postprocessing/groundtruth_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ground Truth problems\n",
    "\n",
    "These are problems for which the data generating process is a known model, \n",
    "\n",
    "$$ y = \\phi^*(\\mathbf{x}, \\theta^*) $$\n",
    "\n",
    "We assess how well symbolic regression algorithms find the form of the model, $\\phi^*$, with some leniency on $\\theta^*$ (we allow the model to be off by a constant or a scalar). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "figdir: ../../paper/figs/results_sym_data/\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rc('pdf', fonttype=42)\n",
    "import os\n",
    "sns.set(font_scale=1.2)\n",
    "rdir = '../results/'\n",
    "# figdir = 'figs/ground-truth/'\n",
    "figdir = '../../paper/figs/results_sym_data/'\n",
    "\n",
    "print('figdir:',figdir)\n",
    "def save(h=None,name='tmp'):\n",
    "    name = name.strip().replace(' ','-').replace('%','pct')\n",
    "    if h == None:\n",
    "        h = plt.gcf()\n",
    "    h.tight_layout()\n",
    "    print('saving',name+'.pdf')\n",
    "    if not os.path.exists(figdir):\n",
    "        os.makedirs(figdir)\n",
    "    plt.savefig(figdir+'/'+name+'.pdf', dpi=400, bbox_inches='tight')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read data from feather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results = pd.read_feather(rdir+'/ground-truth_results.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# compute symbolic solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.loc[:,'symbolic_solution'] = df_results[['symbolic_error_is_zero',\n",
    "                                                    'symbolic_error_is_constant',\n",
    "                                                    'symbolic_fraction_is_constant']\n",
    "                                                   ].apply(any,raw=True, axis=1)\n",
    "# clean up any corner cases (constant models, failures)\n",
    "df_results.loc[:,'symbolic_solution'] = df_results['symbolic_solution'] & ~df_results['simplified_symbolic_model'].isna() \n",
    "df_results.loc[:,'symbolic_solution'] = df_results['symbolic_solution'] & ~(df_results['simplified_symbolic_model'] == '0')\n",
    "df_results.loc[:,'symbolic_solution'] = df_results['symbolic_solution'] & ~(df_results['simplified_symbolic_model'] == 'nan')\n",
    "\n",
    "# save results for detailed tabulating\n",
    "df_results.to_feather(rdir+'ground-truth_solns.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## summarize results by dataset, including ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bill/anaconda3/lib/python3.8/site-packages/pandas/core/series.py:2206: RuntimeWarning: overflow encountered in multiply\n",
      "  result = self._values.round(decimals)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>dataset</th>\n",
       "      <th>target_noise</th>\n",
       "      <th>data_group</th>\n",
       "      <th>random_state</th>\n",
       "      <th>process_time</th>\n",
       "      <th>training time (s)</th>\n",
       "      <th>feature_noise</th>\n",
       "      <th>model_size</th>\n",
       "      <th>mse_train</th>\n",
       "      <th>...</th>\n",
       "      <th>r2_zero_test_rank</th>\n",
       "      <th>r2_zero_test_norm</th>\n",
       "      <th>accuracy_solution_rate_rank</th>\n",
       "      <th>accuracy_solution_rate_norm</th>\n",
       "      <th>symbolic_solution_rate_rank</th>\n",
       "      <th>symbolic_solution_rate_norm</th>\n",
       "      <th>accuracy_solution_rate_(%)</th>\n",
       "      <th>symbolic_solution_rate_(%)</th>\n",
       "      <th>rmse_test</th>\n",
       "      <th>log_mse_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFP</td>\n",
       "      <td>feynman_III_10_19</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Feynman</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>3933.001081</td>\n",
       "      <td>3939.400296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>5.849199e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.763299</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.763010</td>\n",
       "      <td>4.588062e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFP</td>\n",
       "      <td>feynman_III_10_19</td>\n",
       "      <td>0.001</td>\n",
       "      <td>Feynman</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>3836.068982</td>\n",
       "      <td>3838.458160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.091393e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0.764983</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.782349</td>\n",
       "      <td>4.775190e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFP</td>\n",
