkvUMc1bQRAl+IgROm8Bbl43mIH8L99wxjAXP7qQ4lmDi6CFM1CXkFcruQYiuyJXehVSyVxCirzhoAF6/h5JYPQDqmNFYvztCWdnPx+XndceJTUKIXXIxaZCLxArRR6hxg1F/vwq+egT86Avw/zo68UkI0R365A2rhBC5Q50+FXX61EyHIUSfID0NQgghhOizpKdBCCGE6AG52NMgSYMQQgjRA3KljiGVJA1CCCFED5CeBiGEEEKkRZIGIYQQQqQlF4cn5OwJIfq4mmiQl9ckCEVycRcnRObk4r0npKdBiD7sk8YSblxxGC1Loozvr3jr0iBl+bmxcxNCdD/paRCiD7L1zTj//JBX1w+gxfrAq1hRY3nw7zuJR51MhydEjlDtHr2fJA1C9DG2vpnYEbcR//xdVL6+GfK9kOeFfC8NNxse/N9lJBIyVCFEV+Xi8IQkDUL0MXbRaqIrq6mllJC/FFRyZ+ZRbBhRQf1b1WxfF85skELkgFy894QkDUL0MdGlm9nKKBrpzw//8RqHrt8MgMexqOI86voV87ffbMBxcmU3J0Rm5GJPgxRCCtGHOEs3suPmN4AiAAKJBP9vweu8OWwkBfEYgUIfiaCfqo9DNNTEKBsYyGzAQvRiuZh27zNp0FovBI4CYimzHzHGXNKVF9ZajwLWAsONMZu60tZ+vHY+8CBwCDAGuNEYc8uBjEGIA81uqGHbtN8BXtzdmfvNZ8Smeso2rQYgGvSy4tBKGgvz2bwyRNnAfhmLV4jezsmR3oVU6fY0zM3Wg6rW2m+Mie17zTYs8AZwN/CT7o9KiOwTuf8NitmCwiGfUrYGhlAfK6XR5reuE4gkKKprJub3seT5aiYdK0mDEGK3Lg1PaK2nAHcA04Ew8Bfcb+2x5PL7gJOBMmAjcIsxZn5y8yXJnyu01hb4qTFmbnL6OGPMomQbJwIvGGN8yecLgQ+AUcBM4FbgNq31pcBVwHBgDXCtMea5PcVtjGkBfpFsr6Urn4EQvYXnpkcppBmFpZAGSqNbeHTgmVTUJFDJsywtkPArgi0RViyuJ96SwJfnzWjcQvRWuVLHkGq/CyG11gOBV4AngUrcIYxTgOtSVluEOwRQBswB7tdaT0oum5b8Od4YU2SMmduJl58NzANKgXla68uAa4HzgHLgeuBJrfXY/XlvQuQaG4rgt+E2OzEvcf702ck8eMJkVg8pw/G6CYP1KnyOA46lrqo5g1EL0bv15bMnrtda16U8ZgAXAkuMMb83xkSNMVW4Xf0X7trIGHOvMWaHMSZhjHkEWAqc2A1xP26MeckYY40xYeBKYI4xZokxxjHGPAO8DJzTDa/VrUKhkEzL9AGfVsVB4gRJ3XX97eDpvDByAvOPnczVF85k44ASonleHK+HuMeDwlJamZ8V8cu0TB+I6e6Wi2dPKGv3nv8khwNeaF/ToLW+C7gESP0qogCvMaZIa+0BbgLOBgbj7q0KcYcobu6oEDLN4YmXjDFzUrZpAhwgkRKLD3jIGPOt/Xl/PShXEk7RyyTW1hAb+z1anDx8WK4+9RzuOXJG6/Jvv/guh23eRs3AMpqL87ng5nEcdMyADEYsxAHTI0f0ReqPbfb3x9pLMpo5KKVOwf0yPdBaO0sppYESa+1L6bbRlZqG9bgH2zM6WH4ublJxKrDcGONorQ27fzkdXau2CTe52KVyD+u033Y98CNjzGNpRS5EH+Qd3Z/oH75D4yV/J0wph63dgTrCYpUiGIszPNRAYVOMSF0zzeWFjJhekemQhejVsukbolLqO7h1f38EvpKc3Yw71H90uu10JWl4ELhGaz0bmA9EcYsTDzbGPAuUAHGgGvBorS/GrWN4Orl9Ne7BfxyQesqlAS7SWr+MmzBcnUYsvwBu0lqvwi2wzMMtzqwxxnyypw201kHcBMYD+LTWeUBiP87EEKLXyP/GdCq2NuD94fOctqqJL703gn9NGMs33lxGXtjhzYOGMGVHLZf8eJwUQArRRVk2JPFd4L+steuUUtcm530CjO9MI/tdCGmM2QqcBHwRWAfsBJ4CDkqu8gCwGFgNVAGTgNdStm8GbgAeTtZJXJ9cdAUwFqgFHgXuTyOWe4DbgfuScWxItu3fy2YrcLOs44AfJafv2ddrCdHb5X/rSIqG5uPH4TOb6ong4TfHTOPaWUfRP9KCtyzAkKnlmQ5TCNG9inHPYoTdnSB+3C/8adtnTYPodvKBi4xzquqJ/+MjFv/6Y26ceDSrBpZz9NrNhIsCnPO9sZw/Iy/TIQpxIPVIl8Ar6k9t9vcn2NkZ63pQSj0OvG+t/bFSqtZaW6GU+j/gEGvt19JtR+49IUQf5BlaSuBbRxO6oIzPf7iGrYNKeeyYySw+YiwnTgpmOjwhckKWnXL5HeBLSql1QLFSagXw36RXAtBK7j0hRB+WmBJgwhVRns3fxroxAzhtegHDSrJqHFaIXiubahqstVuUUocDRwAjcIcq3rbWdnRSwh5J0iBEH+eM9nHyrFGZDkOInJMFvQttWLceYXHysV8kaRBCCCF6QDb1NCilNtJBHmOtHZFuO5I0CCGEED0gm5IG4Px2z4fgXrfhkc40IkmDEEIIkeOsta+0n6eUWgg8C/wq3XYkaRBCCCF6QKcqDDMjAozuzAaSNAghhBA9wHqyZ3hCKTWn3awC4HPAvzrTjiQNQgghRA+w2ZMzAAxv97wJuBN4qDONSNIghBBC9IBs6mmw1n69O9qRpEGIPs5ay+a/byAejjP0SyPxyo2qhOgWNsPXXFZKzUxnvQN1a2whRA6wf47wzvOLANj48FqO+cd/ZTgiIXKD9Wa8p+HeNNax7L7R5D5J0iBEH6ZiDjP+sYiyyA5aKOLdF6cTCx2Pv3hvN4gVQvQG1tpOnRmRDrlhlRB9TH1Dgh21cQAm/H4JAyJVxPESoIFDm5fik68SQnQLx6PaPHKB7B6E6EMWvt7I3ffXkkjAVz5fwrEbmlmqjmGLtx8oxbDERkoao3jzpadBiK7KdE1DKqVUCXATcALQn5TbgXfmMtJZ9JaEED1t/t/qSSTc6cefbqCqoZLq/BIqPCGKnDCbvMOhUG6NLUR3sB7V5pFhdwOHAXOACtxbZW8AftGZRqSnQYg+ZG2ToiQ5HfMoasLFTGregte697Gpsg7O0g14Z6RdFyWE6ECWXafhVGCitXaHUiphrf27UsoAC+hE4rDPpEFrvRA4CoilzH7EGHNJJwNu3+4oYC0w3BizqStt7cdrjwceAMYBfmAT8EtjzB8OZBxCHEg/XhhlTWEeMxpqGdwYxuckCJfm463bfeO7UqeR2u88w6B3rshgpELkhizoXUjlAeqT041KqTJgCzC2s42kY64xpijl0aWEoTtprfdn8HUrcAEw0BhTApwD3KK1PrVbgxMiS8QSlhvesBy5dSdDGxoJJhJ4LXw48SAa/T5qgwXUB4IMZD0es47Y8u2ZDlmIXs9RbR8ZtgS3ngHgNeAu4LfAys400qXhCa31FOAOYDoQBv4C3GiMiSWX3wecDJQBG4FbjDHzU94AwAqttQV+aoyZm5w+zhizKNnGicALxhhf8vlC4ANgFDATuBW4TWt9Ke5tPocDa4BrjTHP7SluY0w9uzMucM9TtcB4YI/bCNGbNbQ4eB1LYTyBx+7uWRi8aQdLB4wk7lWgFDVhL0fWrCaxugb/pIEZjFgI0c0uZXfx45XAT3CPzRd2ppH9LoTUWg8EXgGeBCpxhzBOAa5LWW0RcEgysDnA/VrrScll05I/xyd7L+Z24uVnA/OAUmCe1voy4FrgPKAcuB54Umu9124XrfVSrXUEWApsBx7uRAxC9BrPfpxgQCSK10I4GMQCOA4DaupJeNyEAaCqYCjN3iC8/mlG4xUiF2RZIeR6a+2nANbaamvtJdbas621yzvTSLpJw/Va67qUxwzc7GSJMeb3xpioMaYKN3NpzVqMMfcaY3YYYxLGmEdwD84ndibADjxujHnJGGONMWHcrGmOMWaJMcYxxjwDvIw77NAhY8xUoAi3N+RJ3Bt49KhQKCTTMn3Ap6dVeggFfDhA1OdjZ0EBW0tKCOcFULs7HvAmHIJODDWmf8ZjlmmZPtDT3c2qto8M26qUulspdWxXGlE2patyT5LDAS8YY25pN/8u4BKgObU9wGuMKdJae3DPCT0bGIzb/V+IO0Rxc0eFkGkOT7xkjJmTsk0T7q3LEymx+ICHjDHfSueD0FrfDdQbY67b58pds/cPXIgeUvCzCANrwhy1eScAEa+HQEuEc//9DpGgF2/CUt4Q5jPRTyldfAWBI4ZmOGIhDpgeOaQ/OuSvbfb3X91ydsZSB6XUocC5uF+mHdye9fnW2g87005XahrW4x7Mz+hg+bm4ScWpwHJjjKO1Nuz+5TgdbNeEm1zsUrmHddpvux74kTHmsbQi3zMf7tkUQuSk787w8ZPFxaAUw+vcXP/gmnoq6+px8JJAkUcEZ+Iw/FMHZThaIXq/LCh+bGWtfR94H/g/pdQJuMfoF5VSW621U9NtpytJw4PANVrr2cB8IIpbnHiwMeZZoASIA9WAR2t9MW4dw9PJ7atxD/7jcE953MUAF2mtX8ZNGK5OI5ZfADdprVfhFljm4RZn1hhjPmm/stb6NKAO9wO0wOeA83EvdiFETrr1OC8PfhSjJeQllO+lKc/HDX99Bx8OHhwsUEAj/d+8GpUnl3ARoquyoI6hIyuAj3FPUOjUl+X9LoQ0xmwFTgK+CKwDdgJPsftuWQ8Ai4HVQBUwCfc0j13bNwM3AA8n6ySuTy66Ave80VrgUeD+NGK5B7gduC8Zx4Zk2x2djlkC/Cn5GtW4wyjXGGPSuSOYEL3W/DN8BCvz2TKuglsvLiOvMt66E1BAHmEZQBOim2RTTYNSqkwp9Q2l1IvAp7j1hT8FOnWa1D5rGkS3kw9cZI1/nfdnKufXsmvUsJ+qYnDTLfjypadB9Ck9ckifP/zRNvv7r238aiZrGsLAG7i1DI9ba+v3sckeyZ5BiD4sfk4pO14MMbR6B0rFebvfFM5oTkjSIEQ3sCqrhifGWGu3dLUR2TMI0cfVfX0w2//SH+tYRn91FMEKuWGVEN0hywohu5wwgCQNQvR5/qN9nPqtU4g1ximfVJbpcITIGVnW09AtJGkQQlA0oijTIQiRczJd/NgTJGkQQggheoCTgz0N+33KpRBCCCE6lmWnXCql1KVKqZeUUkuT845XSn21M+1I0iCEEELkvjnAN4A/ACOS8zbh3uwxbZI0CCGEED3AKtXmkWEXA5+31j7C7usFrWX3BRnTIjUNQgghRA/IgkQhlRdoTE7vShqKUualRXoahBBCiB6QTTUNwL+AO5VSQXBrHIC5wILONCJJgxBCCNEDrEe1eWTY/wJDgHqgFLeHYSSdrGmQ4Qkh+rDBb6xj0n3vwKCX4IEr4ZDRmQ5JiJyRLcMTSikv8BXc22GX4CYLG621WzvblvQ0CNFXtUQ57M5XKdzWCEvXw6V3ZzoiIXJKtvQ0WGsTwJ3W2hZr7XZr7Tv7kzCAJA1C9Fmrn1jH4+Vf4lV1Oq9zGsuW9uPpm5dlOiwhRM9YoJSa1dVGZHhCiD7qjbkfMbymBWu9xPGyM9qPgb/5N5u/MY7KYXLTKiG6LEuGJ5LygMeVUm8CG9l9BgXW2gvTbUSSBiH6mNC71bz1lcfZMmIyQ9fU4Uns2ncoSsNNOE5GwxMiZ2RB8WOqZclHl0jSIEQfU/X1J3h35BQAVo8bxJiV2wjG4wzw1fDW8Al8eUctTYMGURiU0UshuiJbCiEBrLU3d0c7+0watNYLgaOAWMrsR4wxl3TlhbXWo3CvRjXcGLOpK23tx2vPAG4ANG6XzWpgrjHmbwcyDiEOJBt32P6Vp/AvX89BE4uZumINF379Qt75+qkMrw1xzNoqnpg2jotf8MATYSaUWpZdU4Q3u74tCdFrWJU9ibdSamZHy6y1L6XbTro9DXONMbek2+iBpLX2G2Ni+16zjQrgr7iX1dwBnAk8rLU+3hjzTjeHKERWCD3+MZG/LyHfV8f0Nct4cOp/8f5BgwnEE2wqL+Kx0oNJeJM7uZIgn1Q385tFLVx1fH5mAxeil8qy4Yl72z0fAARw7z+R9qWkuzQ8obWeAtwBTAfCwF+AG3cdxLXW9wEnA2W4hRe3GGPmJzdfkvy5QmttgZ8aY+Ymp48zxixKtnEi8IIxxpd8vhD4ABgFzARuBW7TWl8KXAUMB9YA1xpjnttT3MaYZ9rN+pvWehlwLCBJg8g54YeX4P3aXfTzhPl3+efwxGFsTYhfP/wSoaJC1g3pxx+njyWxawMF+D18958xNjUqfva5vAxGL0TvlGXDE20uwpK8dsMPgVBn2tnvvhOt9UDgFeBJoBJ3COMU4LqU1RYBh+AmDXOA+7XWk5LLpiV/jjfGFBlj5nbi5WcD83CvajVPa30Z7lWtzgPKgeuBJ7XWY9N8L4OBycDSTsQgRK8RvvwJCqhlZd4EPHF3XnFDM1GvjwmrNlDSHOErSz/dvYFSUOgHj4c7F8UzE7QQosckr93wY+D/OrNduknD9VrrupTHDOBCYIkx5vfGmKgxpgr4SXI+AMaYe40xO4wxCWPMI7gH5RM7E2AHHjfGvGSMscaYMHAlMMcYs8QY4yR7El4GztlXQ1rrQuAJ4B/GmBe7Iba9CoVCMi3TB346z49F4bdtR/ISXg9VFcWMXr2Bkz5cji+RoD2PyoL4ZVqmD8B0t1PtHtnnFKBT50spa+1eV0gOB7zQvqZBa30XcAnQnNoe4DXGFGmtPcBNwNnAYNxzQgtxhyhu7qgQMs3hiZeMMXNStmlKvvHUPZ4PeMgY8629vLdi4J+4dQ1nG2Oie/0wusfeP3AhekB0+XZapswhz+5gwYBZxL0+avsVU1ecR7+d9cS9XjzWIRDczDfOvQTiDjTFwbH848IAsyYGMv0WhOhJPXJIn3f4c23291e+c2rGUgelVJtrMwAFuCcCfNta+0C67XSlpmE97sH8jA6Wn4ubVJwKLDfGOFprw+5fTkfZTRNucrFL5R7Wab/teuBHxpjH0ooc0FpXAM/i1j+cb4yRPliRswKTBhJwfgNAxRFP8I+KUWwa0p/PrFxLQ0UpLYX54FhmrvyIjydvZ8IZQwG5wJMQXZFlhZDnt3veBKy01jZ0ppGuJA0PAtdorWcD84EobnHiwcaYZ3FvihEHqgGP1vpi3DqGp5PbV+Me/MfhVm/uYoCLtNYv4yYMV6cRyy+Am7TWq3ALLPNwizNrjDGftF85WcPwPPAeMNsY8599skLkqFGNq5heZYn7PGztV05lc7Kz0KN4ccLRXDCmLLMBCpEjsqkQEjjcWvvz9jOVUldba+9Mt5H9LoQ0xmwFTgK+CKwDdgJPsfvUjQeAxbjXQKgCJgGvpWzfjHuthIeTdRLXJxddAYwFaoFHgfvTiOUe4HbgvmQcG5Jt+zvY5HJgCu5dv+q11o3Jxw/SeOtC9GqlQ4MEE3EO/3A101auBWd3j2XM46GwTK75JkR3sEq1eWTYjR3M/2FnGtlnTYPodvKBi8zaVsfSkx5iw/ZidpYUUNW/nHBpERG/Q2FzgovvO5IRo2RoQvQpPXJEv/Pol9rs769+Y+YBzxxSLuq0APg8bd/rQcAN1tqR6bYnXymE6GsGlTF1+Xf4jLV8/8IP6b++jpLmCDRDOD9Icak30xEKIbrPros65QF/Splvga3AdzrTmCQNQvRRSikOO3kLq18rw9kMvgIvn/32GMrLZbcgRHfIgiGJ1os6KaUe7MzdLDsiewch+rDiiiiHfmE7s2bNynQoQuScbEgadumOhAEkaRBCCCF6RDYlDUqpEtxrJ50A9CeltsFaOyLddrLnFlxCCCFEDrEe1eaRYXcDh+He0qECt5ZhA+4lC9ImPQ1CCCFED8imngbcCy1OtNbuUEolrLV/V0oZ3LMq0k4cpKdBCCGEyH0eoD453aiUKgO24F4XKW3S0yCEEEL0gCzraViCW8/wIu6FFu8CGoGVnWlEehqEEEKIHpBlV4S8FPfqzeDeGboZKCPlztTpkJ4GIYQQogdkQaLQylq7JmW6GveGkp0mPQ1CCCFED8imngblulQp9ZJSamly3vFKqa92ph1JGoToi8IR+O69HDHneQa9vSHT0QiRk7IpacA91fIbwB+AXddl2ARc25lGZHhCiL7ohvnwq38yCBjwwWaY/RUYPSjTUQmRU2zG84Q2LgYOtdbWKKV+m5y3lt13pk6LJA1C9EF21VY+yZ/G6tLh+BJxplzyOMNf/HamwxJC9Bwv7tkSsPtuy0Up89IiwxNC9EE1oXLeHTCB2rwSthdW8OEyH9vvXZLpsITIKVk2PPEMcKdSKghujQMwF/fiTmmTpEGIPihcb0koD5W19YzfXE0or5zfPRLiJ2e9xdaNLZkOT4ickGVJw9VAJe4FnkpxexhG0smaBkkahOiDgus2UNHSzKCGJvKjMd4+ZDzby/tR5Svlnh+syHR4QuQER6k2j0xQSg0GsNY2WGu/iFsEOQMYY639krU21Jn29lnToLVeCBwFxFJmP2KM2a9zPFPaHYVbhDHcGLOpK211MY7Tcbtt7u3qexIi24Ve3cyWEx7CTwkFQyMMDq7F68Sx8Wk8Pu0QAo7ljE8z9u8oRE6xZLx3AdwrPpakPP+dtfas/W0s3ULIucaYW/b3RXqS1tpvjInte809blsK/Ap4vXujEiI7bTrtMfKwRP1+xjWson9kJzsKCrnruCOI+vwAvDysP0RiEPRnOFohercsGJIA/iNzObErjXXp7Amt9RTgDmA6EAb+Aty46yCutb4POBn3UpUbgVuMMfOTm++qulqhtbbAT40xc5PTxxljFiXbOBF4wRjjSz5fCHwAjAJmArcCt2mtLwWuAoYDa4BrjTHP7eMt3AncC0zswscgRK9Q99pWaIm5exBrGRCqI4GH1/of2powADQEvdiPN6EOGZ2xWIXIBVmSNNh9r5K+/a5p0FoPBF4BnsQtrjgKOAW4LmW1RcAhuEnDHOB+rfWk5LJpyZ/jjTFFxpi5nXj52cA83GKOeVrry3CLOc4DyoHrgSe11h3evUtrfVoytjs68bpC9Foff/UlvFgieCmMN9NEOasKRxJJlHD6e6sA8CQctvgL2XLWrzIcrRCim/iUUicppWYqpWa2f56cl7Z0k4brtdZ1KY8ZuDe5WGKM+b0xJmqMqQJ+QsrNL4wx9xpjdhhjEsaYR4CldLFrJOlxY8xLxhhrjAnj3nxjjjFmiTHGMcY8A7wMnLOnjbXWJcBvgUuMMfFuiCdtoVBIpmU6I9OJSAKwNBNka0kRJ115BTeffCYAF7z6Ib//3dMcYz6FBDQnVFbELNMyfSCn/397dx5fV1Xuf/zzZGjTNh3pQEtb0tIWqEzCQkBAAQWxiIJ4L+IApSLgFS9eQVFmWpnx4uUKPwGlBWQQpKAgAjKUS1WGJVBApg6kE23TIW3TJh2SrN8fawdO0gynTc45ycn3/XqdV/Z8nr3OydnPXmvtvTtaJ7l6ogK4g1ir/ltgdZPx32zPxiyE1msukuaAp5v2aXDO3Ux84EVN6vaAQu99qXOuALgcOBnYmVhF0ofYRHFFSx0h02yeeNZ7PzVlnY1APVCXEksRcLf3/nvN7NNtwGrv/c+S8RlAbZY6QnZoVZFIupbPeJ+Fp/+V/mzikq8dxR8O2pvSms1cOHM2ZSvX8fbQQVz7OUd/NlNx5FJs0v65DlkkWzJyRL/w+Nca/d5f9egnO0V7RXu0p0/DQuLB/LgW5p9CTCqOAd723tc75zwffzj1Lay3kZhcNBjRzDJN110IXOa9fzCtyGNM/ZN+EBDvioVz7vPe+7I0tyHSpew8eQKr/ryY5X9YwIrSUgA29yjmXxN3pWbVOtb0KmFMVTXrSnsqYRDpALm6zDKT2pM03AWc55ybAtwLbCF2TpzgvX+CeIlHLbASKHDOTSb2Y3gsWX8l8eA/nvjQjAYeOM059xwxYfhRGrHcCFzunJtL7GBZQuycucp7/24zyx9M433/7yTW89N4L5Eua+yVjurXKzn+pbm8t8sQxqzfyPjV68GMoZs2s1fVRqacPSjXYYrkhU7SEbJD7XBHSO/9cuBI4ASgHKgEHubjh1/cCbwEzAOWAhOBF1LWrwEuAe5L+klclMw6BxgHrAEeAGakEcvtwHXA9CSORcm2m71mzHu/3Hu/pOFFvPKj2nv/YZq7L9Il9Z7Qn0/N/RrD3CBu+/WjnDz7rUbzd92pkOP2K8lRdCL5pZP0aehQbfZpkA6nApecu/mqRfSa8Sq9N9Twzp5lhB5FVPct4fQrx/OJ8UoapNvJyBH9/BPfbPR7f8PDe3f5zEFPuRTphnr1K+bl/Sd+NH7pDbsyYmTPHEYkIl2Bnj0h0g2d8PXBjB1fQs+SWnbfb6USBpEMCNb4lQ9U0yDSDQ0aXMxPpo3m0Ue366m4IrIdOsmzJzqUkgYREZEM0CWXIiIikpZ8uWIilZIGERGRDFDSICIiImmpz7+cQVdPiIiISHpU0yAiIpIBap4QERGRtNTrkksRERFJRz7WNKhPg0g3Fupg9T978n+/LWd9xeZchyOSV+qt8SsfqKZBpBtb/mwvKt/oyQoW884zKznjzgMoLNa5hEhHyMebO+nXQaSb2rp+Cxvf+fgnYO2Hm9hYuTWHEYnkl3x8NLaSBpFu6sUz/06fD2s+Gi8oLqD3gOIcRiQinZ2SBpFuZmtd4Mp7VrDohQoGraqiesNWQuVG1lXW23iJOQAAIABJREFUsLa8KtfhieSNfOzToKRBpJu57M8bufifPXhu7C68PmIwIyvXMaqyij0rKrn72y/lOjyRvBGwRq98oKRBpBtZt3orix9fzrnvv8+4TdVYSSHTj/4kt31hf/rVrmdc+RKqxl9CWLo616GKdHn1Zo1e+aDNqyecc7OAQ4DUHlL3e+/PaM8bO+fKgA+AUd77Je3Z1g6+/6nABcBoYA0wHbjCex+yHYtIttx+zUKGVtRAj95s7b2FOw7fmw8HlAKweNCXmX3d/+P9iiHsd+gVFJbflONoRbq2fEkUUqV7yeU07/3PMxrJDnLOFXvvt6vLt3NuX+AO4KvAo8DuwHPAUuD2Dg9SpJNYungzy3r3pqaoiJFba6no2+ujecv79SEAo1jG1sUbKcxdmCJ5IV/6MaRq130anHN7Ab8ADgCqgXuASxsO4s656cDngQHAYuDn3vt7k9XnJH/fc84F4Frv/bRk+HDv/exkG0cAT3vvi5LxWcDrQBlwFHAVcI1z7rvAucAoYAFwgff+qRZC3w2o8N7/KRl/N9nuvu0pD5HObOG6wD/796cgSQfWDR/G/gsreHnscAC+8eIbbKGYjRTTu35DLkMVyQv5eBvpHe7T4JwbCjwPzARGEJswjgZ+lrLYbGA/YtIwFZjhnJuYzGs4QO/uvS/13k/bjrefAtwE9Aducs6dSWxq+CYwELgImOmcG9fC+k8CHzrnTnTOFSTJz2eAP7WwfIepqqrSsIZzMjy3opplvT+uWcCMXnWBB391L3/433u58LHnqaeAzfQArFPErGENZ3NY2mYhtN6En5yBHwSk3mP2WOAwYJL3/qiUZU8i1hg0e7B2znngDu/9LS31aUizpmGB935KyjpvAdd57+9KmfYo8FJLzSrOue8DVwO9gcIk7p+2WhgdQ30mJCe21AVGX7WBcUs2UAQU1tfz/LghXP7IM3zjxTcA2EgxO7GG0tGF9Ft4RW4DFsmejFQJnDx5YaPf+9/P2LXLVz2k2zxxZdODr3Pu28Chzrm1KZONeADGOVcAXA6cDOxMPFj2AYa0M2aA8ibjY4CbnXOpPbeKgGY7WDrnTk9iOxp4hdgZ8n7n3FTv/aUdEJ9Ip9Oj0Fh0YSmTzl7N0BXr2TCgJ3WFw7jkpKO5/6B9OHTuEr76+huM/vcy+t341VyHK9LlqU9DYwuJNQDHtTD/FOAM4Bjgbe99fVLT0FCM9S2st5GYXDQY0cwyTdddCFzmvX8wrchjH4xnvfcNF6WXO+fuITZ7KGmQvNWj0Pj2cf144c4N7P/eImbvtguVfUpYNqAfwzdsYtmuu9L/xmNyHaZIXujOV0805y7gPOfcFOBeYAuxc+IE7/0TQD+gFlgJFDjnJhP7MTyWrL+SePAfT+MaAQ+c5px7jpgw/CiNWG4ELnfOzSV2sCwhJgarvPfvNrP834D/cc4d4L3/p3NuFPAt4NU0912kyzryqIH8+YEKPvfye+zz/lKWDihl2LoqZh34CTYeMybX4YnkDXWETOG9Xw4cCZxAbC6oBB4GxiaL3Am8BMwjXso4EXghZf0a4BLgPufcWufcRcmsc4BxxHsnPADMSCOW24HriPdaqAQWJdtu9kb63vv7gOuJTRJVwMvAW6SXoIh0aaP7GUOOHs6SIQMYunYD+5Yvp2LIQCp69+JzkwbmOjyRvFFnjV/5oM2OkNLhVODSKby/rJZn//0pqqqLqerXi35bazn7kUMoHdwz16GJZFtGDulfOmNJo9/7x34zssunDrqNtEg3NWF4EVuH9MBKiui3tRYDCoq6/G+aiGRQu27uJCJdW0HDL0BS41i3VRVhIh0lH6+eUE2DSDc2+LObKewZn7930Gm70neImiZEOko91uiVD1TTINKN9R5dz27nbuSLX5hEcYmeNiHSkep0yaWI5BsrQAmDSAbkY/OEkgYREZEMqMuTJolUShpEREQyIF/uzZBKHSFFREQkLappEBERyQA9e0JERETSoqsnRCRvVK/cRMmv1lBEPbVHbqGotEeuQxLJK7W5DiADlDSIdEM1qzbx2p4z2GP1agDmjb6VPdb8IMdRieSXfKxpUEdIkW5ohV/N4HXreWP4Ljy3x55sqA1sKl+f67BE8kqtNX7lA9U0iHRDBcP78OKY3SgvGwkFRvnIEYzYUM+IXAcmkkdq8/A+DappEOmGVqyrp1dRDRTEH7VQWMDz9yzNcVQi0tkpaRDphnafdCH7rnrjo/F6gxlLe7Cuui6HUYnkl63W+JUPlDSIdEMF1Rt5tf/e9F+8hk3rNjN75HBeHz2Ub96xga/cv4WFa+tzHaJIl7fVrNErH7TZp8E5Nws4BNiaMvl+7/0Z7Xlj51wZ8AEwynu/pD3b2sH37wNcDfwb0BdYDHzDe/9atmMRyaa6pWu55cDvsOfcDymrXATA8DVV9Dt0L56vLqWyoo5n59ex6oISehblxw+dSC5sbXuRLifdjpDTvPc/z2gkO8g5V+y9367PxjlnwCNADfAp7/1i59xYYGMmYhTpTDbNmseqgf0YXPn+R9N2XbaaJaUlhD7FUAAbNsPidYFxOylpENlR1XlSu5CqXVdPOOf2An4BHABUA/cAlzYcxJ1z04HPAwOIZ/I/997fm6w+J/n7nnMuANd676clw4d772cn2zgCeNp7X5SMzwJeB8qAo4CrgGucc98FzgVGAQuAC7z3T7UQ+jHAocBI7/0aAO/9gvaUhUhX0bOuhv7rC6jYqR9DV8fLLOeMHkZ5cTFr+/eCEKC6lsF9chyoSBdXk385w473aXDODQWeB2YCI4hNGEcDP0tZbDawHzFpmArMcM5NTObtm/zd3Xtf6r2fth1vPwW4CegP3OScOxO4APgmMBC4CJjpnBvXwvpHAvOBC5xzFc65+c65q5xzxdsRww6pqqrSsIZzOrz1j3Oo6lfCgrLBvDVyMK+WDeO+gyZSvLUO6gOYQa8iFqys7jQxa1jD2RjuaFuwRq98YCGEVhdIzuwPAjanTD4WOAyY5L0/KmXZk4g1Bs0erJ1zHrjDe39LS30a0qxpWOC9n5KyzlvAdd77u1KmPQq81FyzinPuN8B3iLUkFxNrJx4HZnjvr2y1QNqv9QIXybD6l+Zy7U+W4V7/gAHrawB4d+dB/PBbx7BmWCkAfQphzX8W0qMwP37oRNqQkS+6/XBNo9/78MtBXf4fKt3miSubHnydc98GDnXOrU2ZbEBhMr8AuBw4GdiZeLDsAwxpZ8wA5U3GxwA3O+duSplWBLTUwbIKqAMu8t5vBuY6524GvgFkOmkQyamCg8Zj9R9SuvHj84AJK9bwX3/9B3/fewy1XxrHfccXKGEQaa88/BdqT5+GhcQagONamH8KcAax/8Db3vv6pKahoRhbuqZrIzG5aNDcTeqarrsQuMx7/2Bakcc+Ec1RLYB0CysHDaB85BDGLVwBQClrGP/hQM76ZT+GHKEbxYp0CHWEbOQu4Dzn3BTgXmALsXPiBO/9E0A/4kO+VgIFzrnJxH4MjyXrryQe/MfTuEbAA6c5554jJgw/SiOWG4HLnXNziR0sS4idM1d5799tZvmZwDXAFc65S4FdgO8Bd6S15yJdWAiB2qIiXjxgAktG7ERRbS1h82ZWF/bgM5/cOdfhiUgntsMdIb33y4kdCk8gNhdUAg8DY5NF7gReAuYBS4GJwAsp69cAlwD3OefWOucuSmadA4wD1gAPADPSiOV24DpgehLHomTbzXZs9N5XAV8gXkFRCcwC7gNuaHvPRbo2M2Nk3zowY8mIwawe1Jd3y3ah/Njd2Llf/p0ZieSMWeNXHmizI6R0OBW45NzLfgNP/+gVhlWtYuLyd3lz1N4c89Akykb1yHVoIrmQmY6Q569t3BHyhgFdPnNQ46VINzTMtvDl959grxVzAdi78j36DGupe5KI7JgunyNsQ8+eEOmGaldvYsTaio/GSzdXY1WbchiRiHQFShpEuqGRhwxhwYRPftRWtulLh8BOfXMak0jesSavPKDmCZFuqGffYvZ98T948qpBlPTcwhEXn57rkETyT54kCqmUNIh0U8W9i9h6UP/4JL486dkt0rnk3/+VkgYREZFMyL+cQUmDiIhIZuRf1qCkQUREJBPyL2fQ1RMiIiKSHtU0iIiIZIJqGkQkn9TXBba8t4UN5ZW5DkUkD+XfjRqUNIh0U1u31PPyvYN56M39+eHPlrHgnjdzHZJIfsm/nEFJg0h39ebMBSwrHExhXR3VPXrxxMyKtlcSkfTl4VMu1adBpBvaPG81f5pewZhQQL0ZiwYNYI71ZdOGWkpK9bMgIs1TTYNIN/TB7f+iIBSwsG8vttZuYsDatTw7eiTzn16e69BE8kceNk/olEKkG6ocMYBHNg3ml79/iD1WrGRTURG7rDiA3ruXASNzHZ6IdFKqaRDphmZW9GRIVSWjV6xjnfWmR20du1Stof4P7+Y6NJE8kn9VDW3WNDjnZgGHQHyuTeJ+7/0Z7Xlj51wZ8AEwynu/pD3b2sH3/ypwOTAWWApc7L1/MNtxiGTbazf+H9WvVDIyDOedotFgRp/6GjYVGvNK+rBbrgMUyRf5kSc0km5NwzTvfWnKq10JQ0dyzhXvwDoHA78Dfgj0A84H7nHOHdTB4Yl0OsW3/oWXdtmNz7y6kAWjh7Bo5wFsDsWM/2AFj4WBuQ5PJH/kX0VD+/o0OOf2An4BHABUA/cAl3rvtybzpwOfBwYAi4Gfe+/vTVafk/x9zzkXgGu999OS4cO997OTbRwBPO29L0rGZwGvA2XAUcBVwDXOue8C5wKjgAXABd77p1oI/avAk977Z5PxR51zfwPOAl5qT5mIdGr19TzSdy/eHDCcx44spqZvLyb/8R+U1NdRt6U/X/vzm1Q+OZyBXxid60hF8kCeZAopdrhPg3NuKPA8MBMYQWzCOBr4Wcpis4H9iEnDVGCGc25iMm/f5O/uSe3FtO14+ynATUB/4Cbn3JnABcA3gYHARcBM59y4FtYvYNtPsyCJVSR/LVnNA7vvR48tdawfWMqQyg0Mq6z6aHYBsPzGN3IXn0g+ycOahnSThoucc2tTXgcDpwJzvPe3eu+3eO+XAlcn0wHw3v/We7/ae1/nvb8feAM4ogPi/oP3/lnvffDeVwP/CUz13s/x3td77x8HngO+3sL6jwHHOueOds4VOedOBA4lNlVkVFVVlYY1nLvhof3Zr+JD6gx6bN7C2r69qOnZuIVvwDEjcx+nhjWcg2Fpm4UQWl0gaQ542nv/8ybTbwbOAGpStwcUeu9LnXMFxI6GJwM7AwHoQ2yiuKKljpBpNk88672fmrLORqAeqEuJpQi423v/vRb2azJwHrAL8AJQBYzz3h/caoG0X+sFLpJha/42nzOvX8lhc1fx1IF7cPCCcia+s5TKHn3Zo2INh2/tNF2WRLIlI/UAdllNo9/7cEWvLl/f0J4+DQuJB/PjWph/CjGpOAZ423tf75zzfPzh1Lew3kZictFgRDPLNF13IXDZ9lz94L2fAcxoGHfOvQI8k+76Il3VoEN3wx7oyfqVmzh42Wro1ZdXPvUJntl5MLfM/1euwxPJH10+RdhWe5KGu4DznHNTgHuBLcTOiRO8908Qq/prgZVAQXJmvy+xaYBkej0wHki95NIDpznnniMmDD9KI5Ybgcudc3OJHSxLiJ0zV3nvt7nw3DlXBOyTLFtKvHpiVLIdkbxX8unBLF68hlEbY0Vhn9o6Jr/8NqU/3iPHkYnkkTx53kSqHe4I6b1fDhwJnACUA5XAw8T7HgDcSbwSYR7xPggTic0ADevXAJcA9yX9JC5KZp0DjAPWAA+QUhvQSiy3A9cB05M4FiXbbulyzELgNmAt8aqOfYDDvPcr2txxkTwwfngRS/r0+mi8oK6e3hs30W9YSQ6jEpHOrs0+DdLhVOCSc9NeqOPhe1ewqa6OfVZWUli9hfL+pdxzYg/KThnb9gZE8ktm+jRM3dS4T8OlJV2+6kHPnhDpho4sM2aUlrCiDt7pHy8aOv79hex0YHNdiERkx3T5HGEbevaESDd02KgCfnfuAL7UbzlHVK3kvLCSu68eTd9xGb/qWKT7yMP7NKimQaSbOmRkAasOmA/A8ccfn+NoRKQrUE2DiIiIpEU1DSIiIpmQJ00SqVTTICIiImlRTYOIiEgm5OHNnZQ0iIiIZEL+5QxqnhAREZH0KGkQERGRtKh5QkREJBPysHlCSYOIiEhG5F/WoKRBREQkE/IvZ1CfBhEREUmPahpEREQyQTUNIiIi0l0paRAREZG0KGkQERHJBGvyam4Rs3Iz2yuLUbWLkgYRERFJi5IGERGRTDBr/Ep7NTvVzN40szfM7GEzG5pM/4eZHZgM32Jm/0qGi8xslZn1ych+pFDSICIikglpNE9ss0psqrgGOCaEsA/wFvC/yexngM8lw4cBNWY2HDgQeCeEsLHDYm+BLrnMMjN7EhiczfcsKioaXFtbuyqb79mZqTwaU3lsS2XSWDcojydCCMd29EbD+UU7ctHlkcDjIYRlyfitwJxk+FngQjO7B1gNPE9MIsYQE4qMU9KQZZn4YrbFOee99y7b79tZqTwaU3lsS2XSmMojqwwITaY1jP8N2B84jpgkPA9MISYNl2YjODVPiIiIdB7PAJPMbOdk/LvA0wAhhM3Aq8BPk2kvAocC+yTDGaeaBhERkdx62sxqU8YvBP5qZgFYAJyVMu8ZYh8GH0KoNbN5wAchhC3ZCFRJQ/dwW64D6GRUHo2pPLalMmlM5ZEhIYSyFmbd2cLyVwNXp4xPykBYLbIQmjadiIiIiGxLfRpEREQkLWqeyEPOud7AdOAAoBY433v/WCvLlxA711TnYw/pdMvDOfcVYg/knsQezHd473+RzVgzxTk3gVjduRPxUq1TvfdzmyxTCNwEHEvsrX2N9/432Y41W9Isk0uArxO/N7XAhd77J7MdazakUx4py+4OvAbc4r0/P3tRSq6ppiE/nQ9Uee/HAccDv3HOlbay/JXAP7ISWW6kWx7LgeO993sBnwa+55w7PItxZtKvgZu99xOAm4nXfjf1TWAcMB44BLjcOVeWtQizL50yeRk40Hu/L/HStt8753plMcZsSqc8GpLLW4FHshibdBJKGvLTycQfAJIzBQ98sbkFk4PieODurEWXfWmVh/f+Je/9h8nwOuAdYNcsxpkRzrmhxGu770sm3Qfs75wb0mTRk4Hbvff13vuVxIPCv2Uv0uxJt0y8909676uT0TeINVA7ZS3QLNmO7wjEy/0eA97PUnjSiShpyE+jgYUp44uAUU0Xcs71AX4JfC9LceVKWuWRyjm3B3Aw8Q5sXd0oYKn3vg4g+fsh25bBdpdTF5ZumaQ6FZjvvV+ShfiyLa3ycM7tA3wBuDHrEUqnoD4NXZBz7lXiD3xzhm3Hpq4nVkcudc6Nb39kudGB5dGwveHAH4HvN9Q8SPfmnPssMA04Otex5Ipzrhi4HTjde1/nXN51f5I0KGnogrz3+7c23zm3iFitvjKZNBp4rplFDwMmOecuBUqAgc65N7z3+3RkvJnWgeXRUE37NHC99/6BjowzhxYDuzjnCpMf+0JgRDI9VUM5vZKMN615yCfplgnOuUOA3wFf8d6/l+U4syWd8hgO7AY8niQMAwBzzvXz3p+Z9YglJ9Q8kZ8eJLmDWFKDcCDwRNOFvPf7eO/LvPdlxB7ib3a1hCFNaZWHc24n4K/Ar/LpqgHvfQXwOnBKMukU4LWk30KqB4HvOucKkrbsE4CHshdp9qRbJs65A4HfA1/z3r+a3SizJ53y8N4v8t4PTvnN+CWxD4wShm5ESUN+uh4Y4JybR+ywdKb3vgrAOTfVOXd2TqPLvnTL46fABOAs59zryev03ITc4c4GfuCcex/4QTKOc+5x93E9893EW9bOJd7Hfqr3fkEugs2SdMrkFqAXcGvKd2Lv3ISbcemUh3RzuiOkiIiIpEU1DSIiIpIWJQ0iIiKSFiUNIiIikhYlDSIiIpIWJQ0iIiKSFiUNImkyszIzC2Y2MsPvc7aZ3Z0y/hcz+0km31OaZ2bzzGxymstm5fuRDWbW08zmmtkeuY5FOhclDdLhzGysmT1oZsvNbIOZLTazh82sRzJ/spnNa2a9lqZ/K/kxvrSZebPMbHPyPuvM7DUzOykze5Z5ZtYHmApc3jAthPDFEMJ1OQuqDclnc1iu4+gOMlHWZnaEmdWmTgshbAZuIN7jROQjShokEx4HlgG7A32Jj1l+kviEwB1xJrAGOMPMCpuZPy2EUEp8+uB9wO/NbMIOvleufQt4M4QwP9eBSLd3H3CUmY3LdSDSeShpkA5lZjsRk4VfhxDWhWhJCOHXydnL9m5vT+Bw4DTive+bfcQ3QAihlngHv0Jgm7v2mdk5ZvZak2ljzKzOzMqS8elJzUiVmb1tZt9oJbbLzezpJtNmmdnFKeN7mdmTZrbKzBaZ2dVmVtzKLp9AvJV1s9tMqQI/LYlvo5k9bmYDzewaM6tIani+n7L+5KSa/QIzW5Ys84vUONrabzPbx8yeMLOVZrbGzP6aTJ+TLPJUUtvT7O23zay3mf1P8h6rzOwRMxudMn9WEtNDSQzzzewrLRVSyj79l5ktSda5wcx2Srax3szeTT0rN7MiM7vUzBYk+/CMme2VMr/YzP47pQwvaOZ9Dzez2cn6883sPDNLOxk2s5PMbE5SKzbHzE5suk9Nlp/RUKYtlbWZlSf7NTuZ7s3swOa2kTKt3GIN3gjgL0Bhsu4GMzsNIISwnvgcki+nu3+S/5Q0SIcKIawG/gX8xsxONbOJ2/Oj2oyziGfejxFrMFq8z73F5o/vA1uBOc0scg+wp5ntlzJtMjArhFCejM8G9iM+jGcqMMPMJu5I4GY2FHgemEl8+M8hxKck/qyV1fYH3k5j8ycRHzg2GigDXgLmJ+9zOvDL1IMy8UFUo4GxSRzHA+enzG9xv81seLIfzyfvtTNwLUAIYd9k/WNCCKUhhDNaiPdG4qPGD05iWQU8ao1rjk4D/hvoD/wKuNPMerdSBrsm8Y5NyuIHxAPg9cBAYrlPT1n+x8THW08iJqAvAH81s37J/J8CXwI+DYxJ9nXXhpXN7BPE7+D1wBDgOOAc4NutxPgRMzuE+B38KbFW7ELgPjM7KJ312yjrs4FzgUHAH4DHU/artW1+SEzE65JtloYQ7kxZ5E3id1IEUNIgmXEEMAv4IfEhOCvM7JImycMYM1ub+iLWEnzEzEqIP8h3JJN+C0yybTuaXZSsvwT4CnBSCGGbvhEhhEriI69PT7ZvxAPVHSnL/DaEsDqEUBdCuB94I9mfHXEqMCeEcGsIYUsIYSlwdTK9JQOB9Wlse1oIYU2SpD0GbA0h3B5CqA0h/AWoBD6Zsnw98OMQQk3S9HEdSTlAm/v9bWBeCOHqEMLGZF8a1bC0xswKiPt8cQhhaQhhI/G7sSfwqZRFfx9C+FsIoR64jZg8tPbI9hrgiiSeOcRE8ZUQwoshhDrikynHmVn/ZPnTgWtDCO8mtV5TgTriwZ8kxmtDCPNCCDXEpCr1PvvfAx4MIfwxKad3iclNa59nqtOBh0IIf0k+pz8DDwNT0ly/Nb8NIfwzhLCFmNDVEBOg9lpPTEREACUNkgEhhFUhhAtDCPsTzwR/AlxKykEK+CCEMCD1BfxHk039G1BK/PGHeJZXATQ9m70y2cbQEMKnQwiPthLedOCbSa3EUUl8MyEe3Mxsqpm9l1QfrwX2JZ5V7ogxwKFNEqM7iGfqLakE2jxDJPYZaVDdZLxhWt+U8YoQQnXKeDkwEtLa7zLg/TRiaskQ4qPXP3r4VQhhA/GzHJWy3LKU+RuTwdR9aKoiSTAaNC2Hhv1t2MaoJjHUE8uhIYaRyXhqDBUp2xsDnNLk87yMWGuRjkbvn5hP4zLYUeUNAyE+UGgRyefbTv2I/YlEACUNkmEhhOoQwgzimet+bSze1FnE/glvmdlyYk3CIOA71nyHyHQ8BWwinoVNBu5PziohPg74DGLV/8AkkZlDyx04NwB9mkwbkTK8EHi6SXLUP+m02ZLXgB1qDmnD0CZV/WXE8oS297uc1s/423rq3UpgM/GgC4CZlQJDgcXphd8hFjeJoYBYDg0xLE3GG+b3IcbYYCFwR5PPs18I4RM78v6JsSnv39b3CVou69S4jdgU1fD5NtqumRXReL9SE6+m9iJ+J0UAJQ3SwSx2yLvaYgfA4qTz2UnEH58XtmM7E4FDgROJyUbD61PEM/VJOxJfcnZ5F/CfwFdJaZognlXVEg9yBWY2hXjG3RIP7G9mByT7eQ6NDwp3Ac7MpphZSXJGP9bMjm1lm48An9/+PWtTAXCNmfUys7HEqveGtuu29vt3wO4WO1L2Tj7Xz6XMX04rSUVKmU8zsxFJ8vIL4F3g5Q7av3TMAH5iZhOSmqaLgCLgz8n8u4Efm9luZtaL2ISTmjDeAnzdzI5P+W5PNLPPbsf7n2RmXzCzQjP7IvE72NDv4jVicvel5LtyIvCZJttoqaynmNn+Fju3/hjonbJfHvicxU6/PYErgdTOuMuJHSEbJTRm1pf4//anNPdPugElDdLRthDPYmYSqzVXAhcDPwghPLgd2zkLeDWE8GgIYXnK6w3gwWT+jpoOfJbYRJJ60LqT2KFwHvGscyKtJDohhFnEg98TxGrxYcDfUuYvB44kXhFRTmx6eJh4dtmSu4F9kwN7R1pI3KcPiPv4BPGgCG3sd9JZ7ghiJ84lwAog9cqCi4CpZlZpZre28P7/RTx4vUKsOh8OfDnpe5At1xMvI3yKuA9HETsVNvQhuZp4afCLxHJaRCw3AEIIbxFrqH5I/LwriIlAWs1XIYS/E/vQ3ED8LlwHfCuE8GIyfz6xM+NtxP+dY4GHmmympbK+Dbgp2e7JwHEhhHXJvHuIB/5Xic0hi4ifc0Nc7xMTopeTZpeGjp2nAM/2aEsqAAAAlElEQVSFEOams3/SPVhs/hKRzsLMzgYODSGk1Ss/je1NJnZC1PX2ecjMyomf7+/aWnY7ttkTeIuY2L3TUduVrq8o1wGISGMhhF8Dv851HNJ9JVeXtNaPRbopNU+IiIhIWtQ8ISIiImlRTYOIiIikRUmDiIiIpEVJg4iIiKRFSYOIiIikRUmDiIiIpEVJg4iIiKTl/wOtxiutlCzidwAAAABJRU5ErkJggg==\n", 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ptgxbb1yELXobUZHMwPItANR94yXsLV0Y3TGMZBIDBwF4yaHhEKab1le66abPhEQEOr3XfTuI/JdNAzSSFPfsP9FmUDTay0iaCJBGQ+Ili4aDg4c0AUDDThtoqRhg004xnRQh0XDQiRNBx2EE9RTVSra0FOAInU69iLXeMcQoxCPTNHaHSGbdijCSTrG9r1cC3kwOv5NDSodAPIcmIduWZvVyPzJfWcW1IMueTtKxpG2n4zri5GH4EjmELTEyDu9e8/YeP4cx51WjO7Lnti9vX7uIrmXtrPv5u9gJm9jKLlbf/E6/PFWzKxHbf7VS0vhcA3Z679fXb7fl+QbWPbIZO+3QvKiN5X9au8f0r/5wEZmo2xikOzK8dt1i2ld3Y6dslt6zltYVnSy6ezWtK7qwMw7ZuE02nmPh3GWku90OwHRXhtd/spRsPEesPsHrc5ftcZ+dD0ucOKTaM7x0w5IBv7e+lt63kYbF7dhphzVP1LPpxcae1965dz2NSzqw0w6rHq1jy8JmFv95PU3LOrHTDisfqaPutZZ92u/eLLxzDR2bE5C2OWf5RpLbEvzvkzG6k5LmLocfPxTrl765McO8h1opau9G2BJfV5pLN2xGFHjBo4MQbI7BzMkh0DUMYHpTb/A6vrkDbzLH6M5uOr0GM9tjBPLzF4SUjO+KExOCOq+HrOPgr/LQ4PHgCEG3oVPoc+8wACAEoAkM26YokSFhGLT7PLxWXsjaSIBNQT+2gPJUFr/d+51MeAyeqR3OKxUl6JpGMJvvJZSSaS0dABQO83LqWPeL3VQSJOvRieUED5YMp9sRvPNkMxsXd/Vsc+JppSwYW0V9UZisrnPPSsGCLYffzaY+yPdp+JFpmt/r8/wjwCxgqWVZf8ivqzdN82fAL3ADBCzL+lOfPA/ltzEbWLFfpYZ/Wpb1Qn45YZrmN4GfWJa1vffiKdM0XwQ+DexqWKUA6NphXScw6FPlo9EoBQUF+7TcVyabZfv5iO04bD8fkPTGtrvbTrpcZ9QT57P21Edw0Hq+zDktjG5vPyxavpYAmcpQRAwN90yg7z6KaaaALhoZST2T6aINDymSRIhRDB6NknNH4nns7R1+MpI0XroZQQ4/IRnFG0ngdO/5+AkkPloRehHspq3MbolBqHeMs41hlLGtp5cjiY8iolRl4pR2dlIfLCbj+CiJu92tw4rDtCZA5uu2tq4g7515PrG3GmjxlJPVvMTjCYrzQVI0GiXzZpTkAxuJ5CSOyJIK6jhObwF39Vkcd9MxbH1iC6mGVP6Q7Lozs993oNihcESQ6MYYQoLmFbvd/q6W97b9gaTvKxGP7zVNLBbb5frdfs/7fFGkdPaafiC/l2Qy2bOcyWT2WuZEojf9/vxm3+/xdJz+7xd27gXyZG38QpLMH6hEwmFtU+97OmlrK3WGwTkdLYze3M7qUZXkdJ1PNLbhDesUtnbR4vUQydl0eb1YYT85TcMf9PH1MQJW9e5ri/Bw5pYGqlu7qIgnaCgK46CzZng5mnRrjqM64jT7PKwpDPKfylKKNIkMGfnfp4BAvnYqDNO9Is3w7iQJj0Ekk2V6UzvlNUEuu30Ky1Z30fZOlJZhQZK7aZqi0Sh+X4hF76bJeHZdXR+oOvbAGBqBQl8D7Wm41bKsoj6PN4Ba4BTTNDu3P4A/AxUApmlqpmn+xDTN1aZpduVfnw4MOwDl3rTD81rgrh3KcgYwYjf5o7i9FH0VAXtpsvZf3y/n+1qeNRm+eS4EvDBjDN65l/ak0f/nqzCyFAqDiD9+1Z3IuJdthmZVMfzWkxAhD/5JRYz85Ulwx+fJ6QGkMLA/ewYcNQqA4Z+oRtt+tgFo5SFEwCD0jZkUfqIW4fdQdnoR3jER4pSQJUyKMJohGXXv2RRV6xhIbHTARpAjQCteYuTwopPBv2Ur/u56HNzoPImfdgpp8xSTRZBDJ4HXDXAqivHZNiESCBzCgTSlgS5sNBqoImMH8WeyCCnx2lnGZlbjpwlJjsrhafzlXiIk8ZKjwE5Tk2yjINs7LUZsijFlahqPTOOTKaalF1NbmSA2Yxp2OMiEb0xi5Cmj+h3P5n9sQubHYzUJgUIv039z4l4/35N/eyKB4QE8hR6Ov/14Co8uYdwPpqEHdcKTC5l487E75T3h9uMIVQTwRjwcd/tx6D59wN+l6rMqGXfhaHS/znCzlKlfHL/H9Kf8bCbeArcC9xV7OfmnMyiZGMEI6Ez/8niqjxvBzK9NpGxKIbpXwxPS8YQMZt1wNL6I23NVOqKEk26cjidkEB4Z5KQbjt5jOYsuFmgh8Jd4mX3LjAG9rx2Xp19eS+XMEgy/xoSPj2DKubU9aU74ymQqjinG8GtMumAU1bPKmXHlWIZPL8Lwa0y+cBQTz67ep/3ubXnWNydQPDoIPp1np9YSrArypXPDRAKC8kKNH3820i99eYWXOZ8uIxHyg5QIx8GXSvPlaAOG7eDJOYxrjbE8bqBls+SA1aEAVe1Rzp9kkCsLkdPdRtvJSUKFBrqUjEhl0CU0+Tzk8ldRpHQdj6FxwdR8g+3RIOhh6ehyprZ1MjyW5JjOLpJ+P5oj0fsEucPSWZCSqMegzusl7dEpDUFhQW8T05TRkOU+NAnhTA4HgTEyyPk/GEeoyIMYVcq24iAn1bUSzOTQheSz7U1ENMmx55ZTO6OQgoICGrek2LYpxXH17QyPJvFIh68dI5hdre33Z3SgfZB7GnZlM+5cg3N38/pngC8BHwJWWJblmKZp0Rt67a4vKQ70nbZftYs0O+bdjHv1w8MDKrk7n+KCHdYdS+88i8PTb7/oPnZ0ymSou+d9b67yuplUXjezd8Xk0+C/TgOg70hm6PxxxH/xKjhuJVF0xzmEPju137YMYExXAmfcD6HVPbMUV5+FdukkUrkEacBBRxIlQjsACSNCSS4/sbK6GC2dgqYEzYwlhU4KP2QF9T2xnyBaPobqtz+PM+lOKuNteEniTyZJEuQdjkGiIYCKVBd6/p/wZCmihI10hnTK1tzEqAIvLRN+h73OLUfhuWNwAmW0P7Qu/95zFL62inP0rYjtV3ycUsPZX/3wbo9l+Jjem+IIj8ZJz32I0OS934Rq+MnlnL9oTr91E2+YzsQbdj9SVn5iOect+sRet70rQhOcdpvJabeZA0pfeeIwLl16Xr91tR/pH4uHKwJc9K/Ze9zOlEvHMOXSMQPap/8oQcVRgjlzPrr3xLvhi3g5/95d38wrUOLjgvtO2WndRTusGwwltWEu++csAL7ZZ/13P7L7K2c+dkEpp54U5P6LXyWXcnuvPlnjsHF1Jx1R9/s5sgiqlrURKyqgLOb2/ky6eiKTL6nmprkNPZ1Y06YFWNCqUZGzyWqCsN2nKpWShqca+Z/ba1mSDrIx3+lY2Z0g6/VSV+hnxbASpnVGGRlPsLa4iC6f298ZN3p7JolnIJrmitN9xP0av1/q7nxYAE6r9PBY/tbVZQWCb99+NMH8PRnGFMJ7Y8vozneaXHe84NZTRwL95+wUl3sJhDSI57j03U2ccX4ZHz+7cmAfgLLf9idouA/4rmmaVwIP4k6PrQEmWJb1NG5Xfw5oATTTNL+A29PwRD5/C27jPx7oe8mlBXw+P7xQBVwzgLLcAdxsmuZa3Ibfjzs5s9WyrFW7SP8o8EvTNL8P/Bb3CooLcedkKDvwHT+CsscvITlvLd4TRuwUMGwnCoNoL34f+fsFUFWE+J7byPo/PwNyDpnX6nj3z01Usw6BpGn4NKZfWYrsSGB850xEJk3kzqfw/ms9enMXGTxsYCwZfD1z/+TpE9FHFlL03BdI/fUdjCXr4Y11BEgxjg10ffgktAnlpO56uye0zBAgThHNiRqaCu+i9KYTKX3+cmK/eg0R8hL+wSwKdQ2trYvG51toMwrolFMxLziGSEkaptfAHgIGgJFXT0RogujSdoZfUjOggEFRBqqgIsD5d81k1VMNFI8OMv3To7mzKccjCxKURDQ+OXsYKx602bwihigOMGFWKeNPdOcNfffbw3lnSYJxY32cfmoBlbUprns4yvJhxQQyWT767iaaAkFqEylGxFLE2rO88Ekfv7YcgkiWLOti4fAyXq4sJ+HReWFkOY4QnNvZxmfPDJGzYX6nxpINNugaBLyEPJKvzfJRVSCoCksa4pKvHaMxrjDM6BKN1qjDZacFegIGgPKQ4IVP6vzpXYfaQsF3zF2fmYcjBl/98RjefLad4mFeTv9E2UH5DPbN0Ohd6EvI3Yyjbpe/WuG5XV1ymZ/U+HPgeNxp8puAP1iWdbdpmkHgr7jzHxLA34AZwMuWZd2cz38dbsDtB26zLOvW/BUZfwam4M59uA/4zQ5XT+xUHtM0Pw98G3eoIos7sfF7lmW9u5v3dRxwFzAN9z4NNx6k+zQMxatwBmzNV16l4Y+rARh7xwmM/Hb/ex44T75L7uN39Txvo5SGcC12LIsW8jBh/rkUnNJ7VuG8tYnM2f8N0RRiWhXeV7+LKPDTeO0rtP1yEQDD2ESCEtKEe/KNa70avbT/2V3d75bz7vd6Jx6OuWosk+7c862nlQNv3rx5AMyZM2cvKZV9sbRZcsx9vfNtpnR3c+HyzTgZGDUxyBd/Nh5Pn7s1/vA/aX6+wD3993o1MvmGUBeS5E1BPLpgwbspPvJ/adKe3is/vneywW0f7n+F0mFsUFr3tLi6X33vk78/4qOIvQYNygH3gT/g0cWt6EGD4KSdz8adhevInXp7z3P7sll4f/dJku+1460pwFu58w2nZGMXcmMbYvpIRLC30kotbQFd4E93seXq10i+7c6IF36dcS1Xo4X7X9rWNK+ORZcs6Hk+6aczGHPNrm/kpAweFTQMrg2dkvF/srePNjKxtZuLWlu5/GsVjJ0WxvDsPNVtWYPNNU9neX5jPpOUFPqh/YcBujtzzP3eJpYJH8+N7J2yNvdMg+tPV0FD3+dDIWhQt5FWDrqCGbvvTtRmjUOf+wns+95AHD0C/+8uQkS8hE/a/bX6oqIQUbHzP8nxT99egZVRcX8JjVfOx25PUfbTWTsFDADD54yi9jtTaHq8jqLjyxj9tUnv+70pyuFuTJHg92dr3PRMBm9XmlO2tBLN2mg+fZcBA8DRlTpv1PdeoSGE4J+f9qJpgk3rUiTiDuNI0tbaxbbhYc6c4uWak1TzMlQmP/alPlXlsKNf/zH06z92QLfpnVBM9cJP7zXd5J/NZPLPZu41naIcya6arjFha4JH/tIEQDiiUzlq9zf5Aphdo/HkGnei0NljNc4e606XHlXrxx/QSCUdTmjt4gufCnDCaTsH5R9MKmhQFEVRhoDZ55YQKTJoacww4+QI4ciem4N/fNLLPYvcYY2rzN7rq0qHefj+3GqWvBVjxGgf083wHrbywaJ6GhRFUZQhY8YpA7+fXdAr+NZuhhyqRvmo2ktPhTI0qKBBURRFUQaF6mlQFEVRFGUAhuLwxOH4b8EURVEURTkMqZ4GRVEURRkEQ7GnQQUNiqIoijIoVNCgKIqiKMoADMWeBjWnQVGUw1p6UzfpjYP+X+sVRRkAFTQoinLYarx9Ce/WPsC7Yx5g2y2LDnVxFOV9kYh+j6FABQ2Kohxe0pLI3R2sPOVRtl3/JmEShEnQPPfNQ10yRfnAU3MaFEU5rIT/GSX4dIIYCQAEEgMH3U4c4pIpyvszVHoX+lI9DYqiHFa09bl+z518NaUhkc4H/j/LK0cUscPjyKd6GhRFOeDefSfGX+5uRDpw6ZeHM/PEgt2mzbalWPLx5+l6s4WsR2Pr2BKOMhrw5BzSPp2itBtEpM8Zj9CGRsWrfDAMxZ4GFTQoyhC2LSr51Rs2AQP+38k6Ed/gVWKNm1O8Mb+Neq+HJS92o6fdf6P8p9824EQzHHdO6S7zrbxlGes2J8hUF5LzeEj7vLw1YwyBeAo9laUuUYgmJZXr7f4ZH1oIr6yAjxwLc447MG+iuRNufxw0Af/vfCjZfbCjKHujggZFUY4o5zyQZUWLg3AkL63OcPtHvZijdPT9PGNv67SJxh1qRngAWFqX496bNxON2fy1uoKLbEFxPm1Wwt/urifREGfyKSUIQ2fY6ACddQnaNsV4/cUW7NIACMh6PWiahpHJYSTTCCAZ0DhaM50AACAASURBVCltTZLRBcn2FMk3thB5ZwXG9fcBIH8/H+eh7yGOrkVMHE52RStasR+jqoBsc4JcUwL/lBKErpFtSpBrTuI/qsTttVjfCICTloiIH3HBT8Fa71b2L6yAN3+KWLkVOuJQUQRjK/bruCnKkU5IuecxQtM0FwAnAdk+qx+yLOtL+7Nj0zRrgI3AKMuytu7PtvZj3wlg+wHotCxr5EHYvRqUVQ6KVE4S+GkaPZmlPJ6h0aMjheDUWp0XvhrC0PctcHjZSvLL/20nZ8OZJwZoGh3h1285ICV+IUl5DbScw8dX11OezKI7DuPXb0EX0FVaCEIwosZH99JWNg8rZfK6LQgpkUBpV5Kahk4kUF8WpiMSACAczeAYHtZNqqRqUyNnNL5KbXJTT5niDEPgpWv8RJJr4+DRKPjBydTf/i4ymSPy0dGUfm0qGy+Zj0zZFM6pYcxxCcSN/wdAlgJsvQCf3YDEg00YEIjaIoyNG+j52d7yWfjRxfv8mSiHrUHpEugU1/ar74vkL474roeB9jTMtSzrlkEtyT4yTdNjWVZ27yl3aeLBDlgUZTA9uDTHf7+RY0yxYPZogW7b2IZOQ4EfsjY4klfqYebvU3xyusEf3sjQ1Z3Dp4GWdihxHI6pNrj2ghC3LBJY2xw8mRzjjRxjOpLUVBqs3JghZ4OQkhdfjzO/McSoziRZj0ZjSRAAx9DYEvEzvaGFjBA0VJTSGvBTlMlSkMuxskkjV1FOu8dA5E9cBJA1tJ7lsq4kHZEA0UiIphEVBDNpZi1dxfDOVpJOFa1kKGMbDhppIrRShrY2QRoPuaxBdO5iSmhGIOn4j033/M34nTTD2Uxg3pvwRLznuOnEyNkeBBIbP9vbELmxE5sgAgmkSN/0LOlfLkWzsxijC/FeMxvjiycdxE9YOZIMxTPE/RqeME1zKvArYCbuWfsDwI3bG3HTNO8FzgaKgDrgFsuyHsxnX5r/u9o0TQn8wrKsufnlUy3LWpjfxmzgOcuyjPzzBcASoAY4E/gp8HPTNL8MfAsYBWwArrUs65n9eX+KciTZ0O5w+b8y2A68UQcPLnXA0MFx3DYw5IGMA5pgWZNk2TNZiGd68hfZDnEpeXVNlvP+kWOzbQACpMHahhzj4oLxmxLueL+UGPnGvqorSXk8TbffoLFPeSrjKeIeD3Gvh+HxBAXRGJv9fjSfj5zhVj2FORtbCPT8toycgwQ6CgN0hwO0lBfTVVoEQhAlyOZ4nKr2TnIIYlSg4QEMHDxk8CAQ5DCw0QFopZJC2tGR6E6WYWyjmEbA6QlWACQ6AeLu+8Xp8y4k5LcFQbJ2AUZ3FA0HViTIfPkhtONHo02rOmCfozJ0DMU5Dft8yaVpmuXAS8AjQBXuEMY5wA/7JFsIHIMbNPwE+ItpmlPyr03P/51oWVbYsqy572P3VwJ3AoXAnaZpXgVcC1wKFAM/Ah4xTXPcXrbzpmmaLaZpLsgHJ4MuGo2qZbU8KMutCYndt73DbdwRQMTrBg3GDj/5PnVa37Oi7r59d0KAJsjk50Fsv+xx+0VkI6MpAE5saOO0jU2M7Epw/JYWZta30hgO8m55Ge9UDCMYS3BsfSOO6LNTXee1KeOI+XwURFMIx2FddSnrasppLivAMXR3/3lJnxeA4TRSTjN+bAxsDHKAwEHb6ewuhzvvwkuKYloBH+BHopPDS5IIyZ4ZGAY6SQQZHCQauZ5DJHdVXUqJbI4eNt8Btbx/ywfe0LvkcqBzGk4A0n1WfwSYBXzMsqwz+6S9CLfHYJeNtWmaFvBny7Lu3t2chgH2NGywLOvKPnmWA7+0LOu+PuvmAW/ualjFNM0wMBVYDHhwg5BfAidYlrVsjwdk/w3FHivlMOA4kgsezPD4KpuID44qhdeb8xVV2G04yTqQcC9hHD9MY+3WNOTcnohCBwptG12DL5xbwC/e092kqRyhRIZj22NU+iSnnxBk3osJDOmgScgB7YbOia0dALxYW87y8iJKEinGNsXQ85XlxYuWYTiSzoIwLaXFIARNAT/tPg/m+jpmLt+EvzuHkZUkwh4aRxfiaIK66gp0CUYux4feXEJ5RycjqevzziUJAmyiBoMcOg5ZPIAgSIwSWmhmBGE6Gc36Prls2qgmhw8bSSUb0LHJ4CdGOQAR6tDwAhJEjlioGmJp9HwwoZ09Ed9TX0V4dJQj2qC06G3iun71fan86REfOQx0eOLWHRtf0zQvA04xTbOzz2pBvi/PNE0NuBn4FFCB21iGgGH7WWaATTs8rwXuMk3zzj7rDGCX8xUsy4oBb+SfZoD/Nk3zE8AlwGAHDYoyKDRN8NilXja0S8pCgogPZtybZUkz+R4HAR6NX83xcuF4jZpijdVtXroTDmVBQVvMwSdgRIlOSVjjv06VtMQlmvQyPOwl1h2kMKIR9Gt86qMRhIBU2uGjD6TZ1i0ZF4+T0TWWVbhn7a3hAJojGNcSI60J1pQWMaWlg6JojFEVGmffcBRd9Ule/+qbRMtCGCkHT9atY0OxLAUdKdrLw0xcXk8s4mfLqFI2H1/DsfP/jUTrqeUFDiERY/Ir5+MJaVDgRwIymcXIpMj5A0Q0DXv5NuQXfoOIu+c/Ma0cx/GiIYn87lzSlQXEP/MAesbttbDR2MzRlN9xOuFZ5YjqUgoKQuSWNSB9HoywgRhTitDUPfKUXRuKwxP7M6dhM24PwLm7ef0zwJeADwErLMty8j0N24+is5t8cdzgYrtdDRbumHczcJNlWQ8PqOS75jBU+o+UDywhBGNLe7/Gz3/Gw52LHRriEDDArBB8bkpvIzexVINS93ltaf+z5WK/oNjfu60Cf2++suLtaXWevsrgBy87/NM7ijHtsX7biAmBKDMIhzQCI6qY5o3gD+tM//RoAkVeRk6JsKkrTiCTI+PzEIj33otBAhOXN+DLuD/3cZ4sZ71wFvo9Gs7mduw/vIJI5xDYiGEFBE7Z9byCnkpuUglyzPXw0OswZQT+8aMR/16FxxyB/5NT3bRLvk33V+dBMoswayg/rZqCT0/uPb6A54Tq3R5/RRnq9idouA/4rmmaVwIP4p6x1wATLMt6Gojg9ly2AJppml/AncfwRD5/C25DPZ7+PQIW8HnTNF/EDRiuGUBZ7gBuNk1zLe4ESz/u5MxWy7JW7ZjYNM0TgRiwCvcYXA6cDlw3wPeuKEeEkoDg5lMGt+u8Kiy472M6w4Pwv8uKqDQkLQmQWYcP1Uoevqw0f3lnyS7zz7j3FN654lWcZJZEyMCXsokV+okVBRi1tasnXZnHwVfghWvOAsD50CRyV90PmsD40+UDKquYUQMzagDwAt5TRvd73Tt5GGUvXblTPkVRXPscNFiW1Wia5hnAz3GvYAjgDhv8IZ/kr7hXN6zDvbLib8ArffInTdO8Afg/0zT9wG2WZd0KfAP4M9AOrAD+AvxmL2W5xzTNDHAv7lBFFne+wvd2k6UWmAtUAqn8fuZYlqX+966i7KPbZuvcNnvHtb695qucU01lazWP/2Id9Q+sIRvw4hg6wnZIe3V8Gbf3obO6sF8+7dxpeOt/cWAKryiDYCgOT+x1IqRywKkDrii7IB3JqgWtLPzaQnK6gZ61CSQzBFI2ti7wnVrJ+Q+ffqiLqQxNg9K6N4sb+tX35XLuER9FqBk8iqIcFoQmmHzmMEq+ohOqtak5fRhogmTQIOPTCQeO+PpW+cAZepdcqqBBUZTDinecoPgrOuf88WRGzxkFgCdsMO3rk/eSU1EOL3KHx1Cg/mGVoiiHJSEEp/3uRGI/mIa30Iu3wHOoi6Qo78tQnNOgggZFUQ5r4ZGhvSdSFOWgUEGDoiiKogwC1dOgKIqiKMoAqaBBURRFUZQBUD0NiqIoiqIMyFC5YqIvFTQoiqIoyiBQPQ2KoiiKogzQ0Asa1M2dFEU56Lo7srzyZCvvvd39vvO2v9LExt+tJLb2/edVFGX/qJ4GRVEOqlTS5r9/uJ725iwA511RyWlzygaUt+mJOhZdsgAkrJ27lFPf+jiB0eFBLK2i7LuhODyhehoURTmomrem2dQpeauqlFWlEVa9Ex1QPifnsPbuVT2zy3JdWZqf2TaIJVWU/aNuI60oirKfvKVe/n50LVGPW/1UFSe4agD5XrvxHba8005R/rkEVv3mPUZeNhbdrw9WcRVlnw3FngYVNCiKsl+yOcnfX0nSnXDw+jSiKclFJ/qpLnMb8seWZ3m7zuYTRxmcUG2wJa1T3t3F8R0x1pcU8O+0h+GPxxkdS/Fek6RLq+Lc0Y0ANN23ltSaLopLs2x7YhupoEGHEBhZh/JME00dBi9dt5jiyUXUnFJGw7/rCIwIUvO5sQht1xW2XNuEc9/riNoyxBWnIMTQq9iVw8NQDBqElEOl0+SIoQ64MqTc+EA3j76eIgvk8g1weURj/vXFPL3G5pP3JwDwGbD422FyG7p59OY1CMAG/jymmsmZLGU5G4A6j4Eck+TvgTQbr3kTAB2bbWVBuiJBACLpbuKeAtI+A0d3R1n9UlLY7O5r4nePYsoPj96prLI9hj3pBmhxh0S0ueejXf/xwTo0ypFjUFr3TeLn/er7GvmDIz6KUD0NiqIMiCMld7/tMH91lg1NWQIGFIU1VmwT+PwGnlQOAJ9tU7gtwS9+HOMxXwFoBmOjCY7ujvPHHzZQGU/iCIEmJTrwkcYW4pGCnv0My9m81FFAwwPLiIW9hPQuSrs60dsjSDSq0lvptMtwAlmSfgNPJodEkJMOSZ9GMJ3B/5vHcTa9QfS4o0netZBwRzPeaz6C54QanJYYCQqRCEIvrMF7/SE6oMqQNxTPEPcaNJimuQA4Ccj2Wf2QZVlf2p8dm6ZZA2wERlmWtXV/trWP+78cuBaoBtqBe4EfW5Y1FD9nRdlvt7xsc9OLWUhmYXuXvp0DKaE0xMjWOL50DrMzStB2aNsIFWHBexWlbCsIclZbJ76uJPGcDULDRqJJhzGd3WzyeIgG/ADUewwm1EdJrewgIiXgYQvV4EBRa4YoFRhAUSJDKmjA9mEIKbENjcrEFsa0LYY/QsEf51OYr7rlt+4i+8AP6ApVkY67VV96hUPFQT6OinIkG2hPw1zLsm4Z1JLsI9M0PZZlZfeesl+e6cCfgQuBecBE4EWgHrjngBdSUY5wmzoc/vB2FrJ2b8AAbqeuBGyH+mEhShJZ9PYuckJgaxrl6SwISHp0tvl9FCaSdAf86LZDMJPBcSS2rhGMx+lwHKq6Y5RrGomAn1iRZHrHe9RpNQS8Dm3hEJFojmDaIYEXGw1NgpGxcYQgpwNCUJJpp45aokQoZxs2AVKEKKWBrs8/ip7LkSFMFh92k8B7/ypKPjfpEB1ZZSgbinMa9mt4wjTNqcCvgJlAAngAuHF7I26a5r3A2UARUAfcYlnWg/nsS/N/V5umKYFfWJY1N798qmVZC/PbmA08Z1mWkX++AFgC1ABnAj8Ffm6a5peBbwGjgA3AtZZlPbOboo8Fmi3Lejz/fFV+u9P353goylDkOJLZ9yTY1i0AAdJxAwcp3YcmwGcghaAtovPcmHJmb2oFITCAmkSKTeEAb5YXMSKV6Qk6bE2jIJXE1jSKbIei7iiRaByAzYbOQx86mbMefpaYXsbLI6oZ29hBUToNgJccLVoYX8bOV8sSENiGoJ5acrjDHe1UEcAdNmmjkmAuiY1BFs/2d8eWy57DUxWm4MyRB+uQKh8QQzFo2Of7NJimWQ68BDwCVOEOYZwD/LBPsoXAMbhBw0+Av5imOSX/2vYGeqJlWWHLsua+j91fCdwJFAJ3mqZ5Fe5Qw6VAMfAj4BHTNMftJv98YJtpmheYpqnlg5/TgMd3k/6AiUajalktH1HLsQxs7nAbZYQAXXODBSEh6IHSQL/eh/agr9/zUfEkH9/cwMzWzn7rY14Pa0uKWF1STFrXQdN6xoDDqTTtoUJWBCez0VfFyEQ9xYneshlImkeFe6pkkXMo6kxR0prAm+yt1vQ+o8oSnRzeftW4yL+eXNZ6yI+zWj70yweaRPR7DAV7vXoifwZ+ApDus/ojwCzgY5Zlndkn7UW4PQa7bKxN07SAP1uWdffu5jQMsKdhg2VZV/bJsxz4pWVZ9/VZNw94c3fDKqZpfh34GRAE9Hy5f7DHg3FgqDkTyhHn439N8OQa2dvo53LucsBwH32CgaMbO5ne2AVC4ABF8QThXI7l5YWEclCcyuIAm0IBMobb2VmQTjN9WyOl0TgSWFlVQSTZzVnPrSNMF7OyL9JELU3UABD1eVg+bgTF7QkM28EXzSI9brCgZx38SRuJwEOWEBkEAoGDnwyCHGkCbP8p6kV+Jrx1Cb7xRSgfWIPSoq8Vt/Wr78fL7x/xkcNAhydu3bHxNU3zMuAU0zQ7+6wWuA0wpmlqwM3Ap4AK3F9oCBi2n2UG2LTD81rgLtM07+yzzgB2OcHSNM0r8mU7B3gbdzLkQ6Zp/sSyrBsPQPkUZUh57HMBHluR450GBx2Hxi6DgE/QbQueWpGiOergAUIabAwHaR9lcGxTJ0XZLOGcOzzQGvLzbGUpVdEk3T6DaVu7erYf83p5rWo4p2yuRxg6nlyM2a+vQaARdmJoSCrZQJh2EhSgU0x1IzRFSpFCx+vRCCVyGLbE9mhEAjGKQza5uhg+kmTyPQzhiyfje+INRCqFhoNEJ/jmjSpgUAbJER8j7GR/5jRsxu0BOHc3r38G+BLwIWCFZVlOvqdh+1F0dpMvjhtcbFe1izQ75t0M3GRZ1sMDKrk7B+MFy7LezD/fZJrmA7jDHipoUJQdGLrg4mkeLp6282t3DYNv/CtB1m/QWRAA3J6ACR1xvvzRMPWLYNOKOJOau1hUVcqm4hAFqQwB2yaZ72kI2A4TOroJS8my4giRGWkaGyuoXNZCmxxGnBAh4oTpxItGMOOwwjcBR9OQmiAV1Mh5dErbU+BISm8/m5rjQrSbvyeaDuEFgh+tZfjDFyJv0eGGf7mF/8yJiAmlB+koKh80Q2VIoq/9CRruA75rmuaVwINABndy4gTLsp4GIkAOaAE00zS/gDuP4Yl8/hbcxn88/XsELODzpmm+iBswXDOAstwB3Gya5lrcCZZ+3MCg1bKsVbtI/yrwW9M0Z1qWtcg0zVHA54DFA3zviqLkfX2Wn1NqDW57S/Jgn1/bWReWMedDPnLnl7BxeZyMLrgoI1mxuptt/6gjnMnQEfDTHgpSOynIF79dw/I6G0/3MsaHosy+4hwa32rFaErQtHYKVQ8+irO1i8Vlk+nwhcl4dGSfYREnoFP7xfGM/PxYiqe7gUDp+u8SemQFcmQJgfMmAiCuPw/54WkQT8Pp6qoJRXk/9jlosCyr0TTNM4Cf417BEMAdNvhDPslfca9uWId7ZcXfgFf65E+apnkD8H+mafqB2yzLuhX4Bu7lkO3ACuAvwG/2UpZ7TNPM4N5roRb3nhKLge/tJv3/maY5EndIogKIAU8xsABFUZQdHDPC4IbTJE9vytGegtGFcOXJXgAMj8b4Y92rGY4CZo2Q/P7eOEgIZbIU5rJceUkV4yYEGDcN5s1zJ6YVDPdTMKf3igb7aJ1F33qZlcWjAXe80wgI7KQ7bDz1inFM+/7UfuUSI4oI/NfJO5VXHDfmQB8CRdnJUOxpULeRPvjUAVeGrJaEZHWbZNowQaF/1xVm8/o4/3vV0p7nI4+OcPkdvY39vHnzAJgzZ85OeV/6r9dYM7+p5/kZd5gU1xYggbIpal6Css8GpXVfJX7dr76fJK854qMI9a+xFUU5YIYFBbNGabsNGACG1QYZd2IxALpHcPKndjVtaddKJpe4l3sCmiNxOjOUTilSAYNyWBqKl1yq/z2hKMpBJTTBJXMn0bwxQajIQ7jUO+C8Hp9OIG0jhXubiGzn+7oZrKIcVEMlUOhL9TQoinLQCU0wfGzofQUMADUXVFM4tgBNQkF1iDEXjx6kEirK/pM7PIYC1dOgKMoRw1/i42NPn0N8a4LgiCCGXz/URVKU3RqKPQ0qaFAU5Yii+3QiYwv2nlBRlANOBQ2KoiiKMghUT4OiKIqiKAMyVOYx9KWCBkVRFEUZBKqnQVEURVGUAVFBg6IoiqIoAzIUhyfUfRoURTnksk0Jul+sJ9eRBqBoTQssWn+IS6Uo+0fdEVJRFOUAS67sYOUpj2F3pPGMCDF18kpqn1sFPAHXXQS3Xnqoi6goSp7qaVAU5aCLJhwWvpumrjnHkoe2sKCinC3DInS2pql+bjUZguTw4/x6HrE36ml5civZrsyhLraivE9ih8eRT/U0KIpyUHUnHC7/RTtbW2zSQYO1FZNJX3kUxfEk1//7VY5eOhocHwCpVIb3TnoGiUZwQoQT3joXT+H7u/W0ohwqQ2VIoi8VNCiKckA8sSzNunUpTvKkKB3moWZmESutbhrrMowYF2DysWGEECxek2Friw1AIJZgysYEScOgNJqgIJnoCRgA6qlG5jtEE2u66Xy1mWEfG3lI3p+ivF9DcSKkChoURdlvNz6bZu5SHQhR22bzxTdXoo8upL3DAcAWghM/WsolV1UxqlxHk5IpdQ2ctH4LAEbWxpfKgQ4pzcDv5AAQQvTUvFJAUgy9Mzdl6FI9DYqiKEAq5bD4rRiJpIPj1fj7/2fvzsPkqsrEj3/Prb1673R3ujt7QhKIwQQ4YJDFgILKIioi4sKSAUfHbUR/MiMKyCaOgzgoKiqLoICCoAMDCBgIhv2wJEAWsqeTTu977VX3/P64lU53zNKdTqebzvt5nnr61q17T526Sd371jnvOfdFmBnPsaU4woZxxdx92DSmdiWY5cQJuxbHWsyznZzzxVo2teaIxGJMaWnrLS8b8OHkXLpLCnkyMp/31m8klHCpLy4n4/gJJzK0jC/CeayeSR+uRTkKG0/j/vl1KC/Ad/rcHWU98ja2I4Hvk4fTdOsqUqvaqPrGe/GF/cSe2ULkqCocHzT/2JDZFqfw2PGUnP8eAjPKRuJQijFsLLY0KGv3/LG01s8AxwJ9b1x/nzHm4qG8sdZ6KrABmGSM2TKUsvbx/QuAHwLnAEVAHfBZY8zrw/zWY/H/kTiI5HKWH15Zx/r1KbKOw/LxJRzZ0IECegI+/jq7hpR1IJWlvD3G+XUNuI4ih2LCV2by3VcV5ODzzy/n+DXeV9/JuTRXjaMwkcCx1ruVsKOY+84WJnW3AxAPBuiO+ik65wje+6sFpI//CfaFDQD4vv9RAlefQeryR0lf/xQADeOm0dEa8Mr3WaIRF9uTwfFZXGtJ5rtBHHIURbNMfuNCAjPLD+zBFKPFsDQJvKhu7Xe+X2D/9V3f9DDQloZrjDHXDmtN9pHWOmCMyex9y377KOAvQAI4xhhTp7WeDsSGo45CjCXtbVk2rkuScxzaIgEq46neM25hJsf05h5WVhRDwCHh8/FKWQmT0mlmbmvkiUdaOCZleaeihPve9x7aCiLM39hIdWuMTCiAE48D3hncAuMyrZTQQJoCfOkC0uEQqXtfZoM/gfNyJ53+KSjXUvU/LzE+lKXh1nfopJYQGXpaoYA4ORySuTDjelaTpIRkrogkYQrpIkyCTkpx4jF6bl9G2QfLYdVWOOsYmFQxUodYiFFrSN0TWuu5wI3AUUAc+ANwxfaLuNb6DuBDQCneL/lrjTH35Hdflv+7WmttgR8ZY67JL59gjFmaL2Mh8JQxxp9//gzwBjAVOBm4HrhBa30J8A1gErAeuMwY88Ruqn4qcBww0RjTBmCMWT+UYyHEwaKkxIfPB02hIE9NH8+M9hiTuxKAd6E/rD1O1uejyUI057K+MEq6xzI/m+Okt+sAWLipgV8cfRiVbXE++NoGWsqidGbLsOz4yVcRb2Fu8mV8uFgUTcykyZnEm1Mmc8qvn2NDYBKpTBgFbOuByu+toUWNx+e1UzCdbYTwciOSxOhmAqDw4VJJIzVsBWA89aTJ4t6wDW5o8N78ugfgzZ9CRfGBOqxiDBqLOQ37PE+D1roKWAI8CNTidWGcAvxnn82WAvPxgoargTu11nPyr83L/51tjCk0xlwziLdfBNwMlAA3a62/CFwGfA4oAy4HHtRaH7Kb/U8C1gGXaa2btNbrtNbXa60Dg6iDEAelbNaScRWNBWFSAR8rqop5paaUhM9HVyCAqxQ1XQkiqSzxggA9RSE2FxWQ9fl6ywhmcxS39LC2oICNlcWgFNPW1bOipoKtpYUUpnpYuPEVfHiJlApLmG4qe7pIBoJ0RgsoycZRfRp/21UZKt/dGiDbGzAAhEmzIxxxiJDsfS1AlkLiROjcUVhDB7wuvyPE0NidHmPBQIOGy7XWHX0eC4DzgWXGmFuNMWljzFa8HIHzt+9kjLnNGNNqjMkZY+4DlgML90O9HzDGLDbGWGNMHPg6cLUxZpkxxjXGPAo8DXxmN/tXAHMBHzAZ+AhebsN39kPd9qi7u1uWZfldvZzNxakq91GaTBPIeRf1FVXFdAYD5BzvlJIF0sUhOscV0F0WoW18EU0Fkd4yHphYTYvPxyuTq/juaccC8ND7ZvOrk+Zzw2kLuPwTJ+HLKtz8hd4CKQppKSwimM1QkojT7Qv3OxEX043Nj67I4ifNjiDFT6J3WZEjSE/vc4vFkiNF4Y51pVGYO/mAH1tZHtnl/W0sTiM90ETIp3bOadBa3wJcDH2+jV4o7zPGFGqtHeAq4FygGu97X4DXRfGD3SVCDrB7YrEx5uo++8QAF8j1qYsfuNsY8+VdfKabgK8BBcaYVH7dv+MlQh6zxwMydGMl4BQHsZ7uHL//bQPPNyhWlxSQaE7jT2QYn87gouhyFCsnl9MZ3THnwlmbG/lQuo3G0gJ+nCjuvcBj4cuvruKvxx3KxsqS3u3vvu0eotksk7u2YLMROgJldId9VHX3UHrCNHzRIN2vNONPpxg/2aVmfpC69nKaX08Qqo7SubieEmJkcYgUxJkUW0OKAgpoI4uPFOWEiKMmLB4hNwAAIABJREFUlZKuqCJ83ccIdzbC6q3w6ePgMJkP4iAyLFf0peq3/c73x9uLRzRyUEqdgvdjuspae6ZSSgPF1trFAy1jKDkNm/Au5qfv5vXz8IKKU4EVxhhXa23Y8Y/j7ma/GF5wsV3tLrbZed9NwJXGmPsHVHMvJ2JX5IIuxAAUFvn40jcn8KX88xuv3MSGrV4S45qCCBXxFEd0+XgmWgVAQSrLx08p5sIzJwDw6KINvFXs5Qv4sGydWk3Y7jifFibTbCscTyTrUl/knQJULodyLbG50zjvvhPwh3e0JGw3Jf+wOZdXj/wrbcu9bYq/MJ+CxzZQsMn7fdLNZNqZgFMeZspLFxKt2d7KMHM/HiVxsBtNFxSl1Nfw8v5+C3wqvzqB19X//oGWM5Sg4S7gW1rrRcA9QBovOXGWMeZxoBivlbIZcLTWF+LlMTyS378Z7+I/E+g75NIAF2itn8YLGC4dQF1uAq7SWq/BS7AM4yVnthhjVu1i+weBG4AfaK2vACYAXwZuH9AnF0L087XvTuLhR9p5rS7LEa1xjo0oPvL1Km5fCS++k+GT0+Cs9xcBkO3J8L0HXuC+o2bSWBzllFWbqSCLPWoO/pwl5Xeo7krSUVZGoKWNQC7nzdxvLYd+ajLHfGX2LgOGvpTPYd4zp9F45xr85SHGf+EQaJgL9/4DJlUQKajGt7KNgo8fgr+mcI9lCbGvRlmXxL8DH7TWblRKXZZftwqYPZhC9jloMMY0aK1Pwrv4Xg9EgI3ArflNfoc3umEt3siKu4F/9Nk/obX+PnCv1joM/NgYcx3wVbyLdxuwArgT+Ole6vIbrXUauAOYhjenxGvAt3ezfbfW+sPALUA70JLf978HdRCEEAAEQw5nnz2Os3da/+Wj4ctH9z/N+AsDVB9bwflLV5NzIBX1o4Bp9c10Fnp5D5FMlmRBhG2haiZv3oqylmhFiAVfm02kPMRABMpCTPzmjkmfqC2Hb50FeE2ZBafP2MdPK8S70vb5iGBHI0gA7wf/gO01p0Hsd3LAxUEvF8+y9f6NNL3UzMY/b+pdv6G2nA