More python3 fixes (and just plain bugs)

Author: justinvf

sklearn/linear_model/stochastic_gradient.py

@@ -648,7 +648,7 @@ class SGDClassifier(BaseSGDClassifier, _LearntSelectorMixin):
     SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
             fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',
             loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,
-            random_state=None, rho=None, shuffle=False,
+            random_state=None, rho=None, seed=None, shuffle=False,
             verbose=0, warm_start=False)
     >>> print(clf.predict([[-0.8, -1]]))
     [1]

sklearn/linear_model/tests/test_ridge.py

@@ -214,7 +214,7 @@ def test_ridge_individual_penalties():
 
     # Test error is raised when number of targets and penalties do not match.
     ridge = Ridge(alpha=penalties[:3])
-    assert_raises(ValueError, ridge.fit, X, target)
+    assert_raises(ValueError, ridge.fit, X, y)
 
 
 def _test_ridge_loo(filter_):

sklearn/metrics/metrics.py

@@ -1199,7 +1199,7 @@ def f1_score(y_true, y_pred, labels=None, pos_label=1, average='weighted'):
     >>> f1_score(y_true, y_pred, average='macro')  # doctest: +ELLIPSIS
     0.66...
     >>> f1_score(y_true, y_pred, average='micro')  # doctest: +ELLIPSIS
-    0.80...
+    0.8...
     >>> f1_score(y_true, y_pred, average='weighted')  # doctest: +ELLIPSIS
     0.66...
     >>> f1_score(y_true, y_pred, average='samples')  # doctest: +ELLIPSIS
@@ -1619,7 +1619,7 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
     (0.66..., 0.66..., 0.66..., None)
     >>> precision_recall_fscore_support(y_true, y_pred, average='micro')
     ... # doctest: +ELLIPSIS
-    (1.0, 0.66..., 0.80..., None)
+    (1.0, 0.66..., 0.8..., None)
     >>> precision_recall_fscore_support(y_true, y_pred, average='weighted')
     ... # doctest: +ELLIPSIS
     (0.66..., 0.66..., 0.66..., None)

sklearn/preprocessing.py

@@ -748,7 +748,7 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
     >>> enc = OneHotEncoder()
     >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], \
 [1, 0, 2]])  # doctest: +ELLIPSIS
-    OneHotEncoder(categorical_features='all', dtype=<type 'float'>,
+    OneHotEncoder(categorical_features='all', dtype=<... 'float'>,
            n_values='auto')
     >>> enc.n_values_
     array([2, 3, 4])