       "      <td>feynman_III_10_19</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Feynman</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>3997.451181</td>\n",
       "      <td>4004.179745</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.277725e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.832813</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.631359</td>\n",
       "      <td>3.354819e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFP</td>\n",
       "      <td>feynman_III_10_19</td>\n",
       "      <td>0.100</td>\n",
       "      <td>Feynman</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>3246.291704</td>\n",
       "      <td>3248.871595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>3.938359e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0.987881</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.805633</td>\n",
       "      <td>5.001957e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFP</td>\n",
       "      <td>feynman_III_12_43</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Feynman</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>762.994263</td>\n",
       "      <td>764.613111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2.105290e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>2.109261e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7265</th>\n",
       "      <td>gplearn</td>\n",
       "      <td>strogatz_vdp1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>Strogatz</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>1041.010437</td>\n",
       "      <td>1041.791482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2.894002e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0.874341</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.664724</td>\n",
       "      <td>3.659322e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7266</th>\n",
       "      <td>gplearn</td>\n",
       "      <td>strogatz_vdp2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Strogatz</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>803.576279</td>\n",
       "      <td>804.068661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.047183e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.998957</td>\n",
       "      <td>10</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>9.557428e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7267</th>\n",
       "      <td>gplearn</td>\n",
       "      <td>strogatz_vdp2</td>\n",
       "      <td>0.001</td>\n",
       "      <td>Strogatz</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>787.524314</td>\n",
       "      <td>788.384568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.048401e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.998968</td>\n",
       "      <td>10</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>9.557428e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7268</th>\n",
       "      <td>gplearn</td>\n",
       "      <td>strogatz_vdp2</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Strogatz</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>768.643139</td>\n",
       "      <td>769.767922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.087898e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.999002</td>\n",
       "      <td>11</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>9.557428e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7269</th>\n",
       "      <td>gplearn</td>\n",
       "      <td>strogatz_vdp2</td>\n",
       "      <td>0.100</td>\n",
       "      <td>Strogatz</td>\n",
       "      <td>16322.5</td>\n",
       "      <td>792.116633</td>\n",
       "      <td>792.795653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.153631e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.999973</td>\n",
       "      <td>7</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>9.720665e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7270 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     algorithm            dataset  target_noise data_group  random_state  \\\n",
       "0          AFP  feynman_III_10_19         0.000    Feynman       16322.5   \n",
       "1          AFP  feynman_III_10_19         0.001    Feynman       16322.5   \n",
       "2          AFP  feynman_III_10_19         0.010    Feynman       16322.5   \n",
       "3          AFP  feynman_III_10_19         0.100    Feynman       16322.5   \n",
       "4          AFP  feynman_III_12_43         0.000    Feynman       16322.5   \n",
       "...        ...                ...           ...        ...           ...   \n",
       "7265   gplearn      strogatz_vdp1         0.100   Strogatz       16322.5   \n",