1Tqgk5Dr78uWl2WZpjTx3H1IXjKayO7K5IIYZiWJoElqjb+53vP2AXjVjTg1LqAeB1a+11Sqk2a225Uuo7wHxr7WcHWo5MIy2EOOB8UT+TLziE0mMr2fL4VrKxLBYojcWY3NxGS9U4AJTrcuyF05h7oszUKN59RtkvxK8BDyulLgGKlFKrgS7gzMEUIkGDEGLEFM8q4UN//zBNSxtJdWewBQFeeWkl0Y4shTW1nPDZycw8qmTvBQkxCo2mnAZr7Tal1NHAMXhTDdQBL1trdzcoYZckaBBCjKjCaUUUTivqfb6ufCXlJDjzzDl72EuI0W+UtTRgvXyEl/KPfSJBgxBCCDEMRlNLg1Kqjt3EMdbayQMtR4IGIYQQYhiMpqAB+PxOz2vw5m24bzCFSNAghBBCjHHW2iU7r1NKPQM8DvzPQMuRoEEIIYQYBoPKMBwZKby5jQZMggYhhBBiGFhn9HRPKKWu3mlVFDgNeGww5UjQIIQQQgwDO3piBoBJOz2PAT/Bm615wCRoEEIIIYbBaGppsNZetD/KkaBBCCGEGAbWGdn3V0qdPJDtDtStsYUQYkhWNeW46d4Wyja2c+kxPspat3HIw6vZlq7g9Wvv4ZW5U1AfnM1nPl5OUdRhTbvl56+7VEYU3z5aEfaPnl9yQuzM+kb8/+dtA9jGAtMHWqAEDUKIEdGTspx4Sw/N6SiEozTc8Tq/ePIBunwzSTk+GsgR2dDIbf7prG5q59qvlrPwjznqewAsm7oUv/nwnm+RLcTBzFo7qJERAyFBgxDigKrvdPE70JawNKd3tN8ur65ic0kx3bEoAK6C4liCkp44a9dbGmLkAwbPa02W7qSlOWaZVq5QasR/1QnRjzuKchr2FwkahBAHzHVPJfne4ykcBT/7eJgSv6Uz651YZ7Z2MbV7LS4+1oZnUT+1hFzAx6lvrSbjKJb+v3rmRGtZMbEKgFmtHUy6LkBnEs6c4+ehC6L4xuBJWrx7jXROQ19KqWLgKuADQAV9bgcu00gLIUadnGu56okUAK6FKxanOXrjNqZvaaE0kWJmaydri6Yzt/NNNpeOJxfI3w5bKQKuS/wfW/mKW8eyqTUUJdMsmVJL5/hKAB5ekeWlzTneP1VOaWL0GE2jJ4BfABOBq4Hf400r/f+APw+mEPmGCSH6sUtWwf/8DSaWw/XnoArDu97OdUledC+5J1YRmF9J6JKj4a4lMKuWrurZJJbU4ZtfTefKOB0vNJHqyvCIC65jWV8T5ZcfeD+biovZOLuI0kSC6FvriDQkAYi6cUraEhR2pkhGA7RWhilIxVk7cQLt5eWM27aN7z79DK9Mn8HKiROpD4f4yZXNPNQdp6qni6kzwnzwO4eytsHypz+0EM5lmV2ao3x8kJMXTSZSLKc+MfxG2TwNpwKHWWtblVI5a+1flVIGeBi4aaCF7PWbo7V+BjgWyPRZfZ8x5uJBVnjncqcCG4BJxpgtQylrH957AfB9QANhYC1wjTHmLweyHkKMNralG06/EWJeiwDJDPx60S63TV33d5J3LQcg8/gWfH8z+G0PMcpoYysA7f/bQA4fYAnjfdkANo8rIxkIs7UwQjLgA0pZXVXOR+ufQDGBaHOAmlwXAEVdKYoScaZ0tfDkUfNI+B2+9I8lNJSV849xlUxLpJiWSJH0+ShvbieUTLHNxHnoK6/yUmkNyrUUtXWwyVo2AYmuLJ+6YtawHUMhthtlLQ0O0Jlf7lFKlQLbgEMGU8hAw+1rjDHXDqbgA0VrHTDGZPa+ZT/lwB+BC4FW4GPAvVrrE40xr+znKgrx7lHfDrEk3qz5Lry+AZa8DXMnw7giAKy1ZF6sI7t0fZ8dFTnrww8kKCRJABdLDgc/WbI45HDw5e/M21JYAEqR7TMkrTscItk2jZZ0Dj/9O4MrOmP4gYqOTkJBPx2FhaweX01bMEDC51CbSOFaiAf8RJMpFNDa6ZIrcolksuC6uHiduFtW9fDKX+qZdFgR6UQO/A6+gMOE2YXDd1zFQckdVTEDy/DyGf4O/AO4BegB3hlMIUNqo9NazwVuBI4C4sAfgCu2X8S11ncAHwJKgTrgWmPMPX0+AMBqrbUFfmSMuSa/fIIxZmm+jIXAU8YYf/75M8AbwFTgZOB64Aat9SV4t/mcBKwHLjPGPLGrehtjHt1p1V+01m8BxwMSNIiDVvrqJwmQwSENgDVvw8Lvw/hSeOGHMG08XZ97gOS9yymiHhgHKBQuaaL46aaOGVj8gKWEbhKESBABwE8Ov3KZt3kLVR2z6Aj5aY2EADj/dcOR6bWApYMSuoiQw4fCJUoacPnwG2/y5rTpPHrkMQCsjQR4rnY8k2IJZsXTrCsvYUJbB1M6OtlaUU5lLEYom8P1+/DnXFylaG/P8citW/FnswQzWVwF6WCQo88az2n/NvVAH3IhDpRL2JH8+HXgh3jX5vMHU8g+Bw1a6ypgCfBd4EygEvgrkMBLtABYCnwb6ADOAe7SWr9hjFkBzMPrnpi9D90Ti4CPA58AIlrrLwLfAc4G3gQ+AjyotZ5vjFk7gM9SDbwHWD7IeggxZtimbtw/v4pDrndd7w+lxg544AXcRaeSvPdNHDIESeHQg4uDQ44UYeKU5QMGT5AsTZT1Pk8rH8tmjMOXCvLdPy5lS0UxSb9DtdvMGVuX975rKZ0cxlrqqcVHDgeLReFYSBfsyLFY0NDKc7XjqSuIMCWZIexatpaXElIQcF1C2fxnUYqco7B9hmVmfT4CmSyOBcd1MY808dEvTUGNriZl8S42yronNllrcwDW2mZgn1IMBjog5HKtdUefxwK86GSZMeZWY0zaGLMVL3LpjVqMMbcZY1qNMTljzH14F+WF+1LRnTxgjFlsjLHGmDhe1HS1MWaZMcbNtyQ8DXxmbwVprQvwskf/1xjz9/1Qtz3q7u6WZVkencslYdzyIiy7OdHNrEEVh6AqisWHxUVh8ZFD4QUYQRKQ74IAhUXhJ9tbRMO4QiJpC35LKJvjkG3tzK1rYc7W9t5tsvhopYIeSvApLyMi6zi8WVvDunFV3tCLvNaw10oRyLkE8uuD2SyhXI6c4/S7NbGy3mPH8x2f1CpFyfhgb8Aw4v8Wsjwiy/ubVf0fI6xBKfULpdTxQylEWWv3uEG+O+CpnXMatNa34EUqib7lAT5jTKHW2sEbE3ouUI13JinA66L4we4SIQfYPbHYGHN1n31ieJ2wO34iea0odxtjvryHz1YE/B9eXsO5xpj0Hg/G/rHnAy7ECHLf2EL22/fjf3s1KgxqWhUUhuHDR8BXPgpAZnkDseuexZ/uJrByHdkNcWwwQLjaxfoc1tRPJNYdIEiGMEky+IkToScQomF8Ue97BZI5ot1ZIrkkZW4HlaoZV4XIZKPkCHr1UeDYHOvHjaOxqASAdMDP83Om0RkK8sSUGhzX8um31pKNRklGgkxq78L1OTSXFFKYTFOcTBHKZAjgEinys/2UVzU+QKjARyqjKKyJ8IHPT2DchF2PFBFj3rBc0v9U88d+5/tPbzt3xEIHpdQRwHl4P6Zd4F7gHmvtm4MpZyg5DZvwLuan7+b18/CCilOBFcYYV2tt2PGP4+5mvxhecLFd7S622XnfTcCVxpj7B1RzQGtdDjyOl//weWNMdi+7CDHmOfMnEnzqm3vcJvDeakr/+One56GdXi/60tOkb30bi48eCkluDwCc/g2bmbCPzrCPTkK0ZIp4IzKb0o4Y07ubiOS8r6NjIayS/Zp5g5ksz1ZXsr6iFIDDumJ01lQxtamVlmiIlnIvuJhY5lDyZlvvflOOLOEzP3nv4A6IEEMwmhIhrbWvA68D31FKfQDvGv13pVSDtXbAX4yhBA13Ad/SWi8C7gHSeMmJs4wxjwPFQBZoBhyt9YV4eQyP5Pdvxrv4zwT65jQY4AKt9dN4AcOlA6jLTcBVWus1eAmWYbzkzBZjzKqdN87nMDwJvAYsMsbkdt5GCLFvKr8+j86/bCDbGCc8PojbmCZNkFAqS0RBwkKwOEC4tZuuQBis5fmjDqVlXAnReIKPLH2NQ5ub8LuWxXNn8dbUar7y5GIaC4pRQFdRlNZCL7Eyms0xLZakqrOLcd3ddBZGyQT8OFjOPb+SuiczvP1EE4Gww4LPTRrZAyMOOqMsp6Gv1cBKvAEKMwez4z4HDcaYBq31ScANeCMYIsBG4Nb8Jr/DG92wFm9kxd14wzy275/QWn8fb6hjGPixMeY64KvA7UAbsAK4E/jpXuryG611GrgDmIY3p8RreEmYu/KvwFy8O3udrbXevv56Y8z1AzsCQohdCc8pZ876L5BpTBCcXEimrotMSwob9BOZXUqiMUG4IkxufRsb5t/Fc3Nm0TLOax0ojCXpCYe55uyPkgn4aS/0Gh3dwkaWX3gWf2ospSscpDsQ4D9P8HHR/CCl/gihbCW5VA4noGjsgOraIMUlfuYdPZsTFk0hXOgnVCgTOokDaxTkMfTKz8twNvBZYAHwBPAj4H8HVc7echrEficHXAjATWbZVPNzVheU8fuTTwSgIJbg/a+u4Nn3zmJjTQUAtZ3N3P7n6/nTQ//D55/z5lJQWJ7/UoQFUyQQEPvFsFze75n0p37n+8/WfXokcxriwPN4uQwPWGs797LLLsk3TggxIpywn4mvXED2oic4bdUKtsyfwvFVnfxH1Xwmbe3CzaYpy3RyybOP0XTKmZSPK2ZWLEa3z0d5JsuWdQ5I0CBGMTu67rw6w1q7baiFyDdOCDFiAoeUMfMf5/brVF35027+MWMqAKFMNZf9/gWS5TWkM5aqdJaq/BDOVEYa7cToNsoSIYccMMDA52kQQogD4otVKwirLEHX5TsPPUdkahETrjiKDy+IcuRsbyTGe6YFOOvE6AjXVIg9s0r1e4wFktNw4MkBF2IPHn74YVwLZ5xxBirr4gR9/V7PZC0B/9g4AYtRY1j+Q/1u2gP9zvcXbPjUu/4/rnRPCCFGHUeBz1GwU8AASMAg3jXcMdK60Jd0TwghhBDDYDRNI608lyilFiullufXnaiU+vTe9u1LggYhhBBi7Lsa+Bfg18Dk/LotwGWDKUSCBiGEEGIYjLJEyAuBM6y197Ejt24D3iSHAyY5DUIIIcQwGAWBQl8+oCe/vD1oKOyzbkCkpUEIIYQYBqMppwF4DPiJUioEXo4DcA3w8GAKkaBBCCGEGAbWUf0eI+ybQA3QCZTgtTBMYZA5DdI9IYQYcZlYls2P1BEsCY50VYTYb0ZL94RSygd8Cu922MV4wUKdtbZhsGVJ0CCEGFHWtfz9s0tofb0NgOApPsIfD4xwrYQYulHQugCAtTanlPqJtfZ2IAk07WtZ0j0hhBhRyZZUb8AAkH3THcHaCDFmPayUOnOohUhLgxBi/3v0VVh0C1gLv/03OPPofi+3XvcirVe/SDoHr8yait9ROG4+oXtLjuSiGFfOe4nu4ghNpVEemzuJbx7r54m/d5PdmiAZ8rNtSjG/PCPAx2fKbx8xSo2S7om8MPCAUuoFoI4+tzSw1p4/0EIkaBBC7H+LboHGDm/5op9Dy+96X0q/00br954DIAjMaGim2V9MIJUh43dwLDRXldBTHEUB4zsSHLK5nf+KlTBvawIfUJDMUrgtzkWPF0nQIEat0dI9kfdW/jEkEjQIIYbMLl0Nf3sTchn464vQ1IlCAQ5uW4LuY/+b3DvtJHJFqJCPABnSBFFYSjpipBw/qaAff87FUaCyOcY3tuM6ivbSQhJKQTIDQE7BtpII3SE/HQnLoTfEuOoEh88cFyGZtfziDUsiC/82X1EWHlUnbXGQGS2JkADW2h/sj3L2GjRorZ8BjgUyfVbfZ4y5eChvrLWeijcb1SRjzJahlLUP7/054NadVkeA/zPGfOxA1kWIdzv76gY46YeQzQEZFGlAYfGjUCgLHS8myFC6fQ9K6KaDMnL46SZCDh/+tMVvXfyuJVABiaIwAAXJFBucAD1ZqA/66SiP0lYYyhdlWW2DXLg4R1kgwW0NQe5/x2t1fWgNmC/I7yIxcqwaPa1gSqmTd/eatXbxQMsZ6DfqGmPMtQMt9EDSWgeMMZm9b7mDMeYPwB/6lFEC1AO/38/VE2Lse3FtPmAAcLEoyD9s71Lf5EaFix8/WTL4SKk+IyWUAiw9JZHeVa7PYUJPnNUlRWwNBkhHdtreZkk5iidXpXmmywcZF5Ti1QaHZNYSlrtiihEyyronbtvpeSVeD+EWBjGV9JDCcK31XOBG4CggjnchvmL7RVxrfQfwIaAUL/HiWmPMPfndl+X/rtZaW+BHxphr8ssnGGOW5stYCDxljPHnnz8DvAFMBU4Grgdu0FpfAnwDmASsBy4zxjwxwI/yBaAbeGgfDoMQB7eFh2F9DirnkiVKhiIAAsTxkcPFxcULBkChcHHI0U4hJcQptTGaVYlXlvVaCUrbemitLsEC8VCQ05vb+WBrB2HXsrUpwp8On+I1/boWEjnIWm5c6UChi0rmsEoRzaRZvinEMTNk7gcxMkZZ98S0vs/zczd8D+/aN2D73Haita4ClgAPArV4XRinAP/ZZ7OlwHy8oOFq4E6t9Zz8a/Pyf2cbYwqNMdcM4u0XATfjzWp1s9b6i3izWn0OKAMuBx7UWh8ywPL+Fbh9sC0WQgiwnUlyOT+QJkMh29sWMnitBQqHEGly+FC4hInTRQFBcgTIUWm7mJRrptiNES/yoxyXSXWtTNzQhD+ZJefzARDOj65YWVWy42TsqP4Z6vFc72vxgI9v3J84QEdBiHcXa20OuA74zmD2G2jQcLnWuqPPYwFwPrDMGHOrMSZtjNkK/DC/HgBjzG3GmFZjTM4Ycx+wHFg4mAruxgPGmMXGGGuMiQNfB642xiwzxrjGmEeBp4HP7K0grfVxwBzgN/uhXnvV3d0ty7I8ppZVYRjvVJKlzygu+v7GcnEARZYAOfxYnHw3hqeIJJmIQy6s8IVyOH6XWGGYop5/vugHcjvN42B3vCc7/bArDqlh/eyyPLaW9zu102P0OQUY1MQoyvb9wu1CvjvgqZ1zGrTWtwAXA32/1QrwGWMKtdYOcBVwLlCNdzYpwOui+MHuEiEH2D2x2BhzdZ99YvkPvr1jFbyul7uNMV/ey+e7C6g0xnx0jwdi/9nzARfiXSh305O4P3kMtaWBDCWAIkAPDpAhRAsTSBDFR5YgKdL4aaeYKBmCZGiKFrGhtBKUoqqzm4qeOF3+EG/PmczmSZVkfH7AkvH5iAcDPDJrAg3REBXdSVqUD+tafApyhQF88SzKWiYEXJ7/WgG15b4RPjriXWBYLuk3H/1Ev/P91185dcRCB6VUv7kZgCje3A1fsdb+btd7/bOh5DRswruYn76b18/DCypOBVYYY1yttWHHP87uopsYXnCxXe0uttl5303AlcaY+wdU8zytdTlwDgNokRBC7J7vm6fg++YptAe/C5kcBWzDn7/jbpIKCkhQkP99EaKTDRyKD4ckYVpCxdSVlfaWlQx4p6VQLkusIExhIgWkAFgzvhLrOJyyvon/rSlnXueO3ywfXljAtz9bCIQOzIcWYi9GWSLk53d6HgPesdZ2DaaQoQQNdwHf0lovAu4B0njJibOMMY/j3RQjCzQDjtb6Qrw8hkfy+zfjXfxn4mVvbmeAC7TWT+MFDJcOoC43AVdprdfgJViG8ZIzW4wxq/aw3wVAS586CSGGIHjBkaR/+wppCgnQgwICxEjnh1v6SOM/670UpGvpemwTOXxE0hkCmSyZgB+spSSeJKcUDeNKqG7oYNO0KgA6ImGs4/Wori8Ikwr5SStF0FqsA2ceF9ldtYQYEaMpERI42lr73zuvVEpdaq39yUAL2eegwRjToLU+CbgBbwRDBNjIjvkPfoc3umEt3siKu4F/9Nk/obX+PnCv1joM/NgYcx3wVeB2oA1YAdwJ/HQvdfmN1joN3AFMw5tT4jXg23v5GF8EfmuMye1lOyHEAER/fTbBT78XfA5uRyfuY8sJHHcogZpy7Fv1+A6twPnIe5isFO2/W0Hrn9ahyiL0vBYj4ffjy2bZEK2kqzhKIhJm/JZWSlu6aB9XzPraMpYXFhPzOdQXhLnwSD/nzwqzbG2aT+ggU6rlJldidBllQcMVwD8FDXgjKAYcNOw1p0Hsd3LAhejDWsuvz1hKvC2Nq1RvawJAZX072yZVkAv4aZo/nptLJvS+9tsz/PzLEZKvIPaLYbm6/+T9i/ud7y99/uQDHkX0mdTpYeAM+n/W6cD3rbVTBlqeTJcmhBhRSik+fuM8nvvVOpyAoqWnkeRKRUXQz9zvHs7yNWkCIYevfLmW2vU+/m+tywmTHC6aP3pm2xNiFNs+qVMYrxV/Ows0AF8bTGHS0nDgyQEXYg8efvhhAM48c8h38RVioIalBeDG457ud77/1nMnjeToibsGczfL3ZGWBiGEEGIYjKachv0RMIAEDUIIIcSwGE1Bg1KqGG/upA8AFfRpXbHWTh5oOdIpKIQQQgwD66h+jxH2C+BIvFs6lOPlMmzGm7JgwKSlQQghhBgGo6mlAW+ixcOsta1KqZy19q9KKYM3qmLAgYO0NAghhBBjnwN05pd7lFKlwDZgoDd2BKSlQQghhBgWo6ylYRlePsPf8SZavAXoAd4ZTCHS0iCEEEIMA6tUv8cIuwRv1mbw7gydAErpc2fqgZCWBiGEEGIYjIJAoZe1dn2f5Wa8G0oOmrQ0CCGEEMNgNLU0KM8lSqnFSqnl+XUnKqU+PZhyJGgQQow4N51j5fde4+VPLobn0yNdHSH2i9EUNOANtfwX4NfA9nkZtgCXDaYQ6Z4QQoy4dT95m/U3rQBAPQF2otyISrz72RGPE/q5EDjCWtuilPplft0GvJtWDZgEDUKIA+L+h7t44+0kcw8N8Zmziun+1evE7ltB6Mhq4rFI73bKgtvojmBNhRiTfHijJWDHPZAK+6wbEAkahBDD7nkT556HvCHiK95JUdHZxcx/+xsAqWfrUF84GtdROK4lGfYTj4VGsrpC7BejoEuir0eBnyilvglejgNwDd7kTgMmQYMQYth1LtnGZ//vVTJ+H48efzh3LQ0Q+NiZTKproq42yidXbeGQ4CrGxTtYUXgIHS8X8tzddax9upEj/vYy4fYYjeMq2Xrpify8eAKxRA43nWNiicMvPxlhcpmkZ4nRZ5QFDZcCd+FN8BTAa2F4AhlyKYQYTWzOpfyqJZT2ZLFAPOQnSQCqSsn5fFRtbSaRbOawNm9EWNXml+nKFbH47q2c/PobVLS0ABDdspnbHmviuQXVkMgAsHybyxcfSPD4JQUj9fGE2C13FAQNSqlqa22DtbYL+LhSqgqYAtRZaxsGXZ61do8baK2fAY4FMn1W32eM2acxnn3KnYqXhDHJGLNlKGXt4/t/Eu+OX9OBrcD3jDH3H4C33vMBF2IseWE12XN+yitb55PBwacsPcVhXj5sKi/MnU4WWPjaGiZva6O9JMzrM6I8WzON6rYk1zz6LNNSLfisi4OLAjYHxrFi6mSaq0vYWhjlpWkTySmH9dEgHQE/Myp93HOmn/sf6qCrx+XzHyvm+CMje6ulEMNydb/io6bf+f7qx/QBjyKUUl3W2uI+zx+01n5yX8sbaEvDNcaYa/f1TYaT1jpgjMnsfct++ywAfg+cATwDnA78WWu92Rjz0v6vpRAHJ/upH+Ovb6OSzTQyhYjNEuns4fQX32J9dTlWKeaurQegJRLijsOPBsB12pmebM6PCVdYFGn8FGYyuBEfOIrlk2qIZC2Q47CuJM9XFLG21XLu75PM2uIN2/zRb9o44sYaCqLSfSEOvFHSPbFzJRYOpbAhdU9orecCNwJHAXHgD8AV2y/iWus7gA/hTVVZB1xrjLknv/uy/N/VWmsL/MgYc01++QSFrgdhAAAgAElEQVRjzNJ8GQuBp4wx/vzzZ4A3gKnAycD1wA1a60uAbwCTgPXAZcaYJ3ZT9U8CfzPGLM4/f1hr/Rzwr4AEDUIMkbuqnvTx1xBqbaOD8bgUEyFN36lhznnhBb522od59NyTOOutDUxp35HEXZRK95tEJqGCpGyYrmiY1ZNqaC+MkFMOvvzvOJ+14CjwO9RnLe/JZsj4A2Sy8MOvvsMHTi3jlM+MPzAfXoi8URI07NfW7X0Ov7XWVcAS4EGgFq8L4xTgP/tsthSYjxc0XA3cqbWek39tXv7vbGNMoTHmmkG8/SLgZqAEuFlr/UW8CSo+B5QBlwMPaq13d/cuh3+Ovpx8XYUQQ5T+1M8ItzaSJcA2ZpIjSIgs289fQZL89si5rC8rprkoym0L5hDJZZneHQNgbUU5HSFvBIUF6ioqAPj70XPpLCrAUQ61sQRu/vVNkSAEfKAUqYCf+gJv3+ruHtJtaZ68r5FNq2MH+CgIMSr4lVInKaVOVkqdvPPz/LoBG2jQcLnWuqPPYwFexuUyY8ytxpi0MWYr8EP6ZGIaY24zxrQaY3LGmPuA5QyxaSTvAWPMYmOMNcbE8W6+cbUxZpkxxjXGPAo8DXxmN/s/AnxEa32K1tqvtf4EcBxQvJvt95vu7m5ZluUxv+zGkwDYPvH525Nr+OG5H+LnnziO9poAXaFw7/ZWKV7Us/hgayef39zA5zdtI5pySeMng5/m4kI2jCujvXhHwmNJNstbE4p5fkoZrf4dk0HVxJLM7kpQ2xNnXCLZuz6ddEfN8ZHl0bm8v42SGSGbgNuB2/KP1p2e/3YwhQ00EfKpnXMatNa34N3wItG3PMBnjCnUWjt4iYbnAtV4PwgK8LoofrC7RMgBdk8sNsZc3WefGOACuT518QN3G2O+vJvPdSHwLWAC3m1Cu4FDjDEL9nhAhk4SIcWYl3nibdRp1+LPpdjE4cQp5f9d8AkSoSAAlV1dHLNsFVcvPJbOSIiT19VxQnc32yrHgbVM2biVY9esZXJ7J40lxfzHRxbyqz89xsuHTue12TNIhoO8VVnMqzWljGvuIZZTuNEAPdEQn15bTyTnBQjKWipjceYdX8J5l07GcUZFc7EYfYblP8Z3z3y93/n++oePeNf/BxxKTsMmvIv56bt5/Ty8oOJUYIUxxtVaG3b84+xuyrcYXnCxXe0uttl5303AlYMZ/WCMuRO4c/tzrfUrePcZF0IMUeDU90D6D+TaEgS+/jS/iE0kGQz0vr61tITSeI7/efQfZByHgOty7anvY1N5MR9Yu4WjG9r51dwjOc+8Q0sgQjrrBQALVq5j3srN5Hw+XMC1sHL+VH591Dz8sQQ+LMV+Syb/8yFc4OPa2+cSDEkipDjwRsOQy/1tKEHDXcC3tNaLgHuANF5y4ixjzON4Tf1ZoBlw8r/s5+F1DZBf7wIz8W6asZ0BLtBaP40XMFw6gLrcBFyltV6Dl2AZxkvObDHGrNp5Y621H3hvfttC4Nt4CZQ3DfCzCyH2xnHwVRRQedPJfPi4v9JSVEhzNALWpaqpi5qGGK2VYZS1PDdjImurygF46tCpHP/OFi580fvqTuiM8dUly0lGs4TjfhzHQs7FAZqLCjntl0ez1vh4YXmKQ6cGOO/4Uu75XQvWWi5cVCkBgxgxoyQRcr/a56DBGNOgtT4JuAFvBEME2Ajcmt/kd3ijG9bijay4G68bYPv+Ca3194F7tdZh4MfGmOuAr+L1t7QBK/BaA366l7r8RmudBu4ApuHNKfEaXjCwKz68O33NxusueBo43hjTOPAjIIQYiMD4KGcsCnDa5d/hlZppLLjkWs5b30QolaN2S8yb8OniqZDasU+8JNqvjEgmx+uHz2VuPEXszQ4sFgIOH2r6PL6wn+uOAde1vd0PJxxfhLUWNQZP2uLdYywGDXvNaRD7nRxwcfDpTsAnfwQvvMN3Lvoqd5bN5eoHnmP25hYqz5xEW1GYL2Vq2VhRzOnL1nNGVx36Q5PZ8vOVpC10zCzh+B8dSXnE4e3zlpDrzjDzp++j+vzdDZASYlCG5er+7U+82e98/98PHf6ujyIkaDjw5IALsZO1X3uB+p+v6H3ec0Mpp1129gjWSBxkJGgYILn3hBBixE2+Yj6xt9uJr+ig+wRFbk5wpKskxJDZd32I8M8kaBBCjLhgZYR5i08D4OGHB3WnXiFGLTs8DRgjSoIGIYQQYhjIkEshhBBCDMhYHD0hQYMQQggxDCRoEEIIIcSAuGMvZtj3u1wKIYQQ4uAiLQ1CCCHEMJDuCSGEEEIMiCtDLoUQQggxEGOxpUFyGoQQIyKbccllvbvc21gam3OJNyboTvrYPru9zbqk2lN7KEWI0ctV/R9jgbQ0CCEOuFcebeLRX2zG8cGpDe8w5eVVPF97CP/yL5+gI3oK81rambZlNamvL0blLB3zqznh2TMJFgVGuupCDNhYnNxJWhqEEAeUm7M89svNuDlLNm15NjKRNEF+9qFj6YiGAVhWUcZtj8dwct5EvKVvNLD6jxtGtuJCDJJVqt9jLJCWBiHEftXckeMPT8RY3QGZ2ggLCrKUrGrFV99FacQy+yM14FOEO+MEU2nKO3pYE5rAMau28nZ1BecsXUUy4Keiu4vGoggliRTtRRHCS+rJfHY6gaictoQYKXJr7ANPDrgY075wTQvr67MAdEQCrKwp5ptPv8HUpjYAnKBDXVExU7c0EkxlmbmurTfHvD0cJpLMAZAKO8SL/WT8ipzfB8DUj03ihJ8tOOCfSYx5w9IMcNHn1/Y739/x+0Pe9c0NErILIfaL1zekCQIbtmVJ+xSuUgQzOaxS+LPZ3u1iVtHt95H0OZTGMv3O1kXJFFn8+JwMPusnEwhSHO8m6CSI+yN0vhYi9ewmnEPGEagt3GU93O4U7rZufNPLUPlgQ4iRIHe5FEKIXTjz5i4e2QqOtUwuCLKxqgiUYvswiDWVpUxs7WRrRTm3v28OzQUhCpKHcdX9S6hu6sHvWrKO4u3xFczv2sLK2hpcx6EoFeeU9hcI0o4CuhorWfeB9aCg+ucnUf5v8/rVI/tmI+0n34FtiRM4bjKlT12ACkvypBgZYzERcq9Bg9b6GeBYINNn9X3GmIuH8sZa66nABmCSMWbLUMrah/eOAHcB84EZwBXGmGsPZB2EGCu2dbk8stVbdpWirqzACxig9+9DR8zk/as3Y6bV0lwQAiAWDvLEvBn0BAIsOWQyGyuKiUV8/PKBv+E6Xo52dyhKImAJ5c8+xTTjMBPX+mm+8oV/ChriP3sR2xIHIPPcZtJPriN05qHDfASE2LWDMmjIu2a0XlS11gFjTGbvW/ZjgeeBXwA/3P+1EuLgkWpJ4XddHAsnNbayLFRJQyAKQDCbozSepLUwwubyYoqS6X77liRSBIM+JqosqWyG18MFBNwdXRmO6xLLlrHNN4Ny1UyQBIFsmhR+aI/TMO/XUFNCQU2O9INvk+52UDg421OHLriZbCxOTkVxC4twT3oP0dvPRRWFDtjxEQevsTI3Q19D6p7QWs8FbgSOAuLAH/B+tWfyr98BfAgoBeqAa40x9+R3X5b/u1prbYEfGWOuyS+fYIxZmi9jIfCUMcaff/4M8AYwFTgZuB64QWt9CfANYBKwHrjMGPPEruptjEkCN+XLSw7lGAhxsLvv+nVQNIEPNrdyREc3MxIJFk+tIu338ZEVm6iIJfj7YdO599j38MnX13J4YzFbiyOUJDPMau8hVxJlVkc3szq6mVtfT3EsScrfg7WKCU3dtNgpTM+topBuACJqHXV2FtFcguTyNL7lDSToJkkpAAqXKJ1E6CDU3kWGIsDipLpQD7xIvDBCwR3njOAREweLsTiN9D7P06C1rgKWAA8CtXhdGKcA/9lns6V4XQClwNXAnVrrOfnXtrcrzjbGFBpjrhnE2y8CbgZKgJu11l8ELgM+B5QBlwMPaq0P2ZfPNpy6u7tlWZbHzHJXVxf1bTmyjqIs7TX4lSYzXPTSSr689E2mtXVRlMpQ3ZMi7MILh0xmRkeCEze3Ma+pm56CCH19bMUasgUhXn3vXErbs+B6ae1BdswKGbJpCujGhzfKwkeaHDtaDiwOpWwmSlvv8+0UlvSqxhE/brI8OpfF3u11yGX+l/37gL5zuX4EOB44zRhzcp9tz8ZrMdjlxVprbYDbjTG/2F1OwwBbGtYbYxb12ect4L+MMXf1Wfcw8NLeulXy5T11ALtfZMilGFMe/20dX39ZoVB8or4JH+BkMpTEvNyChpJilk2sJprJ4eB1ObiOQ1bB8qIIi95ag+MoAqksh7+8gYpkjLdmT2L8lnbqJxdz+IZ6piQ3U0MdAB1OGRG3A4uPRmYBUOLbRneuClBEaaWCjfna+cgRJksBoMiqIIEHLiH4ybkH+CiJUW5YmgTOvXBTv/P9H++c8q5vehho98R1O19UtdZfAI7TWnf0Wa0AX/51B7gKOBeoxrtYFgCVQ6wz0HtG2G4acIvW+uY+6/zAAU2wFOJg9JGLJ/H0whjPrspQVTiNJf+1hpbCKF2RMK7jkAgFKU4kcf3eKAbXcdga9rO8tIiUo5j5Vj2hZJZILI0/6+KimLN6C+snjiMb8LHskAmsTVRyaEsZM7rqKHYb8efzsmuXnIMbiBCcWULhX17Dre8iGMlC25EwdzK2ohT7ykac8mJsJExo4Sz808pH8nCJg4jkNPS3Ce8X+um7ef084GLgVGCFMcbNtzRsP4zubvaL4QUX29XuYpud990EXGmMuX9ANRdC7FcTDingvHz74qq7g8RaXeIhr8sglM1RlUjSUF4GgIvFn3NJ+bxug65oiOmNPfmSLA6WLA7BlNf94DoOPdEg5ckY/qgPfyyf91xTRvC4aeDz5mJwLj7xn+qlAP+H3zs8H1qIvTiYR0/syl3At7TWi4B7gDRecuIsY8zjQDGQBZoBR2t9IV4ewyP5/ZvxLv4z6d8iYIALtNZP4wUMlw6gLjcBV2mt1+AlWIbxkjNbjDGrdrWD1jqEd05xAL/WOgzk9mEkhhCij89eO5vFv9vCugZLUU2EI+YEaHqugeVuhkxNEWvDQSYuriMHNIWDPDtnIqU9CQp7UhQlk2RwiAcClLX3ME41kAn7mNWxlqpAhuBL34ef/RU643D52b0BgxCj0VhMhNznoMEY06C1Pgm4AW8EQwSv2+DW/Ca/wxvdsBZvZMXdwD/67J/QWn8fuDd/wf6xMeY64KvA7UAbsAK4E/jpXuryG611GrgDr6siA7wGfHsPu60GpuSXTwCuzNf5wr1+eCHEbpXVhDn7P3ZKazpzPB/r8/SLLzQztycBPQlsSSFVXd0EUjkSBFFYKtIxonTg5DqwccWUrjrUsbNRc6rhl/96QD+PEPsqN/ZiBrn3xAiQAy4Oeg890MojD7VjAcexLFzyJtPrmokTxMXBCeXYNqWc+ppxABzatJqz7jgB3jd7ZCsuxqphubyfcfGWfuf7R3478V0fRsg00kKIA+4TnxrHB04qpjvmUjvez2vnbaGtsYessnQWRCj8ho+WFyoh4aUvvVNzqAQMQowC+zxPgxBCDEX5uABTJocIhHxUHFeFVQofDmVuCt9kPzWHFfVuWz171zenEmI0c1X/x1ggLQ1CiBE3/dI5+IsDxNZ0sW5CHYxz+PgP5vDK/VuwLhx9zoSRrqIQgyaJkEIIMQyUUky5xJuoad3D2wAIF/k5YdHUEayVEEOTkyGXQgghhBiIsdIl0ZcEDUIIIcQwyEn3hBBCCCEGYizO0yCjJ4QQQggxINLSIIQQQgwDufeEEEIIIQZkLI6ekO4JIcSo0Jm03LfCZVW8dKSrIsR+kd3pMRZIS4MQYsTFM5bjfp/l7RaABXy99k3OHOlKCTFEY7GlQYIGIcSIM082sqUuzEVvv4zr+pjd3EVrXYqySzVOJMD6+izPPNVOaPEmgtYy86IZzF9YPtLVFmKPsmMvZpCgQQgxsrr/sILIJU/yfOFa5jRvpYlptDOBlqUb6P7zWpJ/+QyX/FcbGRcCgVq+/NDzLH21g57r5nH8WVUjXX0hdisr8zQIIcTQxJ/YQHplGwVnHYLz5kbcy/6XQgW1nW18+wOfRiXKOHF9PbNa2km93swv/2MV43wFTGjppKw9xtrp45m6tZW/LunGVJZx4lSHGSrD2qUtlE+JMu0YaYEQYrhI0CCEOGC67nqLpgseAyBxxeMUdTXh4GMi8Jt5H+XGYz4KSnHL+zP8+a6/MKmjm3BTnLKyIO97bSN//+Dh5Pw+1syawFOU0vBomoAD/776HSrru/5/e/cdHkd1Ln78++6qy71hGxfZ2Bgbgg0cAw6d0AkBQm7ohnApySWQAgkhJAZsCC2EhHvJjxKw6SSEFogBY1rACYQT4wKmucgNy12WrL675/fHGYnRWmUlrSRLfj/Ps49mp77n7GrnnTNnZgA48bpx7HPC4E4spVJeTfdraNCrJ5RS7Scx9xPiN88mMX8ViW2VVPxuHj0ppg8byCzZiiNaN+8BRUUQdByryMrkmYl78a8Rw9l9/Tb2X7yCdyYVsDnqGLaqiD4l2xlUXQNATQI+jubVrefNB1by2iNridUkOrawSiWpEan36g6abWkwxrwFTAFqQqOfstZe3JYNG2MKgBXAcGvtmrasqxXbHgc8DIwFMoE1wO+ttfd3ZBxKdWeJvy8m9s17/JubXqZ8zGgiH23GIcTIpYQ8+rGV7OCn5eP+X/VPiCQcA2MJ9lyzlazYZl7dbzTvjB/CtKffJuL8TXO2ZEZZ1KcHkUSCkdu21y27dVuCfz22mnWFFUydNqZDy6xUWE3zs3Q5qZ6emGGtvaldI2klY0ymtbaln00RcD6w3FobN8bsC8w1xhRaa+ekP0qldiGLCnGP/YP4M4sBR4RyIpUJIh8lKGcIgiNCnGqivNn3a7wxYQjDS0r4xhfL+e6iBdihw5mwbjNSFScrlqAmGuHlyeMYv3YTEec3EXGOseu3cFRuNqOKt9MHKM/JBsCJ4Jxj+bzN3H/Jdg4/bxjDJ/bmPy+uJysngjl1MBlZ2siq2l95N2ldCGtTnwZjzD7AncABQDnwODCtdidujJkJHAP0AVYDN1lrnwgWXxj8/cwY44DbrLUzguHDrLXvBus4Ephrrc0I3r8FLAAKgKOB3wC3GmMuAX4EDAeWA9c0lgBYa7cB20KjXPAaB2jSoFRrrdkEh16HlFaQCcTJJEIG1eSymWEQ9CYXEmRlr+fSs6ZSnJcDwKb8Hlz9jw855uLxzBnXk9lOGLF2M5+NH0l1fi4f52cRiwgZCUdchLL8PPYvq4DMKDVxyJYYIsEWnG+t2FBYyV+nf07vwTls21ANwLqlZZx+7dhOqR61a6nofjlD6/s0GGMGAW8DzwJD8acwjgWuDc32LjAJnzRMB2YZYyYE0yYGf8dZa3tYa2e0YPMXAXcDvYG7jTGXAtcA5wJ9geuAZ40xTbZNGmMWGWOqgEXABuDJFsTQKqWlpTqsw913ePEqKK2oGyfEAaGKXAhdfiYkKO7r6hIGgPlDB/PZyBFM2VpC/5oYiUiEd8YN4R+jhnDQwmWc+p+lvHDI11gwbgTvH7AX23vl1i0bTSSoO6hzrv6FbiJ1CQPA6o9KOq9+dHinHk63aqTeqzsQ51yTMwRH9gcBVaHRJwCHAidZa48OzXsGvsWgwZ21McYCD1lr/9hYn4YUWxqWW2svCi3zEXC7tfaR0LgXgfebO61ijMkEDg9et1prK5qaPw2arnClurJNJbDvT2DdVgCqySUTqCGbtYzFJw6OKDX0ylyKOf9XLOu7GwCXf/w5xQMHAFApwqs985i8YSNfX1bEpDUbASjumce7B+8NOCLxBH0qq4gmEuRUVZPhEl/9LCdc3RGRuASD9ujB+uXlAEw+bTDHX17QAZWhupB22aPLj7fU+713v+/X5TOHVE9P3Jy88zXGnA8cYowpDo0W8N2hjTER4AbgTGAwfmeZDwxsY8wAhUnvRwH3GGPuDo3LwHdwbFJwKuX1IOGZRv2WEqVUSwzoBf++DffSf4ivLMF9sIaajcVEt2xht3Wr2BIfSJxsHFG214zmjSf/wN/GTKRXSQ5Lxo+j9sckxzkmlZaydFA/zn/v07rV9y4tZ2HfXAaUVbF7NeSWlpGVSBDPziJOBMHRq38mw8bns21tJX0HZXHI2bszcFQ+H7+5iey8KHsdpvdxUB2ky6cIO2pLn4aV+BaAkxuZfjZwMXAcsMRamwhaGmqrsbHrocrwyUWtoQ3Mk7zsSuB6a+3TKUXesAz81RRKqbYYNgD5/vFkUP8HJvPaF+h966tsYSgQwSH0ryjnssVvsCayN6vGDgX8FRQjtqxle142a3vm8a/RQznuk5UAvD92KAtG+nmOX7ySAzZtoeewfIry8ygviRHJiHD6z/dg9MReO4Q16QS9e6TqYNoRsp5HgKuMMRcBTwDV+M6Je1prXwF64R/stRGIGGMuxPdjeClYfiN+5z+W+i0CFrjAGPMmPmH4aQqx3AXcYIz5At/BMgffOXOTtfbT5JmNMccDxcCH+BaQk4DzgCtSLLtSqoUybzmVyBFjyHhuITEXhX1GkqhxVN7+HJGScsZv+IQzFr3C1rze7LdmCdcfcRY5VRnkxav5cMRAouK461sH160vWpDP8d+dyIhDB1JVDYWLS9ltVC6DR+U1EYVSqi1a3RHSWlsEHAWchj9dsBV4DhgdzPIw8D6wFFgLTADeCS1fAfwaeNIYU2yMuS6Y9ENgDLAF+AswK4VYHgBuB2YGcawK1p3ZyCK9gIeCbWzEn0a5ylr7YHPbUkq1XvSEvcm97xx63n8mPa88mJ5XTSHz9+dSnNWHuw45ioItq/l64YeUZmXz7vDxfNm3F+M2FlOwrYR+ZRUMKPH9EjLiCU4s28KeJ+9OTu8seg/MYuLR/TVhUDsXkfqvbqDZjpAq7bTClUryztwiDl8wgD02FTFxbSEbMgZw2OIv+ZaUELvmQEpfWsGmcthuN7B8cD9GbCzlwMk9OeiJIzo7dNU9tE9HyKuL63eE/G2fLp856LMnlFKdbvKRu2HWxbEMprDfIG569A2O/Hgle8w8koHfGQrfGUq8Ms47J77G0AVrkQxhxHmTOjtspZrR5XOEHWhLQ8fTCleqARU1jnfXOpa+9yajvtjK4acfTd4+9a90iJXH2PL+RvKG59NjzI6dHZVqpXZqadiW1NLQu8tnEdrSoJTaKeRmCscWCJWLy6mZlL1DwgCQkZfBoKOGdEJ0SrVCl08RdqRJg1JKKdUeNGlQSimlVGq6X9agSYNSSinVHrpfzqBJg1JKKdU+ul/WoEmDUkop1R66X87Q+jtCKqWUUmrXoi0NSimlVHvohi0NmjQopZRS7aL7ZQ2aNCilOkWiOs666z+gfOFmiMXIyI8wWFay/6YVfH6W3iJadQPdL2fQpEEp1TnWTbcU3fph8M4hJCijkr1ZQb9PNsAPzus2TwZUu6hu+P3VjpBKqU5R9dEmMqkhQowoCcBRQw5FOYOIbKpmy73zqNpUCUDhlgR/X1LDmx9VsmBRGduKY50bvFK7KH1gVcfTCle7vJrPN1O0931ILEECqCKbOBEqyYSexUwsXYAAa3qN5J/P3ch5c6AmAdmJBCMrqjiicjvXXbc7I0fndHZRVPfQPg+s+lVZ/QdW3ZTf5ZsetKVBKdXhyh//CIklAP8jFCVOFEcGCcaULa37BR9WspJ75pRS42elKhJhW0YGaxIZ/PPtkk6JXaldmfZpUEp1iPJXlrNx6ovUbKxCcOQSJ4tKtkdyKIwOYljNKvbkCzIS8a+Wycylal0V7NYTgIhz9IvFWJmTzcvztvN4oXDiaf25fD89/lE7oy7fsLCDZpMGY8xbwBSgJjT6KWvtxW3ZsDGmAFgBDLfWrmnLutoYx4nAbODBtpZJKdWwREkV6097BlcVJ04WANlsJYJjfWIAWYk4Y/iESHD2LiZRvhiwB/NGHcxx6zZCJMKaHnnkIeQ5QISF0RzM0m3c8UwWBw7uxeQh3e8HWnVx3fArmWpLwwxr7U3tGkkrGWMyrbU1zc/Z4LK9gT8A89IblVKqVnzJOjZfPZeyqigZOIQYuZQgQYJQQwZRatjMbvRnAxESxCWTDXnDKIpkE99ezjc/XY4dM4yt2TnsVlFFcXY2y3tks9plM3pTCSu/zGHykOxOLqlSSXbhpKFBxph9gDuBA4By4HFgWu1O3BgzEzgG6AOsBm6y1j4RLL4w+PuZMcYBt1lrZwTDh1lr3w3WcSQw11qbEbx/C1gAFABHA78BbjXGXAL8CBgOLAeusdbOaaYIvwMeBMa3oRqUUo1IfLqOwq/NIpbIJosYMTJwRCilP3msJEo2+VRQQi+WYOjNZvblPUjksrDn7uy+vpyM+HYA+pdWsHzUYJYN6MfmnjmY4u30razBAa//YSWH317AoMFZnVtgperpfllDq08EGmMGAW8DzwJD8acwjgWuDc32LjAJnzRMB2YZYyYE0yYGf8dZa3tYa2e0YPMXAXcDvYG7jTGXAtcA5wJ9geuAZ40xY5qI//ggtjtbsF2lVAtUPvwBsYRvARAcru4nR6ggDyFGNV/t6LfRn2JGUZzZj5waIRpP1E3LLy2nV1UNlRnCtpxM+lbWBGuCsriwZEFZRxVLqdRI0qsbSDVpuM4YUxx6HQxMBRZaa++z1lZba9cCtwTjAbDWPmit3WytjVtrnwIWAUemIe6/WmvfsNY6a205cCUw3Vq70FqbsNbOBt4EzmpoYWNML+D/ARdbazv0gu/S0lId1uFdZjjrqD0Qajs2CuErjh2wRoYR/jXNIEYcYUDNas5a/hzZiaq6aRW52ZRnZRFNOKKJBFXRr36+os4xrCC708urw117WDWv2fs0BKcD5ib3aTDG3ANcDFSE1wdErbU9jDER4AbgTGAw/jciH3+K4sbGOtMroIwAABO7SURBVEKmeHriDWvt9NAyZUAC+KrbtT/18qi19gcNlOl+YLO19trg/Swg1kEdIfU+DWqXUvG//2D99H+ztTibSCxGLtX0ZD3FDKtLIyrIJEqcvpQymM/JCn5WyqLZ3Lf3OZCVzcZB/YhnZlCamQGJBPOH9Kd/TYzRveCS7/Zm4uRenVpO1aW1z30arq+of5+GG3O7fHtDW/o0rMTvzE9uZPrZ+KTiOGCJtTZhjLF89eEkGlmuDJ9c1BrawDzJy64ErrfWPp1S5D6m3kE/CIAeAMaYY6y1BSmuQymVgtwrDqfgisOJzJjP8mkL2Ap8jVV10wXoQSW92B68/+p3Nj9eyaTyz3hl1DF146pxxHNzuXAPxw9+MryjiqFUy3X5FGFHbUkaHgGuMsZcBDwBVOM7J+5prX0F6AXEgI1AxBhzIb4fw0vB8hvxO/+xQPiSSwtcYIx5E58w/DSFWO4CbjDGfIHvYJmD75y5yVr7aQPzH0z9sv8uiPXqFLallGqFYb+YyLr7P6NiTQUr2YO+bKcqOD6IEqdaImS6BKUMog9rEBIIsEfRCsr2hfw49CgpY+9la/nUjOXMqSM7t0BKNacbPnui1UmDtbbIGHMUcCv+CoZcoBC4L5jlYfzVDUvxV1Y8CrwTWr7CGPNr4EljTA5wh7X2ZuCHwEPAFmAJMAv4fTOxPGCMqQZmAqPw95SYTyNJgLW2KPzeGFOOPz3xZYrFV0q1UCQzykGrzya2rZpofpS1mTfh+KozY7XLphqIR8qYM+jbCA7BMfCYYVwyJZPFt31MJDides5hmfTrr/emU6qj6bMnOp5WuFLApmG3UrrWD9f+U1RlZDBvVAG9y6qIO58U7HfjJAYcOJA5336DRI1DIvCNp45kt4MHdkrcqltqnz4N0yvr92mYltPlmx40VVdKdYp+H19J1mXPU/biClx5NSD0iNUQzxjD1v5RDr1kL3oU9GDEKb7fwrF/PYqieRsYNHkAgw7ShEF1BV0+R9iBJg1KqU4R6Z1Hr6fOITHhLuKf+HssxCSCQyA7woQr6t9zbcB+/RmwX//OCFWp1ul+OYM+5VIp1bnyHzyDyNj+uIE9WLzvBKJ9hX5ndnZUSqmGaEuDUqpTZU4ZQd/PrwL8PedffPHFzg1IKdUoTRqUUkqp9qCnJ5RSSim1q9KWBqWUUqo96M2dlFJKKZWS7pcz6OkJpZRSSqVGkwallFJKpURPTyillFLtoRuentCkQSmllGoX3S9r0KRBKaWUag/dL2fQPg1KKaWUSo22NCillFLtQVsalFJKKbWr0qRBKaWUUinRpEEppZRqD5L0amgWkUIR2acDo2oTTRqUUkoplRJNGpRSSqn2IFL/lfJiMlVEFovIIhF5TkQGBeP/JSKTg+E/isjHwXCGiGwSkfx2KUeIJg1KKaVUe0jh9MQOi/hTFbcCxznn9gU+Av43mPw68I1g+FCgQkSGAJOBT5xzZWmLvRF6yWUHE5FXgQGdHUeyjIyMAbFYbFNnx9EUjTE9NMb06QpxaowpecU5d0K6V+quzmjNRZdHAbOdc+uC9/cBC4PhN4BfisjjwGbgbXwSMQqfULQ7TRo6WHt8MdPBGGOttaaz42iKxpgeGmP6dIU4NcYuRwCXNK72/Txgf+BkfJLwNnARPmmY1hHB6ekJpZRSaufxOnCSiAwO3l8CzAVwzlUB84FfBOPeAw4B9g2G2522NCillFKda66IxELvfwm8JiIOWA5cFpr2Or4Pg3XOxURkKbDCOVfdEYFq0qBq3d/ZAaRAY0wPjTF9ukKcGuNOzDlX0MikhxuZ/xbgltD7k9ohrEaJc8mnTpRSSimldqR9GpRSSimVEj090Y0ZY/KAmcABQAy42lr7UgPz7Q48hu+V+0VyL2ZjzCXANfhevS8DV1prE81NS2eMTW3LGHMlvgdxrdHAn6y1PzXGHAnMBj4PplVZaw9KNb40xthkHDtJPZ6K74GdHUx7yFp7Z7BMk/E3Edee+GbW/vhLxKZaa79ImicK3A2cgO8lfqu19k9tmdZSaYjz18BZ+LqPAb+01r4aTLsB+B/gy2BV86y1l3dCjI3Gka66TEOMj+A79dXaFzjNWvu3dNWjahttaejergZKrbVjgFOAPxljejQw33bgeuDc5AnGmFHBtCnA2OB1XnPT0h1jU9uy1t5trZ1krZ2E7yBUCTwRWnxJ7fSWJgzpirGpOHaWegSKgFOstfsAXwd+YIw5rLn4m3EvcI+1dk/gHvw158nOBcYEsUwBbjDGFLRxWku1Nc5/A5OttRPxCeyfjTG5oWUfCdVda3d0bY2xqTjSVZdtitFaOzX0v3wBsBV4NYX4VQfRpKF7OxP/T0yQ7VvgxOSZrLXbrLX/wCcPyb4DPG+t3Rgc+T4QrLe5aWmNsQXbOgUostbaFsbRkTGma7m0xmitfd9a+2UwvA34BBjZwjjqGGMG4VuvngxGPQnsb4wZ2EDsD1hrE9bajcDzwH+1cVqHxmmtfdVaWx7MtwjfUtO/pbG0Z4zNaHNdtkOM/w08bq2takkcqn1p0tC9jQBWht6vAoancR3tvf7WzHcR8FDSuD2NMfONMe8bYy5oYXzpjLGxOHa6ejTG7AUcjL8DXa2W1uNwYK21Ng4Q/P2yge219juWjnpLV5xhU4Fl1to1oXFnGWMWGWPmGGOmdGKMjcWRjrpMWz0aY7KAc9jxf7mt9ajaSPs0dGHGmPn4f8CG7NaRsTSmI2M0xgwBjgYuDI2eDwy31m4LmubnGmPWWmvndnCMzcbRlE6oxxeAy2tbHmhj/LsKY8wRwAzg2NDoe4GbrbU1xphjgReMMeOttZs7OLydJY5UnAasstYuCI3rSvF3W5o0dGHW2v2bmm6MWYVvXt4YjBoBvNnCzdSuo9YIYHUK09IdY7Pbwp8DnW2trbuHvbW2JDS8whjzPP4OanND49s9xmbi2GnqMWhingvcYa39S2j7zdZjA1YDuxtjotbaeNABbmhy2UIxfRCKaWUbp7VEOuIkOPJ9DDjVWvtZ7XhrbVFo+DVjzGpgH/wtgDssxmbiSEddpqUeAzu0GKapHlUb6emJ7u1pgjuJGWPG4jsJvtLCdTwDnGaMGWiMieBvafqXFKalO8ZUtnUhST80xpghxhgJhvsBxwELaJk2x9hMHDtFPRpj+gOvAf+X3HO+NfVord0QzHN2MOps4MPgPHZy7JcYYyLB+e/TgjjbMi1l6YjTGDMZ+DPwHWvt/PBCxl+dVDs8CSgAPqMF0hRjU3G0uS7T9HljjBkGHEb9zsxpqUfVdtrS0L3dAcwyxiwF4sCl1tpSAGPMdOBLa+29wRHBSvyldr2NMWvwlyzeYK1dboyZwVf3NZ+DP5qiqWnpjrG5bRljDgF6Ur+nNcAZ+KsAavDf90estS90QoyNxrET1eMvgD2By4wxtbet/YO1dmZT8Tfj+8DDxphp+J7wU4OYZgPTgg6rjwIHAbWX5k231i4Phls7raXaGucfgVzgPmPqrlg+31q7GPiNMeYA/OdSHYyvO2ruwBibiiNdddnWGMG3GL5ord2StO501aNqA70jpFJKKaVSoqcnlFJKKZUSTRqUUkoplRJNGpRSSimVEk0alFJKKZUSTRqUUkoplRJNGpRKkYgUiIgTkWHtvJ3vi8ijofcvi8jP23ObqmEislRELkxx3g75fnQEEckWkS9EZK/OjkXtXDRpUGknIqNF5GkRKRKR7SKyWkSeE5GsYPqFIrK0geUaG39e8GM8rYFpb4lIVbCdbSLyoYic0T4la38ikg9MB26oHeecO9E5d3unBdWM4LM5tLPj2BW0R12LyJEiEguPc85VAb/F3/9DqTqaNKj2MBtYB4zD33BpCv6mS9LK9V0KbAEuFpFoA9NnOOd64J8q+CTwZxHZs5Xb6mznAYudc8s6OxC1y3sSOFpExnR2IGrnoUmDSisR6Y9PFu51zm1z3hrn3L3B0UtL1zcef0vZC4AhNPy4ZwCcczH8nfmiwNcaWNcPReTDpHGjRCQuIgXB+5lBy0ipiCwRkXOaiO0GEZmbNO4tEflV6P0+IvKqiGwSkVUicouIZDZR5NPwt3JucJ2hJvALgvjKRGS2iPQVkVtFZEPQwnN5aPkLg2b2a0RkXTDPneE4miu3iOwrIq+IyEYR2SIirwXjFwazzAlae+rdfjq0fJ6I/CHYxiYReV5ERoSmvxXE9EwQwzIRObWxSgqV6ScisiZY5rci0j9YR4mIfBo+KheRDBGZJiLLgzK8LiL7hKZnisjvQnV4TQPbPUxE3g2WXyYiV4lIysmwiJwhIguDVrGFInJ6cpmS5p9VW6eN1bWIFAblejcYb0VkckPrCI0rFN+CNxR4GYgGy24XkQsAnHMl+OdDfCvV8qnuT5MGlVbOuc3Ax8CfRGSqiExoyY9qAy7DH3m/hG/BuLSxGcWf/rgcqAEWNjDL48B4EZkUGnch8JZzrjB4/y4wCeiDP00wS0QmtCZwERmEf5jOs/gH90zBP/3w2iYW2x9YksLqzwAOxT/spwB4H1gWbOd7wO/DO2X8A4JGAKODOE4Brg5Nb7TcIjIkKMfbwbYGA7cBOOcmBssf55zr4Zy7uJF478I/avvgIJZNwItSv+XoAuB3QG/g/4CHRSSviToYGcQ7OqiLK/A7wDuAvvh6nxma/2f42xqfhE9A3wFeE5FewfRfAN8Evg6MCspa93AvEdkb/x28AxgInAz8EDi/iRjriMgU/HfwF/hWsV8CT4rIQaks30xdfx/4EdAP+CswO1Suptb5JT4Rjwfr7OGcezg0y2L8d1IpQJMG1T6OBN4Cfox/gM16Efl1UvIwSkSKwy98K0EdEcnB/yDXPoTqQeAk2bGj2XXB8muAU4EznHM79I1wzm3FP/L5e8H6Bb+jeig0z4POuc3Oubhz7ilgUVCe1pgKLHTO3eecq3bOrQVuCcY3pi9Q0sT0WjOcc1uCJO0loMY594BzLuacexl/3//9QvMngJ855yqCUx+3E9QDNFvu84GlzrlbnHNlQVlSfiS2iETwZf6Vc26tc64M/90YDxwYmvXPzrl5zrkEcD8+eRjbxKorgBuDeBbiE8UPnHPvOefi+GdqjBGR3sH83wNuc859GrR6Tcc/x+DkYPrUYPpS51wFPqkK32f/B8DTzrkXgnr6FJ/cNPV5hn0PeMY593LwOf0deA7/RMe2etA59x/nXDU+oavAJ0BtVYJPRJQCNGlQ7cA5t8k590vn3P74I8GfA9MI7aSAFc65PuEX8D9Jq/ovoAdfPVBpNrABSD6avTlYxyDn3Nedcy82Ed5M4NygVeLoIL5nwe/cRGS6iHwWNB8XAxPxR5WtMQo4JCkxegh/pN6YrUCzR4j4PiO1ypPe147rGXq/wTlXHnpfCAyDlMpdAHyeQkyNGQjkAHUPJXLObcd/lsND860LTS8LBsNlSLYhSDBqJddDbXlr1zE8KYYEvh5qYxgWvA/HsCG0vlHA2Umf5/X4VotU1Nt+YBn166C1CmsHnH+g0CqCz7eNeuH7EykFaNKg2plzrtw5Nwt/5DqpmdmTXYbvn/CRiBThWxL6Af8tDXeITMUcoBJ/FHYh8FRwVAn+Ub4X45v++waJzEIa78C5HchPGjc0NLwSmJuUHPUOOm025kOgVadDmjEoqam/AF+f0Hy5C2n6iL+5p95tBKrwO10ARKQHMAhYnVr4abE6KYYIvh5qY1gbvK+dno+PsdZK4KGkz7OXc27v1mw/MDq0/ea+T9B4XYfjFvypqNrPt956RSSD+uUKJ17J9sF/J5UCNGlQaSa+Q94t4jsAZgadz87A//i804L1TAAOAU7HJxu1rwPxR+ontSa+4OjyEeBK4NuETk3gj6pi+J1cREQuwh9xN8YC+4vIAUE5f0j9ncIjgBGRi0QkJziiHy0iJzSxzueBY1pesmZFgFtFJFdERuOb3mvPXTdX7seAceI7UuYFn+s3QtOLaCKpCNX5DBEZGiQvdwKfAv9OU/lSMQv4uYjsGbQ0XYd/zPffg+mPAj8TkT1EJBd/CiecMP4ROEtETgl9tyeIyBEt2P4ZInK8iERF5ET8d7C238WH+OTum8F35XTg8KR1NFbXF4nI/uI7t/4MyAuVywLfEN/pNxu4GQh3xi3Cd4Ssl9CISE/8/9vfUiyf2gVo0qDSrRp/FPMsvllzI/Ar4Arn3NMtWM9lwHzn3IvOuaLQaxHwdDC9tWYCR+BPkYR3Wg/jOxQuxR91TqCJRMc59xZ+5/cKvll8N2BeaHoRcBT+iohC/KmH5/BHl415FJgY7NjTaSW+TCvwZXwFv1OEZsoddJY7Et+Jcw2wHghfWXAdMF1EtorIfY1s/yf4ndcH+KbzIcC3gr4HHeUO/GWEc/BlOBrfqbC2D8kt+EuD38PX0yp8vQHgnPsI30L1Y/znvQGfCKR0+so59098H5rf4r8LtwPnOefeC6Yvw3dmvB//v3MC8EzSahqr6/uBu4P1ngmc7JzbFkx7HL/jn48/HbIK/znXxvU5PiH6d3DapbZj59nAm865L1Ipn9o1iD/9pZTaWYjI94FDnHMp9cpPYX0X4jsh6vX23ZCIFOI/38eam7cF68wGPsIndp+ka72q68vo7ACUUvU55+4F7u3sONSuK7i6pKl+LGoXpacnlFJKKZUSPT2hlFJKqZRoS4NSSimlUqJJg1JKKaVSokmDUkoppVKiSYNSSimlUqJJg1JKKaVSokmDUkoppVLy/wGsIOGr9TnS0QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap.plots.beeswarm(shap_values[:, :, 0])\n", - "shap.plots.beeswarm(shap_values[:, :, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<1x15 sparse matrix of type ''\n", - "\twith 4 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est[0].decision_path(X_test[:1])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3], dtype=int64)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est[0].apply(X_test[:1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Regression Forest: Random Forest Regressor with confidence intervals" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(123)\n", - "n_samples = 2000\n", - "n_features = 10\n", - "n_outputs = 2\n", - "true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_outputs - 1))])\n", - "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_outputs - 1))])\n", - "# true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n", - "# np.ones((X.shape[0], n_outputs - 1))*np.arange(1, n_outputs).reshape(1, -1)])\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "y = true_te(X) + 0.0 * X[:, [0]] + np.random.normal(0, .1, size=(n_samples, 1))\n", - "X_test = X[:min(100, n_samples)].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), min(100, n_samples))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "est = RegressionForest(n_estimators=400, min_samples_leaf=5, max_depth=None,\n", - " min_impurity_decrease = 0.0, max_samples=0.45, min_balancedness_tol=.45,\n", - " warm_start=False, inference=True, subforest_size=4,\n", - " honest=True, verbose=0, n_jobs=-1, random_state=1235)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RegressionForest(n_estimators=400, random_state=1235)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "point, lb, ub = est.predict(X_test, interval=True, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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o+xKXDuf9hJjKNeTWvH7YRfFOB8kuxzFOKEqLWghxUE9QmwGL/PJmZmTFk+Q6uHN4QnspM8r+QseMT6Fzj17RWSnpo+5f3k2QPZ/FRQ9hNw/fH3FcSv8nFFu7/H2usieEOPV4zQDNncFP2QVVbXR4TU6blHrwAK3J23Uflj2G2Mvu6/3EfqisROdhk2GGw8gJasMGlz5CjKeOuSW/O+yi3BQXbR6zz91dtIbGDmlVCyGgttXb23u6vqyRlNgopmQcnNySW/M6WY3raVtxO8Rl9M7VONTktLijfhZqIyeoAcbm0TrzBqaXP0VSW3Hvjwea+CIzFIUQcHBYXk2rh32NnSydkNI70sPub2dR4UO0JM0i5ayvAMGlKlJiD3aLRNsNxh5y0nG4jKygBtT5d+OzJ7Ck4B7QwS6NrEQndqP/iS/NHTJETwhB72qb68sasRuKxeMPjpueV/JbYrz1dKx8EGU7eAJxXMrBYJ6Q5sIwjlh4aBiMuKBOSM2gaN5tpLdsY3LFCwDYDYOcpBjKGzv6/J2mTmlRC3Gqc3v8dHgD+EyLrQdamJuTSGz3LMTk1l1MK/8H+yZeR/bsMw/7vUO7Pw5dA2Q4jbigBsg880ZqU/JYUPwo0d5GAKZmxnGguYu99e1HHd/uMeWEohCnuKruzUT21LnxmhaLulvTSgdYWvAzvFHJOC6866gWc8/IspRYx2GjQ4bTiAzq7CQX+5f/HLvZyaKiXwJwxpR0UmOjeGFzBV7z6M1tW7qkVS3EqcoMWBRWtwFQVOPG6TCYkBpcrnTq/v9HamsBZYvvYGx2dp+/Py7FFbbWNIzQoAaYMTePokmfZ2LVf8hsWEeU3eDqRWNp6fTzRkHtUcdLP7UQp67iWjedvgCW1hTVuJmaEY/NUMR4apm3+zFq0pYz7qzP9vv741NdjE8d+q1iB2vEBnW804Hv9O/hduWytOBn2AIeJqbFsmxSKh+VNlLWcHh/dbP0UwtxSvL4A+yqCramq1q6aPeazMiKB2DxrgcwLD8t5z5AfEz/3RrxTgdR9vDF5YgNaoDZuZlsnncn8Z0HmL13FQAfm51FssvBC5srsA6Zbt4sQ/SEOCUVVLXhDwSzoKjGjQKmZ8aTU/sOubVvsXvGV5k6c354ixzAoIJaKbVPKbVDKbVVKZUf6qIGK8pu4B13JqU5lzOr9EkS3SVE2Q0umpNNY4eP3TUH91xs7fJjWX1sNyyEGLXavSYltQdzoKi6jdxUFwmGhyUF99ISNwXX2d/BFoYhd8fjeFrU52qtF2it80JWzQlIjHGwZcb38dnjOG3nT1E6wKzsBOKj7b0LrkBwa66+tuwSQoxeu6ra6GmftXb5qWr1MCMrgfkljxHjrWdX3s+ZkJkU3iIHYUR3fUBwFStvVDKbZ95KWst2ppY/g81QLJ6QTHGNm5ZD+qaln1qIU4cZsA6bW1Hc/Qn7nNh9TCt/mpLcaxm/4JwwVXd8BhvUGnhDKbVJKXVzXwcopW5WSuUrpfLr6+uHrsIB9Cw3uG/MZVSlnc783b/C1VXFkgnBhb7zyw/uwyv91EKcOiqau3r7pgGKatrIiIGLS++l05nJgUU/ICdp+KeDn4jBBvXpWutFwMXA15VSZx15gNZ6VfdO5Xnp6elHX0OIJPTs+qIUG+bcCcDSnT8jOcbB1Mw48vc1EehZJLxTuj6EOFUcOvLLZ1rsqWvnttj/ktS+l42zf8LcSWPDWN3xGVRQa62ruv9fB7wILA1lUccjLtpOz4qDnTFj2DbtFsY0rGFC1cssnZBKm8ekuCY4NKe504fWckJRiNGu02f2rusBwbU9Jutyrux4hrIxl2FNvYCMhKM3zI5UAwa1UipWKRXf8zVwIbAz1IUNllKKeOfBRbxLxl9HfdICFhc+yPxkHwnOgycVzYDG7TXDVaoQYpiUNXT0Lmfq9vh5t6iGX7v+jN+RyOaZtzIpDEuVnozBtKgzgQ+VUtuADcB/tdavhbas45NwSFBrZWP93LuxB7o4rfA+8iakUFLbTmP3Ti+NsjWXEKPeod0ebxTU8j/8l+mBEjbNvB3TmcyYEdI33WPAoNZal2qt53f/m621vnc4CjseCTGH72nWFjeJ7VO+Rm7tm1zn2oShFGtLg4s3bdnfTIe0qoUYtRravbR1BV/jB5o6aTmwk+/Zn+VA5krKsy8iKzEmrLMMT8TIqrYfh7aoexRNvJHGxDmcVfIAp4+BTeXNePwBPH6L93fXYwZkNT0hRqOS2uAKmpbWvLLtAL+MehzL4WLj7B+DUn3u2hLpRkdQ97HRpDbsrJv7Mxymmx/yBD7TIr+7r7q508/avY3DXaYQIsTq3J7ebo9tB1o43/0iC9RuNs+8HU90GjaDETMk71CjI6idR2/nDtAaP5WdU77CzMY3uTFxK2tLG3uH6lU0d1FQ1TqcZQohQsiyNBvLgvMmfKZFccEWbnU8x4GMc9g35lKAEdntAaMkqO02g9hoW5+X7Zp0E40Js7jVXIWts4Fd3WvSAhRUttHlO3rtaiHEyLOruq13g+u1e2r5qfUYls1J/uyfQPe+iCOx2wNGSVBD3/3UANpwsG7evTitDh5yPsnakoOzJk1Ls/VAy3CVKIQIkTaPv/cTstvjZ+qeJ1lk7GHznB/R5cwAwFAjs9sDRlNQx/Td/QHQGj+F7dO+wXlsYEHrm6wvO9g/XdbQIbuUCzHCbSxromd8QNH29XzLeI49qedRnn1J7zFZic4R2e0BoyioE/s4oXioook3Upc0n59H/R/rt+5kZ+XB/ukt+5uP8ZtCiEhW1tBBbVtwnkRNk5sv1D+IxxbLtvl39XZ5KAVzcxLDWeZJGTVB3V/XRw+tbKyfdy9OI8Bjrsd5Nr+c0u6NcGvbvBxo6hyOMoUQQ8hrBtjcvfCaP2CRsPER5hj72DD7TrzRKb3HTcuMIzUuOlxlnrTRE9QDtKgB3LHj2TLj+yyxtnGz8x3+tq6chu4Zi4eeZBRCjAxb97fgNYN9Hns2r+bzgX+yNeUS6sde2HtMbLSNeWMjf83pYxk1Qe102AbV/7Qn9xqq0k7nO/ydSaqSFzZXYmlNY7tPlkEVYgSpc3vYWx8cM11WWcNNdffTZM+gZNGPDztu8fhkHLaRHXUju/ojLMpNYsAddZRi3dx7sGxO/hi7iorGVjaUBSfC7OnuChFCRDZ/wOp93bZ7TaZue5DxRh2bFt6P3xHfe1xuiouxySNzSN6hRlVQT0qP47wZGUQP0LL2ONPZMPtOcj3F/DThZV4rqKGl00dZQwd+mVouRMTbUNZEW5eJpTUH1r3ANeotNuV8lqb0Jb3H5Ka4WD45NYxVDp1RFdQAGQlOLpydSbLr6D7rhBg7c3ISADiQfSF7c67gBt/z5FHIv7ZW4jcP37pHCBF5imvclDcGT/7vLNrNtzv+l4royeydfUvvMdOz4jljalrEb1o7WKMuqAHinQ4umpPFaZNSiIkK/onTs+K4aHYW88YmsXRi8Gzwplk/pMOVw2+cf6CmtpZtFa3sqZPuDyEiVb3b2zuctqKpnUtKf0ac8rJlycNYtigA5o9LZPH45HCWOeRGZVBDcEOByelxfHzeGC6ak8Xi8SnYu08oTMmIY+nEFEx7LGvnP0BSoIGHXX/j1Z3VVLV4eteuFkJEjoClWbu3AUuDxx9AbVjFWcZ28qd/D3f8FAAmp8cye8zIHS/dn1Eb1D3sNoOU2Kijfj4lI47F45NpTJrHjilf5ULrfS7wreadojqKa91hqFQIcSzFNW46vAG01mzN/5BvWX9jd9KZlE+8HoC0uKjeTa1Hm1Ef1McyPSueCWkudk3+IrXJi7k3+kmqSnewvrSpd3y1ECL8fKbVO9dha2kl32y6H489gR2Lfg5K4YqycebUdIxR0id9pEEHtVLKppTaopR6OZQFDbelE1JIjHWydv4DKFs0jzl+w6tby9lY1igb4QoRIQqr2/CZFpUtXeQV/oLJRhUbFz2ANzoFu6E4a1o6MVF9r6A5GhxPi/oWoDBUhYSL3WZwxtQ0zLhs1s/7GbNVGde0/pnVxQ2UyIlFIcKuyxeguMZNly9A47p/cK1tNdvH30Rd2nIMBWdMTeuze3M0GVRQK6XGApcCfwptOeGR4HRwxtQ0asespDj3Or5ofxX39pdZu6cBj1/WqxYinHZWtWJamnWb87nD+iMVcXMpnPF1AJZMTBlxG9WeiMG2qP8XuBXodzaIUupmpVS+Uiq/vr6+v8MiVnZiDOfPzKRo3q3UuaZyn/otazZvY1N5s3SBCBEmrZ1+9ta1U1xRzzcb78VmM9i0+CG04WBuTiKT0+PCXeKwGDColVKXAXVa603HOk5rvUprnae1zktPTx+yAodTSmwU588dz64zHsNpBPhy/b2s3lXFu8X1eE1pWQsx3Dbua6LdazJt+y+YZ5SxYd69dLhyyE5yMnfs6BuG15/BtKhPBy5XSu0DngHOU0r9PaRVhVFstJ3T8payef7d5Bm7GbvlYcobO3m9oJaWTlm0SYjhUtbQQZ3bS9PG5/i0eo387Oupyl6JK8rG8kmjY2r4YA0Y1FrrH2qtx2qtJwDXAe9orT8T8srCKCbKRs4Zn2FT+lV8Tv+b0rX/pLnDx5u7anv3ZBNChI7PtNiyv5mafbv4Suuj7I2awZ55P0ApWDE5Fadj9I7w6MspPY76WCakxdK18ucciJ7Kd9t/yWtr1tPuMXl/dz0+UxZuEiKUtlW0UFHXxEW7bgNlY+uyR7G6+6UzEpzhLm/YHVdQa63f1VpfFqpiIk3e5GwKTn+MaJviR+77+duHxdS2elizt0FOMAoRIpUtXWwub2bcujuZpfbxwZyf440dS26KizkjeDutkyEt6mNwOmzMnbeAjQvvY65Rxpc6VvGvbVVUt3jYVtE68BUIIY5LU4ePdwprafzgT1zFatbm3ETruJWkx0ePmiVLT4QE9QBykmIYc9onKJh0E9fb3mZGzUvsa+igsLqN1k7prxZiqHR4Td4triX/o9V8z/84e+PyKJ/7LRJi7Jw1bfQsWXoiJKgHYUpGHMbKn1CdvIR7HU9Qsu1DApZmW0VLuEsTYlQwAxbvFtezrbiU77fcS4c9ia1Lf0lynJNzpmcQbT+1Th4eSYJ6kGbmpNB48R/pdCRzj/cBSsr2UdHcJUuiCjEEtle2sr+hlQsL7yDTaGbj0v9l+uSJXDgri7hoe7jLCzsJ6uMwa8okNi//NemqlYuL78Dv87Fd+qqFOCktnT6KqttIXvcgpxs7WDP9Ryw782PMyUkctavhHS8J6uNgGIrM6St4c+JtLGMHGRvup7rVQ12bJ9ylCTFibShromvr81zne4E1SZeTfe6XSOxjK71TmQT1cZqSEUfHrGt5NebjXOx+npjC59hW0SrD9YQ4AXvq2inbuY7rqx6kwDaTprN+xrSM+IF/8RQjQX2cnA4b41NjqVtxF/lqNpeU3Y977zreKaqTlfaEOA4ef4B3NxVw2a7v4VZxbF/xGAsnZkp3Rx8kqE/A9Mx4HFHRbMh7lDqSOGvzt2mpPcBrO2tkZxghBum/W8tZtP5bpKpW3l/8KzJzchmb7Ap3WRFJgvoEJMdGkZkQTVJaNv+Y+AAxVgcL1nyNrs523tpVKyNBhBjAzooW7K/dyiKKeGvaXQQy57Mod3TtHD6UJKhP0IzsBADGzljC75JvY6JvN5nvfJfmDh/rSpsIWNJnLURfGtq9FP/7Qa4IvMnq9M/imP8pLpqTRZJrdO/ScjIkqE9QTlIMKyan4rAZ5Cz/FK9m3sx5gQ8JrL6f9WWN7KiUYXtCHMlrBtj29jNcVfd7PrAvJ/PKe7hwVqaE9AAkqE/ChLRYLpydSZzTTuuir7Mr4zK+YTxPy/qn+fu6cukCEeIQbR4/6z56nxVbb6NAT6D9kt8wKycZpeTk4UAkqE9SkiuKi2ZnMSE9lu0L7qYmaREPR/2RfZvf4r5XijADsiSqEFUtXbyXv4MFH3yZFiuGv+Tez7lzJ4a7rBFDgnoIRNkNVkxO4/QZY8hf9hgeVzZPOB9l05aNfOfZrbg9sniTOHVVtnSxZlc5y9Z9FYevle8Yt3PNeUtPucX/T4YE9RAal+Li/MUzqb7070Q77Dwd8zBrt1sl8BEAABitSURBVBXz+3f3UtHcGe7yhAiL7eUNLM3/Hsntu/mmeQsLlp7F/HFJ4S5rRJGgHmJOh43ps+cT/dlnyVJNPOF8hH9v3MPbhXXsb5SwFqeWA40djF13F+ObPuTuwE1MXn4Vl8zNltb0cRrMLuROpdQGpdQ2pVSBUuru4ShsxBu3FPWJx5lHCXf5HmH1rio27muS2YvilNL6xgPMrf4nq6wriD/9S0zLimdGVkK4yxpxBtOi9gLnaa3nAwuAi5RSy0Jb1igx6woCH3uAC22buGDfw+ypdbO+rCncVQkxLJrX/Jk5xb/mhcAZtCy/nfGpLs6cmkaUXT7IH6/B7EKutdbt3d86uv/JbI5Bsi//CvtnfZlP298mceOjFNe4Ka1vH/gXhRjJil8j8c0f8F5gHq9P/gnjUuNYPimNzFNwY9qhMKi3NqWUTSm1FagD3tRar+/jmJuVUvlKqfz6+vqhrnNES/74z9medglf0f+Plvd/zwcl9XR4zXCXJURo7F+H9eyNFOjx/Dz2ds6Ynk3e+GRyU2UdjxM1qKDWWge01guAscBSpdScPo5ZpbXO01rnpaenD3WdI1p8TBSt5/+S4sQz+L7/cUpX/5X/bKuipdMX7tKEGFKd+7fi/9snqdSpfMF/K5fmTWXpxBSmZsrSpSfjuDqLtNYtwLvARSGpZhSbnpPC1tMeZX/cPH7s+xX5bz/HPzdVUN3aFe7ShBgS5SXbsf56FY3+KK7tvI1lc6dz9cKxTM+SkD5Zgxn1ka6USur+OgY4HygKdWGjTUa8k2lj09m04vfUx0ziHs8DvPfWS/z5gzIJazHiVZSXEP30J/H6/XzddieXnrmUH182S7o7hshgdo3MBv5PKWUjGOzPaq1fDm1Zo9PC3GQmpsWyNeavzHvzOn7X9QCfXmNQ03YGj12/UNY8ECNSV1MV6q9XEBto49djf8k3zryAZZNSiYmSsdJDZcCg1lpvBxYOQy2nhCRXFOcsmsW+5H+S9uwV/F09yKd2OPjXzEyuWpgT7vKEOC66o5GWP15CstnAqtyH+PwnrmZMUky4yxp1ZEBjmEyYOBXHTS/jiInjH9H38dKbbxOQBZzECOA1A9S5PeyrqKTh95eQ4qnggaS7+OKnb5CQDhEJ6jCKTp+E86b/EuVw8IuOH/PK6vfDXZIQx1Tv9vDrt0p46F8b8P/lShLdJfzI8QO+dOPniXPKzuGhIkEdbmlTUJ97GaUUyz/8HP5aOU8rIlO928tP/lXAX9/dwadLvs0E/17ujf0h19zwRcalyEnDUJKgjgCxObN4ZdEqtA7g/dOl+GokrEVkqXN7ePyDUtYWlPJc3EPMtZWz++zfcNMXvsZpk1LDXd6oJ0EdIa65+Hy+6bgbj9/EfOISako2h7skIQBwe/y8tLWKl9fv4pmY+5lu7cV27V+Zfd4NjE+NDXd5pwQJ6gjhdNi4dOV5XO/7MW5vANc/ruCd1W/KDjEirMyAxXvF9bz00Xb+zN1MV/tR1z4FMy4Nd2mnFAnqCHL90lyuv3Ql92U+QrvlYNG7N/K/f/kHTR2y96IIj437mnn1oy083P4jJhs1BK59GqbLxOThJkEdQew2g+uW5PLVqy9gy3lP0WWL56v7v8vPHvsDH5bUS+taDKs9dW5Wr9vAD6q+Q66tkfrL/07U9AvCXdYpSWk99CuW5uXl6fz8/CG/3lNNW91+PE9eQWLnAW6xbiFu3se5ZE42UzLiZWquCKnC6jbWrfuQi7d+DZfys+PcP3P62dKSDiWl1CatdV5fl0mLOoIlZOSS8c238afN5Le2R9BbnubhN3bzVmEN7bJMqggBy9J8tLeRwvzVXLXtSxgK3l72F+aetjLcpZ3SJKgjnSuFuJtfQY8/g19G/YGz65/ikTd3859tleGuTIwy1a1dvLGrhsL3n+Oi/C/Rpl38e+GfWLb8DBJkMktYDWZRJhFu0fHYP/s8vue/zG1FTzNGt/C7d77I2dMyZMquOGkFVa3sqWunusVD+/r/49udj7HXmMDqvN9x7uI5ZCfKcyzcpEU9UtijibrmCToW3cxn1avc3vEgj72+g1CcYxCnjqKaNrYdaKWsvh3z3V/w/a5fURK7iC0rn2LRrOmylnSEkBb1SGIYxH78FzTGZHHxmnsYU/A11u/8O8vmzgh3ZWIEau30s+1AC5UNrUzZcAdXqfcoSLuYhvMe5ryxaWTI/oYRQ4J6pFGK1Au+xwZ/KvPWf5+mF6+gmKeYOnsxhiHrWYvBsSzNR6UN7Kuo5Kwt32O5UcDGCV9m8ifvYXZcdLjLE0eQro8RasGF/8PvJ/wKR6CTnBcu58Xn/0ar7MEoBml7ZSu7dm7mU1s/T55RzNsz7yHl0jtJkZCOSBLUI1SU3eDaq6/ivpzfUmGlcmXBt/jjQ7fy5IelMjFGHFNBVSsb33qOLxR+kRTVweuLVuFYeD2T0+PCXZrox2D2TBynlFqtlCpUShUopW4ZjsLEwMYkurj54+dQevk/2Z10JrfqJ4l7/Ts8+tp2yho6wl2eiDD+gMV7xbUU/PMBbtr3Axpt6bx5xtP4x53G0okp4S5PHMOAMxOVUtlAttZ6s1IqHtgEXKm13tXf78jMxDCwLDre+Bmx6x5lmzWZDxY+wpl5C5g/LinclYkIUOf28NbWUjLeu43zzfdZ61hG2ZmPEuWK54ypaTIELwIca2biYPZMrAaqu792K6UKgRyg36AWYWAYxF70U/YnzGLyG99i7NbP8t+u+zBPv4zF45PDXZ0Ik6YOH9sqWlifn8+Vu29jGgf4b8YX8S+/hbwxyUxKj8Vhkx7QSHdcoz6UUhMIbnS7vo/LbgZuBsjNzR2C0sSJyF1xDe/qMYx/82Y+XfxN/rZ/HeUX3MaVC8fJqJBTTFFNG2v3NFK34Tm+1vooKINX5z3G1NOvZGpGnOx6P4IMelEmpVQc8B5wr9b6hWMdK10f4eUzLf6zcTfj1vyQpe3v8G5gPi9NuovvX7VCZjKeIkrr23lufSnj8u/jBl6lNHoGzRf/kdmz5+J02MJdnujDSS/KpJRyAP8EnhoopEX4RdkNVswaj/uS37N+9k843VbArfu+wG+eeIJ3Cuvw+APhLlGE0PrSRh78xytctOFGbuBV8rOuhc+/yuIFCySkR6jBnExUwP8BTVrrbw/mSqVFHVkCldto/OtnSPMc4B+OqzHO+xGXLMglyRUV7tLEELEszbYDLTzxYSkxu57lLvuTaFsUG+fezdRzrmdssiyLG+mO1aIeTFCfAXwA7AB6Buj+SGv9Sn+/I0EdefxdbnY+8XUW1v+bnXoSz+TcwbWXnM/csTIqZKR7aWslf3yvlKrqSu5xPMlltnXUpS5h94pfMm3adDLiZSr4SHBSQX0iJKgjU5cvwHv//jPLd91DtNXFL63r6Fz4JX52xVxscuZ/RMrf18Q1f/yICx3beNDxOAnajTr3R3D6LWBIN8dIIhsHCABiomwsv/TzlHziTeozVnCH7W98fOuX+fETL+H2+MNdnjhOPtPiJ8+s4eHox/mD8SCJaWNQN6+GM78rIT3KSIv6VKU1esvf6Hr5doyAj38m3sgZn7mT8RmJ4a5MDNLTf/kNK8seIs1wY5x+C5xzO9hlrY6RSlrU4mhKoRb9D85bNlIUu4RPt/0J/6rz2LjmLVkrJNK1VtL85LVcv+8OuqJSMb70Dpx/l4T0KCZBfYozEnOY8e2XeDzzThL8DSx+45Pk//4LNDfWhbs0cQTL76P5rUfw/TqPmPJ3+BU3EPeN92HMgnCXJkJMglrgjHJwzY3f4oUVL/Ki42KW1L+IfiyP/Bd/TSAgY67DzeMPULDmZWoePo3kD+/mA990rtAPM+0Td5KaKCvenQqkj1r0CliaPXVu3nr7dVbs/gUL1W72Oqbiv+A+JixcKZMlhlGH16SmtYuCndvIWv9zlno/okKn8bvoLxI773JuXDGRsSkyNno0keF54rhorSmobGXNv/7A5fV/JFs1sTZqBcWzv8PypcuYkZ0Q7hJHrYrmTl7bWcP23aUsq3ySTwRew8TGv+Ovw1r2dS5aMIE0Wdx/VJKgFifEsjQf7NpH/RuPcHHrs0Tj49/GSmxn/4DLzlyCXcZeD5mWTh/v7a7jmQ8KWVzz//iy/WVcykt+0sXYV/6EWdOnERMln2hGMwlqcVIsS1NSVkrLa/exqP5FtFbszLqSaZ+8i7h0WSnxZO2pc/PXdwtI2vkXPqf+Q4pqpyr7fOwX3EnGpPnhLk8MEwlqMSS01uRv3U7Vy/dwifkOGAaNkz+B69zvkpAzPdzljTiWpXnm/W10rvkDV/uCAX0g9QySL7mTuMmnhbs8McxOauMAIXoopViycD6FmU9w8zOvc0HTP/jknuex73mWmnEXkXL+d4gavzTcZUY0M2BR5/awdtMWbBv+wBWe14lVXgrillF7zq1MX3yerBsujiItanFCKls6+d3qvewsLubi9n/xGdtbxKkuPJmLcJ7xdZh5OdhldT4ItpwrWzp5p7COii1vcFr985zLRiwMtiedj/2sWxgzLY/0eDlJeCqTrg8REh1ek+KaNt4pqmftrjLmNfyXz9vfYLyqwedMJTDvBhxLPoc9fUq4Sw2bd4vr+NeabYw/8BIfD7zFFKOKVpVAae6nmHbpLcRmjA93iSJCSFCLkDIDFuVNnXxYUs8z68vJrP+QG2zvcJ6xBbuyKHXNwz3tE6QsvZbMjEyi7KN7tIgZsPjPpn3sfPdZ8txvsdLYTJQKUO6ag3feZxh/9meJjpGJKuJwEtRi2Git2by/hXeL6ygtLWF23ct8zHyXyUY1XhwUxOTROflSxi67mtwxY0ZFf6wZsNjX2Mne6kb2579C+oHXOVevJ1F10mZLwT3lcuJP/wIJufPCXaqIYBLUImxau/ys39tAweb3yT3wMst9HzJGNeHHTqlrHvFzL2XMkisgdQqMoM1WLUvT2OGj6kApFfkvk1T5LvM9m4hTXbQTS+PY88g580bsU84Fm5yzFwOToBYRod7tZfuBJgo2rCat8k3yvOuZZlQC0O7MJnrq2TgmnwPjl0PS+IgM7oqK/ezf+hZq31pyWjeR6y8FoE4ns921jIRFV7P03CvlRKo4bie7FdcTwGVAndZ6zmBuUIJaHIvWmpo2D9srWli7aQtRpW+zMLCd5UYhycoNQFdUCq0pC/BnzSMqZz7xuQtwZUwc3vBur4PaAnTtTuqKPkJVbSbDrA7Wp6PYpKdR6FyIe9y5zJi3jCWT0mTkhjhhJxvUZwHtwF9He1Brv5cubzNtnY34A14sHcDSFlprLCwsDVFRsTidScQ4k7AbdhQKQxloNJa2MC0TS1sEdACtNX7Lj9fnxutpxeNto8vbhsfXhtf0gGFHG3ZincksGHcmCTEpR9XU7munpG4b+5p24zc7sfwe0AHSXRlkJ04gK2kyps1Bm78dt99NwAqgAn6UrwNt+bG66/GaXXR53XT53fisAJZSBAwb8TGpTEifw4TkKSREhWcNj4Cleb2ghmc3lNNUtpW5upiFxh4WqhImG9W9x3VoJ5VGNrWOsbQ6c/C6srDic9Dx2bQb8biNBDq1E7+l8QcslFJkJTjJTXGRFh/N3vp29tS2U9XcSZzdJMvRRZqtg1hfAy5vLS5PHa6O/SR1VZBpVpJMW+9tV+kUtukpVLpm056+iOgJeUzOSmFciovxqS6cDgNLWygUSqngfR7w4g148QV8+C0/punHsrwYyobNsGMYDqzu5w1AjD2GWEcsLocLQw18wtXSFiWNRWzd/x5ubwu6e0tTu+Egyu7EaXcR74gl1RFPiiMOFwba9KBNL2bAg8/fhTfgwW8FCNijCdijUIaDOHs0sUY0MUYUpt2BaXOg7DEkxaThciaj7NGgDDCG6KRwwA+WCVYAtAVoQIFhD/6zOU7uDdoKgOmFgC94PcoWvF5HZO0ledJdH0qpCcDLoQ7q57/6MZL3NfTc6JFVoIK1dH/V/RMV/Krne03w7zn6r9JordHd/w8GryagrWCwYmFqq/f3h5tC4TKiiLU7Ma0Afh3Aa/nxaXPYarBhYCiF0f1/W/ebUM/9qrtfQA7DTrQRhcPmwKYMlDKCxykbqvv4YAAFsCyLgDaxrACWDqABrRQQDLOAZRLQwaVUg7dlYFoWphXA1MHfQVtoLBRg1zr4Dyt4Xb3/FFod/L7nOWHTYNdgA/xK41dgKoWhNfbu6+OQ6zCVIoAi0Pu8MgBb8Omout+4e6+95/f0kD9vDAxsqudx6Hlcgm8CoNBAZ8CDqYd3GVoDhb37MTa6nx8uWzRxjljiHHFE2Z0Yhj0Yhoe8hjUa0zLxB3x4/R14/B10mZ5ggwaN1vTei73/1/qQn9F7H3cnAQq6n2fBow7mw0HBh7f/Ryf4xtr7VTDHe//jsPvfhoHNMLApG4YyMDBQqvs1ooL3iJ4xlUX3/+6E7tthmZmolLoZuBkgN/fE1n9o93cQY3Xv3ad6HoqgIx+4np/29QAc/lAd/rOeB7MnjKIMO4Yyuh8EG3Zl620pH/oA9jzpdPcTo+cF2/MGgDr44Pa8mHp+1vMiOzQEDWUEWyXKwLRM2jwtuP1uGn1uHMqGw7ATb3cRY48hJioWpyMOGyrY4tAWvoAPX3drTRk27LZobLZg3WgLbQW6X849NRndf6dCGTaUsoFhw7RMvGYXHrMLX8DXHa7BQAp+MugOye6/QQMdpodm3X5C4XTo26ytpyZlwCH3q1IKm7IRbbNjU1HdoRBsQZra7H4jszDUwcfU0MFIVWjQuvdFbGqNB00AiMIgShm4lEEAMAFfz32kDJRh4FAG0cqGredTkmVhEWwp25UNm7KhlOr9y3tf0ND9HOl+rnSHrKLn/z3x0v2s7Q4mpYze51bPm1qg+00t2IgIENAHHw/0wVtOikogPiqB+OhEHLaDfeK65/mpLcyAD9P04A94sCyT7hvuDaGeT4RK2VCGDQ3BN0/LxNKB3jdflAoGrWViarM3HAM6QJO/nXr/wU8fdgzsKriAVE+QBji6ERSlgq+3ntgN3o+qtzGmlOoNwODX3feVPiS0exsJCt396Vdr66gEUN1/96ENO7rf3OlpiGgdfPvvbdBZ3c+57gYHFgEz+Lf0p7a6kUX9XnrihiyotdargFUQbFGfyHV87vEPgh9TTuWz5AFzRPz9VsCkxV2Jx9+Bx9+FN9CFicbUFibgdMQR40wgxuHC1f2GE22L7n4TE6OJ1dVCac0mdtTkU9tVT4OnmSZ/G4ayEW1z4rTHkOhMJtWVQWpsJmPjc5mYOJHY6ITu4NXBBkhvt4QNDMfQda0MBcuCgBcsE8v04jU9+EwPvoAHn+nFr/34A360KzUkNx9ZiaDUiAipkBohf79hs5OSJLPqBBgxSUyZuJIpE1eGu5TQMQwwYoJfRscTA8QM580P420JIYQ4AQMGtVLqaeAjYLpSqkIp9YXQlyWEEKLHgJ+ztdbXD0chQggh+iZdH0IIEeEkqIUQIsJJUAshRISToBZCiAgnQS2EEBFOgloIISJcSNajVkrVA+VDfsWDkwY0hOm2I43cF0FyPxwk90VQJN4P47XW6X1dEJKgDielVH5/K1CdauS+CJL74SC5L4JG2v0gXR9CCBHhJKiFECLCjcagXhXuAiKI3BdBcj8cJPdF0Ii6H0ZdH7UQQow2o7FFLYQQo4oEtRBCRLhRGdRKqYeUUkVKqe1KqReVUknhrikclFKfUkoVKKUspdSIGYo0lJRSFymlipVSe5RSt4e7nnBRSj2hlKpTSu0Mdy3hpJQap5RarZQq7H5t3BLumgZjVAY18CYwR2s9D9gN/DDM9YTLTuBq4P1wFxIOSikb8FvgYmAWcL1SalZ4qwqbvwAXhbuICGAC39NazwSWAV8fCc+JURnUWus3tNZm97frgLHhrCdctNaFWuvicNcRRkuBPVrrUq21D3gGuCLMNYWF1vp9oCncdYSb1rpaa725+2s3UAjkhLeqgY3KoD7CTcCr4S5ChEUOcOCQ7ysYAS9KMTyUUhOAhcD68FYysJGx5XUflFJvAVl9XHSH1vrf3cfcQfCjzlPDWdtwGsz9cApTffxMxqMKlFJxwD+Bb2ut28Jdz0BGbFBrrc8/1uVKqRuBy4CVehQPFh/ofjjFVQDjDvl+LFAVplpEhFBKOQiG9FNa6xfCXc9gjMquD6XURcBtwOVa685w1yPCZiMwVSk1USkVBVwHvBTmmkQYKaUU8GegUGv9SLjrGaxRGdTAb4B44E2l1Fal1B/CXVA4KKWuUkpVAMuB/yqlXg93TcOp+4TyN4DXCZ40elZrXRDeqsJDKfU08BEwXSlVoZT6QrhrCpPTgc8C53Vnw1al1CXhLmogMoVcCCEi3GhtUQshxKghQS2EEBFOgloIISKcBLUQQkQ4CWohhIhwEtRCCBHhJKiFECLC/X+/ut8qdrkogQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for