sklean style api

.travis.yml

@@ -22,7 +22,7 @@ install:
   - conda info -a
 
   # Replace dep1 dep2 ... with your dependencies
-  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION scipy numpy
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION scipy numpy scikit-learn
   - source activate test-environment
   - python setup.py install
 

bayes_logistic/__init__.py

@@ -29,4 +29,5 @@
 __email__ = '[email protected]'
 __version__ = '0.1.0'
 
-from .bayes_logistic import *
+from .bayes_logistic import *
+from .sklearn import BayesLogisticClassifier, BayesLogisticRegressor

bayes_logistic/sklearn.py

@@ -0,0 +1,178 @@
+# Copyright (c) 2015 MaxPoint Interactive, Inc.
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
+# following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
+#    disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products
+#    derived from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from __future__ import absolute_import
+from sklearn.utils.validation import NotFittedError
+
+from .bayes_logistic import fit_bayes_logistic as _fit, bayes_logistic_prob as _predict
+import six
+import numpy as np
+from abc import ABCMeta, abstractmethod
+from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
+from sklearn.linear_model import LogisticRegression
+
+
+class BayesLogisticBase(six.with_metaclass(ABCMeta, BaseEstimator), ClassifierMixin):
+    """Bayesian Logistic Regression Solver.
+
+    Assumes Laplace (Gaussian) Approximation to the posterior of the fitted parameter vector.
+    Uses scipy.optimize.minimize.
+
+    Initial priors for the coefficients and the hessian need not be provided.  If not provided these will be initialized
+    to an uninformative initial solution equivalent to an L2 penalty.
+
+    Parameters
+    ----------
+    coef : array-like, shape (n_features, ), optional
+        array of prior means on the parameters to be fit
+    H : array-like, shape (n_features, n_features) or (n_features, ), optional
+        array of prior Hessian (inverse covariance of prior distribution of parameters)
+    solver : string
+        scipy optimize solver used.  this should be either 'Newton-CG', 'BFGS' or 'L-BFGS-B'.
+        The default is Newton-CG.
+    bounds : iterable of length p
+        A length p list (or tuple) of tuples each of length 2.
+        This is only used if the solver is set to 'L-BFGS-B'. In that case, a tuple
+        (lower_bound, upper_bound), both floats, is defined for each parameter.  See the
+        scipy.optimize.minimize docs for further information.
+    maxiter : int
+        Maximum number of iterations for scipy.optimize.minimize solver.
+    """
+
+    def __init__(self, H=None, coef=None, solver='Newton-CG',  bounds=None, maxiter=100):
+        self.maxiter = maxiter
+        self.bounds = bounds
+        self.H_ = H
+        self.coef_ = coef
+        self.solver = solver
+
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training vector, where n_samples in the number of samples and
+            n_features is the number of features.
+
+        y : array-like, shape (n_samples,)
+            Target vector relative to X.
+
+        sample_weight : {array-like, None}, shape (n_samples,)
+            Optional weight vector to weight each observation by.  Weights expected to be in [0,1].
+
+        Returns
+        -------
+        self : object
+            Returns self.
+        """
+
+        self.partial_fit(X, y, sample_weight)
+        return self
+
+    def partial_fit(self, X, y, sample_weight=None):
+        """Update the model according to the given training data.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training vector, where n_samples in the number of samples and
+            n_features is the number of features.
+
+        y : array-like, shape (n_samples,)
+            Target vector relative to X.
+
+        sample_weight : {array-like, None}, shape (n_samples,)
+            Optional weight vector to weight each observation by.  Weights expected to be in [0,1].
+
+        Returns
+        -------
+        self : object
+            Returns self.
+        """
+        self._ensure_valid_wh(X, y)
+        self.coef_, self.H_ = _fit(y=y, X=X, wprior=self.coef_, H=self.H_, solver=self.solver, maxiter=self.maxiter,
+                                   weights=sample_weight, bounds=self.bounds)
+        return self
+
+    def predict(self, X):
+        """Probability estimates.
+
+        The returned estimates for all classes are ordered by the
+        label of classes.
+
+        Parameters
+        ----------
+        X : array-like, shape = [n_samples, n_features]
+
+        Returns
+        -------
+        T : array-like, shape = [n_samples, ]
+            Returns the probability of the sample for the model
+        """
+        if not hasattr(self, 'coef_') or self.coef_ is None:
+            raise NotFittedError("This %(name)s instance is not fitted"
+                                 "yet" % {'name': type(self).__name__})
+        return _predict(X, self.coef_, self.H_)
+
+    def predict_proba(self, X):
+        """Probability estimates.
+
+        The returned estimates for all classes are ordered by the
+        label of classes.
+
+        Parameters
+        ----------
+        X : array-like, shape = [n_samples, n_features]
+
+        Returns
+        -------
+        T : array-like, shape = [n_samples, ]
+            Returns the probability of the sample for the model
+        """
+        if not hasattr(self, 'coef_') or self.coef_ is None:
+            raise NotFittedError("This %(name)s instance is not fitted"
+                                 "yet" % {'name': type(self).__name__})
+        return _predict(X, self.coef_, self.H_)
+
+    def _ensure_valid_wh(self, X, y):
+        """Ensures that there are valid values for the coefficients and the hessian.
+
+        If not initialized this sets the coefficients and hessian to be equivalent to the L2 penalty
+        """
+        dim = X.shape[1]
+        if self.H_ is None:
+            self.H_ = np.diag(np.ones(dim)) * 0.001
+        if self.coef_ is None:
+            self.coef_ = np.zeros(dim)
+
+
+class BayesLogisticClassifier(BayesLogisticBase, ClassifierMixin):
+    pass
+
+
+class BayesLogisticRegressor(BayesLogisticBase, RegressorMixin):
+    pass
+
+__all__ = ["BayesLogisticClassifier", "BayesLogisticRegressor"]

docs/usage.rst

@@ -1,6 +1,6 @@
-========
+=====
 Usage
-========
+=====
 
 To use Bayes Logistic Regression in a project::
 
@@ -18,3 +18,17 @@ Methods
 .. autofunction:: get_pvalues
 
 .. autofunction:: fit_bayes_logistic
+
+
+======================
+scikit-learn interface
+======================
+
+For an interface similar to scikit-learn the following classes can be used.
+
+Classes
+-------
+
+.. autoclass:: BayesLogisticClassifier
+
+.. autoclass:: BayesLogisticRegressor

tests/test_bayes_logistic.py

@@ -35,7 +35,9 @@
 
 import unittest
 
-from bayes_logistic import bayes_logistic
+from sklearn.datasets import load_iris
+from bayes_logistic import bayes_logistic, BayesLogisticClassifier
+
 
 class TestBayes_logistic(unittest.TestCase):
 
@@ -47,3 +49,20 @@ def test_something(self):
 
     def tearDown(self):
         pass
+
+
+class TestSklearnModel(unittest.TestCase):
+
+    def setUp(self):
+        self.data = load_iris()
+
+    def testLR(self):
+        c = BayesLogisticClassifier()
+
+        ind = self.data.target[self.data.target <= 1]
+
+        X = self.data.data[ind, :]
+        y = self.data.target[ind]
+
+        c.fit(X, y)
+        c.predict(X)