fix to a minor bug with intercept

Atthemoment · Oct 13, 2014 · 04f6d12 · 04f6d12
1 parent 20a5952
commit 04f6d12
Show file tree

Hide file tree

Showing 2 changed files with 14 additions and 20 deletions.
diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py
@@ -396,7 +396,6 @@ def _partial_fit(self, X, y, alpha, C,
             raise ValueError("The number of class labels must be "
                              "greater than one.")
 
-
         return self
 
     def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
@@ -452,6 +451,7 @@ def _fit_binary(self, X, y, alpha, C, sample_weight,
                 self.intercept_ = self.average_intercept_
             else:
                 self.coef_ = self.standard_coef_.reshape(1, -1)
+                self.standard_intercept_ = np.atleast_1d(intercept)
                 self.intercept_ = self.standard_intercept_
         else:
             self.coef_ = coef.reshape(1, -1)
@@ -484,6 +484,7 @@ def _fit_multiclass(self, X, y, alpha, C, learning_rate,
                 self.intercept_ = self.average_intercept_
             else:
                 self.coef_ = self.standard_coef_
+                self.standard_intercept_ = np.atleast_1d(intercept)
                 self.intercept_ = self.standard_intercept_
 
     def partial_fit(self, X, y, classes=None, sample_weight=None):

diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
@@ -208,25 +208,18 @@ def test_plain_has_no_average_attr(self):
         assert_false(hasattr(clf, 'standard_coef_'))
 
     def test_late_onset_averaging_not_reached(self):
-        eta0 = .001
-        clf1 = self.factory(average=12, learning_rate="constant",
-                            eta0=eta0, n_iter=2)
-        clf2 = self.factory(learning_rate="constant", eta0=eta0, n_iter=2)
-
-        clf1.fit(X, Y)
-        clf2.fit(X, Y)
-
-        assert_array_almost_equal(clf1.coef_, clf2.coef_)
-        assert_almost_equal(clf1.intercept_, clf1.intercept_)
-
-        clf1 = self.factory(average=13, learning_rate="constant",
-                            eta0=eta0, n_iter=2)
-        clf1.fit(X, Y)
-
-        assert_array_almost_equal(clf1.coef_, clf2.coef_,
-                                  decimal=16)
-        assert_almost_equal(clf1.intercept_, clf2.intercept_,
-                            decimal=16)
+        clf1 = self.factory(average=600)
+        clf2 = self.factory()
+        for _ in range(100):
+            if isinstance(clf1, SGDClassifier):
+                clf1.partial_fit(X, Y, classes=np.unique(Y))
+                clf2.partial_fit(X, Y, classes=np.unique(Y))
+            else:
+                clf1.partial_fit(X, Y)
+                clf2.partial_fit(X, Y)
+
+        assert_array_almost_equal(clf1.coef_, clf2.coef_, decimal=10)
+        assert_almost_equal(clf1.intercept_, clf1.intercept_, decimal=10)
 
     def test_late_onset_averaging_reached(self):
         eta0 = .001