t in range(n_outputs):\n", - " plt.plot(X_test[:, 0], point[:, t])\n", - " if est.inference:\n", - " plt.fill_between(X_test[:, 0], lb[:, t], ub[:, t], alpha=.4)\n", - " plt.plot(X_test[:, 0], true_te(X_test)[:, t])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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y81Be2PdG01EAAIDLRFHrMmMjrZSSTNr9EQAA5ixFrcusWrIwH1p7tfFHAACYwxS1LjQ+Mpyf7n0jW3cfbjoKAABwGShqXWj9HcPpK/FUDQAA5ihFrQstX7QgH71+eSY37kyttek4AABAmylqXWp8dDgvvXokm1851HQUAACgzRS1LvXZ21ZlXn/JxEbjjwAAMNcoal1q6eD8/NqNQ3l4465MTxt/BACAuURR62Ljo8N55eBb+fH215qOAgAAtJGi1sXuvnVl5g/0ZWKDw68BAGAuUdS62OKF8/Kpm1fk4U27ctL4IwAAzBmKWpcbGx3OvsPH8v0XX206CgAA0CaKWpf71C0rMji/P5MbjT8CAMBcoah1ucH5A7n71pV5dNOunDg53XQcAACgDRS1OWBsZDivHTmRJ543/ggAAHOBojYHfOLmoSxeOJCJDQ6/BgCAuUBRmwMWDPTnN9atyuNbdufY1Mmm4wAAAJdIUZsjxkeHc/joVL733P6mowAAAJdIUZsjPnbD8lw1OM/4IwAAzAGK2hwxr78v99w+nG89sydvHTf+CAAA3UxRm0PGR4dz5PjJfPvZvU1HAQAALoGiNod8+LplGVq8wPgjAAB0OUVtDunvK7n3juF8Z+veHD56ouk4AADARVLU5pjx0eEcm5rOt57Z03QUAADgIs2oqJVS7imlbC2lbCulfOk91n2olHKylPK32heRC/GBNVeltWRhJjfsajoKAABwkc5b1Eop/Um+nGR9knVJvlBKWfcu6/5lksfbHZKZ6+srGRtt5Xs/3ZeDR443HQcAALgIM3midleSbbXWF2qtx5N8Lcl951j3j5L8SRJbDjZsbGQ4J07WPL5ld9NRAACAizCTorY6yfYzrnecvvdzpZTVSf5mkq+0LxoX647VS/LXlg1mcqPxRwAA6EYzKWrlHPfqWdf/Nsnv1lrf86TlUsr9pZSnSilP7du3b6YZuUCllIyPtPIX2/Zn/xvHmo4DAABcoJkUtR1J1pxxfU2Ssw/qujPJ10opLyX5W0l+r5TyN87+RrXWB2utd9Za7xwaGrrIyMzE2Ohwpmvy6GbjjwAA0G1mUtSeTHJjKeW6Usr8JJ9P8tCZC2qt19Va19Za1yb5epJ/WGv9L21Py4zdvHJxblyxyOHXAADQhc5b1GqtU0m+mFO7OT6T5I9rrVtKKQ+UUh643AG5OKWUjI208uRLB7L79aNNxwEAAC7AjM5Rq7U+Umu9qdZ6fa31/zp97yu11ndsHlJr/Xu11q+3OygXbmx0OLUmD2+yqQgAAHSTGRU1utP1Q4tyW+tK448AANBlFLU5bmyklZ9sP5jtB440HQUAAJghRW2OGxsZThJnqgEAQBdR1Oa4NVcP5pfWLDX+CAAAXURR6wHjo608vetQnt/3RtNRAACAGVDUesC9dwynlGRyg/FHAADoBopaD1i1ZGE+tPbqTGzcmVpr03EAAIDzUNR6xPhoK9v2vpGtew43HQUAADgPRa1HrL99VfqMPwIAQFdQ1HrE8kUL8rEblht/BACALqCo9ZDxkVZ+9uqRbHrl9aajAAAA70FR6yGfvW1V5vUXh18DAECHU9R6yJLBefn4jUOZ3LAz09PGHwEAoFMpaj1mbHQ4O18/mh9vf63pKAAAwLtQ1HrM3beuzIKBvkzY/REAADqWotZjFi+cl0/evCIPb9qVk8YfAQCgIylqPWh8tJV9h4/l+y++2nQUAADgHBS1HvSpW1ZkcH6/8UcAAOhQiloPumJ+f+6+dWUe27wrJ05ONx0HAAA4i6LWo8ZHW3ntyIn8xbb9TUcBAADOoqj1qI/ftDyLFw4YfwQAgA6kqPWoBQP9+extq/KnW3bn2NTJpuMAAABnUNR62PhoK4ePTeW7W/c1HQUAADiDotbDPnr9slw1OC+TG40/AgBAJ1HUeti8/r6sv2M433x6T44cn2o6DgAAcJqi1uPGRobz1omT+faze5uOAgAAnKao9bgPX7csQ4sXZNLujwAA0DEUtR7X31dy7x3D+fbWvTl89ETTcQAAgChqJBkfHc7xqel865k9TUcBAACiqJHkA2uuyuqlVzj8GgAAOoSiRvr6Su4dGc73ntuXg0eONx0HAAB6nqJGkmR8pJWp6ZrHt+xuOgoAAPQ8RY0kye2rr8zaZYPGHwEAoAMoaiRJSikZG2nlief3Z/8bx5qOAwAAPU1R4+fGR1uZrsmjmzxVAwCAJilq/NzNqxbnxhWLjD8CAEDDFDXeZny0lSd/diC7Xn+r6SgAANCzFDXeZmxkOLUmD2/0VA0AAJqiqPE27x9alNtaV2ZSUQMAgMYoarzD+GgrP9l+MNsPHGk6CgAA9KQZFbVSyj2llK2llG2llC+d4/X/qZSy8fSfJ0opo+2Pymy5947hJMnExp0NJwEAgN503qJWSulP8uUk65OsS/KFUsq6s5a9mOQTtdaRJP88yYPtDsrsWXP1YD5w7dJM2v0RAAAaMZMnancl2VZrfaHWejzJ15Lcd+aCWusTtdbXTl/+ZZJr2huT2TY+0srTuw7l+X1vNB0FAAB6zkyK2uok28+43nH63rv5+0kevZRQNO/ekeGUEk/VAACgATMpauUc9+o5F5byyZwqar/7Lq/fX0p5qpTy1L59+2aeklm38sqFuWvt1Xlowyup9Zz/uQEAgMtkJkVtR5I1Z1xfk+Qdu0yUUkaS/EGS+2qtr57rG9VaH6y13llrvXNoaOhi8jKLxkZbeX7fm3l29+GmowAAQE+ZSVF7MsmNpZTrSinzk3w+yUNnLiilXJvkG0n+bq31ufbHpAnrb1+V/r6SSbs/AgDArDpvUau1TiX5YpLHkzyT5I9rrVtKKQ+UUh44vez/TLIsye+VUn5SSnnqsiVm1ixftCAfvX5ZJjbsMv4IAACzaGAmi2qtjyR55Kx7Xznj63+Q5B+0NxqdYHyklX/6Jxuz6ZXXM3LN0qbjAABAT5jRgdf0rs/etirz+ksmNhh/BACA2aKo8Z6WDM7Lx28cyuTGXZmeNv4IAACzQVHjvMZHW9n1+tH86OXXzr8YAAC4ZIoa53X3upVZMNBn/BEAAGaJosZ5LVowkE/dsiIPb9qdk8YfAQDgslPUmJHx0Vb2v3Es33/hnGeZAwAAbaSoMSOfvHlFBuf3Z8Lh1wAAcNkpaszIFfP785l1K/Po5t05cXK66TgAADCnKWrM2NhIKwePnMh/37a/6SgAADCnKWrM2MdvWp7FCwcyuWFX01EAAGBOU9SYsQUD/bnntlX50y27c/TEyabjAADAnKWocUHGRls5fGwq331uX9NRAABgzlLUuCAfvX5Zrn7f/ExuNP4IAACXi6LGBZnX35d7bl+Vbz29J0eOTzUdBwAA5iRFjQs2PtLKWydO5tvP7m06CgAAzEmKGhfsruuuzorFCzKxweHXAABwOShqXLD+vpLP3TGc72zdl8NHTzQdBwAA5hxFjYsyPtrK8anpfPPpPU1HAQCAOUdR46J88NqlWb30CuOPAABwGShqXJRSSsZGhvPnP92fg0eONx0HAADmFEWNizY+2srUdM1jm3c3HQUAAOYURY2LdlvryqxdNujwawAAaDNFjYtWSsn4aCtPPL8/+w4fazoOAADMGYoal2RspJXpmjy62VM1AABoF0WNS3LzqsW5aeWiTG5Q1AAAoF0UNS7Z+EgrP3jpQHa9/lbTUQAAYE5Q1LhkY6OtJMnDNhUBAIC2UNS4ZNctf19uX31lJhQ1AABoC0WNthgbaWXD9oN5+dUjTUcBAICup6jRFvfeMZwkmdy0s+EkAADQ/RQ12mLN1YP54LVLM2H3RwAAuGSKGm0zNtLKM7sOZdveN5qOAgAAXU1Ro23uHRlOKcnkRuOPAABwKRQ12mbllQtz19qrM7FhZ2qtTccBAICupajRVuOjrTy/7808u/tw01EAAKBrKWq01frbV6W/r2Rig/FHAAC4WIoabbVs0YJ89Pplmdy4y/gjAABcJEWNthsfbeXlA0eyccfrTUcBAICupKjRdp9dtyrz+o0/AgDAxVLUaLslg/PyiZuG8vCmXZmeNv4IAAAXSlHjshgbaWXX60fzw5dfazoKAAB0nRkVtVLKPaWUraWUbaWUL53j9VJK+XenX99YSvlg+6PSTe5etzILBvoyafwRAAAu2HmLWimlP8mXk6xPsi7JF0op685atj7Jjaf/3J/k99ucky6zaMFAPn3rijy8aVemTk43HQcAALrKwAzW3JVkW631hSQppXwtyX1Jnj5jzX1J/nM9tR/7X5ZSlpZShmutu9qemK4xNtLKI5t256t/8WKuW76o6ThwTo6RgN5USmk6wpzi71I63fyBvvz6zSuajnFBZlLUVifZfsb1jiQfnsGa1UneVtRKKffn1BO3XHvttRealS7zqVtWZMkV8/IvHnm26SgAAPSw5YsW5Kl/dnfTMS7ITIrauX7ldPavTWayJrXWB5M8mCR33nmnX73McQvn9eeb/+Tj2Xv4WNNRAADoYQP93fcUfSZFbUeSNWdcX5Pk7B0iZrKGHrTiyoVZceXCpmMAAEBXmcmuj08mubGUcl0pZX6Szyd56Kw1DyX5zdO7P34kyes+nwYAAHBxzvtErdY6VUr5YpLHk/Qn+WqtdUsp5YHTr38lySNJPpdkW5IjSX7r8kUGAACY22Yy+pha6yM5VcbOvPeVM76uSX67vdEAAAB604wOvAYAAGD2KGoAAAAdRlEDAADoMIoaAABAh1HUAAAAOoyiBgAA0GEUNQAAgA5TTh2B1sAPLmVfkp818sPf2/Ik+5sOAe/Be5RO5z1KN/A+pdN5j/aGv1ZrHTrXC40VtU5VSnmq1npn0zng3XiP0um8R+kG3qd0Ou9RjD4CAAB0GEUNAACgwyhq7/Rg0wHgPLxH6XTeo3QD71M6nfdoj/MZNQAAgA7jiRoAAECHUdTOUEq5p5SytZSyrZTypabzwJlKKWtKKd8ppTxTStlSSvmdpjPBuZRS+kspPy6lTDadBc5WSllaSvl6KeXZ03+f/krTmeBMpZR/cvrf+c2llD8qpSxsOhPNUNROK6X0J/lykvVJ1iX5QillXbOp4G2mkvyvtdZbk3wkyW97j9KhfifJM02HgHfx/yR5rNZ6S5LReK/SQUopq5P84yR31lpvT9Kf5PPNpqIpitov3JVkW631hVrr8SRfS3Jfw5ng52qtu2qtPzr99eGc+p+L1c2mgrcrpVyT5N4kf9B0FjhbKeXKJB9P8h+TpNZ6vNZ6sNlU8A4DSa4opQwkGUyys+E8NERR+4XVSbafcb0j/ieYDlVKWZvkA0m+32wSeId/m+SfJpluOgicw/uT7Evyn06P5/5BKeV9TYeCv1JrfSXJv07ycpJdSV6vtf5ps6loiqL2C+Uc92yJSccppSxK8idJ/pda66Gm88BfKaWMJdlba/1h01ngXQwk+WCS36+1fiDJm0l8Jp2OUUq5Kqcmuq5L0kryvlLK/9xsKpqiqP3CjiRrzri+Jh4102FKKfNyqqT9Ya31G03ngbN8LMlfL6W8lFPj458qpfx/zUaCt9mRZEet9a+mEb6eU8UNOsXdSV6ste6rtZ5I8o0kH204Ew1R1H7hySQ3llKuK6XMz6kPbj7UcCb4uVJKyanPVTxTa/2/m84DZ6u1/m+11mtqrWtz6u/Qb9da/SaYjlFr3Z1keynl5tO3Pp3k6QYjwdleTvKRUsrg6X/3Px0b3vSsgaYDdIpa61Qp5YtJHs+pHXa+Wmvd0nAsONPHkvzdJJtKKT85fe9/r7U+0mAmgG7zj5L84elfyr6Q5LcazgM/V2v9finl60l+lFO7Pf84yYPNpqIppVYfwwIAAOgkRh8BAAA6jKIGAADQYRQ1AACADqOoAQAAdBhFDQAAoMMoagAAAB1GUQMAAOgwihoAAECH+f8B2iLHQysh/lsAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,5))\n", - "plt.plot(est.feature_importances(max_depth=4, depth_decay_exponent=2.0))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "explainer = shap.Explainer(est, shap.maskers.Independent(X, max_samples=100))\n", - "shap_values = explainer(X[:200])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap.plots.beeswarm(shap_values[:, :, 0])\n", - "shap.plots.beeswarm(shap_values[:, :, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Combining with Double Machine Learning" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(123)\n", - "n_samples = 5000\n", - "n_features = 10\n", - "n_treatments = 3\n", - "n_outputs = 2\n", - "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n", - "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n", - "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n", - " np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "W = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "for t in range(n_treatments):\n", - " T[:, t] = np.random.binomial(1, scipy.special.expit(X[:, 0]))\n", - "y = np.sum(true_te(X) * T, axis=1, keepdims=True) + 5.0 * X[:, [0]] + np.random.normal(0, .1, size=(n_samples, 1))\n", - "y = np.tile(y, (1, n_outputs))\n", - "for j in range(n_outputs):\n", - " y[:, j] = (j + 1) * y[:, j]\n", - "X_test = X[:min(100, n_samples)].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), min(100, n_samples))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 11.3 s\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est = CausalForestDML(cv=2,\n", - " criterion='mse', n_estimators=400,\n", - " min_var_fraction_leaf=0.1,\n", - " min_var_leaf_on_val=True,\n", - " verbose=0, discrete_treatment=False,\n", - " n_jobs=-1, random_state=123)\n", - "%time est.fit(y, T, X=X, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "res = est.const_marginal_effect_inference(X_test)\n", - "point = res.point_estimate\n", - "lb, ub = res.conf_int(alpha=.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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600 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X Y T \n", - "0 Y0 T0 0.066 0.025 2.660 0.008 0.025 0.107\n", - " T1 1.077 0.040 26.652 0.000 1.011 1.144\n", - " T2 2.071 0.039 53.520 0.000 2.007 2.134\n", - " Y1 T0 0.132 0.050 2.660 0.008 0.050 0.213\n", - " T1 2.155 0.081 26.652 0.000 2.022 2.288\n", - "... ... ... ... ... ... ...\n", - "99 Y0 T1 0.842 0.087 9.706 0.000 0.699 0.985\n", - " T2 1.887 0.074 25.641 0.000 1.766 2.008\n", - " Y1 T0 4.139 0.187 22.148 0.000 3.832 4.447\n", - " T1 1.684 0.174 9.706 0.000 1.399 1.970\n", - " T2 3.774 0.147 25.641 0.000 3.532 4.016\n", - "\n", - "[600 rows x 6 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res.summary_frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "res = est.effect_inference(X_test,\n", - " T0=np.zeros((X_test.shape[0], n_treatments)),\n", - " T1=np.ones((X_test.shape[0], n_treatments)))\n", - "point = res.point_estimate\n", - "lb, ub = res.conf_int(alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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200 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X Y \n", - "0 Y0 3.214 0.072 44.951 0.0 3.097 3.332\n", - " Y1 6.428 0.143 44.951 0.0 6.193 6.663\n", - "1 Y0 3.212 0.086 37.447 0.0 3.071 3.353\n", - " Y1 6.424 0.172 37.447 0.0 6.142 6.706\n", - "2 Y0 3.194 0.074 43.012 0.0 3.072 3.317\n", - "... ... ... ... ... ... ...\n", - "97 Y1 9.551 0.413 23.151 0.0 8.872 10.229\n", - "98 Y0 4.798 0.185 25.954 0.0 4.494 5.102\n", - " Y1 9.596 0.370 25.954 0.0 8.988 10.204\n", - "99 Y0 4.799 0.183 26.212 0.0 4.497 5.100\n", - " Y1 9.597 0.366 26.212 0.0 8.995 10.199\n", - "\n", - "[200 rows x 6 columns]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res.summary_frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.98217628, 0.00347145, 0.00153771, 0.00260605, 0.00102379,\n", - " 0.00138967, 0.00223861, 0.00133064, 0.0030675 , 0.00115831],\n", - " [0.98217628, 0.00347145, 0.00153771, 0.00260605, 0.00102379,\n", - " 0.00138967, 0.00223861, 0.00133064, 0.0030675 , 0.00115831]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.feature_importances_" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for tree_id, tree_per_output in enumerate(est[:2]):\n", - " plt.figure(figsize=(15, 5))\n", - " for j in range(n_outputs):\n", - " plt.subplot(1, n_outputs, j + 1)\n", - " plot_tree(tree_per_output[j], max_depth=2)\n", - " plt.title(\"Tree #{} for output y{}\".format(tree_id, j))\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[GRFTree(max_features='auto', min_var_leaf=0.020617404788809485,\n", - " min_var_leaf_on_val=True, random_state=914636141),\n", - " GRFTree(max_features='auto', min_var_leaf=0.020617404788809485,\n", - " min_var_leaf_on_val=True, random_state=914636141)]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "shap_values = est.shap_values(X[:20])" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(25, 15))\n", - "for j in range(n_treatments):\n", - " for i in range(n_outputs):\n", - " plt.subplot(n_treatments, n_outputs, i + j * n_outputs + 1)\n", - " plt.title(\"Y{}, T{}\".format(j, i))\n", - " shap.plots.beeswarm(shap_values['Y' + str(i)]['T' + str(j)], plot_size=None, show=False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Custom Linear Moment Forest\n", - "\n", - "We can even easily create an estimator class that solves a customer linear moment that we want to define on the fly. The `BaseGRF` class can handle the estimation of any linear moment problem of the form:\n", - "\\begin{equation}\n", - "E[\\text{pointJ} \\cdot (\\theta(x); \\beta(x)) - \\alpha \\mid X=x] = 0\n", - "\\end{equation}\n", - "\n", - "The user simply needs to create a child class that inherits from the `BaseGRF` and implement the abstract methods `_get_alpha_and_pointJ` and `_get_n_outputs_decomposition`. The first function takes as input all the variables passed at fit time (i.e. `X`, `T`, `y`, and any other keyword based variables the user passes) and returns for each sample the $\\alpha$ vector of the linear moment and a flattened version of the $\\text{pointJ}$ matrix in the linear moment (in Fortran contiguous form, i.e. the first row is first, the second row next etc). The second function returns two numbers, the first number corresponds to the size of the concatenated parameter vector $(\\theta(x); \\beta(x))$ and the second number corresponds to the size of the prefix $\\theta(x)$ that we care about ($\\beta(x)$ is a nuisance parameter).\n", - "\n", - "As a simple expository example let's see how we can create a forest that solves a moment of the form:\n", - "\\begin{equation}\n", - "E[(y - \\theta(x)' (T; T^2)) (Z; Z^2) \\mid X=x]\n", - "\\end{equation}\n", - "i.e. where we expand the treatments and instruments to include their squares and solve this quadratic in treatment heterogenous IV model." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1227)\n", - "n_samples = 2000\n", - "n_features = 10\n", - "n_treatments = 2\n", - "true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n", - "Z = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "U = np.random.normal(0, .2, size=(n_samples, 1))\n", - "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "for t in range(n_treatments):\n", - " T[:, t] += U[:, 0] + Z[:, t]\n", - "y = np.sum(true_te(X) * T, axis=1, keepdims=True) + 10 * U[:, [0]]\n", - "X_test = X[:1000].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.grf._base_grf import BaseGRF\n", - "from econml.utilities import cross_product\n", - "\n", - "class CustomGRF(BaseGRF):\n", - " \n", - " def _get_alpha_and_pointJ(self, X, T, y, *, Z):\n", - " T = np.hstack([T, T**2])\n", - " Z = np.hstack([Z, Z**2])\n", - " if self.fit_intercept:\n", - " T = np.hstack([T, np.ones((T.shape[0], 1))])\n", - " Z = np.hstack([Z, np.ones((T.shape[0], 1))])\n", - " return y * Z, cross_product(Z, T)\n", - " \n", - " \n", - " def _get_n_outputs_decomposition(self, X, T, y, *, Z):\n", - " n_relevant_outputs = T.shape[1] * 2\n", - " n_outputs = n_relevant_outputs\n", - " if self.fit_intercept:\n", - " n_outputs += 1\n", - " return n_outputs, n_relevant_outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "est = CustomGRF(criterion='mse', n_estimators=400, min_samples_leaf=40,\n", - " min_var_fraction_leaf=0.1, min_var_leaf_on_val=True,\n", - " min_impurity_decrease = 0.001, max_samples=.45, max_depth=None,\n", - " warm_start=False, inference=True, subforest_size=4,\n", - " honest=True, verbose=0, n_jobs=-1, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CustomGRF(min_impurity_decrease=0.001, min_samples_leaf=40,\n", - " min_var_fraction_leaf=0.1, min_var_leaf_on_val=True, n_estimators=400,\n", - " random_state=123)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(X, T, y, Z=Z)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "point, lb, ub = est.predict(X_test, interval=True, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for t in range(n_outputs):\n", - " plt.plot(X_test[:, 0], point[:, t])\n", - " if est.inference:\n", - " plt.fill_between(X_test[:, 0], lb[:, t], ub[:, t], alpha=.4)\n", - " plt.plot(X_test[:, 0], true_te(X_test)[:, t])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 10))\n", - "plot_tree(est[0], impurity=True, max_depth=2)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "explainer = shap.Explainer(est, shap.maskers.Independent(X, max_samples=100))\n", - "shap_values = explainer(X[:200])" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "for t in range(n_treatments):\n", - " plt.subplot(1, n_treatments, t + 1)\n", - " plt.title(\"T{}\".format(t))\n", - " shap.plots.beeswarm(shap_values[..., t], plot_size=None, show=False)\n", - "plt.tight_layout()\n", - "plt.show()\n", - "plt.figure(figsize=(15, 5))\n", - "for t in range(n_treatments):\n", - " plt.subplot(1, n_treatments, t + 1)\n", - " plt.title(\"T{}**2\".format(t))\n", - " shap.plots.beeswarm(shap_values[..., t + n_treatments], plot_size=None, show=False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/Interpretability with SHAP.ipynb b/notebooks/Interpretability with SHAP.ipynb deleted file mode 100644 index 26a052d28..000000000 --- a/notebooks/Interpretability with SHAP.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Interpreting Heterogeneous Effect Models with SHAP\n", - "\n", - "[SHAP](https://shap.readthedocs.io/en/latest/) is a popular open source library for interpreting black-box machine learning models using the [Shapley values methodology](https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html).\n", - "\n", - "Similar to how black-box predictive machine learning models can be explained with SHAP, we can also explain black-box effect heterogeneity models. This approach provides an explanation as to why a heterogeneous causal effect model produced larger or smaller effect values for particular segments of the population. Which were the features that lead to such differentiation? This question is easy to address when the model is succinctly described, such as the case of linear heterogneity models, where one can simply investigate the coefficients of the model. However, it becomes hard when one starts using more expressive models, such as Random Forests and Causal Forests to model effect hetergoeneity. SHAP values can be of immense help to understand the leading factors of effect hetergoeneity that the model picked up from the training data.\n", - "\n", - "Our package offers seamless integration with the SHAP library. Every `CateEstimator` has a method `shap_values`, which returns the SHAP value explanation of the estimators output for every treatment and outcome pair. These values can then be visualized with the plethora of visualizations that the SHAP library offers. Moreover, whenever possible our library invokes fast specialized algorithms from the SHAP library, for each type of final model, which can greatly reduce computation times.\n", - "\n", - "\n", - "## Notebook Contents\n", - "\n", - "1. [Single Treatment - Single Outcome](#1.-Single-Treatment---Single-Outcome)\n", - "2. [Many Treatments - Many Outcomes](#2.-Many-Treatments---Many-Outcomes)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## Ignore warnings\n", - "from econml.dml import CausalForestDML, LinearDML, NonParamDML\n", - "from econml.dr import DRLearner\n", - "from econml.metalearners import DomainAdaptationLearner, XLearner\n", - "from econml.iv.dr import LinearIntentToTreatDRIV\n", - "import numpy as np\n", - "import scipy.special\n", - "import matplotlib.pyplot as plt\n", - "import shap\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.linear_model import Lasso" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Single Treatment - Single Outcome" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(123)\n", - "n_samples = 5000\n", - "n_features = 10\n", - "true_te = lambda X: (X[:, 0]>0) * X[:, 0]\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "W = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "T = np.random.binomial(1, scipy.special.expit(X[:, 0]))\n", - "y = true_te(X) * T + 5.0 * X[:, 0] + np.random.normal(0, .1, size=(n_samples,))\n", - "X_test = X[:min(100, n_samples)].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), min(100, n_samples))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = CausalForestDML(random_state=123)\n", - "est.fit(y, T, X=X, W=W)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0'])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = LinearDML(random_state=123)\n", - "est.fit(y, T, X=X, W=W)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0'])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = NonParamDML(model_y=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " model_t=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " model_final=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " random_state=123)\n", - "est.fit(y.ravel(), T.ravel(), X=X, W=W)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = DRLearner(model_regression=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " model_propensity=RandomForestClassifier(min_samples_leaf=20, random_state=123),\n", - " model_final=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " random_state=123)\n", - "est.fit(y.ravel(), T.ravel(), X=X, W=W)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0_1'])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = DomainAdaptationLearner(models=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " final_models=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " propensity_model=RandomForestClassifier(min_samples_leaf=20, random_state=123))\n", - "est.fit(y.ravel(), T.ravel(), X=X)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0_1'])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exact explainer: 21it [00:25, 1.20s/it] \n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Xlearner.shap_values uses a slow shap exact explainer, as there is no well defined final model\n", - "# for the XLearner method.\n", - "est = XLearner(models=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " cate_models=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " propensity_model=RandomForestClassifier(min_samples_leaf=20, random_state=123))\n", - "est.fit(y.ravel(), T.ravel(), X=X)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0_1'])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "est = LinearIntentToTreatDRIV(model_y_xw=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " model_t_xwz=RandomForestClassifier(min_samples_leaf=20, random_state=123),\n", - " flexible_model_effect=RandomForestRegressor(min_samples_leaf=20, random_state=123),\n", - " random_state=123)\n", - "est.fit(y.ravel(), T.ravel(), Z=T.ravel(), X=X, W=W)\n", - "shap_values = est.shap_values(X[:20])\n", - "shap.plots.beeswarm(shap_values['Y0']['T0_1'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Many Treatments - Many Outcomes" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(123)\n", - "n_samples = 5000\n", - "n_features = 10\n", - "n_treatments = 2\n", - "n_outputs = 3\n", - "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n", - " np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n", - "X = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "W = np.random.normal(0, 1, size=(n_samples, n_features))\n", - "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n", - "for t in range(n_treatments):\n", - " T[:, t] = np.random.binomial(1, scipy.special.expit(X[:, 0]))\n", - "y = np.sum(true_te(X) * T, axis=1, keepdims=True) + 5.0 * X[:, [0]] + np.random.normal(0, .1, size=(n_samples, 1))\n", - "y = np.tile(y, (1, n_outputs))\n", - "for j in range(n_outputs):\n", - " y[:, j] = (j + 1) * y[:, j]\n", - "X_test = X[:min(100, n_samples)].copy()\n", - "X_test[:, 0] = np.linspace(np.percentile(X[:, 0], 1), np.percentile(X[:, 0], 99), min(100, n_samples))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est = CausalForestDML(n_estimators=400, random_state=123)\n", - "est.fit(y, T, X=X, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 98%|===================| 391/400 [00:22<00:00] " - ] - } - ], - "source": [ - "shap_values = est.shap_values(X[:200])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(25, 15))\n", - "for j in range(n_treatments):\n", - " for i in range(n_outputs):\n", - " plt.subplot(n_treatments, n_outputs, i + j * n_outputs + 1)\n", - " plt.title(\"Y{}, T{}\".format(i, j))\n", - " shap.plots.beeswarm(shap_values['Y' + str(i)]['T' + str(j)], plot_size=None, show=False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/Metalearners Examples.ipynb b/notebooks/Metalearners Examples.ipynb deleted file mode 100644 index c058c49f1..000000000 --- a/notebooks/Metalearners Examples.ipynb +++ /dev/null @@ -1,513 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Metalearners Estimators: Use Cases and Examples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Metalearners are binary treatment CATE estimators that model the response surfaces, $Y(0)$ and $Y(1)$, separately. To account for a heterogeneous propensity of treatment $P(T\\mid X)$, the two modeled responses $E(Y(0)\\mid X)$ and $E(Y(1)\\mid X)$ are weighted in different ways in the final CATE estimation. For a detailed overview of these methods, see [this paper](https://arxiv.org/abs/1706.03461). \n", - "\n", - "The EconML SDK implements the following `metalearners`:\n", - "\n", - "* T-Learner\n", - "\n", - "* S-Learner\n", - "\n", - "* X-Learner\n", - "\n", - "* DomainAdaptation-Learner\n", - "\n", - "* DoublyRobust-Learner\n", - "\n", - "In this notebook, we compare the performance of these four CATE estimatiors on synthetic data and semi-synthetic data.\n", - "\n", - "**Notebook contents:**\n", - "\n", - "1. Example usage with synthetic data\n", - "\n", - "2. Example usage with semi-synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Main imports\n", - "from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner\n", - "\n", - "# Helper imports \n", - "import numpy as np\n", - "from numpy.random import binomial, multivariate_normal, normal, uniform\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Example Usage with Synthetic Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1. DGP" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the data generating process (DGP) from [Kunzel et al.](https://arxiv.org/abs/1706.03461). The DGP is described by the following equations:\n", - "\n", - "$\n", - "Y = \\mu_1(x) \\cdot T + \\mu_0(x) \\cdot (1-T) + \\epsilon \\\\\n", - "T \\sim Bern(e(x)), \\; e(x) = P(T=1|X=x)\n", - "$\n", - "\n", - "where \n", - "\n", - "$\n", - "\\mu_0(x) = x^T\\beta,\\; with \\;\\beta\\sim Unif([-3, 3]^d),\\; X_i \\sim N(0, \\Sigma)\\\\\n", - "\\mu_1(x) = \\mu_0(x) + 8 \\mathbb{I}(x_2>0.1) => CATE(x) = 8 \\mathbb{I}(x_2>0.1)\n", - "$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Define DGP\n", - "def generate_data(n, d, controls_outcome, treatment_effect, propensity):\n", - " \"\"\"Generates population data for given untreated_outcome, treatment_effect and propensity functions.\n", - " \n", - " Parameters\n", - " ----------\n", - " n (int): population size\n", - " d (int): number of covariates\n", - " controls_outcome (func): untreated outcome conditional on covariates\n", - " treatment_effect (func): treatment effect conditional on covariates\n", - " propensity (func): probability of treatment conditional on covariates\n", - " \"\"\"\n", - " # Generate covariates\n", - " X = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n)\n", - " # Generate treatment\n", - " T = np.apply_along_axis(lambda x: binomial(1, propensity(x), 1)[0], 1, X)\n", - " # Calculate outcome\n", - " Y0 = np.apply_along_axis(lambda x: controls_outcome(x), 1, X)\n", - " treat_effect = np.apply_along_axis(lambda x: treatment_effect(x), 1, X)\n", - " Y = Y0 + treat_effect * T\n", - " return (Y, T, X)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# controls outcome, treatment effect, propensity definitions\n", - "def generate_controls_outcome(d):\n", - " beta = uniform(-3, 3, d)\n", - " return lambda x: np.dot(x, beta) + normal(0, 1)\n", - "treatment_effect = lambda x: (1 if x[1] > 0.1 else 0)*8\n", - "propensity = lambda x: (0.8 if (x[2]>-0.5 and x[2]<0.5) else 0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# DGP constants and test data\n", - "d = 5\n", - "n = 1000\n", - "n_test = 250\n", - "controls_outcome = generate_controls_outcome(d)\n", - "X_test = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n_test)\n", - "delta = 6/n_test\n", - "X_test[:, 1] = np.arange(-3, 3, delta)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "Y, T, X = generate_data(n, d, controls_outcome, treatment_effect, propensity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2. Train Estimators" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate T learner\n", - "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "T_learner = TLearner(models=models)\n", - "# Train T_learner\n", - "T_learner.fit(Y, T, X=X)\n", - "# Estimate treatment effects on test data\n", - "T_te = T_learner.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate S learner\n", - "overall_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "S_learner = SLearner(overall_model=overall_model)\n", - "# Train S_learner\n", - "S_learner.fit(Y, T, X=X)\n", - "# Estimate treatment effects on test data\n", - "S_te = S_learner.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate X learner\n", - "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n", - " min_samples_leaf=int(n/100))\n", - "X_learner = XLearner(models=models, propensity_model=propensity_model)\n", - "# Train X_learner\n", - "X_learner.fit(Y, T, X=X)\n", - "# Estimate treatment effects on test data\n", - "X_te = X_learner.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate Domain Adaptation learner\n", - "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "final_models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n", - " min_samples_leaf=int(n/100))\n", - "DA_learner = DomainAdaptationLearner(models=models,\n", - " final_models=final_models,\n", - " propensity_model=propensity_model)\n", - "# Train DA_learner\n", - "DA_learner.fit(Y, T, X=X)\n", - "# Estimate treatment effects on test data\n", - "DA_te = DA_learner.effect(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The sklearn.ensemble.forest module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n" - ] - } - ], - "source": [ - "# Instantiate Doubly Robust Learner\n", - "from econml.dr import DRLearner\n", - "outcome_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "pseudo_treatment_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n", - "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n", - " min_samples_leaf=int(n/100))\n", - "\n", - "DR_learner = DRLearner(model_regression=outcome_model, model_propensity=propensity_model,\n", - " model_final=pseudo_treatment_model, cv=5)\n", - "# Train DR_learner\n", - "DR_learner.fit(Y, T, X=X)\n", - "# Estimate treatment effects on test data\n", - "DR_te = DR_learner.effect(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.3. Visual Comparisons" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "### Comparison plot of the different learners\n", - "plt.figure(figsize=(7, 5))\n", - "plt.plot(X_test[:, 1], np.apply_along_axis(treatment_effect, 1, X_test), color='black', ls='--', label='Baseline')\n", - "plt.scatter(X_test[:, 1], T_te, label=\"T-learner\")\n", - "plt.scatter(X_test[:, 1], S_te, label=\"S-learner\")\n", - "plt.scatter(X_test[:, 1], DA_te, label=\"DA-learner\")\n", - "plt.scatter(X_test[:, 1], X_te, label=\"X-learner\")\n", - "plt.scatter(X_test[:, 1], DR_te, label=\"DR-learner\")\n", - "plt.xlabel('$x_1$')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Visualization of bias distribution\n", - "expected_te = np.apply_along_axis(treatment_effect, 1, X_test)\n", - "plt.violinplot([np.abs(T_te - expected_te), \n", - " np.abs(S_te - expected_te),\n", - " np.abs(DA_te - expected_te),\n", - " np.abs(X_te - expected_te),\n", - " np.abs(DR_te - expected_te)\n", - " ], showmeans=True)\n", - "plt.ylabel(\"Bias distribution\")\n", - "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Example Usage with Semi-synthetic Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1. DGP\n", - "\n", - "We use the Response Surface B from [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162) to generate sythetic outcome surfaces from real-world covariates and treatment assignments (Infant Health Development Program data). Since the original data was part of a randomized trial, a subset of the treated infants (those with non-white mothers) has been removed from the data in order to mimic the observational data setting. For more details, see [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162).\n", - "\n", - "\n", - "The DGP is described by the following equations:\n", - "\n", - "$\n", - "Y(0) = e^{(X+W)\\beta} + \\epsilon_0, \\;\\epsilon_0 \\sim N(0, 1)\\\\\n", - "Y(1) = X\\beta - \\omega + \\epsilon_1, \\;\\epsilon_1 \\sim N(0, 1)\\\\\n", - "$\n", - "\n", - "where $X$ is a covariate matrix, $W$ is a constant matrix with entries equal to $0.5$ and $w$ is a constant calculated such that the CATT equals $4$." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.data.dgps import ihdp_surface_B" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "Y, T, X, expected_te = ihdp_surface_B()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Train Estimators" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# T-learner\n", - "T_learner.fit(Y, T, X=X)\n", - "T_te = T_learner.effect(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# S-learner\n", - "S_learner.fit(Y, T, X=X)\n", - "S_te = S_learner.effect(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# X-learner\n", - "X_learner.fit(Y, T, X=X)\n", - "X_te = X_learner.effect(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Domain adaptation learner\n", - "DA_learner.fit(Y, T, X=X)\n", - "DA_te = DA_learner.effect(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Doubly robust learner\n", - "DR_learner.fit(Y, T, X=X)\n", - "DR_te = DR_learner.effect(X)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3. Visual Comparisons" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Visualization of bias distribution\n", - "plt.violinplot([np.abs(T_te - expected_te), \n", - " np.abs(S_te - expected_te),\n", - " np.abs(DA_te - expected_te),\n", - " np.abs(X_te - expected_te),\n", - " np.abs(DR_te - expected_te)\n", - " ], showmeans=True)\n", - "plt.ylabel(\"Bias distribution\")\n", - "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/OrthoIV and DRIV Examples.ipynb b/notebooks/OrthoIV and DRIV Examples.ipynb deleted file mode 100644 index 263df4089..000000000 --- a/notebooks/OrthoIV and DRIV Examples.ipynb +++ /dev/null @@ -1,1642 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OrthoIV and DRIV: Use Cases and Examples\n", - "\n", - "OrthoIV, DMLIV, and DRIV are a suite of algorithms that use Double Machine Learning approach and Doubly Robust machine learning approach to estimate the heterogeneous treatment effect with an endogeneous treatment and an instrument.\n", - "\n", - "The EconML SDK implements the following classes:\n", - "\n", - "* **OrthoIV classes**: solve the moment equation $E[(Y-E[Y|X]-\\theta(X) \\cdot (T-E[T|X]))(Z-E[Z|X])] = 0$\n", - "* **DMLIV classes**: minimize the square loss $E[(Y- E[Y|X] - \\theta(X) \\cdot (E[T|X, Z] - E[T|X]))^2]$\n", - "* **DRIV classes**: minimize the square loss\n", - "$$E[(\\theta_{pre}(X) + \\frac{(Y- E[Y|X]-\\theta_{pre}(X) \\cdot (T- E[T|X]))(Z-E[Z|X])}{E[T\\cdot Z|X]-E[T|X] \\cdot E[Z|X] }-\\theta(X))^2]$$\n", - "* **Intent to Treat DRIV classes**: a special case of DRIV where the instrument is the assignment in a randomized controlled trail with non-compliance\n", - "\n", - "\n", - "\n", - "\n", - "In ths notebook, we show the performance of OrthoIV and DRIV on estimating average treatment effect and heterogeneous treatment effect.\n", - "\n", - "**Notebook contents:**\n", - "\n", - "1. [Example Usage with Average Treatment Effects](#Example-Usage-with-Average-Treatment-Effects)\n", - "2. [Example Usage with Heterogeneous Treatment Effects](#Example-Usage-with-Heterogeneous-Treatment-Effects)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 2;\n", - " var nbb_formatted_code = \"# Helper imports\\nimport numpy as np\\nfrom scipy import special\\nfrom sklearn.linear_model import LinearRegression, LogisticRegression\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport matplotlib.pyplot as plt\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Helper imports\n", - "import numpy as np\n", - "from scipy import special\n", - "from sklearn.linear_model import LinearRegression, LogisticRegression\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example Usage with Average Treatment Effects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DGP\n", - "We construct the DGP as below. The instrument corresponds to a fully randomized recommendation of treatment. Then each sample complies with the recommendation to some degree. This probability depends on both the observed feature $X$ and an unobserved confounder that has a direct effect on the outcome.\n", - "\n", - "\\begin{align}\n", - "W \\sim \\; & \\text{Normal}(0,\\, I_{n_w}) \\tag{Observed confounders}\\\\\n", - "Z \\sim \\; & \\text{Bernoulli}(p=0.5) \\tag{Instrument}\\\\\n", - "\\nu \\sim \\; & \\text{U}[0, 5] \\tag{Unobserved confounder}\\\\\n", - "C \\sim \\; & \\text{Bernoulli}(p=0.8 \\cdot \\text{Sigmoid}(0.4 \\cdot X[0] + \\nu)) \\tag{Compliers when recommended}\\\\\n", - "C0 \\sim \\; & \\text{Bernoulli}(p=0.006) \\tag{Non-Compliers when not recommended}\\\\\n", - "T = \\; & C \\cdot Z + C0 \\cdot (1-Z) \\tag{Treatment}\\\\\n", - "y \\sim \\; & \\theta \\cdot T + 2 \\cdot \\nu + 5 \\cdot (X[3]>0) + 0.1 \\cdot \\text{U}[0, 1] \\tag{Outcome}\n", - "\\end{align}\n", - "\n", - "Moreover, the constant treatment effect is presdefined here, that's what we want to learn from the model.\n", - "\\begin{align}\n", - "\\theta = \\; & 10 \\tag{ATE}\\\\\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 3;\n", - " var nbb_formatted_code = \"# test performance\\ndef dgp(n, p, true_fn):\\n X = np.random.normal(0, 1, size=(n, p))\\n Z = np.random.binomial(1, 0.5, size=(n,))\\n nu = np.random.uniform(0, 5, size=(n,))\\n coef_Z = 0.8\\n C = np.random.binomial(\\n 1, coef_Z * special.expit(0.4 * X[:, 0] + nu)\\n ) # Compliers when recomended\\n C0 = np.random.binomial(\\n 1, 0.006 * np.ones(X.shape[0])\\n ) # Non-compliers when not recommended\\n T = C * Z + C0 * (1 - Z)\\n y = (\\n true_fn(X) * T\\n + 2 * nu\\n + 5 * (X[:, 3] > 0)\\n + 0.1 * np.random.uniform(0, 1, size=(n,))\\n )\\n return y, T, Z, X\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# test performance\n", - "def dgp(n, p, true_fn):\n", - " X = np.random.normal(0, 1, size=(n, p))\n", - " Z = np.random.binomial(1, 0.5, size=(n,))\n", - " nu = np.random.uniform(0, 5, size=(n,))\n", - " coef_Z = 0.8\n", - " C = np.random.binomial(\n", - " 1, coef_Z * special.expit(0.4 * X[:, 0] + nu)\n", - " ) # Compliers when recomended\n", - " C0 = np.random.binomial(\n", - " 1, 0.006 * np.ones(X.shape[0])\n", - " ) # Non-compliers when not recommended\n", - " T = C * Z + C0 * (1 - Z)\n", - " y = (\n", - " true_fn(X) * T\n", - " + 2 * nu\n", - " + 5 * (X[:, 3] > 0)\n", - " + 0.1 * np.random.uniform(0, 1, size=(n,))\n", - " )\n", - " return y, T, Z, X" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 4;\n", - " var nbb_formatted_code = \"func = lambda X: 10\\nn = 5000\\np = 10\\ny, T, Z, X = dgp(n, p, func)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "func = lambda X: 10\n", - "n = 5000\n", - "p = 10\n", - "y, T, Z, X = dgp(n, p, func)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Estimators\n", - "We train multiple estimators from OrthoIV and DRIV classes, and see whether they could all recover the true estimate\n", - "### OrthoIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 5;\n", - " var nbb_formatted_code = \"model = lambda: LinearRegression()\\nmodel_clf = lambda: LogisticRegression()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = lambda: LinearRegression()\n", - "model_clf = lambda: LogisticRegression()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 10.04 0.138 72.517 0.0 9.768 10.311