       "7266   gplearn      strogatz_vdp2         0.000   Strogatz       16322.5   \n",
       "7267   gplearn      strogatz_vdp2         0.001   Strogatz       16322.5   \n",
       "7268   gplearn      strogatz_vdp2         0.010   Strogatz       16322.5   \n",
       "7269   gplearn      strogatz_vdp2         0.100   Strogatz       16322.5   \n",
       "\n",
       "      process_time  training time (s)  feature_noise  model_size  \\\n",
       "0      3933.001081        3939.400296            0.0        56.5   \n",
       "1      3836.068982        3838.458160            0.0        43.0   \n",
       "2      3997.451181        4004.179745            0.0        60.0   \n",
       "3      3246.291704        3248.871595            0.0        48.5   \n",
       "4       762.994263         764.613111            0.0        13.5   \n",
       "...            ...                ...            ...         ...   \n",
       "7265   1041.010437        1041.791482            0.0        55.5   \n",
       "7266    803.576279         804.068661            0.0         4.0   \n",
       "7267    787.524314         788.384568            0.0         4.0   \n",
       "7268    768.643139         769.767922            0.0         4.0   \n",
       "7269    792.116633         792.795653            0.0         3.5   \n",
       "\n",
       "         mse_train  ...  r2_zero_test_rank  r2_zero_test_norm  \\\n",
       "0     5.849199e-01  ...                 10           0.763299   \n",
       "1     6.091393e-01  ...                 12           0.764983   \n",
       "2     4.277725e-01  ...                 10           0.832813   \n",
       "3     3.938359e+00  ...                  9           0.987881   \n",
       "4     2.105290e-07  ...                  6           0.999991   \n",
       "...            ...  ...                ...                ...   \n",
       "7265  2.894002e-01  ...                  9           0.874341   \n",
       "7266  1.047183e-06  ...                  6           0.998957   \n",
       "7267  1.048401e-06  ...                  6           0.998968   \n",
       "7268  2.087898e-06  ...                  6           0.999002   \n",
       "7269  1.153631e-04  ...                  3           0.999973   \n",
       "\n",
       "      accuracy_solution_rate_rank  accuracy_solution_rate_norm  \\\n",
       "0                              10                     0.000000   \n",
       "1                              12                     0.000000   \n",
       "2                              11                     0.000000   \n",
       "3                              10                     0.000000   \n",
       "4                               5                     1.000000   \n",
       "...                           ...                          ...   \n",
       "7265                           11                     0.000000   \n",
       "7266                           10                     0.857143   \n",
       "7267                           10                     0.800000   \n",
       "7268                           11                     0.800000   \n",
       "7269                            7                     0.700000   \n",
       "\n",
       "      symbolic_solution_rate_rank  symbolic_solution_rate_norm  \\\n",
       "0                               8                          0.0   \n",
       "1                               8                          0.0   \n",
       "2                               7                          NaN   \n",
       "3                               7                          NaN   \n",
       "4                               6                          0.5   \n",
       "...                           ...                          ...   \n",
       "7265                            7                          NaN   \n",
       "7266                            3                          1.0   \n",
       "7267                            1                          1.0   \n",
       "7268                            1                          1.0   \n",
       "7269                            1                          1.0   \n",
       "\n",
       "      accuracy_solution_rate_(%)  symbolic_solution_rate_(%)  rmse_test  \\\n",
       "0                            0.0                         0.0   0.763010   \n",
       "1                            0.0                         0.0   0.782349   \n",
       "2                            0.0                         0.0   0.631359   \n",
       "3                            0.0                         0.0   0.805633   \n",
       "4                          100.0                        50.0   0.000459   \n",
       "...                          ...                         ...        ...   \n",
