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[MRG] Use base.is_classifier instead of isinstance #9482

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Merged
merged 1 commit into from
Aug 8, 2017
Merged

[MRG] Use base.is_classifier instead of isinstance #9482

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4 changes: 2 additions & 2 deletions sklearn/ensemble/weight_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@
from numpy.core.umath_tests import inner1d

from .base import BaseEnsemble
from ..base import ClassifierMixin, RegressorMixin, is_regressor
from ..base import ClassifierMixin, RegressorMixin, is_regressor, is_classifier
from ..externals import six
from ..externals.six.moves import zip
from ..externals.six.moves import xrange as range
Expand Down Expand Up @@ -231,7 +231,7 @@ def staged_score(self, X, y, sample_weight=None):
z : float
"""
for y_pred in self.staged_predict(X):
if isinstance(self, ClassifierMixin):
if is_classifier(self):
yield accuracy_score(y, y_pred, sample_weight=sample_weight)
else:
yield r2_score(y, y_pred, sample_weight=sample_weight)
Expand Down
4 changes: 2 additions & 2 deletions sklearn/multioutput.py
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Expand Up @@ -18,7 +18,7 @@
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from .base import BaseEstimator, clone, MetaEstimatorMixin
from .base import RegressorMixin, ClassifierMixin
from .base import RegressorMixin, ClassifierMixin, is_classifier
from .model_selection import cross_val_predict
from .utils import check_array, check_X_y, check_random_state
from .utils.fixes import parallel_helper
Expand Down Expand Up @@ -152,7 +152,7 @@ def fit(self, X, y, sample_weight=None):
multi_output=True,
accept_sparse=True)

if isinstance(self, ClassifierMixin):
if is_classifier(self):
check_classification_targets(y)

if y.ndim == 1:
Expand Down
5 changes: 3 additions & 2 deletions sklearn/neural_network/multilayer_perceptron.py
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Expand Up @@ -13,6 +13,7 @@
import warnings

from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
from ..base import is_classifier
from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
from ._stochastic_optimizers import SGDOptimizer, AdamOptimizer
from ..model_selection import train_test_split
Expand Down Expand Up @@ -268,7 +269,7 @@ def _initialize(self, y, layer_units):
self.n_layers_ = len(layer_units)

# Output for regression
if not isinstance(self, ClassifierMixin):
if not is_classifier(self):
self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
Expand Down Expand Up @@ -491,7 +492,7 @@ def _fit_stochastic(self, X, y, activations, deltas, coef_grads,
X, X_val, y, y_val = train_test_split(
X, y, random_state=self._random_state,
test_size=self.validation_fraction)
if isinstance(self, ClassifierMixin):
if is_classifier(self):
y_val = self._label_binarizer.inverse_transform(y_val)
else:
X_val = None
Expand Down
4 changes: 2 additions & 2 deletions sklearn/tree/tests/test_export.py
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Expand Up @@ -6,7 +6,7 @@

from numpy.random import RandomState

from sklearn.base import ClassifierMixin
from sklearn.base import is_classifier
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.tree import export_graphviz
Expand Down Expand Up @@ -292,7 +292,7 @@ def test_precision():
len(search("\.\d+", finding.group()).group()),
precision + 1)
# check impurity
if isinstance(clf, ClassifierMixin):
if is_classifier(clf):
pattern = "gini = \d+\.\d+"
else:
pattern = "friedman_mse = \d+\.\d+"
Expand Down
5 changes: 3 additions & 2 deletions sklearn/tree/tree.py
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Expand Up @@ -29,6 +29,7 @@
from ..base import BaseEstimator
from ..base import ClassifierMixin
from ..base import RegressorMixin
from ..base import is_classifier
from ..externals import six
from ..utils import check_array
from ..utils import check_random_state
Expand Down Expand Up @@ -123,7 +124,7 @@ def fit(self, X, y, sample_weight=None, check_input=True,

# Determine output settings
n_samples, self.n_features_ = X.shape
is_classification = isinstance(self, ClassifierMixin)
is_classification = is_classifier(self)

y = np.atleast_1d(y)
expanded_class_weight = None
Expand Down Expand Up @@ -413,7 +414,7 @@ def predict(self, X, check_input=True):
n_samples = X.shape[0]

# Classification
if isinstance(self, ClassifierMixin):
if is_classifier(self):
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)

Expand Down
10 changes: 5 additions & 5 deletions sklearn/utils/estimator_checks.py
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Expand Up @@ -35,8 +35,8 @@
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis


from sklearn.base import (clone, ClassifierMixin, RegressorMixin,
TransformerMixin, ClusterMixin, BaseEstimator)
from sklearn.base import (clone, TransformerMixin, ClusterMixin,
BaseEstimator, is_classifier, is_regressor)
from sklearn.metrics import accuracy_score, adjusted_rand_score, f1_score

from sklearn.random_projection import BaseRandomProjection
Expand Down Expand Up @@ -208,10 +208,10 @@ def _yield_clustering_checks(name, clusterer):
def _yield_all_checks(name, estimator):
for check in _yield_non_meta_checks(name, estimator):
yield check
if isinstance(estimator, ClassifierMixin):
if is_classifier(estimator):
for check in _yield_classifier_checks(name, estimator):
yield check
if isinstance(estimator, RegressorMixin):
if is_regressor(estimator):
for check in _yield_regressor_checks(name, estimator):
yield check
if isinstance(estimator, TransformerMixin):
Expand Down Expand Up @@ -980,7 +980,7 @@ def check_estimators_partial_fit_n_features(name, estimator_orig):
X -= X.min()

try:
if isinstance(estimator, ClassifierMixin):
if is_classifier(estimator):
classes = np.unique(y)
estimator.partial_fit(X, y, classes=classes)
else:
Expand Down