From d8d7e5d4ab2cb3f2a8e4aac3836d2a2c67da6aa7 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 1 Nov 2010 18:44:31 +0100
Subject: [PATCH] s/explained_variance/explained_variance_score
---
scikits/learn/base.py | 4 ++--
scikits/learn/glm/base.py | 7 ++-----
scikits/learn/metrics.py | 2 +-
scikits/learn/tests/test_metrics.py | 10 +++++-----
4 files changed, 10 insertions(+), 13 deletions(-)
diff --git a/scikits/learn/base.py b/scikits/learn/base.py
index dab6fa7d10..58db218cf4 100644
--- a/scikits/learn/base.py
+++ b/scikits/learn/base.py
@@ -10,7 +10,7 @@ import copy
import numpy as np
-from .metrics import explained_variance
+from .metrics import explained_variance_score
################################################################################
def clone(estimator, safe=True):
@@ -242,7 +242,7 @@ class RegressorMixin(object):
-------
z : float
"""
- return explained_variance(y, self.predict(X))
+ return explained_variance_score(y, self.predict(X))
################################################################################
diff --git a/scikits/learn/glm/base.py b/scikits/learn/glm/base.py
index 65f7dfe70e..12e02b6334 100644
--- a/scikits/learn/glm/base.py
+++ b/scikits/learn/glm/base.py
@@ -13,6 +13,7 @@ Generalized Linear models.
import numpy as np
from ..base import BaseEstimator, RegressorMixin
+from ..metrics import explained_variance_score
###
### TODO: intercept for all models
@@ -43,9 +44,7 @@ class LinearModel(BaseEstimator, RegressorMixin):
def _explained_variance(self, X, y):
"""Compute explained variance a.k.a. r^2"""
- ## TODO: this should have a tests.
- return 1 - np.linalg.norm(y - self.predict(X))**2 \
- / np.linalg.norm(y)**2
+ return explained_variance_score(y, self.predict(X))
def _center_data (self, X, y):
"""
@@ -64,7 +63,6 @@ class LinearModel(BaseEstimator, RegressorMixin):
ymean = 0.
return X, y, Xmean, ymean
-
def _set_intercept(self, Xmean, ymean):
"""Set the intercept_
"""
@@ -73,7 +71,6 @@ class LinearModel(BaseEstimator, RegressorMixin):
else:
self.intercept_ = 0
-
def __str__(self):
if self.coef_ is not None:
return ("%s \n%s #... Fitted: explained variance=%s" %
diff --git a/scikits/learn/metrics.py b/scikits/learn/metrics.py
index ab5401067a..244f25a891 100644
--- a/scikits/learn/metrics.py
+++ b/scikits/learn/metrics.py
@@ -505,7 +505,7 @@ def mean_square_error(y_true, y_pred):
return np.linalg.norm(y_pred - y_true) ** 2
-def explained_variance(y_true, y_pred):
+def explained_variance_score(y_true, y_pred):
"""Explained variance regression loss
Best possible score is 1.0, lower values are worst.
diff --git a/scikits/learn/tests/test_metrics.py b/scikits/learn/tests/test_metrics.py
index bd7a85f919..5b7782bdc6 100644
--- a/scikits/learn/tests/test_metrics.py
+++ b/scikits/learn/tests/test_metrics.py
@@ -12,7 +12,7 @@ from .. import svm
from ..metrics import auc
from ..metrics import classification_report
from ..metrics import confusion_matrix
-from ..metrics import explained_variance
+from ..metrics import explained_variance_score
from ..metrics import f1_score
from ..metrics import mean_square_error
from ..metrics import precision_recall_curve
@@ -209,8 +209,8 @@ def test_losses():
assert_almost_equal(mean_square_error(y_true, y_pred), 12.999, 2)
assert_almost_equal(mean_square_error(y_true, y_true), 0.00, 2)
- assert_almost_equal(explained_variance(y_true, y_pred), -0.04, 2)
- assert_almost_equal(explained_variance(y_true, y_true), 1.00, 2)
+ 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)
def test_symmetry():
@@ -223,8 +223,8 @@ 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(y_true, y_pred) != \
- explained_variance(y_pred, y_true))
+ assert_(explained_variance_score(y_true, y_pred) != \
+ explained_variance_score(y_pred, y_true))
# FIXME: precision and recall aren't symmetric either
--
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