       "7265                         0.0                         0.0   0.664724   \n",
       "7266                        87.5                       100.0   0.000978   \n",
       "7267                        80.0                       100.0   0.000978   \n",
       "7268                        80.0                       100.0   0.000978   \n",
       "7269                        70.0                       100.0   0.000986   \n",
       "\n",
       "      log_mse_test  \n",
       "0     4.588062e-01  \n",
       "1     4.775190e-01  \n",
       "2     3.354819e-01  \n",
       "3     5.001957e-01  \n",
       "4     2.109261e-07  \n",
       "...            ...  \n",
       "7265  3.659322e-01  \n",
       "7266  9.557428e-07  \n",
       "7267  9.557428e-07  \n",
       "7268  9.557428e-07  \n",
       "7269  9.720665e-07  \n",
       "\n",
       "[7270 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results2 = df_results.merge(df_results.groupby(['dataset','target_noise'])['algorithm'].nunique().reset_index(),\n",
    "                              on=['dataset','target_noise'],suffixes=('','_count'))\n",
    "# count repeat trials\n",
    "df_results2 = df_results2.merge(\n",
    "           df_results2.groupby(['algorithm','dataset','target_noise'])['random_state'].nunique().reset_index(),\n",
    "           on=['algorithm','dataset','target_noise'],suffixes=('','_repeats'))\n",
    "\n",
    "# accuracy-based exact solutions \n",
    "df_results2['accuracy_solution'] = df_results2['r2_test'].apply(lambda x: x > 0.999).astype(float)\n",
    "\n",
    "# get mean solution rates for algs on datasets at specific noise levels, averaged over trials \n",
    "for soln in ['accuracy_solution','symbolic_solution']:\n",
    "    df_results2 = df_results2.merge(\n",
    "        df_results2.groupby(['algorithm','dataset','target_noise'])[soln].mean().reset_index(),\n",
    "                                  on=['algorithm','dataset', 'target_noise'],suffixes=('','_rate'))\n",
    "                                       \n",
    "# # rankings\n",
    "for col in [c for c in df_results2.columns if c.endswith('test') or c.endswith('size')]:\n",
    "    ascending = 'r2' not in col\n",
    "    df_results2[col+'_rank_per_trial']=df_results2.groupby(\n",
    "                        ['dataset','target_noise','random_state'])[col].apply(\n",
    "                                                                              lambda x: \n",
    "                                                                              round(x,3).rank(\n",
    "                                                                              ascending=ascending).astype(int))\n",
    "    \n",
    "df_sum = df_results2.groupby(['algorithm','dataset','target_noise','data_group'],as_index=False).median()\n",
    "# rankings and normalized scores per dataset\n",
    "for col in [c for c in df_sum.columns if any([c.endswith(n) for n in ['test','size','rate']])]:\n",
    "    ascending = 'r2' not in col and 'solution' not in col\n",
    "    df_sum[col+'_rank']=df_sum.groupby(['dataset','target_noise'])[col].apply(\n",
    "        lambda x:  round(x,3).rank(ascending=ascending).astype(int) )\n",
    "    df_sum[col+'_norm'] = df_sum.groupby(['dataset','target_noise'])[col].apply(lambda x: (x-x.min())/(x.max()-x.min()))\n",
    "# df_sum['success_rate'] = df_results2.groupby(['algorithm','dataset'])['solution'].mean().reset_index()\n",
    "for soln in ['accuracy_solution','symbolic_solution']:\n",
    "    df_sum[soln +'_rate_(%)'] = df_sum[soln+'_rate'].apply(lambda x: x*100)\n",
    "df_sum['rmse_test'] = df_sum['mse_test'].apply(np.sqrt)\n",
    "df_sum['log_mse_test'] = df_sum['mse_test'].apply(lambda x: np.log(1+x))\n",
    "df_results = df_results2\n",
    "df_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# save summary data\n",
    "used for statistical comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sum.to_csv(rdir+'/symbolic_dataset_results_sum.csv.gz',compression='gzip', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# solution rates by alg/dataset/noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target_noise</th>\n",
       "      <th>0.000</th>\n",
       "      <th>0.001</th>\n",
       "      <th>0.010</th>\n",
       "      <th>0.100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFP</th>\n",
       "      <td>20.48</td>\n",
       "      <td>19.00</td>\n",
       "      <td>16.31</td>\n",
       "      <td>12.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFP_FE</th>\n",
       "      <td>26.23</td>\n",
       "      <td>21.23</td>\n",
       "      <td>20.00</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIFeynman</th>\n",
       "      <td>52.65</td>\n",