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 10.04 0.138 72.517 0.0 9.768 10.311\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 6;\n", - " var nbb_formatted_code = \"from econml.iv.dml import OrthoIV\\n\\nest1 = OrthoIV(projection=False, discrete_treatment=True, discrete_instrument=True)\\nest1.fit(y, T, Z=Z, X=None, W=X)\\nprint(\\\"True Treatment Effect: \\\", func(X))\\nest1.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "est1 = OrthoIV(projection=False, discrete_treatment=True, discrete_instrument=True)\n", - "est1.fit(y, T, Z=Z, X=None, W=X)\n", - "print(\"True Treatment Effect: \", func(X))\n", - "est1.summary(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Projected OrthoIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 10.026 0.138 72.874 0.0 9.756 10.295


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 10.026 0.138 72.874 0.0 9.756 10.295\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 7;\n", - " var nbb_formatted_code = \"est2 = OrthoIV(projection=True, discrete_treatment=True, discrete_instrument=True)\\nest2.fit(y, T, Z=Z, X=None, W=X)\\nprint(\\\"True Treatment Effect: \\\", func(X))\\nest2.summary(alpha=0.05)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est2 = OrthoIV(projection=True, discrete_treatment=True, discrete_instrument=True)\n", - "est2.fit(y, T, Z=Z, X=None, W=X)\n", - "print(\"True Treatment Effect: \", func(X))\n", - "est2.summary(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DMLIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate
cate_intercept 10.232


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "=============================\n", - " point_estimate\n", - "-----------------------------\n", - "cate_intercept 10.232\n", - "-----------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 8;\n", - " var nbb_formatted_code = \"from econml.iv.dml import DMLIV, NonParamDMLIV\\n\\nest3 = DMLIV(discrete_treatment=True, discrete_instrument=True)\\nest3.fit(y, T, Z=Z, X=None, W=X)\\nprint(\\\"True Treatment Effect: \\\", func(X))\\nest3.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from econml.iv.dml import DMLIV, NonParamDMLIV\n", - "\n", - "est3 = DMLIV(discrete_treatment=True, discrete_instrument=True)\n", - "est3.fit(y, T, Z=Z, X=None, W=X)\n", - "print(\"True Treatment Effect: \", func(X))\n", - "est3.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear DRIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 10.034 0.139 72.211 0.0 9.762 10.306


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 10.034 0.139 72.211 0.0 9.762 10.306\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_formatted_code = \"from econml.iv.dr import LinearDRIV, SparseLinearDRIV, ForestDRIV\\n\\nest4 = LinearDRIV(discrete_instrument=True, discrete_treatment=True)\\nest4.fit(y, T, Z=Z, X=None, W=X)\\nprint(\\\"True Treatment Effect: \\\", func(X))\\nest4.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from econml.iv.dr import LinearDRIV, SparseLinearDRIV, ForestDRIV\n", - "\n", - "est4 = LinearDRIV(discrete_instrument=True, discrete_treatment=True)\n", - "est4.fit(y, T, Z=Z, X=None, W=X)\n", - "print(\"True Treatment Effect: \", func(X))\n", - "est4.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear Intent-to-Treat DRIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n", - "Coefficient Results: X is None, please call intercept_inference to learn the constant!\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 10.03 0.14 71.638 0.0 9.755 10.304


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 10.03 0.14 71.638 0.0 9.755 10.304\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_formatted_code = \"from econml.iv.dr import LinearIntentToTreatDRIV\\n\\nest5 = LinearIntentToTreatDRIV(model_t_xwz=model_clf())\\nest5.fit(y, T, Z=Z, X=None, W=X)\\nprint(\\\"True Treatment Effect: \\\", func(X))\\nest5.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from econml.iv.dr import LinearIntentToTreatDRIV\n", - "\n", - "est5 = LinearIntentToTreatDRIV(model_t_xwz=model_clf())\n", - "est5.fit(y, T, Z=Z, X=None, W=X)\n", - "print(\"True Treatment Effect: \", func(X))\n", - "est5.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example Usage with Heterogeneous Treatment Effects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DGP\n", - "In this section, we keep the same data generation process, but only change the treatment effect $\\theta(X)$ to a function of $X$, and learn the heterogeneous treatment effect. We'd like to see the effect interval learnt from each model could recover the true estimate and the coefficient estimates in the final linear model could recover the true coefficient.\n", - "\n", - "The true effect function is as below:\n", - "\\begin{align}\n", - "\\theta = \\; & 10 \\cdot X[0] \\tag{CATE}\\\\\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_formatted_code = \"func = lambda X: 10 * X[:, 0]\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "func = lambda X: 10 * X[:, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 12;\n", - " var nbb_formatted_code = \"n = 5000\\np = 10\\ny, T, Z, X = dgp(n, p, func)\\n# Generate test data\\nX_test = np.linspace(-2, 2, 100).reshape(-1, 1)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n = 5000\n", - "p = 10\n", - "y, T, Z, X = dgp(n, p, func)\n", - "# Generate test data\n", - "X_test = np.linspace(-2, 2, 100).reshape(-1, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 13;\n", - " var nbb_formatted_code = \"# save output for visualization\\nres_pred = []\\nres_lb = []\\nres_ub = []\\nname_list = [\\n \\\"OrthoIV\\\",\\n \\\"ProjectedOrthoIV\\\",\\n \\\"NonParamDMLIV\\\",\\n \\\"LinearDRIV\\\",\\n \\\"ForestDRIV\\\",\\n \\\"LinearIntentToTreatDRIV\\\",\\n]\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# save output for visualization\n", - "res_pred = []\n", - "res_lb = []\n", - "res_ub = []\n", - "name_list = [\n", - " \"OrthoIV\",\n", - " \"ProjectedOrthoIV\",\n", - " \"NonParamDMLIV\",\n", - " \"LinearDRIV\",\n", - " \"ForestDRIV\",\n", - " \"LinearIntentToTreatDRIV\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Estimators\n", - "### OrthoIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 10.043 0.142 70.962 0.0 9.765 10.32
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.245 0.137 1.793 0.073 -0.023 0.513


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 10.043 0.142 70.962 0.0 9.765 10.32\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.245 0.137 1.793 0.073 -0.023 0.513\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 14;\n", - " var nbb_formatted_code = \"est1 = OrthoIV(projection=False, discrete_treatment=True, discrete_instrument=True)\\nest1.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\\nprint(\\\"True Treatment Effect: 10\\\")\\nest1.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est1 = OrthoIV(projection=False, discrete_treatment=True, discrete_instrument=True)\n", - "est1.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\n", - "print(\"True Treatment Effect: 10\")\n", - "est1.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_formatted_code = \"te_pred1 = est1.effect(X_test)\\nte_pred1_lb, te_pred1_ub = est1.effect_interval(X_test, alpha=0.05)\\nres_pred.append(te_pred1)\\nres_lb.append(te_pred1_lb)\\nres_ub.append(te_pred1_ub)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred1 = est1.effect(X_test)\n", - "te_pred1_lb, te_pred1_ub = est1.effect_interval(X_test, alpha=0.05)\n", - "res_pred.append(te_pred1)\n", - "res_lb.append(te_pred1_lb)\n", - "res_ub.append(te_pred1_ub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Projected OrthoIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 10.052 0.142 70.695 0.0 9.773 10.33
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.22 0.137 1.604 0.109 -0.049 0.488


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 10.052 0.142 70.695 0.0 9.773 10.33\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.22 0.137 1.604 0.109 -0.049 0.488\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_formatted_code = \"est2 = OrthoIV(projection=True, discrete_treatment=True, discrete_instrument=True)\\nest2.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\\nprint(\\\"True Treatment Effect: 10\\\")\\nest2.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est2 = OrthoIV(projection=True, discrete_treatment=True, discrete_instrument=True)\n", - "est2.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\n", - "print(\"True Treatment Effect: 10\")\n", - "est2.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 17;\n", - " var nbb_formatted_code = \"te_pred2 = est2.effect(X_test)\\nte_pred2_lb, te_pred2_ub = est2.effect_interval(X_test, alpha=0.05)\\nres_pred.append(te_pred2)\\nres_lb.append(te_pred2_lb)\\nres_ub.append(te_pred2_ub)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred2 = est2.effect(X_test)\n", - "te_pred2_lb, te_pred2_ub = est2.effect_interval(X_test, alpha=0.05)\n", - "res_pred.append(te_pred2)\n", - "res_lb.append(te_pred2_lb)\n", - "res_ub.append(te_pred2_ub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Non-Parametric DMLIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 18;\n", - " var nbb_formatted_code = \"est3 = NonParamDMLIV(\\n model_final=RandomForestRegressor(),\\n discrete_treatment=True,\\n discrete_instrument=True,\\n)\\nest3.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est3 = NonParamDMLIV(\n", - " model_final=RandomForestRegressor(),\n", - " discrete_treatment=True,\n", - " discrete_instrument=True,\n", - ")\n", - "est3.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 19;\n", - " var nbb_formatted_code = \"te_pred3 = est3.effect(X_test)\\nres_pred.append(te_pred2)\\nres_lb.append(np.array([]))\\nres_ub.append(np.array([]))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred3 = est3.effect(X_test)\n", - "res_pred.append(te_pred2)\n", - "res_lb.append(np.array([]))\n", - "res_ub.append(np.array([]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear DRIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 10.058 0.141 71.339 0.0 9.781 10.334
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.245 0.137 1.791 0.073 -0.023 0.513


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 10.058 0.141 71.339 0.0 9.781 10.334\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.245 0.137 1.791 0.073 -0.023 0.513\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 20;\n", - " var nbb_formatted_code = \"est4 = LinearDRIV(discrete_instrument=True, discrete_treatment=True)\\nest4.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\\nprint(\\\"True Treatment Effect: 10\\\")\\nest4.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est4 = LinearDRIV(discrete_instrument=True, discrete_treatment=True)\n", - "est4.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\n", - "print(\"True Treatment Effect: 10\")\n", - "est4.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 21;\n", - " var nbb_formatted_code = \"te_pred4 = est4.effect(X_test)\\nte_pred4_lb, te_pred4_ub = est4.effect_interval(X_test, alpha=0.05)\\nres_pred.append(te_pred4)\\nres_lb.append(te_pred4_lb)\\nres_ub.append(te_pred4_ub)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred4 = est4.effect(X_test)\n", - "te_pred4_lb, te_pred4_ub = est4.effect_interval(X_test, alpha=0.05)\n", - "res_pred.append(te_pred4)\n", - "res_lb.append(te_pred4_lb)\n", - "res_ub.append(te_pred4_ub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Forest DRIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 22;\n", - " var nbb_formatted_code = \"est5 = ForestDRIV(discrete_instrument=True, discrete_treatment=True)\\nest5.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est5 = ForestDRIV(discrete_instrument=True, discrete_treatment=True)\n", - "est5.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 23;\n", - " var nbb_formatted_code = \"te_pred5 = est5.effect(X_test)\\nte_pred5_lb, te_pred5_ub = est5.effect_interval(X_test, alpha=0.05)\\nres_pred.append(te_pred5)\\nres_lb.append(te_pred5_lb)\\nres_ub.append(te_pred5_ub)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred5 = est5.effect(X_test)\n", - "te_pred5_lb, te_pred5_ub = est5.effect_interval(X_test, alpha=0.05)\n", - "res_pred.append(te_pred5)\n", - "res_lb.append(te_pred5_lb)\n", - "res_ub.append(te_pred5_ub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear Intent-to-Treat DRIV Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True Treatment Effect: 10\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 9.894 0.162 61.058 0.0 9.577 10.212
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.267 0.155 1.728 0.084 -0.036 0.57


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 9.894 0.162 61.058 0.0 9.577 10.212\n", - " CATE Intercept Results \n", - "===================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------------\n", - "cate_intercept 0.267 0.155 1.728 0.084 -0.036 0.57\n", - "-------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 24;\n", - " var nbb_formatted_code = \"est6 = LinearIntentToTreatDRIV()\\nest6.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\\nprint(\\\"True Treatment Effect: 10\\\")\\nest6.summary()\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "est6 = LinearIntentToTreatDRIV()\n", - "est6.fit(y, T, Z=Z, X=X[:, :1], W=X[:, 1:])\n", - "print(\"True Treatment Effect: 10\")\n", - "est6.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 25;\n", - " var nbb_formatted_code = \"te_pred6 = est6.effect(X_test)\\nte_pred6_lb, te_pred6_ub = est6.effect_interval(X_test, alpha=0.05)\\nres_pred.append(te_pred6)\\nres_lb.append(te_pred6_lb)\\nres_ub.append(te_pred6_ub)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "te_pred6 = est6.effect(X_test)\n", - "te_pred6_lb, te_pred6_ub = est6.effect_interval(X_test, alpha=0.05)\n", - "res_pred.append(te_pred6)\n", - "res_lb.append(te_pred6_lb)\n", - "res_ub.append(te_pred6_ub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performance Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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The `predict` method returns the coordinate that achieves the maximum `y[i]` for each `X` based on the tree policy. The `predict_value` method returns the conditional mean value of all coordinates, conditional on `X`, for the leaf that `X` falls in. These classes can be used as building blocks for other functionalities, such as the `SingleTreePolicyInterpreter` or the `DRPolicyTree` or `DRPolicyForest`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "narrow-dutch", - "metadata": {}, - "outputs": [], - "source": [ - "X = np.random.normal(size=(1000, 10))\n", - "y = np.hstack([X[:, [0]] > 0, X[:, [0]] < 0])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "included-payday", - "metadata": {}, - "outputs": [], - "source": [ - "est = PolicyTree(min_impurity_decrease=.001, honest=True).fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "reserved-lover", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.feature_importances_" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "accepted-collar", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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vnOO4ZuUpDe+vE59Z1erjj+5jYvCZbF+dft31lLWubmBQkxpRr9mP/rIgqWEGtcbMuh65dS2sezPsn7Csd888uOSGpwPkZOGy3lnCseb0wmFHVs7oLVgKJ7649mfKnfDrsGMTDD/R0pfZvKicgSxy9rFDduw/hhfuvf7A4wff/9IOViPNHJP1x7nTXYxUeudeWbvZL/rlztQjSd1uNGx98Z1Ph5Fal2EuX1X/rN/o+Nggd+KL4d/+cfKzhsedM3n4q1fTgdDY6Fm8Bs07HC66rvLnqYJoh2TCB/c9/fd6XfWSO0mHxjNqUi1/fTF87ysHjw+8vnKjvKSGeEatMfbIGaTeJZlLz4LvfZXxl1ZWL7Vc0P90IJwsDNY6VtFw2Hs4nPqK8UH1QHDdXrnvOkeoe/nngv7K/Ps+O+WZvOEM3jH8Jv4hf43/vupUL4mTGuClj1KjXFBEaimDWmPskTNMvUsy23kfYL1FWho9TpEax86JqLmoy848ho0rv2pIkxpkUJMa5YIiUksZ1Bpjj1Rp1emPCYT9UWrYZP3RT22Waok6H2pYb1ySpNmgTh8M+6PUcgY1qZYzXtvYuCRJs0Gt2wImG5fUNIOaVMtF11YWDhl9hzB6XEhEkqQF/Y2NS2qay/NL9Vx0rcFMkqSxan2ETW9fZVxSS3lGTZIkScUsXwUrrq+eQYvK9xXXt241S0kHGNQkSZIkqWS89FGqp52ffyNJ0kw08QO+92yvPAZ7pNRinlGTahltRHu2A/l0I9q6ttOVSZLUOXdcPf7+NKg8vuPqztQjdTGDmlSLjUiSpIPt2dHYuKSmGdSkWmxEkiQdbMHSxsYlNc2gJtViI5Ik6WDnXllZjn8sl+eX2sKgJtViI5Ik6WAuzy9NG1d9lGoZbTiu+ihJ0njLV9kPpWlgUJPqsRFJkiSpQ7z0UZIkSZJKxqAmSZIkSSVjUJMkSZKkkjGoSZIkSVLJGNQkSZIkqWQMapIkSZJUMgY1SZIkSSoZg5okSZIklYxBTZIkSZJKxqAmSZIkSSVjUJMkSZKkkjGoSZIkSVLJGNQkSZIkqWQMapIkSZJUMgY1SZIkSSoZg5okSZIklYxBTZIkSZJKxqAmSZIkSSVjUJMkSZKkkjGoSZIkSVLJGNQkSZIkqWQKBbWIuCAitkXEAxHxrhrPL4iI2yLiWxFxX0S8rvWlSpJULvZHSVK7TBnUIqIHuAF4CXAScFlEnDRh2luAb2fmqcCLgP8eEfNaXKskSaVhf5QktVORM2pnAQ9k5nczcy/wGeCSCXMSODIiAjgCeBTY19JKJUkqF/ujJKltigS1JcD2MY93VMfG+jDwq8Au4B7gP2fm/pZUKElSOdkfJUltUySoRY2xnPD4fOBu4FjgecCHI+LnDtpRxOURsSkiNu3evbvBUiVJKpWW9UewR0qSxisS1HYA/WMeL6XyzuBYrwNuzYoHgO8Bz5m4o8y8KTMHMnNg8eLFzdYsSVIZtKw/gj1SkjRekaC2ETgxIk6o3gD9CmD9hDk/AM4FiIifB5YB321loZIklYz9UZLUNnOnmpCZ+yLircAGoAf4RGbeFxFvrD5/I/DfgJsj4h4ql4K8MzMfaWPdkiR1lP1RktROUwY1gMz8AvCFCWM3jvnzLuDFrS1NkqRysz9Kktql0AdeS5IkSZKmj0FNkiRJxW1dCx86Ga5aWPm+dW2nK5K6UqFLHyVJkiS2roXb3gbDQ5XHe7ZXHgMsX9W5uqQu5Bk1SZIkFXPH1U+HtFHDQ5VxSS1lUJMkSVIxe3Y0Ni6paQY1SZIkFdN3VGPjkppmUJMkSZKkkjGoSfW4qpUkSeMNPdbYuKSmGdSkWkZXtdqzHcinV7UyrEmSZrMFSxsbl9Q0g5pUi6taSZJ0sHOvhN6+8WO9fZVxSS1lUJNq2bO9sXFJkmaD5atgxfWwoB+IyvcV1/sZalIb+IHXUi3RAzlSe1ySpNls+SqDmTQNPKMm1VIrpE02LkmSJLWQQU2qZUF/Y+OSJElSCxnUpFq8WVqSJEkdZFCTavFmaUmSJHWQi4lI9XiztCRJkjrEM2qSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlkDGqSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlkDGqSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJKZ2+kCJEmSNLOs27KTNRu2sWtwiGMX9rH6/GWsPG1Jp8uSukqhM2oRcUFEbIuIByLiXXXmvCgi7o6I+yLiK60tU5Kk8rE/ajZat2Unq//uW+wcHCKBnYNDrP67b7Fuy85OlyZ1lSmDWkT0ADcALwFOAi6LiJMmzFkIfAS4ODOfC/xu60uVJKk87I+ara5afx/D+3Pc2PD+5Kr193WoIqk7FTmjdhbwQGZ+NzP3Ap8BLpkw55XArZn5A4DMfLi1ZUqSVDr2R81Kg0PDDY1Lak6RoLYE2D7m8Y7q2Fi/AhwVEV+OiM0R8ZpWFShJUknZHyVJbVNkMZGoMZYTHs8FzgDOBfqAb0TEnZn5r+N2FHE5cDnAcccd13i1kiSVR8v6I9gjNXMcNb+Xx548+OzZUfN7O1CN1L2KnFHbAfSPebwU2FVjzv/OzCcy8xHgq8CpE3eUmTdl5kBmDixevLjZmiVJKoOW9UewR2rmeM+K59LbM/59it6e4D0rntuhiqTuVCSobQROjIgTImIe8Apg/YQ5nwN+LSLmRsR84GzgO60tVZKkUrE/alZaedoS1rz8VJYs7COAJQv7WPPyU12eX2qxKS99zMx9EfFWYAPQA3wiM++LiDdWn78xM78TEf8b2ArsBz6Wmfe2s3BJkjrJ/qjZbOVpSwxmUptF5sTL6afHwMBAbtq0qSPHliRNr4jYnJkDna5jprBHStLsMFl/LPSB15IkSZKk6WNQkyRJkqSSMahJkiRJUskY1CRJkiSpZAxqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNqmfrWvjQyXDVwsr3rWs7XZEkSZJmibmdLkAqpa1r4ba3wfBQ5fGe7ZXHAMtXda4uSZIkzQqeUZNquePqp0PaqOGhyrgkSZLUZgY1qZY9OxoblyRJklrIoCbVsmBpY+OSJElSCxnUpFrOvRJ6+8aP9fZVxiVJms1cbEuaFi4mItUyumDIHVdXLndcsLQS0lxIRJI0m7nYljRtDGpSPctX2XQkSRprssW27JlSS3npoyRJkopxsS1p2hjUJEmSVIyLbUnTxqAmSZKkYlxsS5o2BjVJkiQVs3wVrLgeFvQDUfm+4nrvT5PawMVEJEmSVJyLbUnTwjNqkiRJklQyBjVJkiRJKhkvfZQkSVJh67bsZM2GbewaHOLYhX2sPn8ZK09b0umypK5jUJMkSVIh67bs5Ipb72FoeASAnYNDXHHrPQCGNanFvPRRkiRJhazZsO1ASBs1NDzCmg3bOlSR1L0MapIkSSpk1+BQQ+OSmmdQkyRJUiHHLuxraFxS8wxqkiRJKmT1+cvo6+0ZN9bX28Pq85d1qCKpe7mYiCRJkgoZXTDEVR+l9jOoSZIkqbCVpy0xmEnTwEsfJUmSJKlkDGqSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlkDGqSJEmSVDIGNUmSJEkqmUJBLSIuiIhtEfFARLxrknlnRsRIRLy8dSVKklRO9kdJUrvMnWpCRPQANwC/DewANkbE+sz8do15HwA2tKNQadp9/u2w+WbIEYgeOOO1cNG1na5KUknYHyVJ7VTkjNpZwAOZ+d3M3At8Brikxrw/Av4eeLiF9Umd8fm3w6aPV0IaVL5v+nhlXJIq7I+avbauhQ+dDFctrHzfurbTFUldp0hQWwJsH/N4R3XsgIhYArwMuLF1pUkdtPnmxsYlzUb2R81OW9fC594Ce7YDWfn+ubcY1qQWKxLUosZYTnh8HfDOzNHTD3V2FHF5RGyKiE27d+8uWKLUAfX+KU/+T1zS7NKy/gj2SM0gX3wnjOwdPzaytzIuqWWmvEeNyjuE/WMeLwV2TZgzAHwmIgCOAS6MiH2ZuW7spMy8CbgJYGBgYGIzk8ojemqHsuiZ/loklVXL+iPYIzWDDD3a2LikphQ5o7YRODEiToiIecArgPVjJ2TmCZl5fGYeD/x/wJtrNSFpxjjjtY2NS5qN7I+SpLaZ8oxaZu6LiLdSWa2qB/hEZt4XEW+sPu919+o+o6s7uuqjpDrsj5q1+hbVPnvWt2j6a5G6WGR25uqKgYGB3LRpU0eOLUmaXhGxOTMHOl3HTGGPVKltXQvr3gz7h58em9MLKz8Cy1d1ri5pBpqsPxb6wGtJkiQJqISxlR+BBf1AVL4b0qSWK7KYiCRJkvS05asMZlKbeUZNkiRJkkrGoCZJkiRJJWNQkyRJkqSSMahJkiRJUskY1CRJkiSpZAxqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNkiRJkkrGoCZJkiRJJWNQkyRJkqSSMahJkiRJUskY1CRJkiSpZAxqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNkiRJkkrGoCZJkiRJJWNQkyRJkqSSMahJkiRJUskY1CRJkiSpZAxqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNkiRJkkrGoCZJkiRJJWNQkyRJkqSSmdvpAiRJkjRzrNuykzUbtrFrcIhjF/ax+vxlrDxtSafLkrqOQU2SJEmFrNuykytuvYeh4REAdg4OccWt9wAY1qQW89JHSZIkFbJmw7YDIW3U0PAIazZs61BFUvcyqEmSJKmQXYNDDY1Lap5BTZIkSYUs6OttaFxS8wxqkiRJKiSisXFJzTOoSZIkqZDBJ4cbGpfUPIOaJEmSCjl2YV9D45KaZ1CTJElSIavPX0Zfb8+4sb7eHlafv6xDFUndy89RkyRJUiGjn5XmB15L7WdQkyRJUmErT1tiMJOmQaFLHyPigojYFhEPRMS7ajz/qojYWv36ekSc2vpSJUkqF/ujJKldpgxqEdED3AC8BDgJuCwiTpow7XvAb2TmcuC/ATe1ulBJksrE/ihJaqciZ9TOAh7IzO9m5l7gM8AlYydk5tcz87HqwzuBpa0tU5Kk0rE/SpLapkhQWwJsH/N4R3WsntcDXzyUoiRJmgHsj5KktimymEitz5rPmhMjfpNKI3phnecvBy4HOO644wqWKElSKbWsP1bn2CMlSQcUOaO2A+gf83gpsGvipIhYDnwMuCQzf1xrR5l5U2YOZObA4sWLm6lXkqSyaFl/BHukJGm8IkFtI3BiRJwQEfOAVwDrx06IiOOAW4FXZ+a/tr5MSZJKx/4oSWqbKS99zMx9EfFWYAPQA3wiM++LiDdWn78RuBI4GvhIRADsy8yB9pUtSVJn2R8lSe0UmTUvp2+7gYGB3LRpU0eOLUmaXhGx2YBSnD1SkmaHyfpjoQ+8liRJkiRNH4OaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlkDGqSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlkDGqSJEmSVDIGNUmSJEkqGYOaJEmSJJWMQU2SJEmSSsagJkmSJEklY1CTJEmSpJIxqEmSJElSyRjUJEmSJKlk5na6AEmSJM0c67bsZM2GbewaHOLYhX2sPn8ZK09b0umypK5jUJMkSVIh67bs5Ipb72FoeASAnYNDXHHrPQCGNanFvPRRkiRJhazZsO1ASBs1NDzCmg3bOlSR1L0MapIkSSpk1+BQQ+OSmmdQkyRJUiHHLuxraFxS8wxqkiRJKmT1+cvo6+0ZN9bX28Pq85d1qCKpe7mYiCRJkgoZXTDEVR+l9vOMmiRJkiSVjGfUJEmSVIjL80vTxzNqkiRJKsTl+aXpY1CTJElSITvrLMNfb1xS8wxqkiRJKqQnoqFxSc3zHjVJkiQVMpIJwMVzvsafzl3LsfEIu/IYPrhvFXBhZ4uTuoxBTZIk6RCs27Kz5nL19cZbcZwFfb1EwOCTwyyc30sm7Bkabvg4Y/dZbz9j50TAivga7+/9GPNjLwBL4xE+MO/jsPU0WL6q6dcnabzI6jsj021gYCA3bdrUkWNLhWxdC3dcDXt2wIKlcO6VNiCpSRGxOTMHOl3HTGGPLL/R8LJzcIgAxv421dfbw+nHLeDr//7ouPHReXMC9lef6J0D+/Y/vf383jn8xaXLDwpaE1dbnMr83jlcesZSvnT/7gNB8Tefs5gv3b+bnYND9EQwknlQ7UV8bd7bWDrnkdpPDrweLrq2wT1Ks9dk/dGgJtWydS3c9jYYHnNzdG8frLjesCY1waDWmNnYI9dt2clV6+9jcGgYgKPm9/KeFc9t6AzUxDNYo8FksjNak531qlcT0FBoOhSHz+vhz192yoFQWAbfO+yVTHpLmmFNKsygJjXqQyfDnu0Hjy/ohz++d/rrkWY4g1pjZluPXLdlJ6v/7lsM7x//O0lvT7Dm5aeOC06ThaqpwtMcYMH8XgafHD4Q5P72m9sPOu4LnrWILT8Y5Mnh/a19oV1iyqAGcNWeaalFmukm64/eoybVsmdHY+OSVBJF74tq9f1Th2LNhm0HhSWA4ZFkzYZtB+6TmuyDlmt9vtdE+4HHnhw+sP2n7vxBzXn/998fPYRXIwCuWlD5fsJvwO+vP/h2ghNfDP/2j/VvLzgwfztED+QI9C2qPDf0WLFbEpq9hWHrWvjiO2Go+u+gbxE892WT11tkP2P1LYKXfKDy54mvM+ZA7h8/r9NX83g7SEcUOqMWERcAfwn0AB/LzPdPeD6qz18IPAm8NjPvmmyfs+3dQs0wdc6odegEtFpk9D/fxDeCB+NI7l90Hs959HYW5uN15z/BYdxz9IWc8OjXeGbuZj9z6GE/P4rFbD99NWde/Iam6tq4/qP037WGZ+ZuHq6zryJzDmX+xO1+PnczwhzmsL+h7evp1jNq7eiP0HyPrHVWafQepCVjwti7193Dp+/8wUH3Jh0+r4f9mQxVzySN3kt1VHWRidFLACfbN1B3/6PHeGLvCBH+TJ2pCp1RU5cIOOHXYcc3x98OctC0OXDG6+C4cw4Ongv6mw92RQJiMyHyULfpO6oyVvRNg0kc0qWPEdED/Cvw28AOYCNwWWZ+e8ycC4E/otKIzgb+MjPPnmy/BjWV2Q3X/TlveuyDzLERzRqZFPrFo968oZzHvWdc03CY2bj+o5y8+d30VVdPq7WvInMa3WfRWg719Y3qxqDWrv4IzffIF7z/nye9j6mvt4ffOWNJ3RB1KPp6e3jfpaew6fuP1j1Tpe5gUFN9c6icu56gmfv8i6wX0MyaAq3a5lBfX9Vk/bHIB16fBTyQmd/NzL3AZ4BLJsy5BPhkVtwJLIyIX2y4Uqkk1jz0vIPOuqi7Ff2lo968vthL/11rGj5u/11rDgpGE/dVZE6j+yy6XSPbz0Kl64+7plhsYmh4hFv+ZXvLQ9rovtds2MYt/1Lj/l51FU+Eqr4693UOD1XORjXijqsPDkYT91NkTjP7LbJNI9s3qUhQWwKM/am7ozrW6Bwi4vKI2BQRm3bv3t1orZJUas/MOstVT7pN7Z+FY/dVZE6j+2xku6Lbz0It64/Qmh557MK+KeeMtPF6w12DQ23dv8rhf42c52Wralyj9/kXWS+gmTUFWrlNo3MaVCSo1Xr/eOL/nkXmkJk3ZeZAZg4sXry4SH2SNGM8HMc0sU3tn4Vj91VkTqP7bGS7otvPQi3rj9CaHrn6/GX09fZMOqenjdesHbuwr637Vzm8Z98f8MmR89iXc8j0XkMVtGBpa+aPHS8yp5n9NvJcI3MaVCSo7QD6xzxeCuxqYo40Y7zgWYt4InttPrNI0f/W9eYN5Ty2n7664eNuP301Qzlv0n0VmdPoPotu18j2s1Dp+uPK05bwvktPYUn1zNrEyNTX28NlZ/dPGeaa0dfbw+rzl3HZ2f1TT9aM9559f8Czn/oUJzz1N3xy5Lx6F7ypWxxR9IrtOtGit6+y4EYjzr2yst1k+ykyp5n9Ftmmke2bVCSobQROjIgTImIe8Apg/YQ564HXRMU5wJ7M/GGLa5Wmzaf/8Pm8of+2A2HNr+742l/9mjj+GEdy59Ev4zGOmHT+43kYdx79Mh5iMfuTA+8mP8TiphfaOPPiN3DvGddU9xk191VkTqP7nGq7rL6+/Yf4+rpcKfvjytOW8H/f9Vs8+P6X8qH/+DyWLOwjqKzM+L5LT+GalaccCHMBLOzr5aj5vQSV1R37ep/+1WB0QaWj5veysK/3oGONBsHRfa88bQnXrDyF3zvnuLr3+R4+rxISPfHWPf5t4CrmXLUHLv2f0Ht4p8tRS0XlA8z/5P7K96gTHWJO5flLP1pZ5REqqz5C5XEzC20sX1XZbkF/pY5a+ykyp5n9TrVN36Lqx0UU3L5JRZfnvxC4jsryw5/IzD+PiDcCZOaN1eWHPwxcQGX54ddl5qTLVbnqoyTNHt246iO0pz+CPVKSZotD/sDrzPwC8IUJYzeO+XMCbzmUIiVJmmnsj5Kkdily6aMkSZIkaRoZ1CRJkiSpZAxqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNkiRJkkrGoCZJkiRJJVPoA6/bcuCI3cD3O3Lw9jsGeKTTRUwDX2d38XV2l7K9zl/KzMWdLmKm6OIeWbZ/l+3i6+wuvs7uUrbXWbc/diyodbOI2FTvE8a7ia+zu/g6u8tseZ2aWWbLv0tfZ3fxdXaXmfQ6vfRRkiRJkkrGoCZJkiRJJWNQa4+bOl3ANPF1dhdfZ3eZLa9TM8ts+Xfp6+wuvs7uMmNep/eoSZIkSVLJeEZNkiRJkkrGoNYmEbEmIu6PiK0R8dmIWNjpmtohIn43Iu6LiP0RMSNW0CkqIi6IiG0R8UBEvKvT9bRLRHwiIh6OiHs7XUu7RER/RHwpIr5T/ff6nztdUztExDMi4psR8a3q63xvp2uSJrI/dofZ0CNnQ38Ee2SZGdTa53bg5MxcDvwrcEWH62mXe4FLga92upBWioge4AbgJcBJwGURcVJnq2qbm4ELOl1Em+0D3pGZvwqcA7ylS/97PgX8VmaeCjwPuCAizulsSdJB7I8z3CzqkTfT/f0R7JGlZVBrk8z8x8zcV314J7C0k/W0S2Z+JzO3dbqONjgLeCAzv5uZe4HPAJd0uKa2yMyvAo92uo52yswfZuZd1T//FPgOsKSzVbVeVjxefdhb/fJGZJWK/bErzIoeORv6I9gjO1jSlAxq0+MPgC92ugg1ZAmwfczjHXThD63ZKCKOB04D/qXDpbRFRPRExN3Aw8DtmdmVr1Ndw/44M9kju5Q9slzmdrqAmSwi/gn4hRpP/Vlmfq4658+onFL+9HTW1kpFXmcXihpjpX7XRVOLiCOAvwf+S2b+pNP1tENmjgDPq97389mIODkzu/r+CpWP/bGr+yPYI7uSPbJ8DGqHIDPPm+z5iPh94CLg3JzBn4Mw1evsUjuA/jGPlwK7OlSLWiAieqk0oE9n5q2drqfdMnMwIr5M5f6K0jYhdSf7Y9ezR3YZe2Q5eeljm0TEBcA7gYsz88lO16OGbQROjIgTImIe8ApgfYdrUpMiIoCPA9/JzGs7XU+7RMTi0RX0IqIPOA+4v6NFSRPYH7uCPbKL2CPLy6DWPh8GjgRuj4i7I+LGThfUDhHxsojYATwf+IeI2NDpmlqheqP7W4ENVG6qXZuZ93W2qvaIiFuAbwDLImJHRLy+0zW1wQuAVwO/Vf3/8e6IuLDTRbXBLwJfioitVH6Ruj0zP9/hmqSJ7I8z3GzpkbOkP4I9srRiBl9xIEmSJEldyTNqkiRJklQyBjVJkiRJKhmDmiRJkiSVjEFNkiRJkkrGoCZJkiRJJWNQkyRJkqSSMahJkiRJUskY1CRJkiSpZP5/N8d9k9cqLecAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('Policy')\n", - "plt.scatter(X[:, 0], est.predict(X))\n", - "plt.scatter(X[:, 0], est.predict_proba(X)[:, 1])\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('Conditional Values')\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 0])\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 1])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "cultural-harbor", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "est[0].plot(max_depth=2)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "revolutionary-germany", - "metadata": {}, - "source": [ - "# Using Them for CATE Policy Interpretation\n", - "\n", - "We can use the `PolicyTree` to interpret a CATE model, in terms of actionable insights. This functionality is wrapped in the `SingleTreePolicyInterpreter` class." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "hazardous-shelf", - "metadata": {}, - "outputs": [], - "source": [ - "X = np.random.normal(size=(1000, 10))\n", - "T = np.random.binomial(2, .5, size=(1000,))\n", - "y = (X[:, 0]) * (T==1) + (-X[:, 0]) * (T==2) " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "affected-static", - "metadata": {}, - "outputs": [], - "source": [ - "est = LinearDML(discrete_treatment=True, linear_first_stages=False).fit(y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "suspended-person", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intrp = SingleTreePolicyInterpreter(include_model_uncertainty=True, max_depth=2, min_impurity_decrease=.001)\n", - "intrp.interpret(est, X, sample_treatment_costs=.2 * np.ones((X.shape[0], 2)))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "first-drunk", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 8))\n", - "intrp.plot(treatment_names=['None', 'A', 'B'])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "generous-internship", - "metadata": {}, - "source": [ - "# StandAlone Policy Learning\n", - "\n", - "We can also learn optimal policies directly from data without the need to train a CATE model first. This is achieved by our `DRPolicyTree` and `DRPolicyForest`, which trains a `PolicyTree` and `PolicyForest` correspondingly, on the doubly robust counterfactual outcome targets. This is the policy learning analogoue of the `DRLearner`, but rather than now trying to learn a good model of the treatment effect heterogeneity, we are learning a good treatment policy directly." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "superior-pocket", - "metadata": {}, - "outputs": [], - "source": [ - "X = np.random.normal(size=(4000, 10))\n", - "T = np.random.binomial(2, .5, size=(4000,))\n", - "y = (X[:, 0]) * (T==1) + (-X[:, 0]) * (T==2) + np.random.normal(0, 2, size=(4000,))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "educational-preference", - "metadata": {}, - "outputs": [], - "source": [ - "T = pd.DataFrame(T, columns=['A'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "floral-packing", - "metadata": {}, - "outputs": [], - "source": [ - "est = DRPolicyTree(max_depth=2, min_impurity_decrease=0.01, honest=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "chicken-consultancy", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.fit(y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "sunrise-africa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PolicyTree(max_depth=2, max_features='auto', min_impurity_decrease=0.01)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.policy_model_" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "commercial-lodge", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('Policy')\n", - "plt.scatter(X[:, 0], est.predict(X))\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('Conditional Values')\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 0])\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 1])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "monthly-milton", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est = DRPolicyForest(n_estimators=1000,\n", - " max_depth=2, \n", - " min_samples_leaf=50,\n", - " max_samples=.8,\n", - " honest=True,\n", - " min_impurity_decrease=0.01,\n", - " random_state=123)\n", - "est.fit(y, T, X=X)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "proper-strand", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 9.99822982e-01, -9.69839399e-19, 2.32900716e-18, 1.13384495e-20,\n", - " 3.81744321e-06, 6.77744462e-05, -1.00826898e-18, 5.86452468e-05,\n", - " -3.68359212e-18, 4.67812812e-05])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.feature_importances_" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "commercial-quantum", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PolicyForest(max_depth=2, max_samples=0.8, min_impurity_decrease=0.01,\n", - " min_samples_leaf=50, random_state=123)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est.policy_model_" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "regulation-breakdown", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('Policy')\n", - "plt.scatter(X[:, 0], est.predict(X))\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('Conditional Values')\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 0])\n", - "plt.scatter(X[:, 0], est.predict_value(X)[:, 1])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "postal-industry", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/Solutions/Causal Interpretation for Boston Housing Price.ipynb b/notebooks/Solutions/Causal Interpretation for Boston Housing Price.ipynb deleted file mode 100644 index ce0dd54b4..000000000 --- a/notebooks/Solutions/Causal Interpretation for Boston Housing Price.ipynb +++ /dev/null @@ -1,1628 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Causal Interpretation for Boston Housing Price\n", - "\n", - "This notebook uses the well-known Boston Housing dataset to showcase how we can interpret a blackbox model from both the **correlation and causation** perspective by leveraging the power of model interpretation tools like [SHAP](https://shap.readthedocs.io/en/latest/index.html) and [EconML](https://aka.ms/econml). This housing dataset collects median home values and neighborhood characteristics for the Boston area, largely from the 1970 U.S. Census. We start with a linear regression to build intuition. We then train a fine-tuned predictive ML model and use SHAP to better understand the correlations between features and target and which features are the strongest predictors. Finally, we train a separate causal model using EconML, which identifies features that have a **direct causal effect** on housing price, instead of just predicting the housing price given a set of characteristics.\n", - "\n", - "Also, this dataset has attracted some controversy because it includes the **share of Black residents (`B`)** in the neighborhood as a possible predictor of home prices. In addition to the fairness implications of using neighborhood racial mix to predict prices, our analysis shows that the statistical relationship between the share of Black residents and home prices is **not a causal effect**. In the 1970s in Boston, Black residents were concentrated in less desirable neighborhoods—for example those with higher pollution, higher crime, and smaller houses. This pattern of correlations makes the share of Black residents an effective predictive feature, as shown in our SHAP analysis, but we find that it has no direct causal effect on home prices. In other words, in this historical dataset neighborhoods with higher shares of Black residents also tend to have lower median home prices, but we show that changing the racial mix of a neighborhood on its own would not change median home prices.\n", - "\n", - "It includes the following sections:\n", - "1. [A Gentle Start: Linear Regression](#A-Gentle-Start:-Linear-Regression)\n", - "2. [Train a Fine-tuned Predictive ML Model](#Train-a-Fine-tuned-Predictive-ML-Model)\n", - "3. [Correlation Interpretation](#Correlation-Interpretation)\n", - " * Feature Importance -- Learn the top predictors for a given ML model\n", - " * Partial Dependence Plot -- Learn the statistical relationship between share of Black residents and housing price\n", - "4. [Causal Interpretation](#Causal-Interpretation)\n", - " * Direct Causal Effect -- Do the top predictors also have a direct effect on outcome of interest?\n", - " * Segmentation -- How different type of houses respond differently to number of rooms?\n", - " * What If Analysis -- How the overall housing price changes with one more room?\n", - " * Policy Analysis -- What is the best policy considering cost?\n", - " * Cohort Analysis -- What is the causal effect on a new dataset?\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Some imports to get us started\n", - "from lightgbm import LGBMClassifier, LGBMRegressor\n", - "from sklearn.model_selection import GridSearchCV\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A Gentle Start: Linear Regression\n", - "\n", - "### Data Description\n", - "\n", - "The data contain features of Boston census tracts in 1970 and was originally published by Harrison, D. and Rubinfeld, D.L. [Hedonic prices and the demand for clean air](https://www.law.berkeley.edu/files/Hedonic.PDF), J. Environ. Economics & Management, vol.5, 81-102, 1978.\n", - "\n", - "Below is a list of data description:\n", - "\n", - "Feature Name|Description\n", - ":--- |:---\n", - "**CRIM**|per capita crime rate by town\n", - "**ZN**|proportion of residential land zoned for lots over 25,000 sq.ft.\n", - "**INDUS**|proportion of non-retail business acres per town.\n", - "**CHAS**|Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n", - "**NOX**|nitric oxides concentration (parts per 10 million)\n", - "**RM**|average number of rooms per dwelling\n", - "**AGE**|proportion of owner-occupied units built prior to 1940\n", - "**DIS**|weighted distances to five Boston employment centres\n", - "**RAD**|index of accessibility to radial highways\n", - "**TAX**|full-value property-tax rate per \\$10,000\n", - "**PTRATIO**|pupil-teacher ratio by town\n", - "**B**|$1000\\dot(Bk - 0.63)^2$ where Bk is the proportion of Black residents by town\n", - "**LSTAT**|\\% lower socioeconomic status by town: $\\frac{1}{2}$(share of adults with less than high school education + share of male workers classified as laborers)\n", - "**MEDV**|Median value of owner-occupied homes in \\$1000's" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We start with a linear regression to learn the correlation between each predictor and the outcome variable, the coefficients could tell us how the housing price will change with one unit increase of each feature, and the p-value tells us the variable significance." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the boston housing data\n", - "from sklearn.datasets import load_boston\n", - "\n", - "boston_data = load_boston()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.741
Model: OLS Adj. R-squared: 0.734
Method: Least Squares F-statistic: 108.1
Date: Tue, 20 Jul 2021 Prob (F-statistic): 6.72e-135
Time: 17:58:02 Log-Likelihood: -1498.8
No. Observations: 506 AIC: 3026.
