addition of r2_score function

scikits/learn/base.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 
-from .metrics import explained_variance_score
+from .metrics import r2_score
 
 ################################################################################
 def clone(estimator, safe=True):
@@ -236,7 +236,7 @@ class RegressorMixin(object):
     """
 
     def score(self, X, y):
-        """ Returns the explained variance of the prediction
+        """ Returns the coefficient of determination of the prediction
 
             Parameters
             ----------
@@ -249,7 +249,7 @@ def score(self, X, y):
             -------
             z : float
         """
-        return explained_variance_score(y, self.predict(X))
+        return r2_score(y, self.predict(X))
 
 
 ################################################################################

scikits/learn/grid_search.py

@@ -176,7 +176,7 @@ class GridSearchCV(BaseEstimator):
     >>> svr = SVR()
     >>> clf = GridSearchCV(svr, parameters, n_jobs=1)
     >>> clf.fit(X, y).predict([[-0.8, -1]])
-    array([ 1.14])
+    array([ 1.13101459])
     """
 
     def __init__(self, estimator, param_grid, loss_func=None, score_func=None,

scikits/learn/linear_model/base.py

@@ -12,7 +12,7 @@
 import numpy as np
 
 from ..base import BaseEstimator, RegressorMixin
-from ..metrics import explained_variance_score
+from ..metrics import r2_score
 
 ###
 ### TODO: intercept for all models
@@ -41,9 +41,9 @@ def predict(self, X):
         X = np.asanyarray(X)
         return np.dot(X, self.coef_) + self.intercept_
 
-    def _explained_variance(self, X, y):
+    def _r2_score(self, X, y):
         """Compute explained variance a.k.a. r^2"""
-        return explained_variance_score(y, self.predict(X))
+        return r2_score(y, self.predict(X))
 
     @staticmethod
     def _center_data(X, y, fit_intercept):

scikits/learn/linear_model/bayes.py

@@ -207,7 +207,7 @@ def fit(self, X, y, **params):
 
         self._set_intercept(Xmean, ymean)
         # Store explained variance for __str__
-        self.explained_variance_ = self._explained_variance(X, y)
+        self.r2_score_ = self._r2_score(X, y)
         return self
 
 
@@ -420,5 +420,5 @@ def fit(self, X, y, **params):
 
         self._set_intercept(Xmean, ymean)
         # Store explained variance for __str__
-        self.explained_variance_ = self._explained_variance(X, y)
+        self.r2_score_ = self._r2_score(X, y)
         return self

scikits/learn/linear_model/coordinate_descent.py

@@ -102,7 +102,7 @@ def fit(self, X, y, maxit=1000, tol=1e-4, coef_init=None, **params):
                           ' to increase the number of interations')
 
         # Store explained variance for __str__
-        self.explained_variance_ = self._explained_variance(X, y)
+        self.r2_score_ = self._r2_score(X, y)
 
         # return self for chaining fit and predict calls
         return self
@@ -354,7 +354,7 @@ def fit(self, X, y, cv=None, **fit_params):
 
         self.coef_ = model.coef_
         self.intercept_ = model.intercept_
-        self.explained_variance_ = model.explained_variance_
+        self.r2_score_ = model.r2_score_
         self.alpha = model.alpha
         self.alphas = np.asarray(alphas)
         return self

scikits/learn/metrics.py

@@ -499,7 +499,7 @@ def precision_recall_curve(y_true, probas_pred):
 def explained_variance_score(y_true, y_pred):
     """Explained variance regression score function
 
-    Best possible score is 1.0, lower values are worst.
+    Best possible score is 1.0, lower values are worse.
 
     Note: the explained variance is not a symmetric function.
 
@@ -512,6 +512,25 @@ def explained_variance_score(y_true, y_pred):
     y_pred : array-like
     """
     return 1 - np.var(y_true - y_pred) / np.var(y_true)
+
+
+def r2_score(y_true, y_pred):
+    """R^2 (coefficient of determination) regression score function
+
+    Best possible score is 1.0, lower values are worse.
+
+    Note: not a symmetric function.
+
+    return the R^2 score
+
+    Parameters
+    ----------
+    y_true : array-like
+
+    y_pred : array-like
+    """
+    return 1 - ((y_true - y_pred)**2).sum() / ((y_true - y_true.mean())**2).sum()
+
 
 
 ###############################################################################

scikits/learn/tests/test_metrics.py

@@ -2,7 +2,7 @@
 import numpy as np
 import nose
 
-from numpy.testing import assert_
+from nose.tools import assert_true
 from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_array_equal
 from numpy.testing import assert_equal, assert_almost_equal
@@ -13,6 +13,7 @@
 from ..metrics import classification_report
 from ..metrics import confusion_matrix
 from ..metrics import explained_variance_score
+from ..metrics import r2_score
 from ..metrics import f1_score
 from ..metrics import mean_square_error
 from ..metrics import precision_recall_curve
@@ -222,6 +223,9 @@ def test_losses():
     assert_almost_equal(explained_variance_score(y_true, y_pred), -0.04, 2)
     assert_almost_equal(explained_variance_score(y_true, y_true), 1.00, 2)
 
+    assert_almost_equal(r2_score(y_true, y_pred), -0.04, 2)
+    assert_almost_equal(r2_score(y_true, y_true), 1.00, 2)
+
 
 def test_symmetry():
     """Test the symmetry of score and loss functions"""
@@ -233,8 +237,10 @@ def test_symmetry():
     assert_almost_equal(mean_square_error(y_true, y_pred),
                         mean_square_error(y_pred, y_true))
     # not symmetric
-    assert_(explained_variance_score(y_true, y_pred) != \
+    assert_true(explained_variance_score(y_true, y_pred) != \
             explained_variance_score(y_pred, y_true))
+    assert_true(r2_score(y_true, y_pred) != \
+            r2_score(y_pred, y_true))
     # FIXME: precision and recall aren't symmetric either