8000 [MRG] Remove duplicate import of warnings & unused variables by rebekahkim · Pull Request #12203 · scikit-learn/scikit-learn · GitHub
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[MRG] Remove duplicate import of warnings & unused variables #12203

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Merged
merged 4 commits into from
Sep 30, 2018
Merged

[MRG] Remove duplicate import of warnings & unused variables #12203

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0a1a543
remove duplicate calls to warnings module
rebekahkim Sep 29, 2018
4098d41
remove unused vars
rebekahkim Sep 29, 2018
dcb585d
mult imports on same line
rebekahkim Sep 29, 2018
db45167
fix indentation
rebekahkim Sep 29, 2018
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1 change: 0 additions & 1 deletion sklearn/ensemble/bagging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -579,7 +579,6 @@ def _validate_estimator(self):
def _set_oob_score(self, X, y):
n_samples = y.shape[0]
n_classes_ = self.n_classes_
classes_ = self.classes_

predictions = np.zeros((n_samples, n_classes_))

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11 changes: 5 additions & 6 deletions sklearn/ensemble/forest.py
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Expand Up @@ -41,8 +41,7 @@ class calls the ``fit`` method of each sub-estimator on random samples

from __future__ import division

import warnings
from warnings import warn
from warnings import catch_warnings, simplefilter, warn
import threading

from abc import ABCMeta, abstractmethod
Expand Down Expand Up @@ -112,8 +111,8 @@ def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
curr_sample_weight *= sample_counts

if class_weight == 'subsample':
with warnings.catch_warnings():
warnings.simplefilter('ignore', DeprecationWarning)
with catch_warnings():
simplefilter('ignore', DeprecationWarning)
curr_sample_weight *= compute_sample_weight('auto', y, indices)
elif class_weight == 'balanced_subsample':
curr_sample_weight *= compute_sample_weight('balanced', y, indices)
Expand Down Expand Up @@ -244,7 +243,7 @@ def fit(self, X, y, sample_weight=None):
"""

if self.n_estimators == 'warn':
warnings.warn("The default value of n_estimators will change from "
warn("The default value of n_estimators will change from "
"10 in version 0.20 to 100 in 0.22.", FutureWarning)
self.n_estimators = 10

Expand All @@ -259,7 +258,7 @@ def fit(self, X, y, sample_weight=None):
X.sort_indices()

# Remap output
n_samples, self.n_features_ = X.shape
self.n_features_ = X.shape[1]

y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
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23 changes: 11 additions & 12 deletions sklearn/ensemble/iforest.py
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Expand Up @@ -5,7 +5,6 @@
from __future__ import division

import numpy as np
import warnings
from warnings import warn
from sklearn.utils.fixes import euler_gamma

Expand Down Expand Up @@ -208,20 +207,20 @@ def fit(self, X, y=None, sample_weight=None):
self : object
"""
if self.contamination == "legacy":
warnings.warn('default contamination parameter 0.1 will change '
'in version 0.22 to "auto". This will change the '
'predict method behavior.',
FutureWarning)
warn('default contamination parameter 0.1 will change '
'in version 0.22 to "auto". This will change the '
'predict method behavior.',
FutureWarning)
self._contamination = 0.1
else:
self._contamination = self.contamination

if self.behaviour == 'old':
warnings.warn('behaviour="old" is deprecated and will be removed '
'in version 0.22. Please use behaviour="new", which '
'makes the decision_function change to match '
'other anomaly detection algorithm API.',
FutureWarning)
warn('behaviour="old" is deprecated and will be removed '
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actually I think you need to fix the indentation here. make sure you run flake8 on this file

'in version 0.22. Please use behaviour="new", which '
'makes the decision_function change to match '
'other anomaly detection algorithm API.',
FutureWarning)

X = check_array(X, accept_sparse=['csc'])
if issparse(X):
Expand Down Expand Up @@ -414,8 +413,8 @@ def threshold_(self):
if self.behaviour != 'old':
raise AttributeError("threshold_ attribute does not exist when "
"behaviour != 'old'")
warnings.warn("threshold_ attribute is deprecated in 0.20 and will"
" be removed in 0.22.", DeprecationWarning)
warn("threshold_ attribute is deprecated in 0.20 and will"
" be removed in 0.22.", DeprecationWarning)
return self._threshold_


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