Df Residuals: 492 BIC: 3085.
Df Model: 13
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 36.4595 5.103 7.144 0.000 26.432 46.487
CRIM -0.1080 0.033 -3.287 0.001 -0.173 -0.043
ZN 0.0464 0.014 3.382 0.001 0.019 0.073
INDUS 0.0206 0.061 0.334 0.738 -0.100 0.141
CHAS 2.6867 0.862 3.118 0.002 0.994 4.380
NOX -17.7666 3.820 -4.651 0.000 -25.272 -10.262
RM 3.8099 0.418 9.116 0.000 2.989 4.631
AGE 0.0007 0.013 0.052 0.958 -0.025 0.027
DIS -1.4756 0.199 -7.398 0.000 -1.867 -1.084
RAD 0.3060 0.066 4.613 0.000 0.176 0.436
TAX -0.0123 0.004 -3.280 0.001 -0.020 -0.005
PTRATIO -0.9527 0.131 -7.283 0.000 -1.210 -0.696
B 0.0093 0.003 3.467 0.001 0.004 0.015
LSTAT -0.5248 0.051 -10.347 0.000 -0.624 -0.425
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Omnibus: 178.041 Durbin-Watson: 1.078
Prob(Omnibus): 0.000 Jarque-Bera (JB): 783.126
Skew: 1.521 Prob(JB): 8.84e-171
Kurtosis: 8.281 Cond. No. 1.51e+04


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.51e+04. This might indicate that there are
strong multicollinearity or other numerical problems." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.741\n", - "Model: OLS Adj. R-squared: 0.734\n", - "Method: Least Squares F-statistic: 108.1\n", - "Date: Tue, 20 Jul 2021 Prob (F-statistic): 6.72e-135\n", - "Time: 17:58:02 Log-Likelihood: -1498.8\n", - "No. Observations: 506 AIC: 3026.\n", - "Df Residuals: 492 BIC: 3085.\n", - "Df Model: 13 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 36.4595 5.103 7.144 0.000 26.432 46.487\n", - "CRIM -0.1080 0.033 -3.287 0.001 -0.173 -0.043\n", - "ZN 0.0464 0.014 3.382 0.001 0.019 0.073\n", - "INDUS 0.0206 0.061 0.334 0.738 -0.100 0.141\n", - "CHAS 2.6867 0.862 3.118 0.002 0.994 4.380\n", - "NOX -17.7666 3.820 -4.651 0.000 -25.272 -10.262\n", - "RM 3.8099 0.418 9.116 0.000 2.989 4.631\n", - "AGE 0.0007 0.013 0.052 0.958 -0.025 0.027\n", - "DIS -1.4756 0.199 -7.398 0.000 -1.867 -1.084\n", - "RAD 0.3060 0.066 4.613 0.000 0.176 0.436\n", - "TAX -0.0123 0.004 -3.280 0.001 -0.020 -0.005\n", - "PTRATIO -0.9527 0.131 -7.283 0.000 -1.210 -0.696\n", - "B 0.0093 0.003 3.467 0.001 0.004 0.015\n", - "LSTAT -0.5248 0.051 -10.347 0.000 -0.624 -0.425\n", - "==============================================================================\n", - "Omnibus: 178.041 Durbin-Watson: 1.078\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 783.126\n", - "Skew: 1.521 Prob(JB): 8.84e-171\n", - "Kurtosis: 8.281 Cond. No. 1.51e+04\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "[2] The condition number is large, 1.51e+04. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n", - "\"\"\"" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Train a linear regression using statsmodels\n", - "X = sm.add_constant(boston_data.data)\n", - "X_df = pd.DataFrame(X, columns=[\"Intercept\"] + boston_data.feature_names.tolist())\n", - "model = sm.OLS(boston_data.target, X_df)\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train a Fine-tuned Predictive ML Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we train a LightGBM regression model and use grid search to do model tuning." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Split data into train and test\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(\n", - " boston_data.data, boston_data.target, test_size=0.2, random_state=0\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "categorical = [\"CHAS\"]\n", - "# Store the numerical columns in a list numerical\n", - "numerical = list(set(boston_data.feature_names).difference(set(categorical)))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# train a lightGBM regression model\n", - "est = LGBMRegressor()\n", - "param_grid = {\"learning_rate\": [0.1, 0.05, 0.01], \"max_depth\": [3, 5, 10]}\n", - "search = GridSearchCV(est, param_grid, n_jobs=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best estimator: {'learning_rate': 0.1, 'max_depth': 10}\n" - ] - } - ], - "source": [ - "search.fit(x_train, y_train)\n", - "print(\"Best estimator: \", search.best_params_)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test set score: 0.7025992540638362\n" - ] - } - ], - "source": [ - "print(\"Test set score: \", search.best_estimator_.score(x_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Correlation Interpretation\n", - "### Feature Importance - Shap Value\n", - "We explain this ML model by understanding the top important features to predict the housing price, internally using **shap value**." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "fitted_model = search.best_estimator_" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "\n", - "# use interventional approach\n", - "background = shap.maskers.Independent(x_train, max_samples=1000)\n", - "explainer = shap.TreeExplainer(\n", - " fitted_model, data=background, feature_names=boston_data.feature_names\n", - ")\n", - "shap_values = explainer(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the feature importance\n", - "shap.summary_plot(shap_values, x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary plot above, we could see the **most important features** sorted by their importance level. It tells us that neighborhoods with a smaller shares of low socioeconomic status residents, higher median number of rooms and less pupil-teacher ratio will have a higher housing price. It's also in line with what we learnt from Linear Regression above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Partial Dependence Plot -- Statistical relationship between share of Black residents and housing price" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "shap.plots.partial_dependence(\n", - " \"B\",\n", - " fitted_model.predict,\n", - " pd.DataFrame(boston_data.data, columns=boston_data.feature_names),\n", - " ice=False,\n", - " model_expected_value=True,\n", - " feature_expected_value=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking share of Black residents as an example, here `B` is a function of Black population in town, the higher of `B`, the lower of Black population(%). From the coefficient of linear regression, the shap summary plot and also the partial dependence plot, we could get the same conclusion that there is a positive correlation between `B` and median housing price. In other word, housing price will decrease with the increasing of Black population(%). However, is that really **causal**? Let us validate that in the following section. \n", - "\n", - "Overall, all the insights above are coming from **corelation** perspective, telling us the positive or negative correlation between each predictor and the target. In order to correctly find the **casusal** relationship, we have to train a different model controlling on all the possible **hidden variables (confounders)** and learn the **direct causal effect** for a given feature. That's what the causal interpretation tool is doing. In the following section, we will explore the causal relationship in different ways." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Causal Interpretation\n", - "### Direct Causal Effect -- Do the top predictors also have a direct effect on outcome of interest?" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "classification = False\n", - "# order feature names according to shap values\n", - "vals = np.abs(shap_values.values).mean(0)\n", - "feature_importance = pd.DataFrame(\n", - " list(zip(shap_values.feature_names, vals)), columns=[\"features\", \"importance\"]\n", - ")\n", - "feature_importance.sort_values(by=[\"importance\"], ascending=False, inplace=True)\n", - "top_features = feature_importance[\"features\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.solutions.causal_analysis import CausalAnalysis\n", - "\n", - "ca = CausalAnalysis(\n", - " top_features,\n", - " categorical,\n", - " heterogeneity_inds=None,\n", - " classification=classification,\n", - " nuisance_models=\"automl\",\n", - " heterogeneity_model=\"linear\",\n", - " n_jobs=-1,\n", - " random_state=123,\n", - ")\n", - "ca.fit(pd.DataFrame(x_train, columns=boston_data.feature_names), y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pointstderrzstatp_valueci_lowerci_upper
featurefeature_value
RMnum4.4956410.39931711.2583212.107348e-293.7129945.278288
LSTATnum-0.4508490.083196-5.4191485.988376e-08-0.613909-0.287789
PTRATIOnum-1.8348130.441934-4.1517833.298943e-05-2.700987-0.968639
CHAS1.0v0.05.5552341.4657173.7901141.505781e-042.6824828.427986
CRIMnum-0.8793860.325632-2.7005526.922443e-03-1.517613-0.241159
INDUSnum-0.4565600.213477-2.1386923.246064e-02-0.874967-0.038154
AGEnum-0.0283040.020342-1.3914401.640919e-01-0.0681730.011565
NOXnum-8.4229136.143376-1.3710561.703575e-01-20.4637103.617883
Bnum-0.0056550.005880-0.9617283.361862e-01-0.0171800.005870
DISnum-0.4230340.447826-0.9446413.448421e-01-1.3007560.454687
TAXnum-0.0068520.010871-0.6303055.284951e-01-0.0281580.014454
ZNnum0.0338440.1269670.2665597.898088e-01-0.2150070.282695
RADnum-0.0307880.208264-0.1478318.824764e-01-0.4389780.377402
\n", - "
" - ], - "text/plain": [ - " point stderr zstat p_value ci_lower \\\n", - "feature feature_value \n", - "RM num 4.495641 0.399317 11.258321 2.107348e-29 3.712994 \n", - "LSTAT num -0.450849 0.083196 -5.419148 5.988376e-08 -0.613909 \n", - "PTRATIO num -1.834813 0.441934 -4.151783 3.298943e-05 -2.700987 \n", - "CHAS 1.0v0.0 5.555234 1.465717 3.790114 1.505781e-04 2.682482 \n", - "CRIM num -0.879386 0.325632 -2.700552 6.922443e-03 -1.517613 \n", - "INDUS num -0.456560 0.213477 -2.138692 3.246064e-02 -0.874967 \n", - "AGE num -0.028304 0.020342 -1.391440 1.640919e-01 -0.068173 \n", - "NOX num -8.422913 6.143376 -1.371056 1.703575e-01 -20.463710 \n", - "B num -0.005655 0.005880 -0.961728 3.361862e-01 -0.017180 \n", - "DIS num -0.423034 0.447826 -0.944641 3.448421e-01 -1.300756 \n", - "TAX num -0.006852 0.010871 -0.630305 5.284951e-01 -0.028158 \n", - "ZN num 0.033844 0.126967 0.266559 7.898088e-01 -0.215007 \n", - "RAD num -0.030788 0.208264 -0.147831 8.824764e-01 -0.438978 \n", - "\n", - " ci_upper \n", - "feature feature_value \n", - "RM num 5.278288 \n", - "LSTAT num -0.287789 \n", - "PTRATIO num -0.968639 \n", - "CHAS 1.0v0.0 8.427986 \n", - "CRIM num -0.241159 \n", - "INDUS num -0.038154 \n", - "AGE num 0.011565 \n", - "NOX num 3.617883 \n", - "B num 0.005870 \n", - "DIS num 0.454687 \n", - "TAX num 0.014454 \n", - "ZN num 0.282695 \n", - "RAD num 0.377402 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get global causal effect ordered by causal importance (pvalue)\n", - "global_summ = ca.global_causal_effect(alpha=0.05)\n", - "global_summ.sort_values(by=\"p_value\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# helper function to plot error bar\n", - "def errorbar(res):\n", - " xticks = res.index.get_level_values(0)\n", - " lowererr = res[\"point\"] - res[\"ci_lower\"]\n", - " uppererr = res[\"ci_upper\"] - res[\"point\"]\n", - " xticks = [\n", - " \"{}***\".format(t)\n", - " if p < 1e-6\n", - " else (\"{}**\".format(t) if p < 1e-3 else (\"{}*\".format(t) if p < 1e-2 else t))\n", - " for t, p in zip(xticks, res[\"p_value\"])\n", - " ]\n", - " plot_title = \"Direct Causal Effect of Each Feature with 95% Confidence Interval, \"\n", - " plt.figure(figsize=(15, 5))\n", - " plt.errorbar(\n", - " np.arange(len(xticks)),\n", - " res[\"point\"],\n", - " yerr=[lowererr, uppererr],\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " capthick=1,\n", - " barsabove=True,\n", - " )\n", - " plt.xticks(np.arange(len(xticks)), xticks, rotation=45)\n", - " plt.title(plot_title)\n", - " plt.axhline(0, color=\"r\", linestyle=\"--\", alpha=0.5)\n", - " plt.ylabel(\"Average Treatment Effect\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "errorbar(global_summ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We learn the **Average Treatment Effect (ATE)** for each feature, assuming they are the treatment. The error bar above is ordered by **feature importance**, and the summary table above is ordered by **causal significance (p-value)**. You could see they are not exact in the same order. Some top features such as percentage of lower status population (`LSTAT`) and number of rooms (`RM`), they are the strongest predictors and also have a direct causal effect on housing price, but others like pupil-teacher ratio (`PTRATIO`) and nitric oxides concentration (`NOX`) they don't really have significant causal effect on housing price. Also on the other side features like whether in Charles River Area (`CHAS`), it doesn't have strong prediction power comparing with others features, but it has a direct causal effect. \n", - "\n", - "Following on the findings we learnt for share of Black residents (`B`), we could see it also gives us insignificant causal effect on housing price, which means race by itself has no direct causal effect on home prices. By learning the correlation between `B` and other features, we could see it's highly correlated with crime rate(`CRIM`) and percentage of lower status population (`LSTAT`), which do have strong causal effects. This pattern of correlations make `B` as a strong predictor but not a direct driver. Using the causal analysis tool has helped us **avoid reaching a controversial and incorrect conclusion**. \n", - "\n", - "To sum up, EconML could give us some extra insights from a **causal relationship** perspective." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Segmentation -- How different type of houses respond differently to number of rooms?\n", - "From the analysis above, we learnt the direct treatment effect of each feature from an overall average level. However, different regions might respond differently on housing price for each potential driver. In the following section, we are going to use number of rooms (`RM`) as an example to learn how different type of houses respond differently to number of rooms? " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bvWYHe9bu4umDEJTaWtgUs6Fh+ya07Nkmfz7YXNDR1cn2NbX/dmzvYVzcS6oTVQBTSzNqNK3Nwe3+JCclo6uni6mFKaYWphrtqzeqwbp5q3hw674kq0KIN0KSVSHEG/Hn5Lkc3X2YZt1a4FzahbiYOG5eus6tKzfVyer540E8uvOQuq0bYGFjyZP7j9m7bjc3Ll3nl7Wz0DPQy9Tnop//wtTCjM5DunHryk0CNvuhb6jP0wdP0DPQo9uwXpw/cY6963ZRzNmB1r3bZWp/7Xwwx/YeoUX3VhgYGbJ/015+/moqY+eMp1zV8tmO5dHdh4zt+y3Gpsa0+qAtxibGnD18mj++n0V8bDyterUFYP/Gvfw9ZR5VG9agebeWpKWl8eDWfYKDruSYrMbHxJGampbj56rUUmJsapxjvdxIT0/n7rU71G3dQOOYa1k39q3fzcPbD165lCA8NBwAEzOTfIlJCCH+TZJVIcQbcfbgKRp1bEK/rwZmW6dplxa06dM+U5lvvap8138kJ/Yfo26r+pmOmZibMmbO9ygUCgBCHz1l79pd1G5Zj08mfg5Aky7N+bzjcPw379NIVu9dv8uEhVPx8PECoGGHxnzW/iOWzVjE1BW/Zhvnwh/nY2phytQVv6JvoJ8Re9cWTP92GmvmrqRRx6boG+hz5tApipd25qtfRubqM3rZtM8nc/nMxRzr2RSz5fcdf752/1mJjYolJTkFC2sLjWPmz8siQsOzTVZVKhWbF23AwMiAirUr50tMQgjxb5KsCiHeCEMTI25evE7402fZ3pJ+eeY0MT6BlOQUirk4YmRixK0rNzSS1QbtGqsTVQC3cu5cOnWBhu3/uRWvUChw8/bg+N4jGucrXcZVnagCGJkYU6dVA7Yt3URkWIQ6QXtZbHQs54+fo9OgriQnJpOcmKw+VrFWZY7tOcKtSzco41sOIxMjnj15xo2L13At557zh/SSPl/0JzY6Nsd6uvp6OdbJreSkJAC0s3iSX1dPN6NOYlK27dfNX82lUxcYMGoIJuaaSwSEECI/SLIqhHgjen/en9nfTWdY8wGU8CyFT82K1GlRD6fSzuo6sdGxrJi5hOP7jhIbFZOpfXxsvEafL69jBTA0NgTAys46U7mRiRFJiUmkJKdk2lLJ3tlBo08Hl4yyp4+eZpmshtx7hEqlYt381aybvzrLsUZFRAHQvn8nLp68wOjeX2PraEf56j5Ub1xTvezhVUqVcc2xTn7T1ctIfFOTUzSOJSdlJOXZJcf7N+5l7dyVNOncnGZdW765IIUQRZ4kq0KIN6J645p4VizD6YATnD8exK7VO9iyeCODx36kfihp+jfTuHruCq0/aE9Jz1IYGBkA8Nuon1Gla26FpFRmvdueUivr8vzYTin9eRyterXN9lb3iyfkHUo4MX3D7wQeOcO5Y4EEHjnDvvW7adCuMcO+/+SV54mNiiE1JTXHeJRKJaaWZq83iGwYmxmjo6ujXnf6ssiwCAAsbCw1jh3fe4T5k+ZQrVENBowaki+xCCFEdiRZFUK8MeZW5jTu1IzGnZoRHxvP/z4cyZo/VtCgXWNio2O5cOIcXYb2oMuQ7uo2yUnJxEbHvZF4Qu490ih79HwvUVsH2yzb2DvZA6ClrZWrGVI9Az2qN65J9cY1SU9LY96EOfhv3keHAZ2xL14s23Y/fzn1ra9ZVSqVuLiX4OalGxrHrl+8hq6+Lo4lHDOVBx09y8wxv1LW15tPp3yZ7R8QQgiRXyRZFULku/S0NBLjEzE0MVKXGRobYudkz4UT54B/Zkn/Pfu5fdkWVOnpbySum5dvcO1cMO4VPAGIi4nl0HZ/SnqVznIJAGRs4+RdtTz71u+hefdWWNtnXooQHR6lnumMiYzOtHZTqaWFi3sJIONp/1cpiDWrkDEDvmzGYoIDL+NZsQyQMabje49QqU6VTOcLDrrCL19OpaRnab6ePhptHXlrlRDizZNkVQiR7xLiExja9EOqNaqBi3tJDI0NuXb+Kqf8T9CwQxMgI3kt61uOLYs3kJqSik0xG4KDrnD5zEVMzN/MNkjFXV2YMmICLbq3wtDYiP2b9hIdGcMnk754ZbsBo4fyXb+RfN31Uxp1bIq9swMxEdHcDr7JuWOBLD68CoCJH32PmYUZHj5eWFhb8ORhCLtW78CxpBMu7iVfeY78XrO6a9V24mLiiHueJF86fYG0tIw/Auq2qo/N85nkpl1a4LdxL9M+n0yb3u0xMDZg9+odpKen0/2jnur+Qh895ccRE0hPT6d2i7qc3H880/lc3EqoE3MhhMhPkqwKIfKdnr4ezbq15PzxIE4fOEVaaio2Drb0HNGH1h+0VdcbMflLFv70F/vW7yYtNRWvSmUZN28iE4b+743E5VHBk/LVKrD6jxUZLwUoXowvf/42x9v7Di6O/LjiV9bNX8WhHQeIjojG1NwEp1LF6f15f3W9Jp2acXjnQXau3EZ8bBwWNpbUaVmPzoO6oaWt9UbGlJ2tSzYR+vip+vtzx4I4dywIAM+KXupkVd/QgHHzJ7J0+iL1Hw5u5T34ZNIXOJRwUrd/+uiJOvFdOE1zGULnId0lWRVCvBEKeZ+zEEWbQqEYWKdV/d8+mfi5YUHH8ia9eIPV4LEfFXQo4jXMGjs9/tD2gE9VKtVfBR2LEKJgyMp4IYQQQghRaEmyKoQQQgghCi1JVoUQQgghRKElD1gJIYqENYGbCzoEIYQQeSAzq0IIIYQQotCSZFUIIYQQQhRakqwKIYQQQohCS9asCiEKtZjIaLYt28zpA6d4+vAJ6enp2Dna4VOrMi26t1Jvbv+yDX+vZdXsZbiX92Di4mnq8uEtB2XaKD87nYd0p+vQHq+s71DCkRkb5+R9YEIIIXJFklUhRKF15+ptJn88ntioGGo2q0PTLs3R0tLi3o27HNruz8Ft/vy1f4lGu0PbA7B1tOPa+as8eRCCnZM9AH2/HkBifKK63tmDpzi65zB9vxqAibmputzFrUSmf7fp20HjHIbG7/U7FIQQotCQZFUIUSjFx8Yz7bNJpKWmMWXZLxqv8uz5SW/Wzlul0e7W5Rs8vP2A0bPHMX3kTxzaHkDnId0BqNqgeqa64U/COLrnMFUaVMPWwS7LOMxtLKjbqn6+jEkIIcTrk2RVCFEo7duwm7CQUD4a/2mW75zXNzSg9+f9NcoP7gjAwsaS8jV8qNawOod2HlAnq2/bi1e8+tSqxOo5Kwi5/5jipYozaMwwSpd14+A2fzb8vZbQR08p4VGSYeM+wam0c6Y+Qu49ZvUfKzh/PIiEuHiKuTjS+oO2NGjXWF0nNSWFDX+tJfDwGULuPyY5OQVnVxc6DOiskaC/iMm3XlVWzl7GozsPsLSzotuwntRuUe+tfC5CCPE65AErIUShdMr/BDq6OtRqXifXbdLT0jiy6xA1m9ZGqVRSq3ldHt99xPULV/McR1pqGtER0RpfiQmJOTcGrp+/ysIf51O7eV26DevB00dPmTpiAvs37WP9n2to2KEJHQZ04e71O/w26pdMbR/dfcjoPl9z89J1Wn3Qln5fDcSmmA1/fD+L7cu3qOvFxyawd/1uPHy86P7xB/QY/gGq9HR+/mIKgYfPaMZ04Rp/jJ9FlQbV6P15f/QNDJg1dgYPbt3P8+ckhBBvisysCiEKpQe37uPg4oiOrk6u25w/fo6oZ5HUalEXAO+q5TGzMufQ9gO4eXvkKY6LJ88zsGFvjfJm3VoyYOSQHNs/uP2A6RtmY1+8GABmlub8/r/fWDp9ITM3/6FeK6ulpcXK2Uu5eek6pcu6AbDwx/mYWpgydcWv6BvoA9C0awumfzuNNXNX0qhjU/QN9DE2NWLOjr8yfVbNu7fk2x5fsG3pJirWrpw5plv3+Gn1bziVKg5A9Sa1+KjFAPw378tytloIIQqSJKtCiEIpIS4eg9d8iOngjgDsitvj+jzZU2ppUaNJLY7uzniISktb67XjKF3GlR6faCar1vbWuWpfrko5daIK4ObtDoBvvaqZHupyr5CRTD95+ITSZd2IjY7l/PFzdBrUleTEZJITk9V1K9aqzLE9R7h16QZlfMuh1NJCqZUxttSUFBLjE0lPV+FVqSxHdh3UiKmsr7c6UQUwtzLHsYQTTx48ydWYhBDibZJkVQhRKBkYGZIQl5Dr+okJiZzyP0HtFnUJufdYXe5e3oNdq7YTdPQsletWee04jM1NKF/d57XbvWBlb5Ppe0Njo4xyu8zJrpFJRnlcdCwAIfceoVKpWDd/Nevmr86y76iIKPW//TbsYfvyLTy8/QCVSqUuVygUGu2si9lolBmZGhMXHZObIQkhxFslyaoQolByKunEreBbpCSn5GopwEm/YyQlJOK3YQ9+G/ZoHD+0PSBPyep/pVRm/WiAUivr8heJZnp6xv+26tVW4zb+C87Pt9g6tCOAeRN+p3LdKrTr1xEzS3OUWkoCtvhxeKfmzGp2Mb2U4wohRKEhyaoQolDyrV+Nq+eCObbnMHVbN8ix/qEdAdgXL0bPLG7Zn/Q/zkn/4xlLC4zejf1R7Z/vDaulrZXjzO6xvUexc7LnmxljMs2kBmzxe5MhCiHEWyG7AQghCqUmnZphZWfFkl8Xcv/mPY3jiQmJLJuxCICI0HAunDxP9cY1qd6klsZX8+6tSU5M5vi+o295FHlnammGd9Xy7Fu/h7CQUI3j0eH/LAF4MVP68u3/Jw9COOl//M0HKoQQb5jMrAohCiVDEyO+nj6GKR+PZ2TPL6jVvA6uZd1Ramvx4NY9ju4+jCo9nQ8+68eRXYdIT0vHt17VLPty83bP2BVgx4FM+5PmRmRoBAe3B2iUK5WKN74v6YDRQ/mu30i+7vopjTo2xd7ZgZiIaG4H3+TcsUAWH854KYJvvaqc3H+MaZ9NwrdeVcKfPmP3mp04uDhy5+rtNxqjEEK8aZKsCiEKrVJepfll7Sy2LdvM6QMnObrnMCoV2DnaUbdVfVr0aA1kLAEwszTD9fmT9v+mUCioXLcK/pv9CH/6DEtbq1zHcPf6HWaPna5RrtRSvvFk1cHFkR9X/Mq6+as4tOMA0RHRmJqb4FSqeKYtpuq3bUh0RCR71u7iwolz2BcvRt+vBhBy/7Ekq0KId55CJSvqhSjSFArFwDqt6v/2ycTP343FnKJImTV2evyh7QGfqlSqvwo6FiFEwZA1q0IIIYQQotCSZFUIIYQQQhRakqwKIYQQQohCS5JVIYQQQghRaEmyKoQQQgghCi1JVoUQQgghRKElyaoQQgghhCi05KUAQgjx3MHtAcweOx0dXR2Wn1iXqzb3btxl7dyVXD5zicSERKztrKnWqAY9R/TJVG/P2p3sXr2Dx/ceYWRqTJX61ej5SW+MzUzUdQK2+DFn3Mxsz9V9eC86Duyat8EJIcQ7SpJVIYQAEuLiWTZjEXoG+qSnpeWqzaXTF5jyyQ8UL+VM+/6dMDQ2JPRxKGGPn2aqt2LWUjYtWIdvvao069qSp4+fsmvVNm5evsHERT+io6sDgFelsnw88XON8+zfuJfLZy5SoUbF/z5QIYR4x0iyKoQQwNq5qzAyMaJc1fKc2Hc0x/qJ8QnMHP0r5av58NUvI1FqaWVZLyI0nK1LNlKjaS0+//EbdblHeU9++mIyfhv30LxbKwDsnOyxc7LP1D49PZ3lMxbhWNKJ0mXd/sMIhRDi3SRrVoUQRd6DW/fZuWo7fb8agFY2See/Hd51iIjQcHp80hullhaJCYlZzshev3CVtNQ0ajevl6m8SoNq6Bvqc2TXoVee5+LJ80SERVC7Rb1X1hNCiPeVzKwKIYq8hT/Op0LNivjUrJRj8vjC+WOBGBgbEh0Rxecdh/Pw9gN09HSp3rgmH347CCMTYwBSklMA0DPQ0+hDV0+PO8G3SE9PR6nMeu7g4PYAFAoFdVpKsiqEKJpkZlUIUaQd23uEK4GX6fvFh6/V7vG9x6SnpjH1kwmU9fXmq19G0qpXW47sOsiPn05CpVIB4FDCCYArZy9lav/g1n2iI6JISkwiLjo2y3MkJyZxyv84Hj5e2Dra5WF0Qgjx7pOZVSFEkZWUkMSSXxfQokdrirk4vFbbxIQEkhKTaNSxKQNHDwWgasMa6Bvqs2r2Ms4fD6JCjYqU9CyFRwVPti7dhKWtFRVqViTsUSgLps1HS1ubtNRUkpOSszzHqYATJMQlyKyqEKJIk5lVIUSRteHvNaSmpNJpULfXbqurl3Fbv07L+pnK6zxfWxoceFld9sVP3+Ja1p0/J/3Bx60GM37wWEp4lKJyXV8ADAwNsjzHoR0H0NbRpkbT2q8dnxBCvC9kZlUIUSRFhIazbelmOnzYmdjoGGKjY4CMp/xVKhVPHz1BV08PcyvzLNtb2lhw/8ZdzK3MMpWbPa8fFx2nLrOwseT7vybx5EEIz548w9bRFmt7G8b0+QYzSzMMTYw0+o+OiObcsUAq1fHF2NQ4fwYthBDvIElWhRBFUlR4JCnJKayZu5I1c1dqHP+41WC8q5bnu3kTsmxf0suVc8eCePbkmXpdKkD4k2cAmFqYarR5eWuq2KgYbl25SY2mtbLs/+ieQ6SlplG3VYPXHpsQQrxPJFkVQhRJtg52fPXrKI3ynSu3ERx4hc+nfY2ZRcasaURoOPGx8dg52aOtk3HZrNm0NpsXrsdvwx68q1VQt9+3cQ8AFWq+egP/Zb8tJj09nda92mZ5/ND2AxiZGFGpjm+exieEEO8LSVaFEEWSoYkRVRtU1yg/5X8CpTI407EVs5ZyYOt+Zm+fj61DxlP5JTxK0qhjU/at301qairlq/lw8/IN/Dfvo1bzurh5e6jbL/hxPslJyZTwKAnACb9jXDp1gV6f9qVUGVeNGELuP+b6has06thU/XYrIYQoqiRZFUKIPBowcgjWxWzYv3EvZw6extLGks6Du9FxYNdM9Up5ubJjxRaO7DqEQgElPUrxzfTR+NavlmW/h3ccADQf3hJCiKJI8WIvQCFE0aRQKAbWaVX/t08mfm5Y0LEI8W+zxk6PP7Q94FOVSvVXQccihCgYsnWVEEIIIYQotCRZFUIIIYQQhZYkq0IIIYQQotCSZFUIIYQQQhRakqwKIYQQQohCS5JVIYQQQghRaEmyKoQQ76mALX50rdiOp4+eFHQoQgiRZ/JSACHEeyMmMpptyzZz+sApnj58Qnp6OnaOdvjUqkyL7q2wcbDVaLPh77Wsmr0M9/IeTFw8TV0+vOUgQh8/zfGcnYd0p+vQHq+s71DCkRkb5+R9YEBifAJblmyirG85yvp6/6e+hBDiXSLJqhDivXDn6m0mfzye2KgYajarQ9MuzdHS0uLejbsc2u7PwW3+/LV/iUa7Q9sDsHW049r5qzx5EIKdkz0Afb8eQGJ8orre2YOnOLrnMH2/GoCJuam63MWtRKZ/t+nbQeMchsb//X0LifGJrJu3CuguyaoQokiRZFUI8c6Lj41n2meTSEtNY8qyX3BxL5HpeM9PerN23iqNdrcu3+Dh7QeMnj2O6SN/4tD2ADoP6Q5A1QbVM9UNfxLG0T2HqdKgGrYOdlnGYW5jQd1W9fNlTEIIITJIsiqEeOft27CbsJBQPhr/qUaiCqBvaEDvz/trlB/cEYCFjSXla/hQrWF1Du08oE5W37YLJ86xbv5q7t24S0pyMuZWFpT19WbY95/w9NETPm41GIB181Y9n2GFem0aMvyHTwG4dv4qS375m9vBtzC1NKNZlxaYWZkXyFiEECI/SbIqhHjnnfI/gY6uDrWa18l1m/S0NI7sOkTt5nVRKpXUal6XgC37uX7hKm7eHnmKIy01jeiIaI1yXX1d9A30s2334OY9po6YgFNpZzoP7oaegT5PHz7hlP9xAEwtzPhw5GAWTJ1P1YbVqdqwBgD2z5csPLh5jwlD/4ehkQEdB3ZFW0ebfRt2o29gkKdxCCFEYSLJqhDinffg1n0cXBzR0dXJdZvzx88R9SySWi3qAuBdtTxmVuYc2n4gz8nqxZPnGdiwt0Z5s24tGTBySPaxnDhHSnIKY2aPw9TSTF3e85OMvvQN9KneqCYLps7H2a2ExlKDVXNWkJqSyvgFU7AvXgyABm0bMaLdsDyNQwghChNJVoUQ77yEuHgMXvMhpoM7ArArbo9rWTcAlFpa1GhSi6O7Mx6i0tLWeu04Spdxpccnmsmqtb31K9sZGhsBcCrgBA3aN0apzP2ugulpaZw7epbKdauoE1UAU0szaresy541O3PdlxBCFEaSrAoh3nkGRoYkxCXkun5iQiKn/E9Qu0VdQu49Vpe7l/dg16rtBD1P/l6XsbkJ5av7vHa7ms1q4795H/Mm/M7ymUsoV8Ub33pVqdmsNto6r54tjo6IJikxCYcSjhrHHFw0y4QQ4l0jyaoQ4p3nVNKJW8G3SElOydVSgJN+x0hKSMRvwx78NuzROH5oe0CektW80tXTZdyfEwk+e5mzR85w/lggs7+bwdYlm5iwaCr6hrL2VAhRdEmyKoR45/nWr8bVc8Ec23OYuq0b5Fj/0I4A7IsXU68JfdlJ/+Oc9D+esbTA6L/vj5pbSqWSMr7lKONbDj7ti//mffzx/SyO7j5Mww5NQKHIsp2phSl6+no8uvNQ49iju5plQgjxrpHXrQoh3nlNOjXDys6KJb8u5P7NexrHExMSWTZjEQARoeFcOHme6o1rUr1JLY2v5t1bk5yYzPF9R99a/DGRmjsIlPQsDUBcTBwA+gZ6Gd9Hx2aqp9TSokLNipw5eIqQ+/8saYgOj+LwjoNvKmQhhHhrZGZVCPHOMzQx4uvpY5jy8XhG9vyCWs3r4FrWHaW2Fg9u3ePo7sOo0tP54LN+HNl1iPS0dHzrVc2yLzdv94xdAXYcoEG7xq8VR2RoBAe3B2iUK5UKareol2279X+u4dLpi1SqUxkbBzviY2LZt34Puvq66jj1DQ1wKOHI0d2HKObigImZCbaOdrh5e9B1WE/OHQ1k3IejaNatJVraGVtX2TjYcvfa7dcagxBCFDaSrAoh3gulvErzy9pZbFu2mdMHTnJ0z2FUKrBztKNuq/q06NEayFgCYGZphqu3e5b9KBQKKtetgv9mP8KfPsPS1irXMdy9fofZY6drlCu1lK9MVn3rVyUsJJQDW/2JjojC2MwE13LufPbj1xRzcVDX++j7ESz6+S+W/rqQlOQU6rVpiJu3B86uLoz9YzxLfl3I+j/XZHopwB/fz8p1/EIIURgpVCpVQccghChACoViYJ1W9X/7ZOLnb2+BphC5NGvs9PhD2wM+ValUfxV0LEKIgiFrVoUQQgghRKElyaoQQgghhCi0JFkVQgghhBCFliSrQgghhBCi0JJkVQghhBBCFFqSrAohhBBCiEJLklUhhBBCCFFoyUsBhBDiuYPbA5g9djo6ujosP7Eux/qpKSmsm7+aA1v3Ex0RjUMJJzp82Imazepo1A06epbNC9dz78Y9UlNTsXO0o2GHJjTt3Byllpa6XmJCImvmrODYviNEPYvEyt6aRh2a0rZP+0z1hBCiqJBkVQghgIS4eJbNWISegT7paWm5avPH+Nkc3nmQ5t1aUtzVhZN+x5gx8meATAnr0d2HmDHyZzx8vOg8uBtaWlqcOXSKBVPnE3LvMf2+HghAeloaU4aP59qFqzTp1Byn0sUJDrrCiplLCAsJZeCoofk/cCGEKOQkWRVCCGDt3FUYmRhRrmp5Tuw7mmP9W5dvcGh7AN0+6kWnQV0BaNShCeMGjGbJrwup1qgmWtoZM6E7VmzFxsGWcfMnoq2Tcdlt2rUF3/X7Fv8tfupk9eT+41wJvMzAUUNp2rVFRr0uLTC1MGXnim007dwcZ7cSb2D0QghReMmaVSFEkffg1n12rtpO368GoJXLW+1H9x5BoVTS7HlSCaBQKGjapQXhT59xNeiKujwhLgETMxN1ovqCmZUFevp66u+vBF4GoHaLupnq1WlRD5VKxdE9h197bEII8a6TZFUIUeQt/HE+FWpWxKdmpVy3uX3lJvZO9hibmWQqdy3nBsCt4JvqsjK+5bh15SZr/lhByL3HPH34hB0rtnLm4Ek6DuiirpeSnIJCoUBHTzdTny8S2puXb7z22IQQ4l0nywCEEEXasb1HuBJ4mV/WznqtdpFhEZhbW2iUW1hbAhARGq4u6/FxbyJCw1n/5xrWzV8NgJa2NgNHDaVRx6bqeg4lHFGpVFwNukK5quXV5ZfPXAIg/Ok/fQohRFEhyaoQoshKSkhiya8LaNGjNcVcHF6rbXJSMjq6OhrlOno66uMv6Orp4lDCiUp10qjRtDba2loc2nGAv6bMxdjMhGqNagBQp2U91s9fzdwfZvPht4MpXtqZq+eusGrOcrS0tUhOSvoPoxVCiHeTJKtCiCJrw99rSE1JpdOgbq/dVldPl5TkFI3ylKQU9fEX/p46j6tBV/hp9W/qh65qNqvDuAGj+XvqPCrXrYK2jjZmluZ8O/M7Zo+dztQREwDQ0dPlg0/7sv7PNRgYGuRlmEII8U6TZFUIUSRFhIazbelmOnzYmdjoGGKjYwBIjE9ApVLx9NETdPX0MLcyz7K9ubUFoY+eavYblnGr3sImYzlAakoKAVv8aPVBO3Wi+kKV+tVY8usCnjwIwbGkEwCePl7M3DKX+zfukRAXT3FXZ3R0dFj089+UrVIuv4YvhBDvDElWhRBFUlR4JCnJKayZu5I1c1dqHP+41WC8q5bnu3kTsmxf0qs0F09dIDYqJtNDVjcuXgOglGdpAGIiY0hLTSM9VXPv1rTn+7mm/WtfV6VSiYt7CfX3pw+cRJWeTvlqPq81RiGEeB9IsiqEKJJsHez46tdRGuU7V24jOPAKn0/7GjMLMyBjFjY+Nh47J3v19lPVG9dky6IN7F6zU73PqkqlYu/aXVjYWOLh4wWAmaUZRiZGnAo4QffhvdB9/mR/eloax/YcRt9QH4dXrJdNTEhk9ZwVWNpaUetfW1oJIURRIMmqEKJIMjQxomqD6hrlp/xPoFQGZzq2YtZSDmzdz+zt87F1sAPAtawbtVvUZe28lcRERlO8tLN6U/8Rk79Q3/JXamnRtm9HVs5eyqjeX1O/TUO0tLU4susgt67cpPvwXmjr/POg1rgBo3Hzdsfe2YHYqBj8N+3j2dMwRs8ah76B/hv+VIQQovCRZFUIIfJo2PcjsHGw5cBWf/au20UxFwdGTP5SY1P/DgM6Y1fcnh3Lt7BxwTqSEhJxKOHE4LEf0bhTs0x1S5dx5cT+Y4Q/eYa+oT5lKpfjy19G4uzq8jaHJoQQhYZCpVIVdAxCiAKkUCgG1mlV/7dPJn5uWNCxCPFvs8ZOjz+0PeBTlUr1V0HHIoQoGPIGKyGEEEIIUWhJsiqEEEIIIQotSVaFEOlpqamyHkgUSs9/NtMLOg4hRMGRZFUI8Sz86TPNTUCFKASePXmWBjwr6DiEEAVHklUhxIEbF2/oJ8TFF3QcQmQSHxvPzUs39IEDBR2LEKLgSLIqRBGnUqkidfV0DqyesyJZdgcRhYVKpWLNHyuSdfV0DqhUqsiCjkcIUXBk6yohBAqFwkrfUP9oWV/v4o07NTPwqOCJvqE+oCjo0ESRoiIxPpGr54LZt353wqXTF+4nxifWVKlUsgxAiCJMklUhBAAKhcIMBX2MjI0GJiclu6WmpOo9P6QEVM+/hMhviudf6QDaOtpJunq61+Ni4/5CxRKVShVVsOEJIQqaJKtCiCwpFIo2wAJgsEql2ljQ8Yj3l0Kh6AjMA/qrVKptBR2PEKJwkWRVCKFBoVB8DIwG2qtUqpMFHY94/ykUimrARmCSSqX6vaDjEUIUHvKAlRBCTaFQaCkUiunAcKCWJKribVGpVCeA2sDHCoXiV4VCofXimEKhMFEoFGMKLjohREGSmVUhBAAKhcIQWA6YAx1VKlVEwUYkiiKFQmFBxgxrOPCBSqWKVygUusBdoJFKpbpcoAEKId46mVkVQqBQKOwAfyAGaCaJqigoz3/2mgFxgL9CobBTqVTJwF/AsAINTghRIGRmVYgiTqFQeAHbgSXAeJVcFEQhoFAoFMD3QG+gFRALnAOcVSpVbAGGJoR4y2RmVYgiTKFQ1AcCyEhSv5dEVRQWqgzjgPFk/IyWJuNNVr0KMi4hxNsnM6tCFFEKhaI38DPQQ6VS7S/oeIR4QaFQ1AN8gP3AJaABsBL4m4xZ1gryh5UQRYd2QQcghHi7nt9e/Q7oDzSQB1ZEIRQFlAU+AUzJmFn9AxgI6AG1gMMFFZwQ4u2SmVUhipDnT1XPJyMRaKNSqUIKOCQhXkmhUDiTMbPaAGgMOAKBKpWqUoEGJoR4ayRZFaKIUCgU5sAGIBropVKp4go2IiFez/O7AmUAW5VK5V/Q8Qgh3g5JVt8TCoVCG3Aj45aZooDDEfkrDripUqni89qBQqEoQcYT/3uAr1QqVVo+xSZEgXieuJYArAGtV9cWIs9SgIdyF6pgSbL6jlMoFNa6Bro/p6eld9Qz0lfoGxmkKZSSq74vVCoVKYnJirjIWF1tXZ39SXGJ36hUqkuv04dCoagCbAJ+VKlUM99IoEK8JQqFQltfX/87bR2dQVpaSjNbG9sUbW15/EK8GUnJSYqQxyF6Oro6t2JjYqelpqYuKuiYiiJJVt9hCoXCWkdf93i5Bj7O1TrV0bGwtyzokMQbEh8Vx4X9gaqDy/ZFpyal1FWpVOf/XUehUNQCjr88a6pQKNoBfwIDVSrVlrcYshD5TqFQaBkbG68t512u2bSfpxlWqlyZjAlWId6clJQUAvwD+GjIsPjw8PCJCQkJUwo6pqJGktV3mK6+7u9l6lUY1OKT9jpywS4agnadUu1fuOtcYmxCxZfLFQqFAxlb/DioVKqE52WfAt8A7VQq1em3H60Q+UuhUHT09PRccvj4ESMDA4OCDkcUMY8ePsTH2ycxLi7OS6VS3SnoeIoSeSnAO0qhUCiBHlXb15JEtQgp16iiIi01zUuhUBT716FBwCqVSpWgUCi0FArFb8BgoKYkquJ9YWZm1n/4J8MlURUFwsHRkQ4dO6i0tLS6FHQsRY0kq+8uR6W2lr61s21BxyHeIm0dbRzcnZKAai/KFAqFDhmJ6R8KhcKIjCf+ywG1VCrV3YKJVIj8l5aWVr1e/foFHYYowpo0a2pgZmbWuKDjKGokWX13mega6KYWdBDi7TMwMVSSsevDC22BW8BTMjZPDwdaqFSqyLcenBBvUEpKiqGZuVlBhyGKMDMzM8jYBlC8RZKsvrsUCoVCFhwXQVns9vARsAU4/vx/PwRSFAqFh0KhsHnL4QnxRimV8mtLFBylUolCtod862S/j/fU+X1n2T5jvfp7LR1t9I31sS5uS6lKblRo5ouBiWGWbfr8PARHT2d1+YMr9zi62p+nt0OIj47H0NQQK2db3Kp64tumxlsbU35aOWYBd87dpGLzKjT/uH2O9bdNX8cFv0CNcvNilgz788tMZVNaj8myj7ofNKZW9wbZnuPIKn8OLtuHpZM1Q+Z+nmNMAAqFwguoSMZt/6lkbPi/FGgIpAIjyNi2Soj32tIlSxk6aAj+BwOoWq0qAJMmTGTyxMlYWFhw+doVTE1NM7Vp3qQZT0KeEHghSF3m5e7Jvbv3AFAoFJiYmODg4EDlKr580PsD6tarq3FuIz1DBgwcwMzfZ2kcO3niJA3q1mfun/Po3ae3uvzE8RP8OGUqFy5c4FnYM6ysrfD09KJV65YM/WhYfnwkb9zpU6dYvnQ5p0+f5uKFiyQnJ3Pz7i3s7e016r78ub6sb/++zJn7R67Ot3vXbiZNmMili5cwMzejU+fOfP/D9xgZGWWq99OP0zh96jRnzpzh8aPH2f5/8+JnJivXbl7D0ckpV3GJN0+S1fdc7Z4NsShmRXpaOvGRsdy/fJeAJXs5sekwHUf2pHi5Eq9sH3zkIhunrsLGxZZKrapjaGZI1NNIHl29z6nNR9/JZPXK4Qs8vHr/tdsptbVo9WnHTGW6BrpZ1nUpX4ryTSpnKrMr+e9nov4R9TSSo2sPoKOfdX+v8AtgRsYSgG8Af2A/MA64pZLtPoQgIiKC32fNZtSY0bmqX87bm8+++AyAuNhYrl+/wZbNm1m+dBk9evVk3p/z0NLK+3sINm3cxAc9elGmbFkGDx2CjbU19+7d4+SJU8yeOfudSVZ379rNgr8XUKZsGVzd3Lh86dVbQL/8ub5QunTpXJ3Lb58fnTt0olr1avz0y0/cuXOX2TNnceP6dTZu2ZSp7vf/+x4bW1t8fSvz+NHjHPse890YSpYqlanMwlK2gixMJFl9z5Wq5JZplrQ68PjGQ1b/bxHrJi1j8JzPMLIwzrb9oeV+WDpa0e/XYWjr6mQ6FhcRm29xxkfFkZ6WhrGlac6V/4OUxGT2/72TGp3rcnDZvtdqq1AoKNfAJ1d1LR2scl0XwO+vHTh5OpOelk5sZMzrhHUTmAisAoIlORVCUwWfCsyeOZthwz/C3Nw8x/r29nb06NkjU9mkKZP4fMRnLPh7AU5Ojnz/w/g8xzNx/ATc3Nw4eOQg+vr6mY49efIkz/3+W2hoKKmpqRQrlv0fyv/FwMGD+OKrLzEwMGDShIk5JqtZfa65NfKbkbi7u7Nj9050dTP+qC9VqiTDhw1nz+49NG3WVF33UvBlSpQsAWTMeuekcZMm6tl4UTjJ4p8iqJirI40HtSIxJoHT2469sm7E43CKuTlpJKrAK5Pc3EhPS+f6yWDWT1rOrL4/8ujqg//UX24cXROAQqmkWsfaeWqvSk8nKT4pV3VTk1NISUrJsd7twBtcP3GFxoNbvX48KtUnKpVqnEqluiKJqhBZGzN2DJGRkcz6Le8vcNPW1mb6zBm4u7szZ/YcoqOj89zXrVu3qORbWSNRBbCzs8tzvwBpaWns3L6DHl2741bSldMnT/2n/l7Fzs6O191GLDk5mbi4uNdqE3wlmMuXLtF/wIfqRBWg5we9MDY2Zv26dZnqv0hUX0dMTAxpafIW6sJKZlaLKK863uyctZHbZ69Tr3eTbOuZ2Zlz7/wtop5GYmZrni/nfvYwjAt7z3BhfyCx4TGY2phTvVMdnMq4qOuo0tNJiEnIVX86ejq5un0e/ugZJzYeoe1XXbJMvnOSlprGL10nkJKYjJ6RPmXqeNPgw+boGWr+wrkYcI7A3adBpcLSyZqaXerj3ahiln3unbeNii2qYuPy335JCSGyVrmKLy1atmDO7DkM/+RjLPN4i1dbW5tuPboxYfwEjh45SvMWzfPUj4uLCwcPHOD+vfsUdy6epz7+7fq16yxdspTly5YR8jiE4s7F+eKrL6he85+lWunp6YSHh+eqP0NDQwwNc56VfB2HDx3GxsKa1NRUnIo78dHw4Yz4bESObyELCgoCwLeKb6ZyXV1dylcoT1Dguf8UV5uWrYmNjUVXV5cGDRswddqPuHu4/6c+Rf6SZLWI0tLWwtLBmojHr75w1ehSj+3T1zN38K84ehaneBkXSvi4UrysC8rXWLOVnJBE8OGLnNt7hgeX76Kjp4N7jTKUb1wZlwqlNC5WUaFR/DHg51z1XbtHQ+r0apRjvX1/bsfJyxnPWuVyHfcLxhYmVO9UB/vSDqjSVdw6e43AXad4cjuED34chJb2P5+Fo5czXrW9Mbe3IOZZNGe3n2Db9HUkxSdqrPE9tfkocVGxuYpfCJF3Y/43ltrVa/Hb9BmMn/BDnvspU7YMALdu3spzH19+8xVDBg7Gu0w5qlWvRo2aNWjQsCG1atdCWzv3v5ZjY2PZsH4DSxcv4eiRoxgaGtKmXVt69+lN/Qb1Na6r9+/dp4yHV676Hj12NGO+G/s6w3qlcuXKUWNwTdzd3XgWHs7ypcsYPXIUjx8/Zuq0qa9sG/I4BCDLB7fs7e25dvVanmIyNDTkgz4fULdePUxNTAgMDGTWb7NoVL8hR44fxdnFOedOxFshyWoRpmuoR3LCq29pl29UCUNTQ05tOsr9S3e4f/EOR9ccwMjCmJYjOuBaxfOV7WMjYjiwZC/Bhy+QnJCMo5czLT5uj1dd7yxnJF8wtjCm+8T+uRqHuX3OsyTXT1zh1pnrfPjb8Fz1+W/1+zXL9H2ZeuWxdLTmwJK9XDl0IdP61D4/ZX66tEKTyiz8bA4Hl+6lfJPK6D6fBY4Nj+bIKn/q922qsTODECJ/VaxYkTZt2zB3zlxGfPYpVlZWeerH2NgEgNjY11pbnskHvT/A2sqKWTNnc/TIEQ4fOsxPP/6Enb0dc/6YQ/OWLV7ZPiQkhPHjvmfDug3ExsZSvUZ1Zs+ZTacunTV2PHiZnb0dW3dsy1WMJUuWfK0x5WTthsy36vv07UP7Nu2YM/t3Phr+0SsTw8TEjLtsenqad9D09PVJSMjdXbh/69S5E506d1J/36ZdWxo3aULTRk2YOmVKrncpEG+eJKtFWHJ8EroGejnWc63iiWsVT1KTU3hyO4RrRy9xassxNkxawYBZn2BVPPutPJ/dD+X83jMolErq9m5MtY510NbJ+cdOW1eHkj6urzWe7KSmpLLvrx34NPPFtqTmX+Z5VaVdLQ4u28edoBuvfJhKS0ebyq2rs+v3zTy+9gCX8hlPnfr9vQszW3MqtpCF/UK8DWP+N5ZtW7cx/ZfpTJw8MU99vEhSXyStufXvWc7mLVvQvGULEhMTuXD+PJs3bWHO7N/p0a0Hx0+dwMPTI9u+rl29xpJFS9DS0mLc+HF8+vln6OnlfC3X19enYaOGrxX3m6JQKPh4xCfs27uPAwcOZNrW69/09TPWxSYlJWscS0pMfO11s69Ss1ZNqlStgv9+/3zrU/x38oBVEZWWkkr4ozAsiuV+7Za2rg6OHsVp0L85zYa1IS01jSuHL7yyTTF3J5oOa4NdSXsOLt3HrN5T2f3HFh5de/XDVOlp6cRGxOTqK6fZ4bPbjhMbHkOFppWJfBKh/gJISkgm8kkEyYmaF8Gc6OjpYGBimKu1tSbWGW/dSYzNqBty4yGXD5yjSruaRIdFqWNKTUklPTWdyCcRxEfHv3ZMQojseXt706FjB+bPncfTp0/z1MflS5cBKFX6n62O9PT0SEhMzLJ+fHzGf8dZPUz1orxK1apMnDyR6TNnkJyczIb167Os+0Jl38pM/2063uW9GT9uPKVdSvH5iM84c/r0K9ulpaUREhKSq6/Y2Pzb7SU7TsUz9jGNCI94ZT37YhmTDI8fa25DFRISgn0+73bg5OSUY0zi7ZKZ1SLqyuGLpCanUqpy3haRO7hnPBQQ++zVT8Tq6utSuVV1KreqzpNbjzm39wyXD5zj7PYTWDvb4t2oEuUaVNDYsio6LP/WrEaFRpGalMKizzVv6Vw+cI7LB87R5ssur7XVFEBSfFLGSxLMjHKsGxmSsTbY0DSjbnRoFAA7Zm7Msv4fA36mQtPKtBzRMcvjQoi8Gf3dGDZt3MSvP//62m1TU1NZvXI1RkZG1KxVU13u4uLCtatXs2zzotzZOef1jy8eIMppb1AjIyMGDx3C4KFDOH/+PEsWLmbN6jXMnzcfLy8vevX+gO49u2tsWfXg/oMCW7OalTu3bgNgbWP9yno+Pj4AnDl9hhovPTCWnJzM+XPnad+xfb7Gdfv2baytXx2TeLskWS2CQm48ZN+f29E3MaByq+qvrHs78AYlK2rejr95OuMCbPmKJQD/ZleqGE2HtKbRh825dvwK5/acJmDxbgIW76FUJVcaD2qFpWPGBSI/16xWaFoZF2/N9VfrJy2nZCU3KrWoir2bo7r82f1QtPV01LsfpCankJaajp5h5ttsR1b5g0pFqcpu6rL4qDiN5DUpPolTW46ib2JAMfeM8zh4ONFpTC+NmA4s3UtSXCJNh7bBzM78leMSQrw+Ly8vOnXpzF/z/8xVAvlCamoqn4/4jGvXrvH1t19nWhvatHkzfp81mxPHT1CtejV1eUJCAosWLsLWzpaKlf7ZDcRvnx+NGmv+gb17524A3D2yXwLwb+XLl+fn6b8w+ccpbN2ylcULF/O/sd8x7rv/0bhJY6b9/BOubhnX8IJasxoeHo6ZmVmmFymkpKTwy8+/oKOjQ/369dTl8fHx3L93HytrK3XC6OnliZeXFwv/XsDgoYPV21etWLac2NhYOnTM2x/1oaGh2Nhk/h22a+cuAs8GMnjI4Dz1Kd4MSVbfc7fOXificXjGG6yi4nhw+Q43Tl3DwNSATiN75bhX6vpJyzGzNce1qieWDlakJKXwMPgeVw5dxNzekvKNK7+yfVa0dLTxquONVx1vokMjOb/vLBf2nSXs3lN1spqfa1ZtS9hjWyLrtarmtua41yiTqWz+sBk4lytJr6kDAYiNiGXhiNl41SuPlVPGhe322evcPH2NEhVd8axZVt32zLbjXDt+GdeqnpjZmBMbEcP5vWeICo2i9Wed1FtmGVuaapwXMnYHSE9Pz/KYECJ/jB4zmg3r1nP16lXc3TXvLoWEPGHlipUAxMfFcf36DTZv2sS9u/fo3rMH3437X6b6X33zFZs2bqRlsxb0/7A/XmW8CA0NZc2qNVy7do0Fixeio/PPdnk9unbH2dmZFq1a4upamoSEBI4fP8GGdespWbIkvftmv34zO7q6uuoHhh7cf8DSJUtYtmQZVy5fVier+b1m9d7de6xcsQKAw4ePAPD7rN8xNjbCzMxM/Sau7du2M23Kj7Tv2IESJVwIj4hgzao1XL50ie/GfYeD4z+TBadPnaZF0+YaM7uTf5xCp/YdadmsBT179eTu3bsZT+43bqyxhdiK5Su4f++fV7sGBQXx45SMHQdatm6Ft7c3AI3qNaSCTwUqVqqEmZkpQYFBLFm8BAdHB74Z9W2+fU7iv5Nk9T13eMV+IGOrKj1jfWyc7ajfpwnlm/piaJrzE+gtR3Tg+olgrh69RGx4DOmpaZjZWeDbpjo1u9ZH3yj7J/pzw9TGnNo9GlKrewNSc7GBfkHQN9KndFVP7gTe5KJfIOnpKiyKWVKvTxOqdaiNQvnP0m+nMi48CL7HuT2nSYhJQEdPh2LuTrT4pEOWM9RCiLfP3cOdbj26s2LZ8iyPX7xwgYH9B6BQKDA2NsbBwYE6devS64Ne1HtpFvAFGxsbAg4dYMrEyWzdupU/5/+JkZERlX19+Xn6LxoJ4px5f7B923a2bNrM48ePSUlJwcXFhaEfDePrb7/GzMzsP43PqbgTo8aMZuToUXl+Uj437ty5ww/fZ94G7NeffwHA2cVZnayWK1cWTy9PVq1cSVhoGLq6uniX92bxsiV07tI5V+dq2qwpa9evZdLESXz1xVeYmZvx4cABfP/D9xp1lyxazKGDh9Tfnzl9hjOnzwDg4OioTlY7denErp278NvnR3x8PPbF7OnXvx8jx4x6Y2/9EnmjkJfevJsUCkVZUxuzo8MXfvNm308qCp2NU1fGBh++OFylUi0p6FiEeJv09fXjbty5aZjXTf2F+K/89vnRv0+/U2FhYbKNy1skuwEIIYQQQohCS5JVIYQQQghRaEmyKoQQQgghCi1JVoUQQgghRKElyaoQQgghhCi0JFkVhcbd87eY0noMd8/fKuhQhBDinXHwwEGM9Aw5eOBgQYcixBsh+6wK8QaE3nnCoRV+3Lt4m5TEZEyszfCsWZb6/ZoBoEpP54J/ENeOXebJzcfER8dhZmOOWzVPanarj57hf9u/Vggh3mUrV6xkYP8B6OnpER4doXH8+rXrjPp2JIcPHUahUFCvfj2mTvuREiVLvP1gxRsnyaoQ+ezu+VusGb8Ea2dbanSuh56RHlFPI4l+Gqmuk5KUwvbp6ynm7kSFppUxtjThya3HnNx8lBunrtJv+kfo6OlkfxIhhHhPxcTEMGbUaIyMjEhNTdU4/vjRI5o0aoK+vh5jvhtLeno6s2fNomnjJhw7eRwrK6sCiFq8SZKsCpGPkhOS2PLzGkr6lKbj6F4otbJeaaOlrUXvaYNxKuOSqbyYmxPbZ6znon8gFZvLntNCiKJn0oRJmJuZU79BAzZt2Khx/OdpPxMVGcn+oLOUKl0KgOYtmlOlki8zfp3BhEkT3nbI4g2TZPU9lJyQxKEV+7l29BIx4THoGuhiXdyWWt0bqF/5+fR2CCc3H+H+xTvEPItG10CXEhVK06B/c8xszdV9nd93lu0z1tNrykCuHb/CpYAg0lJScatehubD26FSqfD7awdXj14iLSWNsvUr0GRIa7S0tdR9TGk9horNq1DCx5VDK/yIeByOpYMV9fo0wa2aV47jCX/0jEPL93E78AbJ8UlYOlpTtX0tyjepnKneme3HCdxxksiQcJRaSkxtzanQxJcq7WrmzwebC5cPnCc2PIZ6fZqi1FKSnJiMto62RtKqpaOtkagCeNQsy/YZ6wm7F/q2QhZCPBcbG8ukCZPUr0E1NjbG08uTb0eNpFHjRgBcuHCB2TNnceTQER49eoSxsTENGjZg4uRJFHcuru5r6ZKlDB00hF17d7Nt6zZWrVxFclISrdu2YebsmaSnpzPy62/ZvGkzycnJdOvejZ+n/4KOzj93VIz0DBkwcAANGjVk0g8TuXXrFqVdXfl+/DhatWmd43hu3rjJhB8m4O+3n+joaFzd3Pjk00/o07dPpnrz587jz/l/cef2bbS1tSnu7Ezffn0Z/snwfPpkcy/4SjBz5/zB2g3rWLtmbZZ1NmzYQNNmTdWJKoCHpwf1GzRg/dp1kqy+hyRZfQ/tmrOFK4cuULlVNWxc7EiMTeTx9QeE3HykTlZvB94g/EEY5Rr6YGJpSkRIOIE7T/L42gMG/j4CHX3dTH3u+3M7BmZG1O7RkJAbDzm/7yy6BrpEhoSjo6dL3Q8acyfoJoE7T2LpYEXVDrUztX8QfI8rhy9SuU119Az0Obf3NOsnraD7hH6UqFA627E8exjGkq/mYmBsSNX2tdA3NuDGqats/20DifGJVG1XC4Bze06z54+teNQsS+XW1UlPSyfs/lMeXLmbY7KaGJdIempajp+rQkuJgbHBK+vcDryBnqEe8VFxzB82g2f3Q9HW1cajVjmaDmmNfg7tY8OjATAweXU9IUT++/STT1m/dh2Dhw6hbNkyREZGcfbMGYKCgtTJ6v59+7l+7To9P+hJsWLFuHXrNn//+RenT53mVOBpDA0NM/X5zVdfY21tw+gxowgMDGTp4qUYGxlx+/ZtjIyM+G7cd/jv9+evP/+itKsrIz4bkan98eMn2LB+A0M/GoqJiSlLFi2mR7cebNm+lfoN6mc7luvXrtOwXgMsLCz4eMTHmJubs2vnLoYNHkp0VDQfj/gYgMULF/H5p5/Trn07hg4bQmpqKsFXgjl+7FiOyWpUVBQpKSk5fq5aWlpYWFjkWA/gy8+/pHGTxjRp2iTLZPXRw4c8ffIU3yq+Gsd8q1TGb98+IiIicn0+8W6QZPU9dPNkMD7NfGk8qFW2dSq1rEq1jpkTSrdqniz9ej5Xj12mXAOfTMcMTA3p/kM/FAoFAFFPIzm74yRl61eg7ZddAKjYoirzh83g3N4zGslq6J0n9P5pME5eGbOJFZpWZt7Q6fgv3EX/GdlfEPfO3YqhmRH9ZwxH93kCXallNTb9uIrDy/3waVYFXX1dbpwMxtrZlo6je+buQ3rJ+gnLuHfxdo71zGzN+WjB16+sE/4ojPS0dNaOX0K5RhWp17sJj68/5Pj6Q0Q9ieCDHwepP8OsHFt7AIVSgVdt79cehxDiv9m1Yyf9P+zPtJ+nZVtn0JBBfPr5p5nKWrVuRaP6Ddm8aTM9evbIdMzKypot27eo/7u/e/cef87/k249uvP3wr8BGDh4EJXKV2TJ4iUayeqlixfxC9hP9RrVAejbvy8+3j6MHT2Gw8eOZBvnl59/ibW1NYePH8HIyOh57IPp06s3kyZMpP+A/hgZGbFzx068ypRhxeqVufyU/tGtc1cOHTyUYz1nF2euXAvOsd6G9Rs4euQIp86ezrZOSEgIAHb29hrH7J+XPX78WJLV94wkq+8hPWMDHl17QExYFCbWZlnWeXnmNDkhidSUNCwdrNE30ifkxkONZLV8k8qZkiwHdyfunr9FhZduxSsUChw9ihN8+KLG+ezdHNWJKoC+sQHlGlTk5MbDxEbEYGxhotEmITaB20E3qdW9PqlJKaQm/fMXfKnK7lw5dIGQ6w9x9i6JnrEBMc+ieXT1Pg4exTX6