       "      <td>31.89</td>\n",
       "      <td>12.61</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BSR</th>\n",
       "      <td>2.31</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSR</th>\n",
       "      <td>19.71</td>\n",
       "      <td>19.15</td>\n",
       "      <td>18.92</td>\n",
       "      <td>16.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPLEX</th>\n",
       "      <td>12.02</td>\n",
       "      <td>9.54</td>\n",
       "      <td>8.23</td>\n",
       "      <td>9.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFX</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GP-GOMEA</th>\n",
       "      <td>27.12</td>\n",
       "      <td>10.62</td>\n",
       "      <td>4.69</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITEA</th>\n",
       "      <td>20.77</td>\n",
       "      <td>13.77</td>\n",
       "      <td>7.69</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRGP</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operon</th>\n",
       "      <td>16.00</td>\n",
       "      <td>12.31</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBP-GP</th>\n",
       "      <td>12.60</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gplearn</th>\n",
       "      <td>15.48</td>\n",
       "      <td>16.16</td>\n",
       "      <td>16.05</td>\n",
       "      <td>15.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target_noise  0.000  0.001  0.010  0.100\n",
       "algorithm                               \n",
       "AFP           20.48  19.00  16.31  12.15\n",
       "AFP_FE        26.23  21.23  20.00  12.31\n",
       "AIFeynman     52.65  31.89  12.61   0.86\n",
       "BSR            2.31   0.62   0.08   0.00\n",
       "DSR           19.71  19.15  18.92  16.62\n",
       "EPLEX         12.02   9.54   8.23   9.31\n",
       "FEAT           0.10   0.00   0.00   0.00\n",
       "FFX            0.00   0.00   0.00   0.08\n",
       "GP-GOMEA      27.12  10.62   4.69   1.46\n",
       "ITEA          20.77  13.77   7.69   1.46\n",
       "MRGP           0.00   0.00   0.00   0.00\n",
       "Operon        16.00  12.31   1.92   0.08\n",
       "SBP-GP        12.60   0.69   0.00   0.00\n",
       "gplearn       15.48  16.16  16.05  15.77"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum.groupby(['target_noise','algorithm'])['symbolic_solution_rate_(%)'].mean().round(2).unstack().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "def compare(df_compare=None, x='r2_test',y='algorithm', row=None, col=None, scale=None, xlim=[], est=np.mean,\n",
    "            orient='h', hue=None, **kwargs):\n",
    "    df_compare = df_compare.copy()\n",
    "    if row==None and col == None:\n",
    "        aspect=1\n",
    "    else:\n",
    "        aspect=0.55\n",
    "#     plt.figure(figsize=(8,7))\n",
    "    tmp = df_compare.groupby(['target_noise',y])[x].apply(est).unstack().median()\n",
    "    order = tmp.sort_values(ascending=False).index\n",
    "    \n",
    "    for c in [x,y,row,col]:\n",
    "        if c:\n",
    "            df_compare = df_compare.rename(columns={c:c.replace('_',' ').title()})\n",
    "        \n",
    "    x = x.replace('_',' ').title()\n",
    "    y = y.replace('_',' ').title()\n",
    "    if row:\n",
    "        row = row.replace('_',' ').title()\n",
    "    if col:\n",
    "        col = col.replace('_',' ').title()\n",
    "    \n",
    "    if scale=='log' and len(xlim)>0 and xlim[0] == 0:\n",
    "        df_compare.loc[:,x] += 1\n",
    "        xlim[0] = 1\n",
    "        xnew = '1 + '+x\n",
    "        df_compare=df_compare.rename(columns={x:xnew})\n",
    "        x = xnew\n",
    "    if orient=='v':\n",
    "        tmp = x\n",
    "        x = y\n",
    "        y = tmp\n",
    "    if col and not row:\n",
    "        col_wrap = min(4, df_compare[col].nunique()) \n",
    "    else:\n",
    "        col_wrap=None\n",
    "        \n",
    "    cat_args = dict(\n",
    "                data=df_compare, \n",
    "                kind='point',\n",
    "                y=y,\n",
    "                x=x,\n",
    "                order=order,\n",
    "                row=row,\n",
    "                col=col,\n",
    "                col_wrap=col_wrap,\n",
    "                palette='flare_r',\n",
    "                margin_titles=True,\n",
    "                aspect=aspect,\n",
    "                hue=hue,\n",
    "                legend_out=False,\n",
    "    )\n",
    "    cat_args.update(kwargs)\n",
    "    g = sns.catplot( **cat_args )\n",
    "    if hue:\n",
    "        g._legend.remove() #(title=hue.replace('_',' ').title())\n",
    "        g.axes.flat[-1].legend(title=hue.replace('_',' ').title(),\n",
    "                               fontsize=10\n",