epWGA1uQGJuQYz0d3ZwfeEpOTCYlKQWfZr40/6gdkHFrX9dAlwNL9nI78AalKrll2fa831ku+AVSrWMdrIrbvNYYhBD/nZmZGadPn+bRw4c4ODpmWeflmdPY2FiSkpJwdXPF3NycwLOBGslqn359Ml07q1SpwsGAA/Tt11ddplAoqFKtKhvXb9A4X6XKldSJKoC5uTk9evZg5ozfCAkJUSdoL4uIiGC/nx8jR48kISGBhIR/rm9NmjVl/br1nD1zljp162Bmbsajhw85feoUvlWq5OJT+seUH6cQERGZYz0Dg5zvFMXHxzPq25EMG/4Rrm6u2dZLSEgEQE9PT+OYnn7GLiqJCTlfz8W7RZLV91DDD5uz9dd1zO7/E/alilGqshtl6/tg7WyrrpMQm0DAot0EH7lIYkzm/7CT4hI1+jSzMc/0vZ5RxkXB1MbsX+UGpCSlkJqSirbOPz9elg6aT2daOmaURT2JyDJZjXgYBioVR1b6c2Slf5ZjjYuKBaBG57rcPXeLxV/OxdzOghIVXfGsXY6SPtlf9F4o5pr1L6W8eJHQlv1Xsl+2fgUOLNnLg8t3s0xWb565xs5ZmyhdxYP6fZvkWzxCiNyb/OMUBvYfgHtpdyr4VKBJ0yZ069EdL69/1tZHRETwvzHfsWnjJsLDwzO1j46O0uizePHMfzybmpkC4OTklKnczMyM+Ph4kpKSMiVipV01r2Fuz5O5e3fvZZms3rxxA5VKxZRJU5gyaUqWYw0NzVgX/+VXX3Eg4AD1atejRIkSNGzUkA6dOtKwUcMs272sYqVKOdbJrWlTfyQ5OZmRo0e+sp6BQcbvnqSkJI1jSYkZv7v0c5Eci3eLJKvvIc9a5ShepgTXTlzmTuBNzmw7zvH1h2jxcXv1Q0mbpq7kwZV7VG1fC/vSDuga6IECNk9bjUql0uhTocz61rVCmc17JbLo43W9iKNKu1qUruKeZR1bl4wLtZWTDYP/+JSbZ65x++x1bp25RtCuU5RvUplWn3Z85XkSYuJJy8WaVaVSiaGZ0SvrGFuaEHr3CUbmxpnKX3yf1Qzug8t32Th5BcXcHOnwbXeUWloadYQQb177Du2pUbMG27duY7/ffub9MY/pv0xn1pzZ6oeSevfszfFjxxjx2Qgq+FTA2NgEhUJBv959SU/XvO5pZfPfc3blWV1/X9eLOD4e8QnNmjfLsk7ZcmUBcPdw5+y5QPbs3sO+PXvZs2cPC/5eQJ9+ffhj3txXnic8PJzk5OQc49HS0sLGJvu7RY8fP2bmjJl8/e3XRIRHEBGesa9qXGwsKpWKu3fuom+gj52dnTo5D3n8WKOfF0sEihUrlmNM4t0iyep7ysjCmIrNq1KxeVWS4hNZ+s18Di73o3yTyiTEJnAn6Ca1ezakTs9G6japySm5uh2eF+GPnmmWPcwoM7PLem2RRbGMmVelljJXM6Q6+rp41iqHZ61ypKels3P2Js7vPUPNrvXUfWVlw6QV+bZm1d7VkduBN4gJi8bK6Z+Lc3RYxoNT/052Q24+Ys34JVgUs6LruD4aD7YJId4uOzs7Phw4gA8HDiA6OprGDRoz8YcJ9Onbh4iICPz372fMd2MYPXaMuk1iYiIREZob1+eHmzduaJRdv55R5uzinGWbkqVKAqCtrZ2rGVJDQ0Pad2hP+w7tSUtL4+OPhrNk0RK+/uabTE/c/1vPbj3yZc3q06dPSUpKYuIPE5n4w0SN42U8vKjfoAHbd23HwdERG1tbzpw+o1Hv1MnTuLi4yHrV95Akq++Z9LR0khOT0Tf65w1Ieob6WNhbcufcTQCUL2ZJ//UH/MlNR1FlMTOQH0KuP+TBlXs4eWVcXBNjE7joH4h9aYcslwBARmJXokJpgnafwrdNdUz/tRQhPipOnfzFR8djaPrPWjKllhLbEhl/gSdmsazhZfm5ZtWrjjfH1h0kaPcpSvj8s8vBud2nACj50hKAZw/DWP2/RRiaGtFtQr8cdwoQQrw5aWlpxMbGYmb2z9ImU1NTSpUqif/+O8A/s6H/nv2cNXMW6enpbySus2fOcuL4CapVrwZAZGQkK1esxKeiT5ZLAABsbGyo36ABC/9ewLCPhuFUPPOSg9DQUPVM57NnzzJtoq+lpUW5chkPeEZFRb4ytvxas1qiRAlWrVmlUT5nzh8cO3KUpcuXYmP7zzK2Dh3as2jhIm7dvKVOpq8GX+VAQACffDpCox/x7pNk9T2TnJDE7L4/4lGzLLYli6FnpMfD4PtcO36FCk0ztvrQM9TH2bskxzccIi01DTNbc+5fvsv9i7cxMDXM4Qx5Y+Nix9rxS6jcpjr6hvqc23uGhOh42n7V9ZXtmn7UlqVfz+Pvj2dRoVkVLB2siI+O58nNR9w6e50v1/4PgNXfLcTQ3AgnLxeMLU2IDAnnzLbjWDnZYFcy6wv6C/m5ZtWuVDEqNvMlcNcp0lLTKFnRlcfXH3J+7xnK1CuP4/OHv5Lik1g1diHx0fH4tqnBnaCbmfoxt7dUJ/ZCiDcvJiYGt5KutOvQHm9vb8zMTDlx4gRbt2yl34f9gIzktW69ukz/ZTrJyck4Oztz9OgxDh869MbemlSmbFk6te/I0I+GYmpqxpJFi3kWFsaCRQte2W7GzBk0btCIar5V6fdhf1xdSxP27BnnAoPYt3cfIWFPAGjbqg02NjbUqFkDe3t7bt++zbw/5uHh4YF3+fKvPEd+rVk1MzOjTbu2GuVbt27jhFKpcezrb79mw4aNtGzeguEff4xKpWLWzJnY2dvx2Ref5UtMonCRZPU9o6OnQ6VW1bkTdIPrJ4NJT03DzM6C+v2aUfWl/Ubbfd2VvfO3E7jrJOmp6RQvV4IekwawauyrL4B55eTlTAkfVw4u30fk43AsHKzoOKpnjrf3rRyt6T9jOEdW7edSQJB6BtW6uC0NBzRX1/NpUYXLAec5vfUYSXGJGFuZUraBD7W6N3jra0CbDmuDqa0553af5sapq5hYmlCrRwNqdWugrpMQE090aCQAB5ft0+jDu1FFSVaFeIsMDQ0ZPHQI+/382LFtOykpKbi4uPDDpAl8MuITdb0FixfyzZdfs+CvBaSkpFCrdi127NpJ65bZbxX4X9SoUZ0GjRoycfyEjJcClC7NilUrcry97+buxuFjR5gyeTKrV60iLDQMK2srPD29mDx1srregIEDWLN6DXN+/4PoqCiKFStG9x7d+Xb0SLS1C2eK4ODoyF6/vYz6diQTf5iAQqGgXv16TPlx6ivXxop3lyI/FnOLt0+hUJQ1tTE7OnzhN6YFHUtOXrzBqvnH7Qs6lPfCxqkrY4MPXxyuUqmWFHQsQrxN+vr6cTfu3DS0tLQs6FDeihdvsJr5+6yCDkU857fPj/59+p0KCwuT92G/Rdk8yi2EEEIIIUTBk2RVCCGEEEIUWpKsCiGEEEKIQqtwrp4W75VR2yYVdAhCCPHOiUuKL+gQhCgUZGZVCCGEEEIUWpKsCiGEEEKIQkuSVSGEEEIIUWjJmlUBZLyu9NSmw1w/EUzkkwhU6emY21tSqrI7vm2qY2ar+a7lo2sCOLBkL46ezvT5eYi6fM6HPxH1NDLHc9bu0ZA6vRq9sr6lkzVD5n6e12EJIcQb8+zZM2b9NpPt23Zw984d0tLSKFmyJE2aNWXYR8NwdtF8scdPP07j+/99T7Xq1dh/wF9d7uXuyb2793I85+ixoxnz3dhX1nd3dyfwQlCexyVEYSPJquDJrcesHreIxJgEvOqWp2LLqii1lITefcpF/0Au7g/k0+WjNdpd9A/C3M6Ch8H3iAgJx8I+Y6PuxoNakZyYrK5342QwVw5doNGglhiaGqnLbUvYZ/p3tU51NM6hZ6iXn0MVQoh8cf78edq3aUdEeASdu3Zh0OCBaGtrc/nSZVauWMmK5Su4++CuRrtVK1ZRokQJThw/we1btylZqiQA036aRmxcnLrerh07Wbd2HT/+NA0r639e5VrOu9xL//bO8vWiZqaF/l0xQrwWSVaLuKT4RNZNWEZ6Wjr9pn+EbUn7TMfr92nC4ZX7Ndo9vvGQZ/dD6Tq+L5t/XMUl/yBq98h4/Z97jTKZ6saERXHl0AXcq5fB3E5zhhbAyNKEcg188mdQQgjxBkVHR9O1UxfSUtM4ePQQ3t7emY5/P2E8kydO1mgXePYswcHBbNyyib4f9GHVypWMGpMxEdCmXdtMdR8+fMi6teto07YNLiVcsozD3t6OHj175NOohCi8ZM1qERe06xTRoZE0HNBCI1EF0DXQo+GHLTTKL/kHYWxpQqmKrrjXLMulgHNvI9wsTWk9hl2zN3Ht2GX+Gj6TnzqOY+Fnv/P4+gMALuwPZN7Q6UzrMI7FX84l7N5TjT7CHz1j80+rmdFzEtPa/4+/hs/k/N4zmeqkpaRycPk+Fn4+h+ndJvBTx3Es+nwO145dzjamG6eC+fuTWUzrMI4/Bv1SoJ+TECJ/LPhrAffv3WfS1MkaiSqAsbExk6dqJqsrV6yimEMxGjdpTNv2bVm9cvXbCDdLRnqGjBj+CVs3b6FKpSpYmVlSu0Ytzp7JuO6tWL6Cit4+WJpaUL9OPa5cuaLRx80bN+nXpx8ujs5YmJhTpVIVlizO/Bbo5ORkJv4wgTo1a+No54CVmSV1a9Vh6+Yt2ca0a8dOqlephqWpBd5e5Vi9quA+J1E4SLJaxF07fgUtHW3K1C2f6zbpaelcPnAerzrlUSiVlKlbnvCHYTy8ej/PcaSnphEfFafx9fJygld5GHyfPfO2UaZeBer0akzkkwjWjF/CuT2nObLKH5+mvtTsWo+nd0LYPC3zhe/ZwzAWf/kHj689pGr7WjQe3ApTW3O2/7aBk5uPqOslxScRtPMUTl4u1O3ThHq9m6BSqVg/aTk3T1/VjOnqfXb8thH36mVo+GFzdPV12frrWsLuaybLQoh3x7atW9HT06NL1y65bpOWlsbaNWvo1LkzSqWSLl27cv36dU6dPJnnOFJSUgkLC9P4intpOcGrnDhxki+/+JKu3bsy9n/fcffOXTq278TiRYv5cfJU+vbvxzcjv+HihYv0690vU9vr165Tv049zp4+w8cjPmbaz9Nwdi7OsMFDmT1ztrpedHQ0f//1N9Vr1GDc+HGMG/896enpdO/and27dmvEdPLkSYYNHUabtm2YPHUyRsZGDOw/gOArwXn+nMS7T5YBFHFh955i5WiNtk7ufxRuB90gLjKWMvUyEtwSFUpjZG7MJf8gHD2K5ymOu+dv8VsvzZmISq2q0WxY2yxaZBb2IJTBf3yKRbGMtV1G5sZsm76O/Qt2MWT+FxiaGgKg1NLiwJI9PL7+gGJuTgDsnbsVQzMj+s8Yjq6+bsZ5W1Zj04+rOLzcD59mVdDV10Xf2ICPFn6d6bOq3Lo6Cz79nZMbj1Da1yNzTPeeMmD2J1gXtwXAq7Y3v/efxvm9Z7KcrRZCvBuCrwTj5u6Onl7u19T77fPj6ZOndO2WkeA2aNgAWztbVq1YRZWqVfMUx4GAAFwcNR/iGjJ0CL/+Nj3H9leDgzl7LpBSpUsBYGtny+ABgxgzcjTnLp3Hyirjeqqlrc33343j7JkzVKpcGYAvP/8Sa2trDh8/gpFRxrMIg4YMpk+v3kyaMJH+A/pjZGSEhYUFwdevZvqshn40lJrVajJzxkyaNW+WKaYrl69w4vRJPL08AejQqSOerh4sWbwky9lqUTRIslrEJcUnofuaDzFd8g/CvJglDu4ZyZ5SS4lnHW+uHDxP40EtUWppvXYc9m6O1O/bVKPc1NosV+1dvEuqE1UAB4+M2NyqeaoTVQBHz4xkOjIkgmJuTiTEJnA76Ca1utcnNSmF1KQUdd1Sld25cugCIdcf4uxdEqWWEqVWxs2ItJRUkhOTUaWrKF6uBFcOnNeIybl8KXWiCmBkYYyVkw2RIRG5GpMQonCKjo7Gy9TktdqsWrmKUqVKUdnXFwAtLS06derE2rXr+PHnaWhrv/6v40qVKzF+wg8a5U5OTrlqX7dePXWiClClShUAWrZupU5UAapVqwbA7dt3qFS5MhEREez382Pk6JEkJCSQkJCgrtukWVPWr1vP2TNnqVO3DlpaWmg9/52QnJxMbGws6enp1K5di7Vr1mYZ04tEFcDOzg53Dw/u3L6dqzGJ95Mkq0WcnqEeyQlJua6fnJjMteNXKFuvPOGPnqnLHT2Kc2brMW6euY5bVc9X9JA1AxNDSvq4vna7F0xtzDN9r2ekD4DJv5JdfeOM8sTYjItrxMMwUKk4stKfIyv9yUpcVKz630G7T3Fq81HC7oeCSvVPJYVCo53Zv2LKOL8BCTHyCkUh3mWmpqbExMTkun5cXBzbtmyla7eu3LxxU11epVpV/pjzB3t376FFq5avHYelpSUNGzV87XYvFHfOnNSammXsIuDk5Jip3Mw84zoaGZHxh/bNGzdQqVRMmTSFKZOmZNl3aGio+t+LFixk9szZBAcHo3rpuqnI4rrp7KJ5d87CwpyICPkjvyiTZLWIsypuw5Obj0hNSc3VUoCrRy+RkphM0O7TBO0+rXH8kn9QnpLV/0qp1LzoZZRnvSz7xQXzxf9WaVeL0lXcs6xr65Lx4NlF/yB2ztqEa1VPqneqi6G5EUotJef3nuXyAc0Hp7KLCVXWxUKId4OHpwdBgUEkJSXlainA5k2biYuLY+GChSxcsFDj+KqVq/KUrP5XWsqs74JpZXN37MX1Mj09438/HvGJxm38F8qWKwtkjG34sOG0bNWSz7/6AhsbG7S1tVm6ZClrsnhwKruYXk5yRdEjyWoR517Ni4dX7nHl0AW8G1bMsf4l/yAsilllecv+6rHLXDt+maT4pHdmf9QXSweUWsocZ3aDj1zE3N6Szt99kGlG4Pzes280RiFE4dKqdWuOHzvO+nXr6dmrZ471V61YRenSpRk/UfOW/ZbNW9i6eQsxMTGYmLze0oKC8mJvWG1t7Rxndjeu30DJkiVZs35tpuvm0iVL32iM4v0iuwEUcT4tqmJibcb+v3cSeveJxvHkxGT2L9gFQGx4NHfO3cKzVlk8a5fT+PJtU53UpBSuHrn4toeRZ4ZmRpSoUJqg3RlbeP1bfNQ/T9WqZ2lf+gs/IiSca8c1t64SQry/BgwagKOTI6O+HcXly5r//cfFxTFm1BgAHj9+TIC/P+07dqBDFl/DPhpGQkICGzdsfNvDyDMbGxvqN2jAwr8X8OD+A43jLy8BeDFL+/LM6O1bt7PcukqI7MjMahGnb6RP57G9WP39YhZ+Nocy9crj4O6EUqkk7P5TLh88j0qlouGHzbl84Dyq9HRcq3ll2ZeDR3GMzI256B9E+SaVXyuOuPAYLvoHaZQrFArK1q+Ql6HlWtOP2rL063n8/fEsKjSrgqWDFfHR8Ty5+YhbZ6/z5dr/AeBazZOrRy+xdsIy3Kp5EvMsmrPbT2DlaM2TW4/faIxCiMLDzMyM1evW0KFtB2pXr0WXbl3w9fVFW1ubK1eCWbd2LenpKiZNmcTa1WtJS0ujVetWWfZVpWoV9a4Affr2ea04QkKesHLFSo1ypVJJt+7d8jS23JoxcwaNGzSimm9V+n3YH1fX0oQ9e8a5wCD27d1HSFjG5EfL1q3YvGkzXTp2plXrVjx69Ig/5/2Jm7sb589pPpgqRFYkWRXYuzoy6PdPObnxMNefvxoVlQpze0vKNaiIb9saAFwMCMLQ3AhHj6yfNFUoFLhW9eT8vjPEhEVpPNz0Kk/vhLD1F80nQxVK5RtPVq0crek/YzhHVu3nUkAQ8dHxGJoaYl3cloYDmqvrlW9UifjIOAJ3nOBO0E0silnSeFArIh49k2RViCKmYsWKnDp7ilm/zWLHtu2sX7selUpFyZIl6dGzJx8NHwbAqpUrsbG1pUrVKln2o1AoaNmqJUsWLeHRw4c4ODpmWS8rFy9cYGD/ARrlWlpabzxZdXN34/CxI0yZPJnVq1YRFhqGlbUVnp5embaY+qD3B4Q+DeWv+X/iv9+fUqVLM/WnH7l186YkqyLXFLJo+d2kUCjKmtqYHR2+8Bt5CXQRs3HqytjgwxeHq1SqJTnXFuL9oa+vH3fjzk1DS0vLgg5FFFF++/zo36ffqbCwsLxtjivyRNasCiGEEEKIQkuSVSGEEEIIUWhJsiqEEEIIIQotSVaFEEIIIUShJcmqEEIIIYQotCRZFUIIIYQQhZYkq0IIIYQQotCSlwKIN2rb9HVc8AvM9njvaYNxKuOS5bHIJxEcWLKHx9ceEhsRg1JLiaWDFZVb16BcQ59M75l+4fqJK5zYcJgntx4BCiwdrKjaoXamFwtcPniem6eu8uj6A8IfPsPS0Yohcz//z2MVQoj8kpyczJRJk1m+bDlhoWG4e7jz1Tdf07lL51e2u3vnLmU8sn7L4F8L/6ZHzx4a5du3buO3Gb9xLugcCoWC0q6lGfHZp+oXCxw8cJAWTZtrtHuh34f9+P2POa8xOiFejySr4o3yaV6VEj6uGuV+f+8gPS2dYm7Zv60lNjyGuIhYvOp6Y2pjRlpKGrcDb7Bt+jrC7j+lQb9mmeofX3cQ/0W7ca9Rhgb9My6szx6EER0Wlale4I6TPL7xEHtXB5LiEvNhlEIIkb+GDRnGmlWrGTJsKGXLlmHzps30/SDjdaw5Jawv6jRv2SJTWY0aNTTq/frzr3w3Zixt27Vl4uSJAFy7dp0HDx6o63h4evDXwr812m7bspVNGzfRuEmT1xqbEK9LklXxRjl5OePk5ZypLOz+U+Ij46jYvApaOtn/CDp5OdNzcuZXCfq2qcHa8Us4vfUYdT9ojJa2FgCPbzwkYMkeGvRvTvVOdV4ZU5svO2NsaYpSS8nykX8RGxmTx9EJIUT+Czx7llUrVvK/7//Ht6NGAtDvw/40bdSEUd+OpH2H9mhrv/rXt3eF8lnOov77POO++x8TJ0/i8y+zv7tkZ2eXZV9z5/yBmZkZLVu1zMWohMg7WbMq3rpL/kEAlG3gk6f2prbmpCalkJaSqi47ufEIhmZGVG1fC4Ck+KTs29uYo9SSH30hROG0ft0GlEolg4YMVpcpFAoGDRnMo4ePOHb0WK76iY+PJykp+2vhzN9mYWNrw4jPRgAQE5P7P9xvXL/B6VOnad+xPXp6erluJ0ReyG9s8VapVCouHTiPuZ1FtmtV/y0lMZn4qDgiQ8I5v/cMF/adxdHTGV2Dfy6Qd4JuUMzNibM7TvBbr8n82vUHZvScxOGV+1GpVG9qOEIIke+CAoMoVaoUlpaWmcp9fX3Vx3Py09Rp2FhYY2VmSa3qNdm9a7dGnf1++6lcuTJ/zpuPi5ML9tZ2uDg6M2XS5Byvm6tWrgSge49Xz94KkR9kGYB4qx5cvkvUkwhqdquf5QNSWTm69gBHVweovy/hU5pWn3ZUf58Ym0B8VBwPg+9x99xNanVvgIWDFcGHL3JouR9pKanU69M0v4cihBBvREhICPbF7DXKX5Q9fvw427ZKpZJGjRvTtl0bHBwcuH37DrNmzqRzh06sXL2S1m3bABAZGUlYaCgnT5wkwD+Ab0ePxNXVlQ3r1jPxh4kkJSXx/Q/jsz3PmlVrKO5cnDp1X73sSoj8IMmqeKteLAEo9xpLAMo3roSzd0niImK5fvwKibEJpCb/swQgOTEZgIToeNp+1VX95L9nrXIkxCZwctMRqneui56hfr6NQwgh3pTEhAR0dTVvrevr66uPZ6e4c3G2bN+SqaxHrx5U9qnEqG9HqZPVuNhYAMLCwliweKH6yf/2HdoTERHJrN9m8cVXX2JqaqpxjpMnTnLz5k2+/PqrXE86CPFfyDIA8dakpaRy5chF7N0csXKyyXU7i2JWlPRxpVwDHzqM6oGhuRGr/reI1OdrVrV1dQBQainxquOdqW3Z+hVITU7l8fWH+TcQIYR4g/QNDEhO1lxrmpiYqD7+OiwtLendpw+3bt3i7p27mfrQ1tbW2F2gW49uJCYmcvbM2Sz7W7Xi+RKAnt1fKw4h8kqSVfHW3Dx9jcSYBMrV9/lP/XjWKkfUkwgeXrkHgIGxPtq62hiYGmo8OGVkbgxkLBUQQoh3gb29PY8fad7qD3kcAkCxYsVeu08np4xtAsMjwgGwsLBAX18fK2srtLS0MtW1tbUFMpYK/Ftqairr162nfIXylClT5rXjECIvJFkVb82lgHMotZSUqVf+P/WTkpQCQHJCxsyDQqnErpQD8VHxmXYIAIh5vseqoZnRfzqnEEK8LT4Vfbh9+zbh4eGZyk+dOqU+/rpu374DgI11xl0tpVJJBZ8KhIWGkZycnKnuo4cZd6Ksra01+tm3Zy9hYWH06NnztWMQIq8kWRVvRWJcIjdOBVPCx1U92/my+Kg4nt0PJSXxn4tmXGSsRj1VejoX9p1FoVRgV+qf2YUy9cqjSk/n3N4z6rL0tHTO7z2LvrEBxdyc8nlEQgjxZnTo2J709HT+nDdfXaZSqfj7z78o5lCMGjUzNvcPCwvjavBV4uPj1fVCQ0M1+nv08CFLFy/By8sLp+L/XAu7dO1KWloaixcuUpelpaWxZPESLCwsqOxbWaOvlStXoaWlRZduXfJjqELkijxgJd6Kq0cukpqcmu2DVWe2Hefwyv30nDwAl/KlAPBftJuIR89wqVAKU2sz4qPiuXLoPE9vh1CtYx1MbczV7X2a+XJuz2n2zttG+MMwLIpZcfXoJR4G36P58Hbo6Omo6967eJv7F+8AEBUaSXJCEkdW+QNgW9Iet2pZv6pQCCHehsq+vnTt3o1JEyYRFvZM/QarI4ePsGDxQvULAeb9MZfJEyezc88u6tarC8DY0WO4fes29RvUp1ixYty9e5cFfy0gNjaWn379OdN5+g/oz+JFi/jqi6+4fv0GpUuXYtOmzZw4foKZs2di8K+1sTExMezYtp169evnaSmCEHklyap4Ky4FnEPXQBf36rlPBD1qlOHsjhME7TpFQnQ8Ono62JYsRpsvu2gkvdq6OvSYNICARbu5FHCOpLhErIrb0PbrrpStVyFT3bvnbnF45f5MZQeX7QPAu1FFSVaFEAVu7vy5uLg4s3zZcv7+8y9c3dxYuGQRXbt1fWW7Ro0b8/effzFv7nwiIyIwNzenZq2afDPyGypVzjxTqq+vz/ZdO/jf2O9YvWo10VFReHh6ZHueLZu3EB8fLw9WibdOIRumv5sUCkVZUxuzo8MXfqO5r4h4r22cujI2+PDF4SqVaklBxyLE26Svrx93485Nw39vli/E2+K3z4/+ffqdCgsLq1rQsRQlsmZVCCGEEEIUWpKsCiGEEEKIQkuSVSGEEEIIUWhJsiqEEEIIIQotSVaFEEIIIUShJcmqEEIIIYQotCRZFeI/OL/vLFNajyHySURBhyKEEO+MpUuWYqRnyN07dws6FPEOkJcCiNcSHx3PqU2HuX4imMgnEajS0zG3t6RUZXd821THzNZCo83RNQEcWLIXR09n+vw8RF0+58OfiHoameM5a/doSJ1ejV5Z39LJmiFzP8/rsABITkjixIbDOHuXVL9FSwgh8sOzZ8+Y9dtMtm/bwd07d0hLS6NkyZI0adaUYR8Nw9nFWaPNTz9O4/v/fU+16tXYf8BfXe7l7sm9u/dyPOfosaMZ893YV9Z3d3cn8EJQnscFEBsby2/TZ1Cnbl31m7SEyE+SrIpce3LrMavHLSIxJgGvuuWp2LIqSi0loXefctE/kIv7A/l0+WiNdhf9gzC3s+Bh8D0iQsKxsM/Y0LvxoFYkJyar6904GcyVQxdoNKglhqZG6nLbEvaZ/l2tUx2Nc+gZ6v3n8SUnJnN45X5q01CSVSFEvjl//jzt27QjIjyCzl27MGjwQLS1tbl86TIrV6xkxfIV3H2gOcO4asUqSpQowYnjJ7h96zYlS5UEYNpP04iNi1PX27VjJ+vWruPHn6ZhZW2lLi/nXe6lf3vz2RefaZzDzPS/v1cmNjaWyRMnM3oskqyKN0KSVZErSfGJrJuwjPS0dPpN/wjbkvaZjtfv00TjFaYAj2885Nn9ULqO78vmH1dxyT+I2j0aAuBeo0ymujFhUVw5dAH36mUwt9OcoQUwsjTReNWqEEIUVtHR0XTt1IW01DQOHj2Et7d3puPfTxjP5ImTNdoFnj1LcHAwG7dsou8HfVi1ciWjxmRMBrRp1zZT3YcPH7Ju7TratG2DSwmXLOOwt7ejR88e+TQqId4uWbMqciVo1ymiQyNpOKCFRqIKoGugR8MPW2iUX/IPwtjShFIVXXGvWZZLAefeRrhZuhN0k2Uj/2R694n81Ol7/hjwM9t/2wBA5JMIZvWeCsDhlfuZ0noMU1qPYdv0der2D4PvseSreUzrMI7f+0/j2NoDIK8rFkK8woK/FnD/3n0mTZ2skagCGBsbM3mqZrK6csUqijkUo3GTxrRt35bVK1e/jXCz5L/fn2aNm+Jk74i1uRVlPcowbMhQAO7euUtpl4w7UZMnTsZIzxAjPUMGDxysbn/yxEka1muApakFHq7u/PzTz8ir3sXrkJlVkSvXjl9BS0ebMnXL57pNelo6lw+cp0y9CiiUSsrULc+FfWd5ePU+jh7F8xRHemoa8VFxGuXaejro6utm2y7s3lPWjF+CjbMttXo0QFdPl8gnEVw7dhkAQzMjmg5tw565W3GvUQaPmmUBMH++ZCHs3lNWjl2InqEetbrVR0tbi8Ddp155TiGE2LZ1K3p6enTp2iXXbdLS0li7Zg1du3VDqVTSpWtXli1ZxqmTJ6lSNW+vpE9JSSUsLEyj3MDAACMjoyxaZLhy5Qqd2nfEq4wXI0ePwsjIkDt37rB181YArG2s+XXGr3zx2Re0bdeWtu3bAVCqVCl1+9YtWmFiasK3o75FR0eXhX8vwMg4+3MK8W+SrIpcCbv3FCtHa7R1cv8jczvoBnGRsZSpl5HglqhQGiNzYy75B+U5Wb17/ha/9dKchajUqhrNhrXNosXzWAJvkJaSSrcf+mFo9s9Fsn7fpgDo6uviUasse+ZuxbaEvcZSg4PL9pGWmsYHPw7ColjGmrDyTSozd/CveRqHEKJoCL4SjJu7O3p6uV9X77fPj6dPntK1W0aC26BhA2ztbFm1YlWek9UDAQG4OGo+xDVk6BB+/W16tu3279tPUlISm7ZuxsbGRl0+fsIPABgZGdGuQ3u++OwLynmX01hqMOH7H0hOTmav3z5Klc5IYHv37U2Fsrmf+BBCklWRK0nxSei+5kNMl/yDMC9miYO7EwBKLSWedby5cvA8jQe1RKml9dpx2Ls5qhPMl5lam72ynZ6xPgDXjl+mQpPKKJS5XwGTnpbOrTPXcKvqqU5UIWM2tmz9CpzdfiLXfQkhipbo6Gi8TE1eq82qlasoVaoUlX19AdDS0qJTp06sXbuOH3+ehrb26//qrlS5kjrBfJmTk9Mr25mZZ1xbt2/dRp9+fVG+xrUzLS2NvXv20rJVS3WiCmBjY0O37t2YP29+rvsSRZskqyJX9Az1SE5IynX95MRkrh2/Qtl65Ql/9Exd7uhRnDNbj3HzzHXcqnq+dhwGJoaU9HF97XZl6nhzfu8Zds7ahP+i3ZQoXxrXap6UqeONVg6zxfHRcaQkpWDpaK1xLKsyIYR4wdTUlJiYmFzXj4uLY9uWrXTt1pWbN26qy6tUq8ofc/5g7+49tGjV8rXjsLS0pGGjhq/drnOXzixdvIThw4bz3ZjvqFe/Hi1bt6Jzl87o6r56GVRoaCjx8fG4ubtpHHNzd3/tWETRJcmqyBWr4jY8ufmI1JTUXC0FuHr0EimJyQTtPk3Q7tMaxy/5B+UpWc0rbV0dek0ewP1Ld7l5+iq3A2+w7dd1nNxwmN4/DUbX4L9vfSWEEP/m4elBUGAQSUlJuVoKsHnTZuLi4li4YCELFyzUOL5q5ao8Jat5pa+vz849uzhy+Ai7d+3Gb98+Bn04kN+m/4ZfgB/GxsZvLRZRdEmyKnLFvZoXD6/c48qhC3g3rJhj/Uv+QVgUs8rylv3VY5e5dvwySfFJ+bI/am4plEqcvUvi7F2SBv3h/N4zbP9tA1cOXaBCU18UKLJsZ2hqhI6eDuEPNR9OyKpMCCFeaNW6NcePHWf9uvX07NUzx/qrVqyidOnSjJ+oect+y+YtbN28hZiYGExMXm9pwX+hVCqpU7cOderWYeLkiSxZvIRhg4eyfu06+vbvh0KR9bXTxsYGQ0NDrl+7rnHs+rVrbzps8R6RratErvi0qIqJtRn7/95J6N0nGseTE5PZv2AXALHh0dw5dwvPWmXxrF1O48u3TXVSk1K4euTiW4s/Pjpeo8yutAMAibGJAOjo6zz/PiFTPaWWkpKV3Lh+MpiIx/8saYiPiivQrbiEEIXfgEEDcHRyZNS3o7h8+bLG8bi4OMaMGgPA48ePCfD3p33HDnTI4mvYR8NISEhg44aNby3+Z8+eaZT5VPQBIDIyCkC9m0BERGSmelpaWjRu0pgd23dw6+YtdXloaCirVxXcVlzi3SMzqyJX9I306Ty2F6u/X8zCz+ZQpl55HNydUCqVhN1/yuWD51GpVDT8sDmXD5xHlZ6OazWvLPty8CiOkbkxF/2DKN+k8mvFERcew0X/II1yhUJB2foVsm13ZJU/9y7cwrWKB2a2FiTGJRK06xTaejq4VctYjqBroIelkzWXD53H0tEaAxNDzOwtcPQoTt1ejbl99jrLvv2TSq2qoaWVsXWVma05T2+HvNYYhBBFh5mZGavXraFD2w7Url6LLt264Ovri7a2NleuBLNu7VrS01VMmjKJtavXkpaWRqvWrbLsq0rVKupdAfr07fNacYSEPGHlipUa5Uqlkm7du2XbburkqRw6eJDmLZrj4uJCZGQUC/9egIGBgTpOY2Nj3N3dWbd2HW5urlhaWVGihAtVqlZl7Ljv2Ld3H00aNWbI0CFoa+uw8O8FOLs4c+H8hdcagyi6JFkVuWbv6sig3z/l5MbDXH/+alRUKsztLSnXoCK+bWsAcDEgCENzIxw9sn7KVKFQ4FrVk/P7zhATFoVJDk/yv+zpnRC2/rJWs0+l8pXJqnt1L6JDI7ngF0h8VBz6JgY4eBSn/bfdMj0k1erTTuz7czt+f+8kLSUV70YVcfQojk0JO7pP7I/fXzs5sioAI3MjKrWshpG5sfrFAkIIkZWKFSty6uwpZv02ix3btrN+7XpUKhUlS5akR8+efDR8GACrVq7ExtaWKlWrZNmPQqGgZauWLFm0hEcPH+Lg6JjrGC5euMDA/gM0yrW0tF6ZrLZu05oH9++zfNlywkLDsLC0oEqVKixevgRXt38edp375zy++eprRn07iqSkJHr1/oAqVatStmxZtu7YxqhvRzJ18lRsbG0YNGQwtra2DBs8NNfxi6JNIW+ReDcpFIqypjZmR4cv/Oa/v9hZvFM2Tl0ZG3z44nCVSrWkoGMR4m3S19ePu3HnpqGlpWVBhyKKKL99fvTv0+9UWFhY3ja8FXkia1aFEEIIIUShJcmqEEIIIYQotCRZFUIIIYQQhZYkq0IIIYQQotCSZFUIIYQQQhRakqwKIYQQQohCS5JVIYQQQghRaMlLAcQb9ejaAy76B3H33E2inkaiZ6iHvasjdXo1wv75605zEvU0kkMr/Lh15hqJsYkYW5rg7F2S1p91Utc5tNyPwyv3a7RVKJWM3DIhU9mcD38i6mmkRt0KTSvTckTH1xugEEK8AbGxscz4dTpnTp/h7JmzhIWFMX7iD3z19Ve5an/71m2+/9849vvtJz4+njJly/DNt9/Qpl1bjbohISGM+nYUe3fvITk5mSpVqzL5x8lUqJD5RSupqan88tPPLF+6nPv372NrZ0vXbt0YPXY0BgYG+TJuIbIiyap4o46vO8j9y3fwrFUOu5LFSIhNIHDnSRZ/8QfdxvejhE/pV7YPu/eUZSP/xMDUEN82NTEyNyLmWTQPg+9nWb/p0DboGemrv1coFFnWsy1hT7VOdTKVWRSTjcaFEIXDs7BnTJk0BUcnR8pXqMB+P79ct3344AEN6jVAlZ7ORx9/hIWFBevWrqNHtx4sWb6Ujp3++aM8Li6Olk1bEBoayqeff4qJiQnz586neeNmHDp6ONNbqgZ+OJC1q9fQ84NejPhsBJcuXuK36TO4fOkS6zfJm/zEmyPJqnijqnaoRbuvu6Kl88+PWoUmlZk/bAYHl+17ZbKqUqnY8vMazOws+GDKQHT0dXM8n0etshhbmORYz8jShHINfHI1BiGEeNvsi9lz4/YNijk4cPfOXcp4eOW67S8//UL4s2ccO3WcsmXLAjBoyGDq1a7L119+RZu2bdDR0QHgr/l/cfXqVfb47aVW7VoAdOrSGZ9yFZgw/gcWL8t4Ud7ZM2dYu3oNn3/5BRMnT1Sfy9XNjW+++ppdO3bSvGWL/Bq+EJnImlXxRjl5uWRKVAEMzYxwLleSsPtPX9n2TtBNntx6TJ2eDdHR1yUlMZn0tLRXn1ClIik+EVV6eo6xpaWkkpyYnGM9IYR42/T09CjmkLulUv925MhRypYrq05UAbS0tOjcpQshj0M4dPCQunzD+vV4l/dWJ6oANjY2dOzUke3btpOYmJjR5+GjAHTt3jXTubr16AbAmjVr8xSrELkhM6uiQMQ8i8bAxPCVdW4HXgdAR0+XxV/O5dHV+yi1lJSq5Eazj9piamOu0WbekOkkJySjo6eDazUvGg9sgbGlqUa9+xdv83Pn8aSnpWNqY4Zvm5pU7VAr22UDQgjxrkhOSsLSSnNZk6FhxrrSs2fP0rBRQ9LT07lw/gI9e/XUqOtbxZcFfy8gODgYHx8fkpKTMvowMPxXnxnfB545m9/DEEJNklXx1t09f4tHV+9Ts2u9V9YLf/QMgI1TV1KiQmmqdaxNxKNnHFnlz4rRCxgw62P10gB9YwMqt66Oo6czWjpa3L90l7Pbj/P42gP6/zYc/ZfWsdqUsKdSGRcsnaxJiI7ngl8g+xfsJDY8mkYDW765gQshxFvg5u7GwQMHiYyMxNzcXF1++NBhAB4/fARAeHg4SUlJ2Bez1+jDzj6jLOTRY/Dxwd3dDYAjR45kWsf6Ypb20aNHb2QsQoAkq+Itiw2PZsvPazC3t6RGl1cnqykJGbfo7Us70P7b7upyUxtztvy8hosBQVRsXhWAKu1qZmrrWascDu5ObPl5DWe2HqNW9wbqY13+1ztT3fJNKrN63GJObTmGb9samNla/KcxCiFEQRo8dAjbt22nV/de/DBxPBYWlqxZvZqtW7YCkJCYAEBiQsYtfj09vf+zd9bhUV1bH37PTGTi7i4kISG4u1vRFkrde2/dvb11+XrbW/eWUupAgeLugQDBIcTdjfgkmWTmfH9MCAyTkECB2H6fJ0/JPnufs870ZM3v7L32WkbnUKlUjX31faZMnYq/vz+vvvwfrKysGDx4MLGnTvHE409gampKTU3Ntbg1QTdFxKwKrhm1VTUseXUxDfUNzH/1dswsjB3k+ZiY6d+lIsb2NWjvOaoXCqWC7NiMi46PGNsHKwdr0o+lXLSfJEkMnj0CWacj40Ra6zciEAgEHZiJkyby2RefcfjQIUaPGE1keC++/+57PvjfBwBYW+s3oaos9IK0rq7O6BxnY1UtGkWrubk5K1atxN3Dgztvu4OeIWEsmL+AGxcsoHef3tjYtL6xVSC4XMTMquCaoKnVsPSNnynNK+Gmt+/B2de11THWTvpYUyt7a4N2hVKJhY0ltVWtv8nbOttRU6VuvZ+LHQC1la33FQgEgo7Ovfffxy233cqpkycB6NO3L7t27gKgR+MyvqOjI+bm5uTl5RmNL8jPB8Dd06OpLTQslAOHDhAfF09JSQnBPYJxc3Mj0C/AIDRAILjSCLEquOpo6xtY/s5v5CXlMO8/t+Hd07dN49yD9TthK0vKDdob6htQV6ixtLO66HhZlikvLMPFz63Va5XmnwHAopVzCgQCQWfBwsKCQYMHN/2+bas+V+u48eMBUCgURPaO5PChw0ZjYw7GYGFhQWhoqNGxsJ5hTf8+deoUBfkF3HnXnVfafIGgCREGILiq6LQ6Vn2wlIzjKcx6ej5BA0Ka7acur6Ykq4j681JJhQwNx8TMhONbDhukojrR+HtA/x4G4y/kyPoDqMurCTzvmjWVanRaw7RW2gYt+//ajcJEiX/vwMu+V4FAILjWFBcXkxCfgFp98VWhxIREFi38kRkzZxjMgs6ZO5eTJ06yb+++praioiJWrljJtOnTLlqZSqvV8vILL2Ftbc1999/3z29GIGgBMbMquKps/3EDCftiCejfA22DllM7jhkcP5uY//Da/UT9sZ1b3r0Xv0bBaGVvzcibx7Nz8Wb+eGURocMjKM0r4fDa/XiH+xE2olfTeb685wN6jorE1c8NpZkJ2aczOL37JK7+7vS/bkhTv6QD8exdsoOwEb2wd3OgprKG07uOU5RRwKhbJ2DjbHfVPxOBQCBoC9989TXl5eWUlelXl3bv3I22oQGAm2+5BV8/X779+hvefftdNmzeyOgxowHIzMjk1ptv4boZ1+Hh6UlyUjILv/8BV1dXPvn8U4Nr3P/v+1m86CcWzLuRx558HNvGClZarZb/vPaqQd/bb7kNNzc3eob3RK2uYcmff3L82HEW/vQjXt7e1+ATEXRXhFgVXFUKUvSxUGlHkkg7kmR0vLUqUsPmj0FlbUHM6n1s+2E9FraW9J8+hDF3TEKhPLcwEDGmDznxmSTsjaWhvgE7F3uGzB3JiJvGYnZe5SsXfzecfVyJ3XEMdXk1SlMlrgEezH5uAeGje1+ZmxYIBIIrwKeffEpmRmbT79u2bmXb1q0ADBs+HF+/5kOqbGxt8PLy4vvvvqekuAQ3dzduufUWXnzlJZycnAz6Wltbs2HzBl584SU++d/HaDQaBg0ezA8/LSQk1HAlrF///vz2y6/8tOgnzMzMGDR4EJu2bmb4CMNsLALBlUaSZbm9bRBcBpIkRdi62O17eNFzxhnvBV2alf/3R1V81KmHZVn+ub1tEQiuJSqVqjo5PcXS0dE44b1AcC3YtnUbd99xV0xxcfHg1nsLrhQiZlUgEAgEAoFA0GERYlUgEAgEAoFA0GERYrXzotVpdaKQfTdE16CTAW172yEQXGskSdI1NG4wEgjag4aGBmThf685Qqx2Xoprq2rML0zDJOj6VBSV6YCi9rZDILjWmJmZlTeXwF4guFbk5eai1TaIh/AaI8RqJ0WW5WKlqUlGVmx6e5siuIaoy6spyixQAVHtbYtAcK1paGhYvXb1WjGrJWg3/lr2V2V5WfmK9rajuyHEaiemvlbzzbaFG9SaGuO6zoKuh6zTsW3hhjoTU9N1siyLurCCbodarV705edf1CUnJbe3KYJuyNYtW4neF60A1rS3Ld0NkbqqEyNJksJMZfazlaPNnMFzRlj59w3GwsYCSRKhrF0FGdCo68iOy+DohoPqgpS8RE1N3WhZlivb2zaBoD0wNTW9z8rK6tOHH33EfObsmUoPDw9MTU3b2yxBF6Wuro6U5BSW//WXZvFPizU16pqpsizvbW+7uhtCrHZyJElSADPNrVR367S6kTqt1hK4UmpVQsYcidordL6uj4w50IB0xQLwZYVSWas0UZ6urapZCCwRs6qC7o4kSYOtrKzuVigVs+o19fY6WdexVglllIAJEmLZq63IqIA6JDqUKFFIigZzc/PCmpqa3zUazWJZlsW0fjsgxKqgWST99Ox2YKksy1+3tz2dBUmShgLLgVBZlqva2x6BQHBtkSTJBogHrpdl+UB729NZkCTpIWAeMEEWwkRwAR3rbVTQkbgecAK+b29DOhOyLO9HL/JfbG9bBAJBu/AisE0I1UvmO8AFmNvehgg6HmJmVWCEJEkqIA64V5bl7e1tT2dDkiQv4DgwSJbltPa2RyAQXBskSQoEYoDesizntLc9nQ1JkiagnyAJl2VZhJ8JmhAzq4LmeAo4KoTq5dH4JfUx8EF72yIQCK4pHwD/E0L18pBleRv6F/0n29sWQcdCzKwKDGicFTyBflYwtb3t6axIkmQBnAbukWV5R3vbIxAIri6SJI0HfkDMCv4jJEkKAg6gn53ObW97BB0DMbMquJD3gG+FUP1nyLJcAzwDfCJJkkl72yMQCK4ejX/jnwDPCKH6z5BlOQV9KMB77W2LoOMgxKqgicad7BMQTuJKsQIoBe5rb0MEAsFV5X6gBFjZ3oZ0Ed4FJkqSNKS9DRF0DEQYgABoytcaDXwly/Li9ranqyBJUl9gExAmy3JpO5sjEAiuMJIkOaBPVTVZluXj7W1PV0GSpLuAB4Dhsizr2tkcQTsjZlYFZ7kVfTGBX9rbkK6ELMvH0M+2vNrOpggEgqvDa8AKIVSvOD8DSuCW9jZE0P6ImVUBkiRZAwnAPFmWo9vbnq6GJEku6DdbjZZlOa697REIBFcGSZLCgV3oN1UVtbc9XQ1JkoYBy9CvTIkiK90YMbMqAH0S6+1CqF4dGr/E3gE+bqwMJhAIOjmNf8sfA28LoXp1aPxO2gm80M6mCNoZIVa7GZIkWUmS9MB5vweijwsSzuDq8iXgD0w/2yBJ0ixJkoLbzSKBQNBmJEkKliRp1nlN1wG+wFftZFJ34QXgQUmSAs42SJL0gCRJVu1ok+AaI8Rq9yMEePC83z8APhZJrK8usizXo090/bEkSWaNzXOAMe1mlEAguBTGArMBGv+GPwKebPzbFlwlZFnORp8W7PwiKw8BPdrFIEG7IMRq98MOKAOQJGkcMAD4X3sa1F2QZXkDkAQ80thUjv7/h0Ag6PjYof+bBXgUSJRleWM72tOd+BAYKEnS2MbfyxC+s1shxGr3ww4ovyCJdY0kSeaSJLm2r2ldE0mSbCVJOutYnwJebPysyxAOVyDoLNgBZZIkuaFfmn4KQJIkO0mSbNvVsi6KJEmukiSZNxZZeRb4VJIkJeJFv9shxGr3wx79H/p96BPWL29MvHwUeLgd7erKTADiJUmaL8tyAvqULG+j//9g356GCQSCNmOP/m/2bWCxLMuJkiTNR59jdUJ7GtaFeRg4IknSYOAv9C/49yF8Z7dDlIHsftgBNcAbwCzgv8DtwOPA0na0q8siy/JKSZLygR8lSVqAPvvCHiAHMTsgEHQW7NALpBnAKEmS/gIigLmyLO9vT8O6MK8DccBq9C/5zzX+ezXCd3YrxMxq98Me6APsQ//H7wtEyrK8RBZJd68ajSlY+gHJQBSwBrgRMTsgEHQW7IEFwFpgL/r4835CqF49ZD1/Ar3RZ1P5GX2lxd4I39mtEDOr3Y8A9Juq/ICHZFle0c72dBtkWa4FXpAkaTmwCAgCxAuCQNA5CAYCAS1wnSzLh9rZnm6DLMuFwI2SJN2APg2gMxDbvlYJriViZrX74QTEABFCqLYPsizHoH9h+AMwa6W7QCDoGJii/5sdKIRq+yDL8nL0oRcx6AWroJsgyq0KBAKBQCAQCDosYmZVIBAIBAKBQNBhaZeYVUmSAhRKxc0WVpYLdFqtiyxEc5dCkqR6hUKRWV1R9TPwlyzLJe1tU1uRJMkCmGlqaXYXEC7rZFU7myS4ssiSQirX1ms36Oq1v8iyfKS9DfqnSJLkAsy3sbO9XafTecuyzrS9bRK0hoRSqSjX1Gk21tXW/doYGtSpkSTJCZhnbW52h06WfWUZ8Rx2ISQJnUKSimo09Uu0svyHLMtp1/T61zoMQJKkyWbmZiuHThmhGDhhqMrJzRmlifKa2iC4ujTU15OblkP0xj3VsQdP1mpqNcNkWU5qb7taQ5IkOxOVyR4bD/sA/zEh1g7+zijNxB7EroQsy9RV1lJwPLshbVeCRqtpeE1br/2wve26XCRJ6mmuMt87ePRQs0mzJlv5BPpiaibCoDs6Oq2W8tJyDuyMblj1x9+autrad+tq695pb7suF0mSepibKKP7+niqxoUGWXk72GGqFN/rXQmtTkdRZTVRKem1uxNTdXUN2rmyLG++Vte/pmJVkqQAM5XZqac/fckypG/YNbuuoP3YtWq77vePfirW1Gq8O3oNbVNLs23egwJG9L1jmLkkSe1tjuAqoy6pYufb69R1FTULZFle2972XCqSJJmbq8xznnzjWcep108TD2wnpSi/iAfn3acuLii+vTNuepUkydTcRJn9r1FDnCdHhIhV0m5AbG4Br63erK5r0Pa6VjOs1/TBUigVtwybOkophGr3Yczs8QpnD1cVMLa9bbkYkiS56Rp0I3rfPFgI1W6CpZM14df3szS1NOusldsm+wb5mQqh2rlxcXfhX88+ZGlta/NIe9tymYxzs7VRCaHafYjwdGNMSKBCKUk3X6trXtOHy8LKcsGg8UPMr+U1Be3PsKkjrc1U5nPb245WmOzS00Mjlv27Fx79fGmoqx8vdcI3FEsry7mTZk22aW87BP+cEeNHUqNWj5QkqdM5IHMTkzljQwKt29sOwbVlZHCAysLM9KZrdb1rKlZ1Wq2Lo7tIjdbdcPJwVpiamfq2tx2t4GblYiNepLoZ5tYqGoVqp/uyNTE19XH1cO90IltgjJWNFSampjo6YQlRM6XS18XGWsyqdjNcbKzQybLLtbreNX3AZFlWdrXNVPGHY7l7yE3EHxbFNFrCxMQESerwO0OVChNFl3K4RfF5rLz3J4ri89rblA6NpJB0dMJqfpKEqYlppzP7ohw9cISxPUZw9ECnT9JwySiVik75HCJhaqLsUq6Tk9l5zPziJ05mC9/ZEiYKBbLMNRN0ne8PQ3DFSY9PY+2ilWQkpFFxphwzlTmeAV5Mu20mfUcNaOqn0+nYt2EPR3bGkJmYTmVpBU4ezvQdNYCZd83FwtqyHe9C0BUpyyghYe0JyjJLqKuoQWlmgo2HPT2m9sKjr49R/8r8ck4tjaE4oQAkcAnzIHLBIKxcxGq54NqRFJvIwo+/4+ThE2i1WkJ7hXHvk/fTe1Df9jZN0E1ILixh6aETpBSVUFZTg7mJCb6O9lzfrxeDAwx9Z2JBEdviU0gsKCK9uJQGnY6f774RB6uO853etV6HBJdFUU4B9Zp6Rs4Yyy1P38XMu/XhpZ8+8wE7Vmxp6qep1bDwza8pLyll9Kxx3PL0XfQc2IvNf6zn7fteRVOraa9bEHRRqosq0dZr8RsRTO+bhxA6ozcA+z/fRtrOeIO+NaVq9ry/gfKsUnrO7kvYzD6Uphez5/0N1FXVtof5gm5I0ulEHr35QdJT0rntwTu598n7qSiv4Ok7n+Dk4RPtbZ6gm1BQUUm9VsvEnsH8a9QQFgzU+8631m1jwylD33koPZtNsQnodDJe9rbtYW6riJlVAYMmDGXQhKEGbRNvnMrrd77Ixt/WMe76SQCYmJrw0ndv0KNPqEHfgJ6BLHzrG/Zt2M3YuROvmd2Cro/XQH+8BvobtAVN6MmON9eQtCmWgLHnMoskrj9BvVrDhLfmYO2qd7juvb3Z9uoqkjaeote8gdfSdEE3ZeHH36NUKvlq2Xc4OjsCMGPBbO6YcgtfvPMp365Y2M4WCroDI4L9GRHsb9A2o3dPnly6hpVHY5nW65zvnBYZxg0DIjE3MeH3A0fJOFN2bY1tAx1GrNaqa/n7+2Uc2RlDaXEpKgsVngFezLrneiKG6N8IspIy2PzHehKOxVFapO8TPqgXNz56K07nbdyKWruThW99w/Nfv8rR3YeI3hhFg6aefmMGcefz9yHLOv745BcO7zhIQ30Dw6aO4Jan78LE5NzHcfeQmxg7dyLhg3rx9/d/UZhTgJuPOzc8sIB+o1v/0ivIymfld0uJPXiS2uoa3Hw9mHLzdYyaOdag3/a/NrN9xRaKcgpRKhU4ebgwauZYJt80/cp8sJeJQqnA0dWRlNjkpjYTUxMjoQowYNwQFr71DblpOdfSxGtGQ209cauOkXskg9oyNSYqU2w87Amb2QfXCE8AyrPOkLzlNCWJBdSUVmOiMsWlpwe95g/E0unc3p2MqCSOLNrLqOemkns0k6z9qegatHj086Xf7cOQZZmTS2LIPZyBrkGHz9BAet88BIXJuUWQlff+hP+YEFzDPYlbdYzqwgqs3WwJv74/Hn1b38dWVVBB3KqjFMbm0lBbj7WbLcGTI/Ab2cOgX+r2eNJ2xlNdVIWklLB0ssZvZA+CJ4VfoU/28pAUClQOVpSmFhm05xxKx62XV5NQBbDxsMelpwc5MendSqyqq9X89PmP7Nm0i+LCYiysLPAP8ueOh+9i4MjBAKTEJ7Ns0RJOxByjqEDfZ8DwgTzw3EO4ebo3nWvD8nW8/8K7fPLrF+zdtoctqzbpV2ImjeKpN59Dp9Px1bufs2vzTho09UycNZnH/vMk58fTju0xgpk3zWbA8EH89NkP5GTm4u3vzX1P/YsRE0a1ej/ZGdks+uQHDu2NQV1VjXeADzfefRPT5l1n0O/v31aw6veV5GXlolQqcfNyZ/q8Gcy768Yr9Mm2zslDx+k3bECTUAWwsLRgxMSR/P3rCrIzsvH2875m9rQnNZp6fj94jOjUDM5Uq1GZmuLraM+CgX3o56v3nWnFZ1h17DSxuQWUVFejMjWlr48Hdw0fiKvNOd+5NS6JT7ft5d25UzmQmsnOxFTqtVqGBvjy0Di97/whKoZ9KRk0aHWMDQ3kX6OGcH487cwvfmJqRAh9fTz57eAx8ssr8LSz5fZh/RkS0LrvzC2r4LcDRzmWlYtaU4+XvS1z+kYwMdzQd647Gc+Gk/HkV1ShVEi42lgzKbwHs/q0r+9UKhQ4WVmRWGDoOx0sLdrJorbTYcTqz+//wMGt0UyYNxmvIB/UlWrSTqeQnpDWJFZjD54kLzOX4dNH4+DsQGFOATtWbCXtdApv/fEB5irDzdx/fPwzNvY2zL73BtLj04hauxOVhYqi3ALMVebM/fd8Th88xY4VW3Hz8WDKLYaOL/lkIjHbopkwfyoWVhbsWb2DL174iKc/e4nwgb1avJf8zFzevvdVrGytmXLzdCxtrDix9yg/vv0NNVVqJt+sF6K7V2/nlw9+ZMDYwUyYNwWdVktuWg7JJxJbFavqKjXahoZWP1eFQoGVbds2Oteqa6nXaFBXqjm6+xAn9x83mnFtjrLiUgCs7Trdhuo2ceyXaLJj0gkcH4atlz31ag2lacWUZZQ0idXC07lU5ZfjMywIlb0l1UUVpO9MYE9aMRPenIOJueGf2ok/DmJuo6LnrD6UppeQuTcZE3NT1EWVKM1N6DmnH0Wn80jbmYCVqy09pkQYjD+TUkROTDpBE3piYmFKxp4kDny5gxFPTcalp0eL91KZX86ud9djZmVG8OQITC3NyD+RzZFFe6mv0RA8SX+d9D2JHP9tP579/Qgc3xNZp6Mit5wzyYXQilitV2vQaXWtfq6SQsLMqm0JGBpq69HWa6lXa8g7lknhqRy8Bvk3Ha8praauohaHQOPNqQ4BzhTG5qKprmvz9To7H7/2ITvWb2POrdcTEBJIVUUV8SfjSIxNbBKrh/bGkJWWyeS503B2dSYnM4fVf/xN/PE4Fq3/BZWFYaXhL975FHtHe+589B4STyWwYfk6LCwtyc3KRWWh4t7H7+fwvhhW//E33v4+3HiPYVab2KOn2LlhO9ffPg9LayvWL1vLfx5+mQ8XfUz/YQNoiay0TB6+8d/Y2Nky/+4bsbG1JXrnPt5/8V2qKquYf/cCANYtW8Mnr/+P0ZPHMPe2G9BqtWQkp3PqyMlWxWpVZRUN9a37U6VSgY3dxZdJNZp6VCrj50yl0n+eCSfju41Y/WpXNFFJ6UyPDMPPyZ7qOg1JhcWkFJU0idVjWbnklJUzPiwIRytL8sor2HgqgcSCYr64eQ6qCzYR/rDnILYWKm4e1IfkohK2xiWjMjMlv7wSlakJtw3px7GsPDacSsDTzpY5/Qx9Z3x+EVHJ6czo3RNLM1O2nE7i3fU7eHP2ZPp4t+w7c0rLefav9VirzJjdNwJrczNi0rP5dPteqjUaZvfVX2fz6US+2bWfYYF+XNe7JzqdjszSck7nFbYqVqvrNDToWvedSknCuplnrDlqNPVotFqq6zQcSMvkSGYOIy+Yce0MdBixenzvUcbMnsDNT97ZYp9xN0xi6q0zDNr6jhrAu/e/xuEdBxk+zfAN3drehqc/e4mzKRSL84rYsWILQ6eM4F9v6PMvj7t+Ei8teJo9a3YYidXs5Exe+v4NevTWzyaOnjWOF+c/xbLPf+e1xe+2aOevH/6EjYMtry9+F/NGhz/+hsl89fIn/P39MsbMGY+5hYrjUUfxCvTmkfefauOndI7Pnv2AhCNxrfZz8nDmw7+/aNM5F7//A/s3RgH6mdUBYwdz+7P3tDpu3eK/kRRSm4RtZyT/RDb+o0PofdPgFvsEjA2jxxTDFxiPvr7sfm89uUcy8B0WZHDMzMac4U9Nano21SVVpO2Mx2dIIAPvH914zlC2vrKSjKgkI7FakV3K6Ben4xTsCoDfqB5sfXklp5YdYtyrM1u088TvBzC3MWfcqzMxMdcnaAgcF8bBb3YSt+oY/qNDMDE3Jf94Njae9gx5eFwbP6Vz7P9im36DUytYOlkx5b/z23TOY79Ek7U/FdCLXM/+fvS59dzzVlteA4DKzniGQGWn3yRQW6buNmI1esc+Ztw4i0defrzFPrNvmcuCew1zeo+YMJJHFjzA7s27mDx7isExOwc7Plj0cdMzm5+Tx6rfVzJx1mRe/vBVAGbdMoc7pt7C+r/WGonV1IQUvljyDb36RwIwff4M7ph8M9/890u+W/lji3Z+9ubH2Dna893KH7FonAGafetc3nj8P/z02UJmLJiFhaUF0Tv24d8jgDe/bNk3t8TLD7zA8YNHW+3n5uXOkp3LL9rHN9CX2KOxNDQ0GKzWHY85BkDxBbNaXZmY9GwmR4Rw/6iWfee0XmHM7WfoO4cE+PLc8vVEp2YwLtTQd9pamPPmrHO+s7Ciig0n4xkTGsjTk0Y3njOUB39byZa4JCOxml5Syn9vmE5PD73vnBTegwd/XclPew/x8YKWfee3uw9ga2HOJwtmojLV+87pkWG8v3Envx88xpSIEFSmpsSkZ+PraM9L0y/dd769bhunclv3na42Viy8s22+86ud0exM1PtOhSQxLMiPB8d0vu/qDiNWLa0tST2dTGnhGRxcHZvtc/7Maa26lob6etx9PbC0sSIjPtVIrI6aMZbzc30HRgQTfziW0bPOPUSSJBHUK5iYbQeMruffM7BJqAJY2lgxbNpINv2+jvKSMuyc7I3GVFdUcfrgSWbecz2aOg2aunObjnoP60vM1v2kxaUS1j8cS2tLzhSeITU2mcCI4NY/pPO46bHbqa6sbrWfmXnb64TPuHM2I68bQ2nRGfZvjEKr1aJt0F50TNS6Xexdt5tpt83Ew9+rzdfqTJhamlGaVkRNaTUWDlbN9jl/5rShth5tgxZrN1tMLc0oyygxEqt+I3sYPJuOgS4Ux+fjN+rccpIkSTgGupBzKMPoevb+Tk1CFcDM0hyfoUEkb46ltlzdJNDOR1NdR+HpXMJm9EGr0aLVnPt/69bLi5yYdMrSS3AOdcfU0oza0mrOpBbh2Mxs5cXodeMg6tWtb7ZTmrY960nI9Eh8RwRTU6ome38qOp0O+bzZ27P3omgmNZ6i8Tra+os/y10Ja1tr4k6cpii/CBf35v//nT9zqq5WU6+px9vfB2tbGxJPxRuJ1enzZhg8sz37hHN0/xGmzz83gSBJEuF9Iti1cYfR9UIjw5qEKoCNrQ2T5kxl6cI/KCkqwcnFyWhMZXkFh/bGcMfDd1FXW0ddbV3TscGjh7Jj/XYSTsbTd0g/rG2sKcovIu74aXpe4nLrQy8+QmV5Zav9Lly9a465t93Ah6+8z3vPvs2tD9yO0sSElb/+RdLpRACDe+jqWJmZkVhQRElVNU7WzfvO82dOazT11Gu1eNrbYmVuRnJhiZFYndjT0HeGuLlwIiefyT0NfWeouwt7k419Z7CrU5NQBbA2N2dsaBB/H4ultFrd7A74qto6jmXlsmBQH+oatNSd9704wM+LqOR0kgpLiPRyx8rMjJKqahILighxuzTfee/IQVTVte47zS4hBei8AZFM6BlMSZWanYl639mW2duORocRqwseu43vX/+Sp2c9jG+IP5HD+jB0yki8As8tl1RXVLHsyz84tP0A1RVVBuPVVTVG53S6oACBZWNqJUc3pwvardDU1lGvqcfU7Fw6UDcfdy7E3Ve/TFCcW9SsWC3IykeWZVYvXM7qhc2/gVeWVgAw/c7ZxB2O5a17XsHF05XwwZEMmjCUiMGRzY47H/+ega32uVS8An3wCtSntBg+bRT/9+83+Oy5D3nlh7ea7X8y+hg/vfsdfUb044YHr1khi2tOr/kDObxwDxufXYa9rxOuvbzwGRqIrad9Ux9NdR2xyw+TcyiD+mrDL6PmhJulo6HjNrHQP3cWF7SbWpqh1TSgrdcaiDtrN+OlSGt3fZu6uKpZsVpVUAEyxK85Tvya483ea12Fftd8yPRIiuLy2PXOOiydrXGN8MRroD+u4Z7NjjsfB/8rX/jD1ssBWy8HAHyHBbH7/Q3s/2I7Y17Sr4YozfSfja6Zlytdo0i9FHHc2Xnw+Yd599m3uHH0XHqEhzB49BAmzpyMf4+Apj6V5RV898E37Nq0g4qyCoPxzb0Iu3q6Gfxu1RhP6OruatBubWtNbU0tmjqNwcuyt59xqjEff31bfnZes2I1Oz0bWZZZ/MUiFn+xqNl7LTujD0O65d+3cXT/YR6cdz8e3p4MHDGIMdPGMXDEoGbHnU9orytXAnzGglkUFRTx+7e/sm2tPpuKT4AP9z35L77571dYdqB0QFebe0YM5KOte7j7p2UEujjR39eLsaGB+DraN/Wpqq3jp+jD7EvOoLLO0HeqNca+08XG0EdaNq4QOV/Qbm1uRl1DA/VaLabKc3/7ns2EcZzdAV9QWdWsWM0tr0AG/ow5zp8xzfvO8hq975w3IJITOXk8vWwdbrbW9PXxZGSwP319Wvedwa5X3nf6OTng56T3nePCgnhhxQbeWb+dDy6I9+7odBixOnD8EHr0CeXo7kPEHjzJtmWb2PDLGu588f6mTUlfvfwpyccTmHLLdfiGBmBhqQJJ4ptXPkOWjd8UFC3keG+pHVn+x/ch6/TnmHzzdHoP79dsH+8gvYP28PPknSX/4+S+Y5zaf5yT0cfY9fc2Rs0cyz2vPHDR61SVV7UpZlVSKLB1uPRUFAqFggHjBvPnp79QnFuIs6fhF1LS8QS+eOFjAsKDePDdJ+hqxR7Ox2ugP0493PSxkrG5pG6PI2njSfrdMbxpU9LBb3ZyJrmQ4MkR2Pk6YaoyBQlivt3V7HMlKZovPNRSO/zzZ/OsHUGTwnHv3XzM3FlBaONux8S351JwMpvC2FwKTuaQvisRv5E96H/3iIteRlNVh07b+iympFBgbqNqtZ/xOH0YwKmlMVQXV2LlbNO0/F9bpjbqX1uub1PZdx+RMGbqOCIH9mHv1j0c2hvDyl+W88f3v/HMW883bUp6/bH/cOrISW685yZ6hIfoRZQk8eaTr6FrZualRX+qbP5vX74Cz+xZO+bfvYAhY4Y12yewcebNN9CPxRt/58CuaGKiDnJg937WLFnF9HkzeO69Fy96nYqyCurr61u1R6lQYN/4xX8x7n7sXm68+ybSklIxMzcjuGcP1i5ZDeiFa3dhRLA/4Z5uHEjN5FhWLutOxrHi6EkeGTu8aVPS+5t2cjqvkLl9Iwh0ccLC1BRJgv9u2oWuGd+paKEyckvt8hX4Xj9rx+w+4Qz0b953nhWE3g52fHXLXA5nZHM0M5fDGTlsik1kUs8ePDbh4r6zsraOhjb4ToVCgZ3FpftOhSQxPNCPhXtjKKioxM228+Sf7jBiFcDOyZ6xcycydu5EaqrUvPvv11n53VJGzRzbtLw++/55zLlvXtOY+joN1ZVVFznr5VOQlW/Ulp+pr2jh7Nn89L6Lt372QalUtmmG1FxlzsDxQxg4fgg6rY6f3vuOPWt2MuOuObh6G8/snuWLF/53xWNWL+RsCENNteGsdUZCGh8/9T5u3m48+dHzbVoa6+yo7CwIGBNKwJhQ6ms07P6/DcT9fRS/kT3QVNdRdDqPsFl96Tm7b9MYbX0Dmuqrk3u2qqDCuC1f32bp3PxGN6vGXfIKpaJNM6Qm5iZNqaNknY6ji6PJiEoi5LpIgx33F3Lgq+1XPGb1QnSNm2EaavQCw8LBCnNbFaVpxUZ9S1OLsXS27jbxqmdxdHZk5k2zmXnTbKorq3n05gf58dMfmDbvOirLKzi87xB3PXYvdz16Li69rq6OqjYsh18O2RlZRm1Z6fo29xY2tnj56kOLlEplm2ZIVRYqxkwdx5ip49Bqtfzvlf+y/q+13PrA7XhdZFPTfx5+6YrFrJ7FysbKIOzhUNRBVBYqevXv3abxXQUHSwum9gplaq9Q1BoNzy/fwG8HjzIxvEfj8noetwzuy82D+zaN0TQ0UN2G5fDLIbfc2HfmNK4suNk07zs9GmdjlQpFm2ZIVaYmTamjtDodX+6IZktcEvMHRjadqzneXb/9isesXkidVu871ZrWX846Eh1CrOq0OmprapuW6QEsrC1x9XLjdG4hcN5b/QVvSZv+WN80m3mlSY9LJflkIsGRIQCoK6uJ3hCFX2hAsyEAALYOtoQP6sXOv7cx4cYpOLkZTutXlFY0zXRWlVdibXfuzUahVOAd7Nt4LeMZovO5kjGrFWfKsXU0LEmtqdWwf9NeVJYWTaEPoM908L/H38PG3panP3sJS5vm45C6CrJOR0NtA6aW5z5HUwszrFxsKCrSf6mfmw01fA6TN5++IrP1zVGWXkJJcmFT3KpGXUfW/hTs/ZyaDQEAMLdR4dLTg/RdCQRO6GkUilBXWds001lXVYu59bk3d0mhwNZbP3PQWjzqlYxZrauowdzWcNOUVtNA1oE0TFSmTaEPAJ4D/MnYk0hVYUWTmK7MK6MoPo/gyYabLLoyWq2WGnUN1ud98VrZWOHp68XhfTEAKBT6z/7CWae/Fi1pdlb1SpBwMp7Yo6eIaNxMU1lRyZa/NxISEdpsCACAvZMD/YcPZM2S1Vx/xzxcPQxDEcpKSptmOstLy7FzOOfHlEpl06xrVSsTGlcyZrU5jh88RtTWPcy9/QasurjPPItWp6O2vgGr876DLM3McLez4XiW/rNWNPrOC5/DVcdONzureiVILiwhPq+QsMa41aq6OnYmpBDk4tRixSY7CxV9vD3YFJvAjN49jUIRymtqm2Y6K2pqsT1v1lOpUODvrH9GWxPgVzJmtUxdg/0FKanqGhrYlZiGhalph03+3xIdQqzWqmt4csZDDBw3GJ8eflhYW5JyMpEju2IYPXs8oBevYf3D2fDLGhrqG3BydyHpeDwJR+MMBN+VxCvIh0+eep8J86diaW3J7tU7qCyv4F9vPnLRcbc/dw/v3P8ar976PGNmj8fNx53KskoyEtI4tf8EX+/Qx159+Oi72DraEtw7FHtne4pyCtm2bBMe/p749PC76DWuZMzq1698hqmZCcGRIdg5O1BadIZ963dTlFPI7c/eg2mjs6mpruHDR9+lqqySiTdOJfbgSYPzuHq7NQn7rkJ9bT0bn16G5wA/7HwcMLUw40xKEXlHM5s2Q5lamOEc6k7SxlPoGnRYOllTklRAcWIBZtZXZzbP1sue6E+3NqauMiMjKom6yrqmTAIt0ee2oex+bz3bX1uF/+gQrFxt0VTVUpZRQmFsLjO/vBWAfR9twdxGhWOwKyp7C6qLqkjdHoe1hx12Ps1vgDzLlYxZPfjtLpQmSr0ddhbUlKrJik6huqiSPrcORXne5ozQ6yLJOZRO1AebCJ4UjixD8pZYzO0s6DG15VRzXQ11tZr5I+cwespYgsKCsbKx4vTRWKK27Oa6G/W7na1srOg7pB9/fv8bDfX1uHm6c/LwCY4fPIatg10rV7g8AkICeeH+Z7j+9nlY2VizftkaykvLefl/r1103JOvP80jCx7knhl3MuPGmXj7+1BeWkZibCIxew6w/pg+LvTZu5/E3smBXv0jcXJ1Ii8rl5W/LMc30I+gsItvYL2SMavHDx7jp88XMmjkEOwc7UiOS2Ld0jWE9Arl3if/dcWu09Gpqa/nrkXLGB7kR4CzA5ZmZiTkF7E/NZPJjSEAlmZmRHq5s/zoKRp0OlxsrDmdV8CpnAJsrtKKnZ+jPW+s3cqM3j2xMjNjc1wSFbV1PD354r7zwTFDeW75eh79cxVTwkPwtLeloqaWlKISjmTmsvTfet/56uot2FmoCPdwxcHKgvzyKtadjMPbwY4A54v7zisZs/rBpl2YKJX09HDFwdKCM9VqtsenkF9RyQNjhmJ2XqaKwooqdiSkABDbOLO76vhpLExNsTI3Y0bvnlfMrsulQ4hVM5U5E+ZNJvbgSY7uOYy2QYuzhwvzHr6ZKTefCwL+91uP8vtHi9m5chvaBi2h/cJ47sv/8MGjb18Vu3r0DtUXBfhuGYU5Bbh6u/PI/z3V6vK+u68nry9+j1ULlxO9KYrK0gps7G3xDPBiwWO3NvUbO3cC+zfvZevSjdRUqbF3dmDY1FHMuvf6axoDOnzaKPat383WZZtQV1SjsrLAv2cgtz59N31GnIu7rS6vpCRfv8y68tulRucZcd3oLidWTcxMCBwfRmFsLvnHMtFpZSydrYm4YQDBk8/tOB70r9Gc+OMgabsSkbU6nELcGPnMFPb+b9NVscsx2FVfFODvo1QXVWLlasuQh8a2urxv427HuFdnEr/mOFn7U/Wzqdbm2Hja0+vGc0nz/ceEkH0glZRtcTTUaFDZW+IzNIiwmb1RKK9dlWbfYUFk7kshZVsc9eo6TFRmOPg70fuWIUZxtxYOVox+YRonl8QQ9/dRkCRcwtzptWDQZcXGdlZUKhVzbr2eQ3tj2LstCm1DA+5e7vzrmQebcpIC/Oej1/n87U9Z8+cqGuob6D2oLx/98hlP39lyuqt/Qq/+kQwYPohFn/5AbmYOXn5evPHFO60u7/sE+PLd3wtZ/MUitq7ZTNmZMuwc7PALDuDBF85NHMy8aTZb12xhxc/LqK6sxsnNmUmzp3D7w4YFX642zm7OmJiasvTHP6iurMbV040F997MrQ/cYZS7titjbmLCdZFhHMvK5WBaJg06GVdba+4cNoDZfc/5zmcmj+a7PQfZeCqRBp2OCE833pkzhf+sujq+s6eHq74owIGj5JVX4mFvywvTxra6vO/lYMfHC2byZ8xxdiam6mdQVeb4ONpzz4hzvnNqRAi7ElNZcyKO6joNjlaWjA0N4qaBvVG2tF/mKjAuNIjtCSmsPRFHVV0dlqZmBLs68e/RQ4zibgsqKvn1gGEYzPIjpwB9yEFHEKvSlQg+bisWVhaFb/z6vourl1vrnduZsxWs7nzhvvY2pdMTs20/P7//w+bKssoprfduHyRJej54SsTbkTcO6hAvcBfjbAWrfncMb29TugSrH/ylTqvResiyXNretlwK9o72O59998UxIye2XgGqvTlbwerpt55rb1M6LNP7TapRV6kDZFluPWixA2Frodr00NhhkztDovmzFaweHid85z8lv7ySx/5cXaTWaFxb7/3PuXYyXyAQCAQCgUAguESEWBUIBAKBQCAQdFiEWBUIBAKBQCAQdFg6fHxee7HowJ/tbYJA0CxzF97V3iYIBJfEzqS97W2CQMCaR+5qbxMEl4mYWRUIBAKBQCAQdFiEWBUIBAKBQCAQdFiEWBUIBAKBQCAQdFg6bcxqVXklm35fx9E9hynOLUSn0+Hi6UbksD5MvHEqzh4uRmPW/rSS5V8vISiyB6/88FZT+zNzHqEkz7im+IXMvu8G5tw//6L93f08eW/pR5d/Y4JOT11VLcmbT5N/LJPq4ipknYyViw1uvbwImtATS2fj+tMJ605wesURHINcGPPSuUIYm55bhrqk9bK6YbP60HN2v4v2t3a3ZdI711/+jQm6BOWl5Sxb9Cd7t0WRl52HTqvF08eTwWOGcf3t83D3cjca8+vXi/nho++I6NeLL5d+29S+YOwNFOTkt3rNOx+9h7sfu/ei/X0Cffll0x+Xf2OCTk9FTS1/HzvNwbRMCiqr0Olk3OxsGODrxczePXG1NfadSw+d4Jf9Rwhzd+GDeed8572Ll1HYhpLkNw/qwy1D+l20v5e9Ld/cJnxne9IpxWpmYgYfPfEe1RVVDJ40nPHXT0KhVJCTmk30hij2bdjDZxu/MxoXvSEKF09XUk4m6StSNRYnuOXJO6lV1zb1Ox51hINbo7n5iTuwtj9XytUn2Nfg31Nvm2l0DUvr5msLC7oH5Vln2PvxFuqr6/AaHEDA2DAkpURFThlZ+1PIjE7huk9uMhqXFZ2CpbM1Z1KK9BWpXPTPXeRNg2moa2jql388i5yYdCJvGoSZ9blqOHbeDk3/tvV2aLa8qKmFmVGboHuRHJfEc/c+TWVZBeNnTGD2LXNRKpWkJaay5e+NbF65gb8PrDMat2XVZjy8PYk9eorczBw8fb0AeOTlx6lR1zT1i96xlx3rtvHwS49h52jf1B4UGnTu32HBLLjvFqNrWFtbGbUJug9pxWd4bfUWKmvrGN0jgGmRYSgliYwzZexMSGFHQgq/3mvsO3cmpOBma018fhH55ZW4N5Zfv2/UYGrrz/nOmPQs9iSlc9/IQdieV0nM38nB4N/X9zf2nVZmwne2N51OrNZUqfns2Q/QarW8uugdfHr4GRy/4cGbWPXDX0bj0uNSyU3P4alPXuDrlz8lekMUs++7AYD+YwzL/ZUWnuHg1mj6jxmIs2fzxRnsnB0YPq3jV44RXDvqazREf7YNWatj7CszsPMxrAMdcX1/4lYfMxpXml5MZV45w5+YyMFvd5EVnULYrL4AePY3fL5rSqvJiUnHo58vVs42RucCUNlb4DssqNljgu5LdWU1Lz/wPNoGLd+s+IGgsGCD4/c9/W8Wf/6j0biEU/FkpKTz/sL/8ebjr7Jl1SbufPQeAEZNMqynXpRfyI512xg5aTQe3h7N2uHo4sTk2R22mJ2gHVBrNLy1bhtanY6PbpxBgLOh77xjaH/+iDlmNC65sJis0nJenzmR/27axY6EFG4e3BeAYYGGvrO4qpo9SekMDfTFzbZ53+loZcG4UOE7OyKdTqzu/HsbJfnF3Pvqg0ZCFUBlqWLBY7cZtUdv3IO9iwMRQ3ozYOxg9m86J1avNWdLuUYO68OKb5dSmJ2PV4APd7xwLwE9g9i3fjdrfvqb4rwifHv4cc8rD+AVeEEt36x8Vn63lNiDJ6mtrsHN14MpN1/HqJljm/o01DewZtFKTuw7SmF2AfUaDd5Bvsy4a46RQD9rU9+R/Vn+zRLyMnJxdHVkzr/mM2zKyGvxsXR60nclUnOmmv73jDQSqgAmKlMibzSug561PxWVvSWuEV549vcja39qk1i91pwt5erWy4u4v49SVViJrac9fe8YhoO/M5n7UkhYdxx1cRV2vo70v3sktp72BueoKqggbtVRCmNzaaitx9rNluDJEfiN7NHUR9egJWHtCfJPZlNdWIm2XoudtwMh0yONBPpZmzz6+HB65REq88qxcLSi5+x++AwNvBYfS5dgzZJVFOQW8ML7LxsJVQBLK0sefOERo/Ytqzbh7ObMoJGDGTV5DFvXbG4Sq9eas2VbB48eyo+ffE9ORjb+PQJ46s1nCYvsyea/N/LL14vJz84nuGcwz7/3Ev49AgzOkZ2RzaJPfuDQ3hjUVdV4B/hw4903Me28JeR6TT2/fr2Y/buiycnIQVNXR2BoELc+cIeRQD9r0/DxI/nho2/JTM3Exd2Fex6/j4mzJl+Tz6Wzs/FUIkWV1TwxYaSRUAWwMDPlnhHGvnNHQiqOVpb08/ViWKAfuxJTm8TqteZsKdf+fl78tv8oeeWV+Dra89DYYfRwc2Z7fApLDx2nsLKKQGdHHpswEt/zVh8Acssq+O3AUY5l5aLW1ONlb8ucvhFMDD/nO+u1WpYeOsGhjGzyyiupb9Di5+TA/IGRRgL9rE2DA