    "                              )\n",
    "    for ax in g.axes.flat: \n",
    "        ax.yaxis.grid(True)\n",
    "        ax.set_ylabel('')\n",
    "        ax.set_xlabel(ax.get_xlabel().replace('Symbolic ',''))\n",
    "        if col:\n",
    "            ttl = ax.get_title()\n",
    "            ax.set_title(ttl.replace(col,'').replace('=',''))\n",
    "\n",
    "    \n",
    "    if len(xlim)>0:\n",
    "        plt.xlim(xlim[0],xlim[1])\n",
    "    if scale:\n",
    "        plt.gca().set_xscale(scale)\n",
    "\n",
    "    sns.despine(left=True, bottom=True)\n",
    "    savename = '-'.join(['cat-'+cat_args['kind']+'plot',x+ '-by-'+ y])\n",
    "    if row: savename += '_'+row\n",
    "    if col: savename += '_'+col\n",
    "    \n",
    "    save(g, savename )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving cat-pointplot-Symbolic-Solution-Rate-(pct)-by-Algorithm.pdf\n",
      "saving cat-pointplot-Symbolic-Solution-Rate-(pct)-by-Algorithm_Data-Group.pdf\n",
      "saving cat-pointplot-R2-Test-by-Algorithm.pdf\n",
      "saving cat-pointplot-R2-Test-by-Algorithm_Data-Group.pdf\n",
      "saving cat-pointplot-Accuracy-Solution-by-Algorithm.pdf\n",
      "saving cat-pointplot-Accuracy-Solution-by-Algorithm_Data-Group.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for metric in ['symbolic_solution_rate_(%)','r2_test','accuracy_solution']:\n",
    "# for metric in ['r2_test']:\n",
    "    for kind in ['point']: #,'strip']:\n",
    "        args =dict(df_compare=df_sum, x=metric, est=np.mean, orient='h',\n",
    "                   kind=kind) \n",
    "        if kind=='point': \n",
    "            args['join'] = False \n",
    "            args['markers']=['o','s','x','+']\n",
    "        if metric == 'r2_test':\n",
    "            args['xlim'] = [-1, 1]\n",
    "        compare(**args,\n",
    "                hue='target_noise', \n",
    "                col=None,\n",
    "                ) \n",
    "        compare(**args, \n",
    "                hue='target_noise', \n",
    "                col='data_group',\n",
    "                ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pairgrid_symbolic_solution_rate_(pct)_r2_test_simplified_complexity.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 982.8x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the PairGrid\n",
    "df_plot = df_sum.copy()\n",
    "tmp = df_plot.groupby(['target_noise','algorithm'])['symbolic_solution_rate'].mean().unstack().median()\n",
    "order = tmp.sort_values(ascending=False).index\n",
    "df_plot['size_diff'] = df_plot['model_size']-df_plot['simplified_complexity']+1\n",
    "x_vars=[\n",
    "#         'accuracy_solution_rate_(%)',\n",
    "#         'mse_test',\n",
    "#         'r2_test_rank',\n",
    "#         'r2_test_norm',\n",
    "        'symbolic_solution_rate_(%)',\n",
    "        'r2_test',\n",
    "        'simplified_complexity',\n",
    "#         'size_diff',\n",
    "#         'model_size',\n",
    "#         'training time (s)',\n",
    "#         'solution'\n",
    "]\n",
    "g = sns.PairGrid(df_plot, \n",
    "                 x_vars=x_vars,\n",
    "                 y_vars=['algorithm'],\n",
    "                 height=6.5, \n",
    "                 aspect=0.7,\n",
    "                 hue='target_noise',\n",
    "#                  hue_order=[0.01,0.001,0]\n",
    "#                  hue='dataset'\n",
    "                )\n",
    "g.map(sns.pointplot, \n",
    "#       size=10,\n",
    "      orient=\"h\",\n",
    "      jitter=False,\n",
    "      order=order,\n",
    "      palette=\"flare_r\",\n",
    "      errwidth=2,\n",
    "      linewidth=0.01,\n",
    "      markeredgecolor='w',\n",
    "      join=False,\n",
    "      estimator=np.mean,\n",
    "      n_boot=1000,\n",
    "      markers=['x','o','s','+'],\n",
    "      markeralpha=0.5\n",
    "     )\n",
    "plt.legend(title='Target Noise')\n",
    "titles = [x.replace('_',' ').title().replace('(S)','(s)') for x in x_vars]\n",
    "\n",
    "for ax, title in zip(g.axes.flat, titles):\n",
    "\n",
    "    # remove xlabel\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    # Set a different title for each axes\n",
    "    ax.set(title=title)\n",
    "    \n",
    "    if any([n in title.lower() for n in ['size','complexity','time']]):\n",
    "        ax.set_xscale('log')\n",
    "    if 'R2' in title and 'Rank' not in title:\n",
    "        ax.set(title=title.replace('R2','$R^2$'))\n",
    "        ax.set_xlim([0,1])\n",
    "\n",
    "    # Make the grid horizontal instead of vertical\n",
    "    ax.yaxis.grid(True)\n",
    "save(g, 'pairgrid_'+'_'.join(x_vars))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}