3z4Zf8RskvLcba24pbB/Rgb2rl8Z6fbYHV01yFMzEwZMqnttX11Wh37N+9j8MRhKBQKhkweTn5mHimnki7bDm1DA5VlFUY/dTW1rQ8GUk4m8uuHixg6eQRz77+RotxCPnnyv+xevYPVP65g9KxxzLxrDtnJmXz7n88MxuZn5vLWPa+QdjqVKTdP5+Yn78DZ3Zkf3/6GzX+sb+pXU61m58qt9Ogdyg0PLOCGB25C1sl8/tz/OLHvqLFNp5L48Z1v6T9mIAseuxVzCxXfv/4luWk5l/05dSdyj2aiMFHgPTig9c6NyDod2QdS8Rrkj6SQ8B4cQFVBBWdSiy7bDrlBpq6y1uinoa6+TePPpBRx4vcDeA8JpOecflQXVxH96VbS9yQRv/Y4/qNCCJ3Rh4rsUmK+3WUwtjK/nJ3vrKM0rZjgyRFE3jQYCydrjizaS/KW2KZ+9TX1pO1KwCnYlfC5/Qmf2x9ZJ3Pgyx3kn8w2sqk0tYgjP+3Fo58vvW4chIm5CYd+2ENFbtllf07djaitezA1M2P8dRPbPEar1bJt7VbGTZ+AQqFgwoyJZKVlcfpYbOuDWzpnQwNlZ8qMfs4PJ7gYp4/F8tmbHzNx5iTuefw+8rPzePH+Z1m/bC0/f/kT182fye0P3kFqQgpvPfW6wdistEwemnc/8SfjmH/3jTzy8uO4ebrz/ovvsmzRkqZ+1VXVrP5zFb36R3LfU//ivqf+jU6n4z8PvciBXdHN2vTfl95j5KTRPPjCw1hYWvDus2+RkZx+2Z9Td2J/WiamSgWjQ9ruO7U6HbuTUhkV7I9CkhgdEkBOWQUJ+ZfvOxt0MuU1tUY/tfVt853x+UV8t/sAY0ICuXVIPwoqqnhz7Va2nE5iScxxJoeHcOPAPqSVlPLBJkPfmVNazjPL1pFUWMzsvhHcP2owLjbWfLp9L6vO+3tTa+rZGJtAuIcrtw/tz+1D+yPLMu+u38GhDGPfmVBQxGfb9zI0wJd7RgxCZWrCx1v3kHWm7LI/p/ag082s5qZn4+HrgamZaZvHxB48QcWZcoZMHgFA+KBIbB3tiN4YRVCvHq2Mbp64Q7E8NuVfRu3j503m9mdbn3XITc/h3SX/w9Vbv5nB1smOH974iqWf/8r//fUJ1o1xNwqlkuVf/0laXAoBPfXLE79++BM2Dra8vvhdzBtjb8bfMJmvXv6Ev79fxpg54zG3UGFlY82Hq74w+KwmzJ/C63e8wKbf19F7eD9Dm1KzefO3/+IZoI9HGzR+KM/MfoQ9a3Y0O1stMKQytwxrdzuUpso2jymMzaWuohafIfq3XNdwD8xtVWRFp+AYaLxJsC0Uxeex/gnjohaB48Poc+vQVsdX5pUz8e05WLvaAqCyVXF4YRSnlsYw6b3rMW+MlZUUEqdXHKE0vRgHf2cATvx+AHMbc8a9OhMTc/1zFzgujIPf7CRu1TH8R4dgYm6KmZUZU/473+CzCpoQxvY31pC8KRb3SMOVhIrcMsa/PrtpFtdroD+bnltGRlRSs7PVAmMyktPwDfTFzLzt8XeH9sZQWnyGCTMmAdB/+EAcnB3ZsnoT4X0jLsuOI9GHmTPkOqP2ObddzxOvPd3q+IyUdBZv+A0vP/0z4uDsyHvPvc3X73/Br1uWYOdgB4DSRMn3//uW+JNxhEX2BOCzNz/GztGe71b+iIWlBQCzb53LG4//h58+W8iMBbOwsLTAxs6GJTuXG3xWc2+7gfvn3M2SH/9kyJhhBjalJ6fx45qf8Qv2B2DstPEsGH096/9a2+xstcCQrDNleNnbYapsu+88lpVLmbqW0SF639nH2wN7SxU7E1IIdb8833kiO4/bFhr7zusiw3hgTOu+M7u0nK9unYOHnd532luq+HhrFD/ujeHb265vipVVShI/7z9CUkExPdz0vvPb3QewtTDnkwUzUZnqfef0yDDe37iT3w8eY0pECCpTU6zNzfjxzvkGn9V1vcN4Yska/j4ay0A/Q9+ZeaaMz2+ajU/jLO6IYH/u+WkZW+KSmp2t7qh0OrFaU12Dysriksbs2xiFq7cbgeF6sadQKhg8cRgHtuzj5ifuQGnS9j+Qs/j3DGTeQzcbtTu6ObVpfNiAiCahChAYoV+W6ztqQJNQBQiO1IvpopxCAnoGUV1RxemDJ5l5z/Vo6jRo6jRNfXsP60vM1v2kxaUS1j8chVKBQqmfPG+ob6BWXYss6wjp15MDm/c1Y1N4k1AFsHOyx8PPk6KcwjbdU3enobYeU4u2v0SBPgTAytUGhwC9w5IUCrwGBZB9MI3ImwY3/f+7FOz9nYi4YYBRu4Vj2zawuIS5NwlVAIdG0ezR16dJqAI4Bunbq4sqcfB3RlNdR+HpXMJm9EGr0aLVaJv6uvXyIicmnbL0EpxD3ZEUCs7emq5BS0NtA7Is4xziRvbBNGObQj0Mwg1UdhZYu9uhLqpq0z0JoLpKfckbQLes2oSnrxdhvfViT6lUMm7aeLav28rDLz2Gicmlf4WERoZx/9MPGLW7ejS/P+BC+g3p3yRUAXr2CQdg+PiRTUIVILyffqNMXlYuYZE9qSyv4NDeGO54+C7qauuoq61r6jt49FB2rN9Owsl4+g7ph1KpRNkoBuo19dSoa9DpdPQZ1Jdta7c2a9NZoQrg6OyIb5AfeVm5bbqn7o5aU4/lJUxAAexMSMXDzoaQRrGnVCgYGRzAnqQ07hs1GKXi0n1nsKsTdw4z9p3Obdz8F+nl3iRUAULc9D5ycICPwaausEYxnV9RSQ83Z6pq6ziWlcuCQX2oa9BS13DOdw7w8yIqOZ2kwhIivdxRKhScVSz1Wi219Q3oZJkITzd2Jxn7zkgvjyahCuBgaYG3gx355Z3Ld3Y6sWphZWGwc7816mpqOborhqGTR1CQdS5lSmCvYLYu3cjJ/cfoO9L44WwNazsbIgZHXvK4szi5Oxv8fvZLxNHVUOxa2uj/SKor9Ck1CrLykWWZ1QuXs3rh8mbPXVla0fTvXau2s/mP9eSl5yDLclO7JEnGNjWT7svS1orqis71ULcXJipTGmrbtlwE0FBXT+7RTHyGBFJVcO7/mWOgC6nb4ig4lYNHH59LtsPM2hzXcM9LHneWC0XtWQFu1G6pn3Wqr9a/MFUVVIAM8WuOE7/meLPnrqs497ebvjuR5C2nqcwrA/m8TsaPJhZOxl8WZlZmaKrrjDsLmsXK2hJ1lbrN/WvUNURt3cPEmZPIPm95MbxvBCt++YuDuw8wfPyIS7bD1t6Ogf9gRsfV083gdysbfTqjC8WudWN7ZXklANnp2ciyzOIvFrH4i0XNnrvsTGnTv9cuXc1fi5aQkZLRqu90aybdl7WtDRXlFUbtAmMszUypaeNSO0BtfT370zIZ0yOQ3LJzn3GomwtrT8RxOCOHwQGX7jttVeb09bl83+liY+inrBoFuMsFYteqcca+qnGyKbe8Ahn4M+Y4f8Y07zvLzwsx3BSbyKrjp8k+U9aa68TVxth3WpubUVXXuXxnpxOrHv5eZCSkUa+pb1MowOEdB6mrqWPXqu3sWrXd6Hj0hqjLEqv/FEULb30tz6TpH0lZp//v5JunGy3jn8U7SP9HGr0xip/e/Y6+owYw/Y5Z2DrYolAqiVq7k/2bjGt1t2STbPDnIGgJG097yjJK0NZr2xQKkHs4E21dA+m7E0nfnWh0PCs65bLE6j9FUjTn8lpub1KajV/oQZPCce/t3WxPWy99mpis/SkcXbwP9z4+hEzrhbmNCkmpICMqmewDqZd+bUGr+AX5kxibgKZO06ZQgD2bd1GrrmHtktWsXbLa6PiW1ZsuS6z+U1r2nc3/zZ0VmjqdDoD5dy8wWsY/S2DjTvAtqzbx4cvvM3z8CG7+123YOzmgVCrZuHwdW9dsabNNsng824SPgz0pRSXUa7VtCgXYl5JJbX0Dm04nsum0se/cmZhyWWL1n6Jo5kXmYu1n/Zeu8UGZ3Secgf7N+06/xhRbOxNS+GLHPgb7+zCvfy/sLFQoFQq2xiWzK9HYd7Z0bbmTPZydTqz2Gz2Q5BOJxGyNZvj00a3214cAuDPvIeP8bEd2xXBkZww11TVYXGJoQXvh4q2fVVAqla3O7MZs24+LlyuPffCMwWxA1NqdV9PEbotHXx/OJBeSE5OO7/DW059k7U/BytWm2SX73COZ5B3NoL7m0kML2gurxtABhVLR6sxuTkw6Vi42DH10vMGzmRGVfFVt7M6MmDCKU0dOsnPDdibPmdpq/y2rNuHl593skn3Ull3s2bIbdVU1lp0kP6pXY25YpVLZ6szuzo078PTx5J1v3jd4PjcuN85BK/jnDAn0IS6/kD1J6YwPa9137kxIwcPOptkl++jUTKJTMy4rtKC9OBs6oFQoWp3ZjUpOx93WhleuM/SdW+O6tu/sdNkAxs6diIOrI39+9is5qVlGx+tqaln6+W8AlBWXEnfoFAPHD2HQhKFGPxPnT0VTp+HQ9gPX+jYuG1sHW8IH9dKn8CowrqJVcV4IwNm3/fPfoApzCjiyM+bqG9oNCRgTioWDJSeXxlCRU2p0vKGunlPLDgFQW6amKC4Pr4H+zf4ETQhDq9GSezj9Gt/F5WNuo8KlpwfpuxJQnzGuBFNXeW4ZSzo7E3Xey311USV5RzOutpndlpk3z8bF3ZWv3vuctCTjGZgadQ3fvP8lACWFxRyOPsyYqWMZO22c0c/c2+dRV1vHro07r/FdXD72Tg70Hz6QNUtWU5hXYHS8rOTc36yyGd+Zm5nDni27r76h3ZCpEaE4W1vy494YMkqMfWdtfT2L9up955lqNcez8xgR5M+IYOOfGb3D0DRo2deJMjHYWajo4+3BptgEipqponV+CIBCanw2zzueX15JdGrX9p2dbmbV0tqSxz54lo+f+D9ev/Mlhk4aTkBEEEqlkty0bA5siUbWydz46K0c2LwPnVZHv9HNL/MH9gpuzAqwxyA/aVsoLy5l34Y9Ru2SQrrqeUlvf+4e3rn/NV699XnGzB6Pm487lWWVZCSkcWr/Cb7eoY/H6jt6AId3HuSzZz6g76gBlBaVsn35Ztz9PMlMTL+qNnZHTC3NGProBPZ9soUdb67Be0ggDgHOSAoFlXll+o1Dskyv+QPJOpCGrJNxb2GZ3yHQpTErQKpBftK2UFtWQ2Z0ilG7JElXPS9pn9uGsvu99Wx/bRX+o0OwcrVFU1VLWUYJhbG5zPzyVgDc+/qQeySD6M+34dHXh9pSNak74rF2s6M868xVtbG7Ym1jzTvf/B/P3/s0/5pzLxNmTKRn73CUJkrSk9PYvm4bsk7HA88/zLa1W9FptYyY0Hzhk/C+EU1ZAc7PT9oWzhSVsHnVJqN2hSRd9bykT77+NI8seJB7ZtzJjBtn4u3vQ3lpGYmxicTsOcD6Y/ol/uETRrJ78y5e+vfzjJgwkuKCIv7+bSU+Ab4kx11+ykNB81iZm/Hy9Am8sWYLTy5dw+gegYS4OaNUKMg8U8aepDRkWebuEQPZnZiGTpYZ0sIyf6ibC/aWKnYkphrkJ20LZ6pr2JHQjO9Euup5SR8cM5Tnlq/n0T9XMSU8BE97WypqakkpKuFIZi5L/633nUMCfIhOzeCttdsYEuBDSbWa9Sfj8ba3I7W46/rOTidWAfzDAnjrjw/Y9Ps6ju05zMGt0cjIuHi6MXzaKCbeqF/i2rdxD7YOdk077S9EkiT6jhrAnjU7KC08g4OrcTLilshKzuT71780alcoFVddrLr7evL64vdYtXA50ZuiqCytwMbeFs8ALxY8dmtTv5HXjaHyTAU7VmwhNuYUbt5u3PzEHRRm5wuxepWw93NiwptzSN4cS96xLHJi0pBlsHKxwXdYEEET9Luqs/anYG6rajE9lSRJuPfxISMqmZrSaiwc2r7UWpFdyuEfmn+Rutpi1cbdjnGvziR+zXGy9qdSV1mLubU5Np729LpxYFM/vxHB1FXWkrYjnqLTuVi52hJ502CqCyuEWL2KhESEsmjdLyxd9Cf7tkWxY/02ZFnG08eTybOncP0d8wF9CICDk0PTTvsLkSSJ4eNHsOGvdRTlF+FyCamCUuKTefeZN43aFUrlVRerPgG+fPf3QhZ/sYitazZTdqYMOwc7/IIDDFJMTb1+OmUlpaz6/W8O7zuEl58XD7/8GDkZ2UKsXiWCXZ344pY5/H00lgPpWexJTgMZ3OxsGBcaxMw+et+5IzEFewsVIS08c5IkMdjfh61xyZRUVeN0CWEq6SWlfLTF2HcqpKsvVr0c7Ph4wUz+jDnOzsRUKmpqsVWZ4+Nozz0jzvnOCT2DKa+pZf2peI5n5+JhZ8t9owaTV1bRpcWqdC2DbC2sLArf+PV9F1cvt9Y7C7oMMdv28/P7P2yuLKvssDUWJUl6PnhKxNuRNw7qlC9wgstn9YO/1Gk1Wg9Zlo3XHzsw9o72O59998UxIyeKss9dgen9JtWoq9QBsiwbxyh0YGwtVJseGjts8sjzUncJuj755ZU89ufqIrVG07acc/+QThezKhAIBAKBQCDoPgixKhAIBAKBQCDosAixKhAIBAKBQCDosAixKhAIBAKBQCDosAixKhAIBAKBQCDosAixKhAIBAKBQCDosAixKhAIBAKBQCDosIickhcQfziW9x96q9ljL377OiF9w1ocm3o6hegNe4g7dIrivCIsrC3xDwtkzv3z8AsNMOj7zJxHKMkzLpcK+trVP+z7ren3U/uPc3DbftJPp5CTlo1CqeT7Pb9cxt0JOjtlGSWcXnmUkuQCZK2Mvb8T4XP74xzSeu7iovg8oj4wrhwEMOqFaTj3MDxH2s54UrbFU11YgamlGR79fIm4fgBm1uZG/Yri8ylNK0ZdXIVrL09GPHl1k7sLOh6JsQn8+tViEk8nUlp8BnMLFf5B/tx03y0Mn3BphVI2r9rEu8+8iamZGVtidxgdX/X7Slb+upyc9Gys7WwYOWEU9z/zALb2tgb9xvYY0ez573nifu54+K5LsknQtdgen8KKIyfJLa/A3tKCST17cOPA3k2ldluioqaWrXHJHEzPIqe0nLoGLZ72tkzrFcqk8B4oJMmgf3FVNX8cPMbx7DxKq2twsLKgr48nCwb2wcXmXMGC3YmpbIlLIqOkjMraOuwtVYS4uXDL4L74OTlclc+gMyHEaguMnzeZoF6Gpdrc/TwvOmbDz6tIPJbAwPFDmLTAj6qKKnau2Mpbd7/CU5++QPigyKa+tzx5J7XqWoPx1RVV/P7RYnoN7W3Qvn/zXg5sica3hx/OHi6cKey6VSoELVOWWcLu/9uAua2K0Ot6o1AqyIhKYu//NjHymSk49WhbsY3A8WE4XFA5y8bdzuD32OWHSVx/Eve+PgSOD0NdXEXq9jjK0ksY/eJ0lKbKpr6J609SX1OPg78zmkrDZ1rQfcjNzEVTp2Ha9dNxcnOmprqGPZt38dIDz/PkG88w+5a5bTqPuqqab97/EpWlBdoGrdHx7//3Db998wsjJoxk7q03kJ+Tx4pf/iLhVDxfLPkGM3Mzg/79hw1g6g2GJWGDe15aGU5B12Lz6UQ+376PQf7ezOwTTlrxGf44eIxSdQ0PjR120bHx+UX8vP8wA3y9uWFAJOYmSg5n5PDFjn3E5RXyxMRzL2ZVtXU8vWwdWp2Oab1Ccba2Iru0nI2xCRzOyOHLW+ZgaWYKQFpxKXYWFszu64mtSsUZtZqtp5N5atla/nvDdIJcnK7qZ9LREWK1BUL6hDFk8vBLGjP5luv491uPYWJ67mMdNXMcL9/0NCu+XWogVvuPGWQ0fvvyzQAMm2pYkeaGB2/irhf/hYmpCT+8+RUHtkRfkl2CrsHplUeRlBJjXroOlZ0FAP5jQtj68kpO/HmQcf+Z2abzOAW74j2k5dKBtWVqkjadwmuQP4MfGNvU7hjsyoEvtpOxJ5HA8T2b2kc9Nw0LJyskSWLTc8su7+YEnZ6x08Yxdto4g7br75jHv+bcw9If/2yzWP3p8x+xtrGm/7AB7Nq40+BYSWExf/7wO+Omj+e1T8+tgEX0j+SVB19g3bI1zL3tBoMxXn7eTJ7dYYvnCa4xmoYGFu87TB9vD16dMbGp3crcjGWHTjCjd098He1bHO/rZM+3t12Pm61NU9u0XmF8vHUP2+KTmTcgEm8H/ct/VHI6Z6rVvDlrMv18z012ednb8uXOaI5n5TIsyA+AO4cPMLrW5PAQ7v5pKetPxvPo+OZXCboLImb1ItSqa5t9s2+JHr1DDYQqgK2DLaF9e5KbltPq+OiNUagsLeg3eqBBu4OLo9F5Bd2PkqQCXMI8moQqgIm5KR79fClLL6GqoKLN52qorUen1TV77ExqEbJWNhK0nv18MTE3IetAmkG7pbM10gVLXwIB6EOaXNxdqaqoalP/jOR0VvzyF4+88jhKpdLo+OljsWgbtEyYaRhmMnLiKCysLNi2dmuz562rq6Outu7Sb0DQ5TiRk09FbR3X9TYM6ZveKwwZ2JucftHx7rY2BkL1LCOC/AHIKi1ralNr6gFwtLIw6OtoZQmAucnFv9ftLFSYKU2ortNctF93QCigFvjpve+oVdeiUCro0TuU+Y/cYhQW0FbKikuxtrO+aJ+i3EKSTyQy4rrRmKnMLtpX0D3R1WtRmhn/yZ5tK0svxtrN1uj4hRxdvI+GugYkhYRjsCu95g/E8bywAF29/gXNpJlrKcxMKM88g6yTkRRCoAqMUVer0dRpqKqsYu/WPRzcc4Bx08a3aeynb33MoJFDGDxqCNvWbDE6rmn88lepzI2OmZubk3w6EZ1Oh+K8uMMtqzezdslqZFnGJ9CX2x64gylzp13m3Qk6OylFJQCEuBqGQjlZW+Jsbdl0/FIpqVYDYKtSNbX18nIH4Otd+7lr2ABcbKzIKi3np32HCHVzoY+Ph9F5qmrr0MoyZ6rVrDp2mpr6evr6XDwEsTsgxOoFKE1NGDhuMJHD+2Fjb0NuWg4bf1vL/z34Ji9++zqB4UGXdL74w7GknEpixl1zLtovemMUAMOnjbpoP0H3xdrdjjMphei0OhTKc1/GxYn5ANSUqS86XmGixHOAH26R3pjbmFORW0byplj2vL+B0S9MxyHAuek6AMVJBbhGnHOSFbllTTGpGnUd5tYq44sIuj0fvfoBW1frQ5oUSiWjJ4/hidefanXczg3bORlznEXrW9486hvgC8CJQ8cZOHJwU3tGcjplZ8oAqCyvxK5xGbZX/0jGThuPh48HxQXF/P3bCt577m2qK6u4/o75l3uLgk5MaXUNYDzbCeBgadkkOi+Feq2WVcdicbWxItTtnAgOcXPmgdFD+GX/EZ5dvr6pfaCfN89PHdPsZq5H/ljVZIOFqQkLBvZmckTIJdvU1RBi9QJ69A6lR+/Qpt/7jR7IwPFDePXW51j+1R88+8UrbT5XWXEp3772BS5erlx355yL9t2/KQoHF0fCBkRcrumCLk7g+J4c+3kfhxfuIWR6bxRKidTt8ZRl6DfcaTUXD1lxCnbFKdi16XePvr54DfBn++uriF1+mJHP6OP67P2ccAx2JXnTKSzsLXHt5UlNSTXHfz+ApFQga3XoWrmWoPty6wO3M/X66RQXFLF19Wa0DQ00tBJOVVtTy1fvfcH1d8zD29+nxX49IkLo1T+SJQv/wNnNhUGjhlCQk89nb32MiakJDfUNBsv9Xyz5xmD89Hkz+Nfce1j48fdMmzcDC0tjwSLo2mgatJgoFM2GLpmZKKm8jHCRr3ZGk1NWwaszJmCiNBSgrrbWBLk4MdDfGw87W5KLSlh59BQfbYnixWljjex4dsoYauvrySuvZEtcEhqtFlmWoZuHWgmx2gbcfNzpN3ogh3YcoKGhAZNW4kwA1JXVfPT4e9TX1fPMZy+jsmx5FiotLoW89Fym3jrDYPlKIDifgDEh1JZVk7j+JNmNcaPWbraEX9+f2GWHMFGZXvI5rd1s8ejrS87hDHQNOhQm+udvyINjifluF8d+adzMJ4HPsCCsXG3IO5J5WdcSdA8CegQS0EMf7zx5zlQeu+VhXnnwBb5c+m2LY379ejH19fXc8fDdrZ7/jc/f5s0nX+ejVz8AQJIkJs+ZgpevF3u27MbS2rLFsaZmpsy97QY+evUD4k/G0W9I/0u8O0FnQavTUVFjmJ3E0twMMxMlDTodOlk2SjOladBiZmIcK30xfj1wlK1xydwxrD+DLnjRis0t4O1123n/+mmEeegnCoYG+uJmY81n2/cSk57N4ADDMRGe57K6jAj256HfVmKiUHDHMOMNWN0JIVbbiKObE9oGLbXVta3Gn9bV1PLxU+9TkF3As1+8jFeg90X7R2/QhwBcmAVAILiQnrP7ETw5goqcMpSmSux8HEnfnQiAtXvr8arNYeFohazV0VBb35RDVWVvyajnplFdVEnNmWosXWywdLRi5zvrMLdVYWop4qoFraNQKBg9eQxfvfc5edl5eHgbx+iVFBazZOGf3PbA7VSUV1BRrt8oWKOuAVkmLzsPc5U5js6OADi5OvPpb1+Qm5lDUX4RHj4euHq48dD8f+Hg5IC1zcX9s2ujaKgsa/uGREHno7iqmvt+Xm7Q9viEETg0Lv+XVqtxsrYyOF6qVl9Siqi/j8WyJOY4s/qEM39Ab6Pjm2ITsVWZNwnVswwN9OWz7Xs5nVdgJFbPx8HSgnAPN3Ylpgqx2t4GdBaKcgowMTXBopk4l/NpqG/g8+f+R9rpFB7/8FmCIy8ea6LT6jiwZR/ewb74hvhdSZMFXRRTCzOD5fzC2FyUZiYGbZdCdVElChMFJhbGs6VWLjZYueh3vmqq6ijLKMF7kP9lXUfQPTm7LK+uqm72+JniM9RrNCz6bCGLPltodPzmcfPoP3wgHy3+1KDd09cLT18vACrKKkiMTWBsGzZy5WblAmB/kfREgs6Pg6UFb802zBrh62hParE+bCqxsJhh54nVkio1xVVqJoe3LT50y+kkfoyKYXxoEPeNNE5FCVCmrkEry0btWp2u8b/Gxy5Eo21oyirQnRFi9QIqSiuwdTCcocpMzODonsNEDO6NsnGJoKy4lJoqNS7ebk1hATqtjm9e+YzTh07xwFuPETmsb6vXi405ScWZcqbccl2rfQWCCylOyCf3aCZBE8IwtTg321lbpqa+RoOVi23T0n5dZS3mNobhKOVZZ8g7loVruKfBpq3mOPXXIWSdTNCk8Ct/I4JOT2lJKQ4XVNqpq61j25rNWFpZ4t04g1RSWExVZTVevl6YmJrg4ePJW1+9Z3S+FT8v4+ThE7z26VtG572Qbz/4Cp1Wx/y7FzS1lZWUYn/BOHVVNX/9tBRbe1tCe/e88DSCLoSZiUmzu+itzc2wUZmz/mQ8wwLPTRCtPxWPBAwPOtdWW99AUVUVtioVdhbnfOfe5HS+2LGPIYG+PDZhRIup+7wc7DialcuRjBz6+3k1te9O0odxnT+LW6auwf6CGOqs0jJO5xUS7tG2gi9dGSFWL+Drlz/FzNyM4N4h2DrYkpuWzc6/t2NmbsaNj97S1O+vr/5g77rdfLDyM5w99TNaSz77hcM7D9JraB8a6hvYt2GPwbmb2+kfvWEPkkJi6OSWE/5mJenFMkBWciayVsfqH1cA4OzuzPDpo//xfQs6PsUJ+cStPoZbhBdmNuaUZ54hfU8SDo0lV88ndvlhMvelMPn9G7By1s+MHvxmJ0pTE5yCXTCzsaAyr4z0XYkozZT0mm+4xHT89wNoNQ3Y++qXXnMOZ1Acn0/EvAE4+Dsb9M07lkV5ln62or6mnuqiKuLXHAfAOcQN51D3q/J5CDoWbz7xKqZmZvTq1wtHVyeKC4rZvHIDuVm5PPH605ib60NMvvvwGzat3MAfO/7Cw9sDaxtrRk0y9mFRW3YjSQqjY5+9+TF1dXVNVah2b9rJ0f1H+PezDxHa61zuzJW/rSBqy26Gjx+Bm6c7JYXFrF++jsLcAl54/+UmewTdCzMTE+4cNoAvduzjzbVbGRLgS1rxGdafjGdyRIhBadOkgiJe+nsTNw/qwy1D+gGQWFDMh5t3Y2lmyiB/7ybheZae7q642+l97ozIMLbFJfHuhh1cFxmGh50NyUUlbDmdhK+jPSOCzwnjexf/xYhgf/ydHLA0MyW7tJwtcUlISNw+VMRWC7F6Af3HDGT/pig2/b6O2uoarO1t6D92ELPvvQF3X+N4q/PJSEwH4NT+45zaf9zo+IVita62jqO7DxHaLxxHt5bjZDIS0lj57VKDtrO/h/bvKcRqN0HlYInCREHS5lgaajRYOFrRY0oEIdN7Y2Le+p+yZz9fsvankrT5NA21GsysVXj29yVsVl+j/Kz2fk6kbDlN9sE0JMDO14mhj47Ho6+v0XlzD6eTuS+l6fd6tYa4v48CEDarjxCr3YTJs6ey6e8NrPjlLyorKrGytiK0VxiPvfokQ8deWjXAixHSK5S/flrKtrVbkSTo0TOEd775P0ZMMPSvkf0jiT1yknXL1lJRVo65SkXP3j159u3nDdJeCbofUyJCMFEoWHH0FN/s2o+9pQU3DerDjQP7tDo280wpDTodVXUaPt++z+j44xNGNIlVLwc7Plkwi98OHCUqOY0z1TXYWqiYFN6D24f2x/S8whfX9Q7jeHYeB9OzqK2vx97CgsH+Pswf2PuiFbW6C5LcTDzF1cLCyqLwjV/fd3H1ElPa3YmYbfv5+f0fNleWVXbYmoeSJD0fPCXi7cgbB4kXuG7G6gd/qdNqtB6yLJe2ty2Xgr2j/c5n331xzMiJYmNmV2B6v0k16ip1gCzLBe1ty6Vga6Ha9NDYYZNHBvu3tymCa0h+eSWP/bm6SK3RXN5miUtE5EkSCAQCgUAgEHRYhFgVCAQCgUAgEHRYhFgVCAQCgUAgEHRYhFgVCAQCgUAgEHRYhFgVCAQCgUAgEHRYhFjthkSt3cndQ26iOLewvU0RCAzIiEpi5b0/UV1c2d6mCAQGbFi+jrE9RpCXndfepggERmyNS2LmFz9RUNE1fWe3SNNTVV7Jpt/XcXTPYYpzC9HpdLh4uhE5rA8Tb5yKs4eL0Zi1P61k+ddLCIrswSs/vNXU/sycRyjJK271mrPvu4E598+/aH93P0/eW/rR5d8YUKuuZeNvawjrH07YgIh/dC7BtaeuqpbkzafJP5ZJdXEVsk7GysUGt15eBE3oiaWzcZ3zhHUnOL3iCI5BLox56Vzls03PLUNd0nxJy/MJm9WHnrP7XbS/tbstk965/vJvDGiorSdp0ymcQ91xCbt4jmJBx6O8tJxli/5k77Yo8rLz0Gm1ePp4MnjMMK6/fR7uXsb5c3/9ejE/fPQdEf168eXSb5vaF4y9gYKc/Faveeej93D3Y/detL9PoC+/bPrj8m8MUFerWbLwD/oO6Ue/ISLhemekoqaWv4+d5mBaJgWVVeh0Mm52Ngzw9WJm75642hr7zqWHTvDL/iOEubvwwbxzvvPexcsorGzdd54tDnCx/l72tnxz2z/znTWaelYePUWklzuR3sJ3QjcQq5mJGXz0xHtUV1QxeNJwxl8/CYVSQU5qNtEboti3YQ+fbfzOaFz0hihcPF1JOZlEYU4BZ3PD3vLkndSqa5v6HY86wsGt0dz8xB1Y29s0tfsE+xr8e+ptM42uYWlt+Y/vr66mllU/LIf7EGK1k1GedYa9H2+hvroOr8EBBIwNQ1JKVOSUkbU/hczoFK775CajcVnRKVg6W3MmpYjqokqsXPTPXeRNg2moa2jql388i5yYdCJvGoSZ9blSgXbe5yq02Ho70GNqL6NrnF+69XJpqKsnfvVxwmYhxGonIzkuiefufZrKsgrGz5jA7FvmolQqSUtMZcvfG9m8cgN/H1hnNG7Lqs14eHsSe/QUuZk5ePrqS0w+8vLj1KhrmvpF79jLjnXbePilx7A7L+F5UGjQuX+HBbPgvnNVA89ifV4998ulRl3D4s9/BO4RYrUTklZ8htdWb6Gyto7RPQKYFhmGUpLIOFPGzoQUdiSk8Ou9xr5zZ0IKbrbWxOcXkV9e2ZS8/75Rg6mtP+c7Y9Kz2JOUzn0jB2F7XplV//OqW/k7OXB9f2PfaWX2z31nbX09f8ToCwsJsaqnS4vVmio1nz37AVqtllcXvYNPDz+D4zc8eBOrfvjLaFx6XCq56Tk89ckLfP3yp0RviGL2fTcA0H/MIIO+pYVnOLg1mv5jBjaVXb0QO2eHZkutCrov9TUaoj/bhqzVMfaVGdj5OBocj7i+P3GrjxmNK00vpjKvnOFPTOTgt7vIik4hbFZfADz7Gz7fNaXV5MSk49HPt6nk6oWo7C3wHRbU7DFB96S6spqXH3gebYOWb1b8QFBYsMHx+57+d6PQMyThVDwZKem8v/B/vPn4q2xZtYk7H70HwKhkalF+ITvWbWPkpNF4tPBl7OjixOTZHbaOiKCdUGs0vLVuG1qdjo9unEGAs6HvvGNof/6IOWY0LrmwmKzScl6fOZH/btrFjoQUbh7cF4BhgYa+s7iqmj1J6QwN9MXNtnnf6WhlwbhQ4TuvFV1arO78exsl+cXc++qDRkIVQGWpYsFjtxm1R2/cg72LAxFDejNg7GD2bzonVq81p2NOsnrhCrJTMqmvq8fOyZ6wAeHc88oDFOcW8uzcxwBY9cNy/QwrMOK60dz36kMApJxM4o9PfyYjIR1bB1vG3zAZW0e7drkXwTnSdyVSc6aa/veMNBKqACYqUyJvHGTUnrU/FZW9Ja4RXnj29yNrf2qTWL3WFJ7OJX7NcSqyS9E1aDG3tcAlzIP+d4+guriSzc/rn8f41ceJX62fJfAdHsSAe/UvbmdSCjm5JIayjBLMbS0IGBeKytaiXe5FcI41S1ZRkFvAC++/bCRUASytLHnwhUeM2res2oSzmzODRg5m1OQxbF2zuUmsXmsO7zvEz18sIjUxBU2dBkdnJ/oN7c9z771IXnYeN4+bB8Diz39sEt5T5k7jxf++AkDs0VN89d7nJMYm4uDkwOxb5+LoZPx3Krj2bDyVSFFlNU9MGGkkVAEszEy5Z4Sx79yRkIqjlSX9fL0YFujHrsTUJrF6rTmelcsfMcfJKClFo9XiYGlBby8PHpswgoKKSu77We87/4g53jTDOj4siCcbq9XF5xeyMCqGlKIS7CwsmB4ZioNF1/adXVqsHt11CBMzU4ZMantdap1Wx/7N+xg6ZQQKhYIhk4cTtW4XKaeSCOrV47Ls0DY0UFlWYdRuZm6G+XlLDBeSk5rNx0/9F68Ab2bdcwPmFuYU5RZyZFcMADYOttz2zN38+uEi+o8dxICx+nrXrt5uTeM/ePRtLKwsmXn3XExMTdj197aLXlNwbcg9monCRIH34IA2j5F1OrIPpOI9JBBJIeE9OIDMvcmcSS3CMdA47rpN52yQqausNWpXmikxMTdtcVxFbhnRn27F1suBsFl9UJqZoi6uJPdoJgDmNir63DqE478dwKO/b9Os79mQhYrcMqL+txlTlSmhM/qgMFGQvjsRE/Mu7ZI6BVFb92BqZsb46ya2eYxWq2Xb2q1MnDkJhULBhBkT2bhiPaePxRLe9/LCk7QNDZSdKTNqN1eZY2HZ8hdzelIaL9z/LAE9Arjj4buxsLQgLzuXPZt3A2DvaM/jrz3Fp298xKhJoxk1ZSwAXo0hC+lJaTx91xNYWVty+0N3Ympqypolqy56TcG1Y39aJqZKBaND2u47tTodu5NSGdMjEIUkMTokgG3xySTkFxHqfnm+s0EnU15j7DvNTZSoTFv2nZlnynhj7VZ8HR24aVAfVKamFFRUEp2q9512FioeGD2Eb3YfYFigL8OC9L7To3GGN/NMGf9ZtRlLU1MWDOyDiVLBpthEVKZd23d26bvLTc/Gw9cDU7OWH5wLiT14gooz5QyZPAKA8EGR2DraEb0x6rLFatyhWB6b8i+j9vHzJnP7sy3PPMQePEGDpp6nPn0RWwfbpvZ5D90MgLmFioHjh/Drh4vwCfY1CjVY+e1SGuobePHb13D11m+GGDljLC/Me+Ky7kNw5ajMLcPa3Q6lqbLNYwpjc6mrqMVnSCAAruEemNuqyIpOuWyxWhSfx/on/jRqDxwfRp9bh17UFl2DjuFPTsLc5tzLT8QNAwAwMTfFc4Afx387gJ23g1GoQdzKo+gadIx6YRrWrvpn229kD7a8uPyy7kNw5chITsM30Bcz87bH3h3aG0Np8RkmzJgEQP/hA3FwdmTL6k2XLVaPRB9mzpDrjNrn3HY9T7z29EVtqddo+O/C/2F/Xozh/U8/AICFpQWjp4zl0zc+IjAs2CjU4MdPvqehvp7Pfv8KLz9vAKbdMJ1bJxnHQAquPVlnyvCyt8NU2XbfeSwrlzJ1LaND9L6zj7cH9pYqdiakXLZYPZGdx20LjX3ndZFhPDCmZd95LCuXeq2ON2ZNwu68iaM7hul9p8rUlOFBfnyz+wD+Tg5GoQa/7j9Kg1bH/900DQ87ve+c2LMH//6la/vOLi1Wa6prUFld2tvwvo1RuHq7ERiuf0AUSgWDJw7jwJZ93PzEHShN2v4Hchb/noFNAvN8HN2cLjrO0ka/keDo7kOMmjkWhaLtmcZ0Wh0n9x+n78gBTUIVwNbBlmFTRrJ9+eY2n0tw5WmorcfUou0vUaAPAbBytcEhwBkASaHAa1AA2QfTiLxpMArlpWeis/d3ahKY52PhePFNLKaWeiGTdzQTv5E9kBRSm68p63QUnMrBo49Pk1AF/Wys99Ag0nbEt/lcgitPdZX6kjd/blm1CU9fL8J69wRAqVQybtp4tq/bysMvPYaJyaV/1YRGhjUJzPNx9Wh+b8BZrBt3gUdt28P0eTMuyW9qtVoO7jnA8HEjmoQqgL2TA5NmTebv31a0+VyCq4NaU4/lJUxAAexMSMXDzoYQN73vVCoUjAwOYE9SGveNGozyEp6RswS7OnHnMGPf6dzKBsCzG7D2p2YyKbwHCqntvlOr03EkM4dB/j5NQhX0s7FjQoNYf7Lr+s4uLVYtrCwMdu63Rl1NLUd3xTB08ggKss6lTQnsFczWpRs5uf8YfUcaP5ytYW1nQ8TgyEseN2TiMKLW7OSnd79j2Re/03NgBP1GDWDwpOGYtDLlX1lWgaa2Dnc/480LzbUJri0mKlMaauvb3L+hrp7co5n4DAmkquBcSIljoAup2+KaxN+lYmZtjmu45yWP8x7sT0ZUEkcX7+PUX4dxCXPHo68v3oP9UbTyQldXWYtW04C1u63RMZtm2gTXFitrS9RV6jb3r1HXELV1DxNnTiI7I7upPbxvBCt++YuDuw8wfPyIS7bD1t6Ogc3EHrbGuOsmsOGvdXz48vt898HX9Bs6gBETRjJu+oRWV9nKzpRRW1OLT6Cv0TGfAOM2wbXH0syUmvq2+87a+nr2p2UypkcgueeF44W6ubD2RByHM3IYHHDpvtNWZU5fn0v3naN6+LMlLokvduxj8b7D9PZ2Z3CAL6N6+Lc6W1xRU0tdQwPeDsZ+0su+a/vOLi1WPfy9yEhIo15T36ZQgMM7DlJXU8euVdvZtWq70fHoDVGXJVYvF1NzM5776j8kHovnxL6jxB44yfdvfMXG39by0vdvorIUsaedFRtPe8oyStDWa9sUCpB7OBNtXQPpuxNJ351odDwrOuWyxOrlojQ1YdSzUylJKiD/RDaFsbkcXriHpE2nGPPidExUlzbzIeg4+AX5kxibgKZO06ZQgD2bd1GrrmHtktWsXbLa6PiW1ZsuS6xeLubm5nz86+eciDnOgV3RxEQd5N1n32LJwj/4/M+vsbT65ykDBe2Hj4M9KUUl1Gu1bQoF2JeSSW19A5tOJ7LptLHv3JmYclli9XIxMzHh3blTic0t4HBGNkczc/l46x5WHj3Ff2+YjsUlzhp3F7q0WO03eiDJJxKJ2RrN8OmjW+2vDwFwZ95DxrFJR3bFcGRnDDXVNVhcYmjBP0GhUOgT/vcPh0dgz5qd/Pj2Nxzcuo/Rs8a3OM7G3hYzlTn5GcbVVpprE1xbPPr6cCa5kJyYdHyHt57+JGt/ClauNs0u2eceySTvaAb1NZceWvBPkBQSzqHuOIe6w3x99akji/aSHZOG/6gQoPnlLXMbFUozE6ryjTcdVjbTJri2jJgwilNHTrJzw3Ymz5naav8tqzbh5efd7JJ91JZd7NmyG3VVNZZXID9qW1EoFPQd0o++Q/rx7+ceYsNf63j/xXfZsX4b182fidTC0qu9oz0qCxVZjZtdzicrzbhNcO0ZEuhDXH4he5LSGR/Wuu/cmZCCh51Ns0v20amZRKdmXFZowT9BIUn6hP9e7tw1HLaeTuLT7XvZk5zG5PAQaOH5tLVQYW5iQnapsZ/MaWYTd1eiS5dbHTt3Ig6ujvz52a/kpGYZHa+rqWXp578BUFZcStyhUwwcP4RBE4Ya/UycPxVNnYZD2w9cM/uryo3LpvmF6ndAqiv1y3Rnd/ZXVxhW01AoFfQa2ptjUYcpzD4X0lBRWkH0pqirZbKgjQSMCcXCwZKTS2OoyCk1Ot5QV8+pZYcAqC1TUxSXh9dA/2Z/giaEodVoyT2cfs3sr6syDq+x99PHYNerNQBNO/vrqzUG/SSFAtdenuQdz6Kq8JyDrausJXt/ytUyWdBGZt48Gxd3V75673PSklKNjteoa/jm/S8BKCks5nD0YcZMHcvYaeOMfubePo+62jp2bdx5zewvLy03ausREQJAVUUVAKpGv3mhj1UqlQwaOZh9O/aSc15IQ1lJKVtWizj/jsDUiFCcrS35cW8MGSXGvrO2vp5Fe/W+80y1muPZeYwI8mdEsPHPjN5haBq07EtOv2b2VzSTQSDIVe87q+v0vlLVGONdVWfoO5UKBf19PYlJzyKv/JzvLK+pZVdC1/adXXpm1dLaksc+eJaPn/g/Xr/zJYZOGk5ARBBKpZLctGwObIlG1snc+OitHNi8D51WR7/RzS/zB/YKbswKsIdRM8dekh3lxaXs27DHqF1SSAybMrLFcasXriD+yGn6jOiHs4cL6ko1O//ehpm5GX1HNe4ctFTh7ufJwS3RuPt6YG1ng7OnC0G9ejD3Xzdyav8J3vv3G4yfNxmliZJdf2/D2cOFrKSMS7oHwZXF1NKMoY9OYN8nW9jx5hq8hwTiEOCMpFBQmVdG9sE0kGV6zR9I1oE0ZJ2MewvL/A6BLo1ZAVLxG3lpGStqy2rIjDZ2cpIk4TM0sMVxCWuOU5yQj1tvHyydrKmv0ZC+KwGlmRKPvvrYPhOVKdbutmTHpGHtbouZlTmWLjY4BroQPrsfhady2fN/GwgcH4ak1KeusnS2pjzL+AtIcO2wtrHmnW/+j+fvfZp/zbmXCTMm0rN3OEoTJenJaWxftw1Zp+OB5x9m29qt6LRaRkxovuhJeN+IpqwA0+YZ7+y/GGeKSti8apNRu0KSmDhrcovjfv5yEccOHGXYuOG4e3lQVVHJmiWrMVeZM2KC3t9aWlniE+jL9nVb8Q7wwdbeDg9vD8L7RnD3E/cTE3WQx255iDm33oCJiQlrlqzC3cudlPjkS7oHwZXHytyMl6dP4I01W3hy6RpG9wgkxM0ZpUJB5pky9iSlIcsyd48YyO7ENHSyzJAWlvlD3Vywt1SxIzGVieGX5jvPVNewoxmBKCExNrRl37nk0HFO5uQz0M8HN1trqus0bIxNwMxEyZDGuGgLM1O87G3Zk5SGl70tNipz3GxtCHV34dYh/TiSmcsLyzcwvXcYJgp96ipXW2vSiruu7+zSYhXAPyyAt/74gE2/r+PYnsMc3BqNjIyLpxvDp41i4o36Za59G/dg62BHYIRxEmzQf3n3HTWAPWt2UFp4BgfXtieIzkrO5PvXvzRqVygVFxWr/UYPpKSgmL3rdlNZVoGVrTWBEcE8+M7juPue2yR1738e4I+PF/PnZ7/SoKlnxHWjCerVA+8gH575/CWWfPorq39cYVAU4Me3v2mz/YKrg72fExPenEPy5ljyjmWRE5OGLOtzkfoOCyJogn5nddb+FMxtVS2mp5IkCfc+PmREJVNTWo2FQ9uXWyuySzn8Q/MvUhcTqx79fFGfqSZzXzKaylpMrcxxDHBm8ANjsXY7F+jf/+6RnPzzICeXxKBr0OE7PAjHQBdsvR0Y8fRkTi6JIX7NcYOiAEcW7W2z/YKrQ0hEKIvW/cLSRX+yb1sUO9ZvQ5ZlPH08mTx7CtffMR/QhwA4ODnQs094s+eRJInh40ew4a91FOUX4XIJaYJS4pN595k3jdoVSuVFxerIiaMozC1g44oNlJ8pw8belp59wnnt0zfx9j8nWl74v5f54u1P+eq9L6jXaJgydxrhfSMIDAnkw58+4av3PufnL38yKArw/ovvttl+wdUj2NWJL26Zw99HYzmQnsWe5DSQwc3OhnGhQczso/edOxJTsLdQEdLCcydJEoP9fdgal0xJVTVOlxCqkl5SykdbjH2nQrq4WB0S4EtRZTXb45Mpr6nFRmVOiJszz00Zi+d5m6SemDCS76MOsnBvDPVaHePDggh1d8HPyYG3Zk9mYVQMS2KOGxQF+HR71/WdkizL1+xiFlYWhW/8+r6Lq5fbNbumoP2J2bafn9//YXNlWWWHrZ0oSdLzwVMi3o68cVCXf4ETGLL6wV/qtBqthyzLnWpawt7Rfuez7744ZuREUcq5KzC936QadZU6QJblgva25VKwtVBtemjssMkjg/3b2xTBNSS/vJLH/lxdpNZoLp5L7grRpWNWBQKBQCAQCASdGyFWBQKBQCAQCAQdFiFWBQKBQCAQCAQdFiFWBQKBQCAQCAQdFiFWBQKBQCAQCAQdFiFWBQKBQCAQCAQdFiFWBQKBQCAQCAQdFpFTshlq1bVs+HUNaadTSItLoaqsknkP3cx1d85u0/i1P60kNVY/tqyolLFzJ3LnC/cZ9fu/B98g4Uhci+f5aM1XBsUHdqzYwrZlmyjIysfSxop+owcy76GbsbazvvSbFHRKGmrrSdp4itL0YkrTitFU1RF+Q39Cp/du0/iaM9XErT5GUVweteU1qOwscI3wJHRGHywdzyXEjlt1lPjVx43GSwqJOd/f2fR7UXweUR8YVxk6i9+oHvS/a8Ql3KGgs6KuVrPkh9+JPxlH/Il4ykvLuP+ZB7j137e3+RwFufks+nQhB/ccoLK8EidXJ/oO7scL77/c1CczNYM1f64i7kQcyacTqa2p5cul3xLRr5fR+WL2HGDHhu0knIwnLSkNpVLJltgdV+R+BZ2Leq2WP2OONyXj93awY/6A3ozqEdDq2OKqav44eIzj2XmUVtfgYGVBXx9PFgzsg4vNOb/5+4Gj/BFj7DcVksSqh+80aKutr+e3A8eISk6nTF2Di40Vk8NDmNsvAqVCzCNeiBCrzVBVVsHqhctxcHXEL8Sf2IMnL2n88q+XYOtgR0BEEGVFLecZn3nXXEbPGm/QptPp+Ond73D39TAQqn999QfrFq+i76gBjJ83meK8IrYt3UR6XCov//Ampmaml3aTgk5JXVUt8WuOY+Fgib2vE4Wnc9s8VlNdx8531qHT6ggcG4qFoxWVeeWk7Uqg4GQOE96cg6mF4XPU59YhmFiYNf0uSZLBcRsPewbcZ5yUPu9oJrmHM3Dr5XWJdyjorJSXlrP4i0W4uLvSI7wHh/bGXNL49KQ0Hr/1EWwdbLnhjnk4ODlSXFBE7LFTBv1ij55i+eJl+AT4EtAjkLgTp1s859Y1W9i+bhvBPYNx93KnKL/osu5N0Pn5bPtediemcV1kGH5ODuxLyeC/m3YBXFSwVtXW8fSydWh1Oqb1CsXZ2ors0nI2xiZwOCOHL2+Zg+UF378PjB6Cpfl5fhNDv6nV6Xh9zVbi8wuZFhGKr5M9p3MLWRx9mKKqah4cM/QK3nnXQIjVZrBzduCjtV/h4OJIcW4hz8597JLG/3flZ7h46os63D3kphb7RQwxng07se8o2gYtw6aeEwBlxaVs/HUtgyYO5aF3nmhqD44M5fPnPmT3qu1MmN9hi0MJriAqO0umfngjFg6WVBdXsvn55W0em3MondoyNSOemoxrhGdTu7W7Lcd+jqYoLhfP/n4GYzwH+KGys7yIPRb4Dgsyak/dFoephSnufZqvyS3oeji5OPFX1N84u7mQl53HzePmtXmsLMu8/fQbuHu78+lvX6KyULXYd8SEUYw+NBYrGys2LF93UbF6/9P/5pm3n8fUzJT3nnub7eu2XdI9CboGyYXF7ExI5bYh/VgwqA8Ak8N78MKKDSyMimF4kF+Ls5lRyemcqVbz5qzJ9PM95ze97G35cmc0x7NyGRZk6DeHB/nhYNWy39yfmklsbgEPjhnK9MgwAKb1CsPWQsWa46eZFhGKv7PDP73tLoWYa24GUzNTHFwcW+/YAmeF6uUQvTEKSZIYOvXc0mnKqSS0Wi1DJ4806Nt/zEBUlioObN532dcTdC6UpkosHFp2ghejoaYe0AvM8zkrRpVmzby7ylBfo0HWtb0sc1VBBaVpxXgO9EdpqrwsWwWdDzNzM5zdmq/B3hqH98aQHJfEXY/ei8pCRW1NLQ0NDc32tbW3xcqmbTXcnd1cxKqTgKjkdBSS1CQMQb9KND0yjJJqNafzClscq9bo/aajlaHfdGwUo+Ymxn5TBtQaDboWytnH5uor6o4JCTRoHxsSiAzsSU5r9Z66G2JmtQNRV1PL0d2HCOkXhpObc1N7g0bvtM1VZkZjTM3NyEhMR6fToRBxLoKL4BzqDsCxX/cTMW8Alk76MIDYvw7hEOiCS08PozFbXlpBQ10DSjMTPPr6ELlgECr7i4vlrOgUAHyGBl60n0BwlpiogwCoLFU8OO9+4o6fRmmiZPCooTz5xtO4eri1s4WCzkxKUQnutjbYqMwN2kMav2dTikqI9HJvdmyvxvavd+3nrmEDcLGxIqu0nJ/2HSLUzYU+PsZ+84FfV1BT34C5iQlDAny4d+SgJnEL+vhZCTAzMXyZNzfVS7LkwuLLvteuihCrHYjDO2Ooq6lj+DTDGEB3P/0fQ+KxeIPQgdy0HCpLKwBQV1ZjbWdz7YwVdDocApzpc+sQTq84wu731je1u/X2ZvADY1Aoz73smFqaEzg+DMcgVxQmCkqSCkjdEU9pWjHjXp2JqaXxi9NZsg6kYuFo1SSOBYLWyM7IBuD1R/9D/2H9uem+W8jOyOaXL3/iydsfY+GaxRcNDRAILsaZxk1RF+Jgadl4XN3i2BA3Zx4YPYRf9h/h2eXn/OZAP2+enzrGIHzAWmXOjMgwQt1dMVUqOJ1XwLqT8SQWFPPJgplYNcaxejnYIQOn8wrp431O7J7KyQegpKple7orQqx2IKI3RmFqbsrA8YbB1X6hAQT3DmHjb2uxd3EkcmhvivOL+e1/P6E0UaJt0KKp1YBdOxku6DRYOllj5+eEe29vrFxtKcsoIXnTKQ7/EMXgh8Y2baAKnhRuMM5roD8OAS4c+n43KdviCJvZp9nzn0kppLqwkpDpkUabsQSClqhR1wAQEhHCa5++1dTu5uHG20+/wZZVm5h5U9uysQgEF6Jp0GKqNA5JOjuzqWnQXnS8q601QS5ODPT3xsPOluSiElYePcVHW6J4cdo5vzmrj6HfHBHsTw9XF/63ZTdrT8Q1xcuODQnkz5jjfL59Lw+MHoqvoz2n8wr5df9RlAoJjfbi9nRHxLpxB6G8pIzTMSfpM6I/ltbGy6wPv/ckARFB/Pz+Dzw79zH++9Bb+Ib402dkfwBUzbw1CgTnU5xYQPTn2wmf258eU3rh2c+X8Dn96H3LUHKPZJB/PPui432GBmJuZ0FRXMsZCLL2pzb1FQjairm5fnl24qzJBu1jp49HaaLk5OET7WGWoItgZqKkvhkBeFakXrgcfz6xuQW8vW47tw/tz9x+vRga6MttQ/rxwOihRKdmEJN+cb85NjQQB0sLjmWd85v2lha8OmMCAG+s3cq9P//F59v3cvPgvlibm2NhKuKsL0TMrHYQDmzeh06rM8gCcD72zg688PVrFOYUUFp4BmdPF5zcnHn73v9g62DXrMAVCM4nfXci5jbmOAUbbgD07OfL0Z/2UpJcgEffi+/et3S0QlOtafaYTqsj+2Aadj6O2HqJnayCtuPsqo8ddHA23NhqYmKCrb0dleWV7WGWoIvgaGVBQUWVUXupWt14vOXvz02xidiqzAnzMPSbQwN9+Wz7Xk7nFTA44OJ+09naiqo6Q78Z7uHGd7ffQEZJKWpNPX5O9pgqlfwQdbDF+NnujJhZ7SBEb4rC2s6G3iP6XrSfq5cbof164uTmTFV5FenxqYQPibw2Rgo6NXUVNc3u6pd1Ov1/tRff8S/LMtXFVZhbNx87WHgqB01VHT7NpLISCC5GSK9QAIryDXdla+o0lJeWY+9o3w5WCboKQS5OFFRUUVlbZ9CekF/cdLwlytQ1aJvZ1a9t9JvaVjKlyLJMYWUVts3EXCskiQBnRyI83bA2N+d4Vh46Waavj2cza5kFcAAACPdJREFUZ+reCLH6D6gsqyAvPYe6C/4ALpW8jFzS41IZNHEoJs2kwWiJZV/+jk6nY8pN0//R9QVdj7rKWirzymioO5f+x9rdDk1VHQWncgz6Zh3Qp0mx93MyGH8haTsS0FTW4hrZfKL/rP2pSAoJnyGtV4QRdF/KzpSRkZJBbc25Z2zkpNGYmZux4a916BpFAKD/Xatl0KjB7WGqoIswPMgfnSyz/mR8U5ssy2yMTcDRypLwxlnT8ppaskrLqK0/5ze9HOyorK3jSIah39ydpPeb5wvd8hpjv7n+VALlNbUM8L14gZTa+np+PXAEJytLxoQIH3ohIgygBbYu24i6Uo26shqAuMOxaBtjXoZPG4Wzhwvblm1i1Q/Lef6r/xA2IKJp7L71uynOP5d6IiMhjdU/rgCg36gB+PQwTCAcvWEPAMOmGuZRPZ/f/vcTmjoNvo1jD+04SPzhWOY/cgv+PUV8YHciZVsc9WoN9Wr9slJxfH7TrKjvsCAsna1J3R5H/OrjjHx2Ci5h+t2mgePDyIxK4sCXOwgcH4aVqw1lGSVk7EnCxtMezwHnnstNzy3Da1AAtl4OKE2VlCQXkH0wDVtvBwLHhRrZVF9TT96xLFzCPFpNbSXouqz45S+qKqqoqtQvuR7bfwRtY1zg5DlTcfdyZ+Wvy1n8+Y98/Ovn9Buij7l3cHLgzkfv4fsPv+HpOx9n9JSx5GRks/LX5UQO7M2YqeOarlFVWcWKn/8CIPl0IgAbV6zn8L5DANzx8F1NfVPik9m7LQqA1IQUdDotP3/5EwDuXu5MnjP16n0Ygg5DiJszY0IC+f3gMSpqa/F1dCA6NYPY3AKenjS6aUf/uhNx/BFznHfnTCGycZf+jMgwtsUl8e6GHVwXGYaHnQ3JRSVsOZ2Er6M9I4LP+c17Fi9jVHAA/k4OmJooOZ1bwJ6kNPydHJgeaeg3X1ixgVA3FzztbamsrWPL6SSKq6t5feYkVCJm1QghVltg429rKck7JzhjD5wg9oA+yD+kTyjOHi0nv969ZgcJR+Kafk87nULaaX3uSUdXRyOxun/zXly8XOnR21gEnMUvLIAtf27gwOa9SJKEb4g/j33wDP1GD7ys+xN0XpI3nUJdUt30e2FsLoWx+uB9px6uWDpbNzvOxt2Oca/NIu7vo+TEpFFTVoO5jQq/UT0In9vfIIG/95BAzqQUkXs4A229FksnK3pM6UXojN6YmBs70ryjGWg1DfgMEy9O3ZklC/+goDH9Dujzp57NoRo5sDfuF4nFu/Xft2Nja8Pyxcv46t3PsXWwZc4t13PvU/9Ced5O7srySn785HuDsWv+XNX07/PFamJsglHfs7/3GdxPiNVuxOMTRuBqY832+GQ21CbgZW/HM5NHGyXmvxAvBzs+WTCL3w4cJSo5jTPVNdhaqJgU3oPbh/Y3yDIwNiSQuPwi9qVkUK/V4mJjxdx+vVgwqLeRAO3h6kx0agbFVdWoTE2J9HLnpenj8HMS8f7NIcktVFi4GlhYWRS+8ev7Lq5eIsFzdyJm235+fv+HzZVllR22JqwkSc8HT4l4O/LGQeIFrpux+sFf6rQarYcsy6XtbculYO9ov/PZd18cM3Ji85syBZ2L6f0m1air1AGyLBe0ty2Xgq2FatNDY4dNHhns396mCK4h+eWVPPbn6iK1RnP5JTsvARGzKhAIBAKBQCDosAixKhAIBAKBQCDosFx7sXoNww4EHQNZlpGh4/+P7/gWCq4CskynLLUly8jXMoxLcJXpvP8rxXPYDZGv8QN7TcWqpFDUaerqr+UlBR0ATW0dsk42zsjcsajVahoaWu8m6ErIsozcoDMBatrblktHVmv+Ydo8Qcehvr6+Uz6HsiyrWytXKuh61NVrUUiSca6uq8S1FauSFJcen3otLynoAKTEJtfV1fx/e/fTGlcVx2H8e+ZfkgkKjWJEWkFFdCFIVi5cC6IFV74AV67dKK6kSwVRX0Cp0EU1XaWtEDsaSw0WVIi2tGmYakxGIzF/jM3k3sy9c+7PheAqODgN956ZPJ/1DOe3OMw8l3Nn7v6PRc/Rw9LWnT8OfjQThtbd33ZUqpW3zSy3D93DEkfxwuL1W1z9D4HW8qpKpVIkaeAe1RWn6cLS+gZXTUfMnY1Nyel271cejlxjNdrd+/jqzFybI4OjI+0k+rZxLfNdP130LD3MtdfvVvY2Bu67Avegde2nrszOFT1HP9IknW7MXE7ThF4ddI0Ll7NSqTQ9iOfpPrPzV5vLGQdTR4eZqXGr2d7rJGfyWjPve1ZnWs2VtU8+PJt0Uzb2sIvbkT54493IsmzWzJaKnue/mFninDv19XuzUbQZ+h0LuFdmppX5pv08t9j2if+o6Hn6dCNNkitvv/5mFO1FRc+CPpiZGjOf26enz+3GUfx+0fP0w8xum9nsqYtfRFHC4dSwS73X6fnvkuXN7TVJF/JaN9f/WZUk59zE2Hj9kvd+6tnnp/xDxyfHKtUK/0owRJL9xK82V6KlhcVatVY9H7ej18xsIK5OKiOVtyyzd+4/fix94MnJenW0WhnMn9/gIJaZ7e9End9/aPks9ZvdTvdlM7tZ9Fz9cs7Vxu8bP5t00lemnptKHn/qifrI2Gi59ztRpMx729rY6nzz5bzv7He24yg+aWbXi56rX865Sr1WPZN6/+ozjzycPPbgRH2kUmYfDpFulmVrf+3G3//ya7lccgtRkp40s+281s89Vv9d2LkTkl6UNCmepDVsOpJWJX1mZjsFz/K/OedGJb0g6WlJ4wWPg8OVSfpT0leSbg7isetBnHMTkl6SdELSSMHjoLdM0o6kK5JuDNE+PKZ/9uGjYh8Om66kdUmzZtbKe/HCYhUAAADoheN3AAAABItYBQAAQLCIVQAAAASLWAUAAECwiFUAAAAEi1gFAABAsIhVAAAABItYBQAAQLCIVQAAAASLWAUAAECwiFUAAAAEi1gFAABAsIhVAAAABItYBQAAQLCIVQAAAASLWAUAAECwiFUAAAAEi1gFAABAsIhVAAAABItYBQAAQLCIVQAAAASLWAUAAECw/gZdq8nkJ6d6hwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "ca.plot_heterogeneity_tree(\n", - " pd.DataFrame(x_test, columns=boston_data.feature_names),\n", - " \"RM\",\n", - " max_depth=2,\n", - " min_impurity_decrease=1e-6,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the global level, we know that the ATE of `RM` is 4.5, which means in average adding one more room will raise the housing price by 4.5 units. In the shallow tree above, we could see although overall `RM` has a significant positive effect on housing price, housing price will be more expensive for one more room in regions with lower pupil-teacher rate, and the effect will be insignificant in the regions with higher pupil-teacher rate and lower retail business rate. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Policy Analysis -- What is the best policy considering cost?\n", - "To take a step further, we'd like to know the sub-population where the treatment effect will still be positive after taking cost into consideration. Assuming the average cost of adding one more room is 4 units, let us see what kind of houses have a housing price that will increase more than their cost. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "ca.plot_policy_tree(\n", - " pd.DataFrame(x_test, columns=boston_data.feature_names),\n", - " \"RM\",\n", - " treatment_costs=4,\n", - " max_depth=2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You could see if we follow the recommended policy above, on average, the housing price will increase by 2 more units compared with no more room added. Similarly, it will increase by around 1.4 units compared with adding one more room for every house. To be more detailed, we could also output the individualized policy. In the following table, I will only print the top five houses ordered by policy gains." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TreatmentEffect of treatmentEffect of treatment lower boundEffect of treatment upper boundCRIMZNINDUSCHASNOXCurrent treatmentAGEDISRADTAXPTRATIOBLSTAT
35decrease5.1930294.1054456.2806134.222390.018.101.00.7705.80389.01.904724.0666.020.2353.0414.64
37decrease4.9712833.9781395.9644278.267250.018.101.00.6685.87589.61.129624.0666.020.2347.888.88
17increase4.9036313.1067256.7005370.0401180.01.520.00.4047.28734.17.30902.0329.012.6396.904.08
12decrease4.8294104.1652815.4935396.288070.018.100.00.7406.34196.42.072024.0666.020.2318.0117.79
91decrease4.7281593.7514515.70486811.160400.018.100.00.7406.62994.62.124724.0666.020.2109.8523.27
\n", - "
" - ], - "text/plain": [ - " Treatment Effect of treatment Effect of treatment lower bound \\\n", - "35 decrease 5.193029 4.105445 \n", - "37 decrease 4.971283 3.978139 \n", - "17 increase 4.903631 3.106725 \n", - "12 decrease 4.829410 4.165281 \n", - "91 decrease 4.728159 3.751451 \n", - "\n", - " Effect of treatment upper bound CRIM ZN INDUS CHAS NOX \\\n", - "35 6.280613 4.22239 0.0 18.10 1.0 0.770 \n", - "37 5.964427 8.26725 0.0 18.10 1.0 0.668 \n", - "17 6.700537 0.04011 80.0 1.52 0.0 0.404 \n", - "12 5.493539 6.28807 0.0 18.10 0.0 0.740 \n", - "91 5.704868 11.16040 0.0 18.10 0.0 0.740 \n", - "\n", - " Current treatment AGE DIS RAD TAX PTRATIO B LSTAT \n", - "35 5.803 89.0 1.9047 24.0 666.0 20.2 353.04 14.64 \n", - "37 5.875 89.6 1.1296 24.0 666.0 20.2 347.88 8.88 \n", - "17 7.287 34.1 7.3090 2.0 329.0 12.6 396.90 4.08 \n", - "12 6.341 96.4 2.0720 24.0 666.0 20.2 318.01 17.79 \n", - "91 6.629 94.6 2.1247 24.0 666.0 20.2 109.85 23.27 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ca.individualized_policy(\n", - " pd.DataFrame(x_test, columns=boston_data.feature_names),\n", - " \"RM\",\n", - " n_rows=5,\n", - " treatment_costs=4,\n", - " alpha=0.1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that here the `effect of treatment` is the treatment effect of increasing or decreasing 10% of average treatment level minus the cost, and `decrease` or `increase` mean in which direction we will get positive policy gain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What If Analysis - How the overall housing price changes with one more room?\n", - "The causal analysis tool could also answer **what if** types of questions. For a given treatment, we'd also like to know the **counterfactuals** if we intervene it in a different way. In the example below, we will learn how the overall housing price changes with one more room?" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current average housing price on test set: 22.21960784313725\n", - "Average housing price with one more room on test set: 26.916421568627456\n" - ] - } - ], - "source": [ - "cf = ca.whatif(x_test, x_test[:, 5] + 1, 5, y_test)\n", - "print(\"Current average housing price on test set: \", y_test.mean())\n", - "print(\n", - " \"Average housing price with one more room on test set: \",\n", - " cf[\"point_estimate\"].mean(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Histogram of Housing price -- Current vs. One more room')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# distribution comparison\n", - "plt.hist(cf.point_estimate, label=\"With one more room\", alpha=0.7)\n", - "plt.hist(y_test, label=\"Current\", alpha=0.7)\n", - "plt.legend()\n", - "plt.xlabel(\"Housing Price\")\n", - "plt.title(\"Histogram of Housing price -- Current vs. One more room\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary table we could see overall if we add one more room in the test set, the housing price will increase by 4+ units, which is in line with the ATE we learnt above. And the histrogram shows a comparison between the current housing price distribution and the counterfactuals ditribution if we add one more room." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cohort Analysis -- What is the causal effect on a new dataset?\n", - "Causal analysis class could also help us to learn the global and local causal effect of a new dataset given the model trained with training set. From the two tables below, you could see the global effect on test set is similar with training set, and the local effect gives you the heterogeneous treatment effect for each observation." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pointstderrzstatp_valueci_lowerci_upper
featurefeature_value
LSTATnum-0.4030590.078745-5.1185493.078958e-07-0.532583-0.273536
RMnum4.6967920.4804459.7759181.428563e-223.9065305.487054
PTRATIOnum-2.0648640.546016-3.7816921.557659e-04-2.962980-1.166748
NOXnum-5.1114126.638709-0.7699414.413351e-01-16.0311165.808292
AGEnum-0.0194040.018952-1.0238363.059127e-01-0.0505770.011770
CRIMnum-0.7513230.311513-2.4118511.587175e-02-1.263716-0.238930
DISnum-0.4178470.438805-0.9522383.409765e-01-1.1396170.303923
Bnum0.0008730.0055190.1582288.742774e-01-0.0082050.009952
INDUSnum-0.5468750.307997-1.7755857.580141e-02-1.053485-0.040265
TAXnum0.0012190.0132460.0920079.266925e-01-0.0205690.023007
RADnum-0.1532490.258233-0.5934535.528784e-01-0.5780060.271507
ZNnum0.0881380.1587700.5551345.788031e-01-0.1730140.349291
CHAS1.0v0.08.1945942.0862543.9278998.569134e-054.76301211.626177
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" - ], - "text/plain": [ - " point stderr zstat p_value ci_lower \\\n", - "feature feature_value \n", - "LSTAT num -0.403059 0.078745 -5.118549 3.078958e-07 -0.532583 \n", - "RM num 4.696792 0.480445 9.775918 1.428563e-22 3.906530 \n", - "PTRATIO num -2.064864 0.546016 -3.781692 1.557659e-04 -2.962980 \n", - "NOX num -5.111412 6.638709 -0.769941 4.413351e-01 -16.031116 \n", - "AGE num -0.019404 0.018952 -1.023836 3.059127e-01 -0.050577 \n", - "CRIM num -0.751323 0.311513 -2.411851 1.587175e-02 -1.263716 \n", - "DIS num -0.417847 0.438805 -0.952238 3.409765e-01 -1.139617 \n", - "B num 0.000873 0.005519 0.158228 8.742774e-01 -0.008205 \n", - "INDUS num -0.546875 0.307997 -1.775585 7.580141e-02 -1.053485 \n", - "TAX num 0.001219 0.013246 0.092007 9.266925e-01 -0.020569 \n", - "RAD num -0.153249 0.258233 -0.593453 5.528784e-01 -0.578006 \n", - "ZN num 0.088138 0.158770 0.555134 5.788031e-01 -0.173014 \n", - "CHAS 1.0v0.0 8.194594 2.086254 3.927899 8.569134e-05 4.763012 \n", - "\n", - " ci_upper \n", - "feature feature_value \n", - "LSTAT num -0.273536 \n", - "RM num 5.487054 \n", - "PTRATIO num -1.166748 \n", - "NOX num 5.808292 \n", - "AGE num 0.011770 \n", - "CRIM num -0.238930 \n", - "DIS num 0.303923 \n", - "B num 0.009952 \n", - "INDUS num -0.040265 \n", - "TAX num 0.023007 \n", - "RAD num 0.271507 \n", - "ZN num 0.349291 \n", - "CHAS 1.0v0.0 11.626177 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# global effect on new dataset\n", - "ca.cohort_causal_effect(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0LSTATnum-0.0866470.198795-0.4358620.662937-0.4136350.240341
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AGEnum-0.0632100.047780-1.3229300.185859-0.1418020.015382
...........................
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ZNnum-0.1591290.091308-1.7427760.081373-0.309316-0.008941
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1326 rows × 6 columns

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" - ], - "text/plain": [ - " point stderr zstat p_value \\\n", - "sample feature feature_value \n", - "0 LSTAT num -0.086647 0.198795 -0.435862 0.662937 \n", - " RM num 9.387421 2.121412 4.425081 0.000010 \n", - " PTRATIO num 0.610745 0.651034 0.938116 0.348185 \n", - " NOX num 25.792624 32.291454 0.798745 0.424439 \n", - " AGE num -0.063210 0.047780 -1.322930 0.185859 \n", - "... ... ... ... ... \n", - "101 INDUS num -0.595808 0.252946 -2.355475 0.018499 \n", - " TAX num -0.016860 0.013712 -1.229612 0.218842 \n", - " RAD num 0.102697 0.492187 0.208655 0.834718 \n", - " ZN num -0.159129 0.091308 -1.742776 0.081373 \n", - " CHAS 1.0v0.0 8.870247 4.602972 1.927070 0.053971 \n", - "\n", - " ci_lower ci_upper \n", - "sample feature feature_value \n", - "0 LSTAT num -0.413635 0.240341 \n", - " RM num 5.898009 12.876833 \n", - " PTRATIO num -0.460110 1.681600 \n", - " NOX num -27.322091 78.907338 \n", - " AGE num -0.141802 0.015382 \n", - "... ... ... \n", - "101 INDUS num -1.011868 -0.179749 \n", - " TAX num -0.039414 0.005694 \n", - " RAD num -0.706878 0.912272 \n", - " ZN num -0.309316 -0.008941 \n", - " CHAS 1.0v0.0 1.299032 16.441462 \n", - "\n", - "[1326 rows x 6 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# local effect on new dataset\n", - "ca.local_causal_effect(x_test)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Solutions/Causal Interpretation for Employee Attrition Dataset.ipynb b/notebooks/Solutions/Causal Interpretation for Employee Attrition Dataset.ipynb deleted file mode 100644 index eddf9663e..000000000 --- a/notebooks/Solutions/Causal Interpretation for Employee Attrition Dataset.ipynb +++ /dev/null @@ -1,1283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Causal Interpretation for Employee Attrition Dataset\n", - "\n", - "This notebook uses the popular kaggle Employee Attrition dataset to showcase how we could interpret a blackbox model from both the correlation and causation perspective, leveraging the power of model interpretation tools like [SHAP](https://shap.readthedocs.io/en/latest/index.html) and [EconML](https://aka.ms/econml). We start with a fine-tuned ML model and learn the top important features to predict employee attrition, it will help us to better understand the correlations between features and target and which features are the strongest predictors. In addition, this notebook will take a step further and focus more on figuring out which features cause the employees leave the company, instead of just predicting how likely they are going to leave. This extra causal interpretation could better help company to make corresponding changes in order to minimize the attrition rate. \n", - "\n", - "It includes the following sections:\n", - "1. [Train a Fine-tuned ML Model](#Train-a-Fine-tuned-ML-Model)\n", - "2. [Correlation Interpretation](#Correlation-Interpretation)\n", - " * Feature Importance -- Learn the top predictors for a given ML model\n", - "3. [Causal Interpretation](#Causal-Interpretation)\n", - " * Direct Causal Effect -- Do the top predictors also have a direct effect on outcome of interest?\n", - " * Segmentation -- How to make individaulized plans to reduce the attrition?\n", - " * Cohort Analysis -- What is the causal effect on a new dataset?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Some imports to get us started\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from lightgbm import LGBMClassifier\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "pd.set_option(\"display.max_columns\", 100)\n", - "pd.set_option(\"display.max_rows\", 100)\n", - "pd.set_option(\"display.max_colwidth\", 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train a Fine-tuned ML Model\n", - "### Load the employee attrition data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
149NoTravel_Frequently279Research & Development81Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
237YesTravel_Rarely1373Research & Development22Other144Male9221Laboratory Technician3Single209023966YYes15328007330000
333NoTravel_Frequently1392Research & Development34Life Sciences154Female5631Research Scientist3Married2909231591YYes11338008338730
427NoTravel_Rarely591Research & Development21Medical171Male4031Laboratory Technician2Married3468166329YNo12348016332222
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" - ], - "text/plain": [ - " Age Attrition BusinessTravel DailyRate Department \\\n", - "0 41 Yes Travel_Rarely 1102 Sales \n", - "1 49 No Travel_Frequently 279 Research & Development \n", - "2 37 Yes Travel_Rarely 1373 Research & Development \n", - "3 33 No Travel_Frequently 1392 Research & Development \n", - "4 27 No Travel_Rarely 591 Research & Development \n", - "\n", - " DistanceFromHome Education EducationField EmployeeCount EmployeeNumber \\\n", - "0 1 2 Life Sciences 1 1 \n", - "1 8 1 Life Sciences 1 2 \n", - "2 2 2 Other 1 4 \n", - "3 3 4 Life Sciences 1 5 \n", - "4 2 1 Medical 1 7 \n", - "\n", - " EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel \\\n", - "0 2 Female 94 3 2 \n", - "1 3 Male 61 2 2 \n", - "2 4 Male 92 2 1 \n", - "3 4 Female 56 3 1 \n", - "4 1 Male 40 3 1 \n", - "\n", - " JobRole JobSatisfaction MaritalStatus MonthlyIncome \\\n", - "0 Sales Executive 4 Single 5993 \n", - "1 Research Scientist 2 Married 5130 \n", - "2 Laboratory Technician 3 Single 2090 \n", - "3 Research Scientist 3 Married 2909 \n", - "4 Laboratory Technician 2 Married 3468 \n", - "\n", - " MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike \\\n", - "0 19479 8 Y Yes 11 \n", - "1 24907 1 Y No 23 \n", - "2 2396 6 Y Yes 15 \n", - "3 23159 1 Y Yes 11 \n", - "4 16632 9 Y No 12 \n", - "\n", - " PerformanceRating RelationshipSatisfaction StandardHours \\\n", - "0 3 1 80 \n", - "1 4 4 80 \n", - "2 3 2 80 \n", - "3 3 3 80 \n", - "4 3 4 80 \n", - "\n", - " StockOptionLevel TotalWorkingYears TrainingTimesLastYear \\\n", - "0 0 8 0 \n", - "1 1 10 3 \n", - "2 0 7 3 \n", - "3 0 8 3 \n", - "4 1 6 3 \n", - "\n", - " WorkLifeBalance YearsAtCompany YearsInCurrentRole \\\n", - "0 1 6 4 \n", - "1 3 10 7 \n", - "2 3 0 0 \n", - "3 3 8 7 \n", - "4 3 2 2 \n", - "\n", - " YearsSinceLastPromotion YearsWithCurrManager \n", - "0 0 5 \n", - "1 1 7 \n", - "2 0 0 \n", - "3 3 0 \n", - "4 2 2 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/EmployeeAttrition/Employee-Attrition.csv\"\n", - "attritionData = pd.read_csv(file_url)\n", - "attritionData.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n", - "attritionData = attritionData.drop([\"EmployeeCount\"], axis=1)\n", - "# Dropping Employee Number since it is merely an identifier\n", - "attritionData = attritionData.drop([\"EmployeeNumber\"], axis=1)\n", - "attritionData = attritionData.drop([\"Over18\"], axis=1)\n", - "\n", - "# Since all values are 80\n", - "attritionData = attritionData.drop([\"StandardHours\"], axis=1)\n", - "\n", - "# change the unit of income related variables\n", - "attritionData[[\"MonthlyIncome/1K\", \"MonthlyRate/1K\"]] = (\n", - " attritionData[[\"MonthlyIncome\", \"MonthlyRate\"]] / 1000\n", - ")\n", - "attritionData = attritionData.drop([\"MonthlyIncome\", \"MonthlyRate\"], axis=1)\n", - "\n", - "# Converting target variables from string to numerical values\n", - "target_map = {\"Yes\": 1, \"No\": 0}\n", - "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(\n", - " lambda x: target_map[x]\n", - ")\n", - "target = attritionData[\"Attrition_numerical\"]\n", - "\n", - "attritionXData = attritionData.drop([\"Attrition_numerical\", \"Attrition\"], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Split data into train and test\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(\n", - " attritionXData, target, test_size=0.2, random_state=0, stratify=target\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "categorical = []\n", - "for col, value in attritionXData.iteritems():\n", - " if value.dtype == \"object\":\n", - " categorical.append(col)\n", - "\n", - "# Store the numerical columns in a list numerical\n", - "numerical = attritionXData.columns.difference(categorical)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make training pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.compose import ColumnTransformer\n", - "\n", - "# We create the preprocessing pipelines for both numeric and categorical data.\n", - "numeric_transformer = Pipeline(\n", - " steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n", - ")\n", - "\n", - "categorical_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown=\"error\", drop=\"first\")),\n", - " ]\n", - ")\n", - "\n", - "transformations = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numerical),\n", - " (\"cat\", categorical_transformer, categorical),\n", - " ]\n", - ")\n", - "\n", - "# Append classifier to preprocessing pipeline.\n", - "# Now we have a full prediction pipeline.\n", - "clf = Pipeline(\n", - " steps=[(\"preprocessor\", transformations), (\"classifier\", LGBMClassifier())]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train a LightGBM classification model, which you want to explain" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "param_grid = {\n", - " \"classifier__learning_rate\": [0.1, 0.05, 0.01],\n", - " \"classifier__max_depth\": [3, 5, 10],\n", - "}\n", - "search = GridSearchCV(clf, param_grid, n_jobs=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'classifier__learning_rate': 0.1, 'classifier__max_depth': 3}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search.fit(x_train, y_train)\n", - "search.best_params_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Correlation Interpretation\n", - "We explain this ML model by understanding the top important features to predict the employee attrition, internally using shap value." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# get the fitted model and transformer\n", - "fitted_model = search.best_estimator_[\"classifier\"]\n", - "fitted_transformer = search.best_estimator_[\"preprocessor\"]\n", - "# get the feature name after featurization\n", - "column_names = numerical.tolist()\n", - "column_names += (\n", - " search.best_estimator_[\"preprocessor\"]\n", - " .transformers_[1][1]\n", - " .steps[1][1]\n", - " .get_feature_names(categorical)\n", - " .tolist()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Passing 1176 background samples may lead to slow runtimes. Consider using shap.sample(data, 100) to create a smaller background data set.\n", - " 95%|=================== | 280/294 [00:13<00:00] " - ] - } - ], - "source": [ - "import shap\n", - "\n", - "# use interventional approach\n", - "background = shap.maskers.Independent(\n", - " fitted_transformer.transform(x_train), max_samples=2000\n", - ")\n", - "explainer = shap.TreeExplainer(\n", - " fitted_model, data=background, feature_names=column_names\n", - ")\n", - "shap_values = explainer(fitted_transformer.transform(x_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the feature importance\n", - "shap.summary_plot(shap_values, fitted_transformer.transform(x_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the summary plot above, we could see the most important features sorted by their importance level. Taking the top 5 important features as an example, employees who often work overtime, have frequently changed jobs or live far away from their worksite are more likely to leave the company, and employees with higher income and stock options are less likely to leave. However, it doesn't mean these are the drivers that directly cause the employee to leave, it might have hidden variables that affect both the top features and outcome. For example, maybe the inefficient collaboration environment forces the employee to work overtime and also causes them to leave, instead of working overtime itself. In order to correctly find the direct reason and make improvements accordingly, we have to train a different model controlling on all the possible hidden variables (confounders) and learn the direct causal effect for a given feature. That's what the causal interpretation tool is doing. In the following session, we will explain the causal relationship for the top 5 important features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Causal Interpretation\n", - "### Direct Causal Effect -- Do the top predictors also have a direct effect on outcome of interest?" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "classification = True\n", - "k = 5\n", - "# get top feature names according to shap values\n", - "vals = np.abs(shap_values.values).mean(0)\n", - "feature_importance = pd.DataFrame(\n", - " list(zip(shap_values.feature_names, vals)), columns=[\"features\", \"importance\"]\n", - ")\n", - "feature_importance.sort_values(by=[\"importance\"], ascending=False, inplace=True)\n", - "top_features = feature_importance.iloc[:k][\"features\"]\n", - "# extract the raw feature name for top features\n", - "top_features = [i.split(\"_\")[0] for i in top_features]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.solutions.causal_analysis import CausalAnalysis\n", - "\n", - "ca = CausalAnalysis(\n", - " top_features,\n", - " categorical,\n", - " heterogeneity_inds=None,\n", - " classification=True,\n", - " nuisance_models=\"automl\",\n", - " heterogeneity_model=\"forest\",\n", - " n_jobs=-1,\n", - " random_state=123,\n", - ")\n", - "ca.fit(x_train, y_train.values)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pointstderrzstatp_valueci_lowerci_upper
featurefeature_value
OverTimeYesvNo0.2089620.0233488.9500433.553429e-190.1632010.254722
StockOptionLevelnum-0.0134640.019818-0.6793814.968964e-01-0.0523060.025378
NumCompaniesWorkednum0.0243160.0077753.1276221.762264e-030.0090780.039554
MonthlyIncome/1Knum-0.0116610.011280-1.0337943.012323e-01-0.0337680.010447
DistanceFromHomenum0.0042060.0024141.7420498.149992e-02-0.0005260.008937
\n", - "
" - ], - "text/plain": [ - " point stderr zstat p_value \\\n", - "feature feature_value \n", - "OverTime YesvNo 0.208962 0.023348 8.950043 3.553429e-19 \n", - "StockOptionLevel num -0.013464 0.019818 -0.679381 4.968964e-01 \n", - "NumCompaniesWorked num 0.024316 0.007775 3.127622 1.762264e-03 \n", - "MonthlyIncome/1K num -0.011661 0.011280 -1.033794 3.012323e-01 \n", - "DistanceFromHome num 0.004206 0.002414 1.742049 8.149992e-02 \n", - "\n", - " ci_lower ci_upper \n", - "feature feature_value \n", - "OverTime YesvNo 0.163201 0.254722 \n", - "StockOptionLevel num -0.052306 0.025378 \n", - "NumCompaniesWorked num 0.009078 0.039554 \n", - "MonthlyIncome/1K num -0.033768 0.010447 \n", - "DistanceFromHome num -0.000526 0.008937 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "global_summ = ca.global_causal_effect(alpha=0.05)\n", - "global_summ" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# helper function to plot error bar\n", - "def errorbar(res):\n", - " xticks = res.index.get_level_values(0)\n", - " lowererr = res[\"point\"] - res[\"ci_lower\"]\n", - " uppererr = res[\"ci_upper\"] - res[\"point\"]\n", - " xticks = [\n", - " \"{}***\".format(t)\n", - " if p < 1e-6\n", - " else (\"{}**\".format(t) if p < 1e-3 else (\"{}*\".format(t) if p < 1e-2 else t))\n", - " for t, p in zip(xticks, res[\"p_value\"])\n", - " ]\n", - " plot_title = \"Direct Causal Effect of Each Feature with 95% Confidence Interval, \"\n", - " plt.figure(figsize=(15, 5))\n", - " plt.errorbar(\n", - " np.arange(len(xticks)),\n", - " res[\"point\"],\n", - " yerr=[lowererr, uppererr],\n", - " fmt=\"o\",\n", - " capsize=5,\n", - " capthick=1,\n", - " barsabove=True,\n", - " )\n", - " plt.xticks(np.arange(len(xticks)), xticks, rotation=45)\n", - " plt.title(plot_title)\n", - " plt.axhline(0, color=\"r\", linestyle=\"--\", alpha=0.5)\n", - " plt.ylabel(\"Average Treatment Effect\")\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "errorbar(global_summ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We learn the Average Treatment Effect(ATE) for each of the top 5 important features, assuming they are the treatment. From the summary table and the error bar plot above, we see the causal effect directions we learnt here are in line with the correlation directions we learnt above. However, features like `StockOptionLevel` or `MonthlyIncome/1K` although they are the strongest predictors on how likely employees will leave, we are less confident to say these are the drivers causing them leave. This is super valuable for the managers when they are trying to make plans to reduce the employee attrition rate, improving work life balance or providing extra support for employees who are living far away from the company might be more effective than raise their salary/stocks. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Segmentation -- How to make individaulized plans to reduce the attrition?\n", - "From the analysis above, we learnt the direct treatment effect of each top features from an overall average level. However, people in different life stage or working experience might have different response to each of this potential reasons. Since the salary related features are not sigificant in an average level, we are interested to find the sub-groups who will respond positively to the income raise. If we could find a sub-group who have sigificant effect on income, we could further help the managers to refine their strategy and make individualized plans to different employees. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "ca.plot_heterogeneity_tree(\n", - " x_test, \"MonthlyIncome/1K\", max_depth=2, min_impurity_decrease=1e-7\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This shallow tree interpreter gives us clear guidance on what we should target. Employees with less than 1.5 years of total working experience have siginificant negative effects on monthly income, which means increasing income will definitely reduce the rate of attrition for them. On the other side, for employees who have already worked for this company for a long time, salary might not be an important driver if they decide to leave." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cohort Analysis -- What is the causal effect on a new dataset?\n", - "The causal analysis class can also help us to learn the global and local causal effect of a new dataset given the model trained with training set. From the two tables below, you can see the global effect on the test set is similar to the training set, and the local effect gives you the heterogeneous treatment effect for each observation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pointstderrzstatp_valueci_lowerci_upper
featurefeature_value
OverTimeYesvNo0.2080870.0462694.4973070.0000070.1319810.284193
StockOptionLevelnum-0.0137890.019792-0.6967160.485980-0.0463440.018765
NumCompaniesWorkednum0.0238020.0074993.1741530.0015030.0114680.036137
MonthlyIncome/1Knum-0.0125820.011563-1.0881370.276535-0.0316010.006437
DistanceFromHomenum0.0042140.0023231.8143520.0696230.0003940.008035
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" - ], - "text/plain": [ - " point stderr zstat p_value \\\n", - "feature feature_value \n", - "OverTime YesvNo 0.208087 0.046269 4.497307 0.000007 \n", - "StockOptionLevel num -0.013789 0.019792 -0.696716 0.485980 \n", - "NumCompaniesWorked num 0.023802 0.007499 3.174153 0.001503 \n", - "MonthlyIncome/1K num -0.012582 0.011563 -1.088137 0.276535 \n", - "DistanceFromHome num 0.004214 0.002323 1.814352 0.069623 \n", - "\n", - " ci_lower ci_upper \n", - "feature feature_value \n", - "OverTime YesvNo 0.131981 0.284193 \n", - "StockOptionLevel num -0.046344 0.018765 \n", - "NumCompaniesWorked num 0.011468 0.036137 \n", - "MonthlyIncome/1K num -0.031601 0.006437 \n", - "DistanceFromHome num 0.000394 0.008035 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# global effect on new dataset\n", - "ca.cohort_causal_effect(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pointstderrzstatp_valueci_lowerci_upper
samplefeaturefeature_value
0OverTimeYesvNo0.1588910.0475753.3398160.0008380.0806380.237145
StockOptionLevelnum-0.0179210.022392-0.8003330.423518-0.0547520.018910
NumCompaniesWorkednum0.0240370.0072143.3320120.0008620.0121710.035903
MonthlyIncome/1Knum-0.0309020.019577-1.5784810.114455-0.0631040.001299
DistanceFromHomenum0.0052030.0022872.2750260.0229040.0014410.008964
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293OverTimeYesvNo0.3366860.0718594.6853700.0000030.2184880.454884
StockOptionLevelnum-0.0418780.038472-1.0885140.276368-0.1051590.021404
NumCompaniesWorkednum0.0279480.0108512.5756540.0100050.0101000.045796
MonthlyIncome/1Knum-0.0548830.029937-1.8332590.066764-0.104126-0.005640
DistanceFromHomenum0.0064100.0041211.5555280.119820-0.0003680.013188
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" - ], - "text/plain": [ - " point stderr zstat \\\n", - "sample feature feature_value \n", - "0 OverTime YesvNo 0.158891 0.047575 3.339816 \n", - " StockOptionLevel num -0.017921 0.022392 -0.800333 \n", - " NumCompaniesWorked num 0.024037 0.007214 3.332012 \n", - " MonthlyIncome/1K num -0.030902 0.019577 -1.578481 \n", - " DistanceFromHome num 0.005203 0.002287 2.275026 \n", - "... ... ... ... \n", - "293 OverTime YesvNo 0.336686 0.071859 4.685370 \n", - " StockOptionLevel num -0.041878 0.038472 -1.088514 \n", - " NumCompaniesWorked num 0.027948 0.010851 2.575654 \n", - " MonthlyIncome/1K num -0.054883 0.029937 -1.833259 \n", - " DistanceFromHome num 0.006410 0.004121 1.555528 \n", - "\n", - " p_value ci_lower ci_upper \n", - "sample feature feature_value \n", - "0 OverTime YesvNo 0.000838 0.080638 0.237145 \n", - " StockOptionLevel num 0.423518 -0.054752 0.018910 \n", - " NumCompaniesWorked num 0.000862 0.012171 0.035903 \n", - " MonthlyIncome/1K num 0.114455 -0.063104 0.001299 \n", - " DistanceFromHome num 0.022904 0.001441 0.008964 \n", - "... ... ... ... \n", - "293 OverTime YesvNo 0.000003 0.218488 0.454884 \n", - " StockOptionLevel num 0.276368 -0.105159 0.021404 \n", - " NumCompaniesWorked num 0.010005 0.010100 0.045796 \n", - " MonthlyIncome/1K num 0.066764 -0.104126 -0.005640 \n", - " DistanceFromHome num 0.119820 -0.000368 0.013188 \n", - "\n", - "[1470 rows x 6 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# local effect on new dataset\n", - "ca.local_causal_effect(x_test)" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "mesameki" - } - ], - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Solutions/tombstone b/notebooks/Solutions/tombstone new file mode 100644 index 000000000..e69de29bb diff --git a/notebooks/Weighted Double Machine Learning Examples.ipynb b/notebooks/Weighted Double Machine Learning Examples.ipynb deleted file mode 100644 index ad49f02c9..000000000 --- a/notebooks/Weighted Double Machine Learning Examples.ipynb +++ /dev/null @@ -1,737 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double Machine Learning: Summarized Data and Interpretability\n", - "\n", - "Double Machine Learning (DML) is an algorithm that applies arbitrary machine learning methods\n", - "to fit the treatment and response, then uses a linear model to predict the response residuals\n", - "from the treatment residuals." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Helper imports\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "%matplotlib inline\n", - "\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generating Raw Data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import scipy.special\n", - "\n", - "np.random.seed(123)\n", - "n=10000 # number of raw samples\n", - "d=10 # number of binary features + 1\n", - "\n", - "# Generating random segments aka binary features. We will use features 1,...,4 for heterogeneity.\n", - "# The rest for controls. Just as an example.\n", - "X = np.random.binomial(1, .5, size=(n, d))\n", - "# The first column of X is the treatment. Generating an imbalanced A/B test\n", - "X[:, 0] = np.random.binomial(1, scipy.special.expit(X[:, 1]))\n", - "# Generating an outcome with treatment effect heterogeneity. The first binary feature creates heterogeneity\n", - "# We also have confounding on the first variable. We also have heteroskedastic errors.\n", - "y = (-1 + 2 * X[:, 1]) * X[:, 0] + X[:, 1] + (1*X[:, 1] + 1)*np.random.normal(0, 1, size=(n,))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating Summarized Data\n", - "\n", - "For each segment, we split the data in two and create one summarized copy for each split. The summarized copy contains the number of samples that were summarized and the variance of the observations for the summarized copies. Optimally we would want two copies per segment, as I'm creating here, but with many segments, the approach would work ok even with a single copy per segment." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.tests.test_statsmodels import _summarize\n", - "\n", - "X_sum = np.unique(X, axis=0)\n", - "n_sum = np.zeros(X_sum.shape[0])\n", - "# The _summarize function performs the summary operation and returns the summarized data\n", - "# For each segment we have two copies.\n", - "X1, X2, y1, y2, X1_sum, X2_sum, y1_sum, y2_sum, n1_sum, n2_sum, var1_sum, var2_sum = _summarize(X, y)\n", - "\n", - "# We concatenate the two copies data\n", - "X_sum = np.vstack([X1_sum, X2_sum]) # first coordinate is treatment, the rest are features\n", - "y_sum = np.concatenate((y1_sum, y2_sum)) # outcome\n", - "n_sum = np.concatenate((n1_sum, n2_sum)) # number of summarized points\n", - "var_sum = np.concatenate((var1_sum, var2_sum)) # variance of the summarized points\n", - "splits = (np.arange(len(y1_sum)), np.arange(len(y1_sum), len(y_sum))) # indices of the two summarized copies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Applying the LinearDML" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n", - "from econml.dml import LinearDML\n", - "from sklearn.linear_model import LogisticRegressionCV\n", - "\n", - "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n", - "# as long as it accepts the sample_weight keyword argument at fit time.\n", - "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n", - " model_t=LogisticRegressionCV(cv=3),\n", - " discrete_treatment=True)\n", - "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n", - " freq_weight=n_sum, sample_var=var_sum)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.07157889])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Treatment Effect of particular segments\n", - "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.87415187]), array([1.26900591]))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Confidence interval for effect\n", - "est.effect_interval(np.array([[1, 0, 0, 0]]), alpha=.05) # effect of segment with features [1, 0, 0, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['A' '2.0109841149829912']\n", - " ['B' '0.07212612018559361']\n", - " ['C' '-0.01525518572828466']\n", - " ['D' '-0.18928230056541373']]\n" - ] - } - ], - "source": [ - "# Getting the coefficients of the linear CATE model together with the corresponding feature names\n", - "print(np.array(list(zip(est.cate_feature_names(['A', 'B', 'C', 'D']), est.coef_))))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Non-Linear CATE Models with Polynomial Features" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n", - "from econml.dml import LinearDML\n", - "from sklearn.linear_model import LogisticRegressionCV\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "\n", - "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n", - "# as long as it accepts the sample_weight keyword argument at fit time.\n", - "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n", - " model_t=LogisticRegressionCV(cv=3),\n", - " featurizer=PolynomialFeatures(degree=2, interaction_only=True, include_bias=False),\n", - " discrete_treatment=True)\n", - "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n", - " freq_weight=n_sum, sample_var=var_sum)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Getting the confidence intervals of the coefficients and the intercept of the CATE model\n", - "# together with the corresponding feature names.\n", - "feat_names = est.cate_feature_names(['A', 'B', 'C', 'D'])\n", - "point_int = est.intercept_\n", - "point = est.coef_\n", - "lower_int, upper_int = est.intercept__interval(alpha=0.01)\n", - "lower, upper = est.coef__interval(alpha=0.01)\n", - "yerr = np.zeros((2, point.shape[0]))\n", - "yerr[0, :] = point - lower\n", - "yerr[1, :] = upper - point\n", - "\n", - "with sns.axes_style('darkgrid'):\n", - " fig, ax = plt.subplots(1,1)\n", - " x = np.arange(1, 1 + len(point))\n", - " plt.errorbar(np.concatenate(([0], x)), np.concatenate(([point_int], point)),\n", - " np.hstack([np.array([[point_int-lower_int], [upper_int - point_int]]), yerr]), fmt='o')\n", - " ax.set_xticks(np.concatenate(([0], x)))\n", - " ax.set_xticklabels([1] + list(feat_names), rotation='vertical', fontsize=18)\n", - " ax.set_ylabel('coef')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "# Getting the confidence intervals of the CATE(x) for different x vectors\n", - "fnames = np.array(['A', 'B', 'C', 'D'])\n", - "\n", - "lst = list(itertools.product([0, 1], repeat=4))\n", - "point = []\n", - "lower = []\n", - "upper = []\n", - "feat_names = []\n", - "for x in lst:\n", - " feat_names.append(\" \".join(fnames[np.array(x)>0]))\n", - " x = np.array(x).reshape((1, -1))\n", - " point.append(est.effect(x)[0])\n", - " lb, ub = est.effect_interval(x, alpha=.01)\n", - " lower.append(lb[0])\n", - " upper.append(ub[0])\n", - "\n", - "feat_names = np.array(feat_names)\n", - "point = np.array(point)\n", - "lower = np.array(lower)\n", - "upper = np.array(upper)\n", - "yerr = np.zeros((2, point.shape[0]))\n", - "yerr[0, :] = point - lower\n", - "yerr[1, :] = upper - point\n", - "\n", - "with sns.axes_style('darkgrid'):\n", - " fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n", - " x = np.arange(len(point))\n", - " stat_sig = (lower>0) | (upper<0)\n", - " plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n", - " plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n", - " ax.set_xticks(x)\n", - " ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n", - " ax.set_ylabel('coef')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Non-Linear CATE Models with Forests" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.dml import CausalForestDML\n", - "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", - "\n", - "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n", - "# as long as it accepts the sample_weight keyword argument at fit time.\n", - "est = CausalForestDML(model_y=GradientBoostingRegressor(n_estimators=30, min_samples_leaf=30),\n", - " model_t=GradientBoostingClassifier(n_estimators=30, min_samples_leaf=30),\n", - " discrete_treatment=True,\n", - " n_estimators=1000,\n", - " min_samples_leaf=2,\n", - " min_impurity_decrease=0.001,\n", - " verbose=0, min_weight_fraction_leaf=.03)\n", - "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n", - " sample_weight=n_sum)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "# Getting the confidence intervals of the CATE(x) for different x vectors\n", - "fnames = np.array(['A', 'B', 'C', 'D'])\n", - "\n", - "lst = list(itertools.product([0, 1], repeat=4))\n", - "point = []\n", - "lower = []\n", - "upper = []\n", - "feat_names = []\n", - "for x in lst:\n", - " feat_names.append(\" \".join(fnames[np.array(x)>0]))\n", - " x = np.array(x).reshape((1, -1))\n", - " point.append(est.effect(x)[0])\n", - " lb, ub = est.effect_interval(x, alpha=.01)\n", - " lower.append(lb[0])\n", - " upper.append(ub[0])\n", - "\n", - "feat_names = np.array(feat_names)\n", - "point = np.array(point)\n", - "lower = np.array(lower)\n", - "upper = np.array(upper)\n", - "yerr = np.zeros((2, point.shape[0]))\n", - "yerr[0, :] = point - lower\n", - "yerr[1, :] = upper - point\n", - "\n", - "with sns.axes_style('darkgrid'):\n", - " fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n", - " x = np.arange(len(point))\n", - " stat_sig = (lower>0) | (upper<0)\n", - " plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n", - " plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n", - " ax.set_xticks(x)\n", - " ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n", - " ax.set_ylabel('coef')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tree Interpretation of the CATE Model" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.cate_interpreter import SingleTreeCateInterpreter" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=1)\n", - "# We interpret the CATE models behavior on the distribution of heterogeneity features\n", - "intrp.interpret(est, X_sum[:, 1:5])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# exporting to a dot file\n", - "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# or we can directly render. Requires the graphviz python library\n", - "intrp.render(out_file='cate_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# or we can also plot inline with matplotlib. a bit uglier\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tree Based Treatment Policy Based on CATE Model" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from econml.cate_interpreter import SingleTreePolicyInterpreter" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=3, min_samples_leaf=1, min_impurity_decrease=.001)\n", - "# We find a tree based treatment policy based on the CATE model\n", - "# sample_treatment_costs is the cost of treatment. Policy will treat if effect is above this cost.\n", - "# It can also be an array that has a different cost for each sample. In case treating different segments\n", - "# has different cost.\n", - "intrp.interpret(est, X_sum[:, 1:5],\n", - " sample_treatment_costs=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# exporting to a dot file\n", - "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# or we can directly render. Requires the graphviz python library\n", - "intrp.render(out_file='policy_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# or we can also plot inline with matplotlib. a bit uglier\n", - "plt.figure(figsize=(25, 5))\n", - "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Appendix: Amendment\n", - "\n", - "To make estimation even more precise one should simply choose the two splits used during the crossfit part of Double Machine Learning so that each summaried copy of a segment ends up in a separate split. We can do this as follows: " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n", - "from econml.dml import LinearDML\n", - "from sklearn.linear_model import LogisticRegressionCV\n", - "\n", - "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n", - "# as long as it accepts the sample_weight keyword argument at fit time.\n", - "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n", - " model_t=LogisticRegressionCV(cv=3),\n", - " discrete_treatment=True,\n", - " cv=[(splits[0], splits[1]), (splits[1], splits[0])]) # we input custom fold structure\n", - "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n", - " freq_weight=n_sum, sample_var=var_sum)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.07249425])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Treatment Effect of particular segments\n", - "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.90614918]), array([1.23883932]))" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Confidence interval for effect\n", - "est.effect_interval(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}