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Parallelize the fit and decision_function methods of FeatureBagging #197

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Parallelize the fit and decision_function methods of FeatureBagging #197

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1921e88
Added parallelization to fit and predict methods of FeatureBagging
Shihab-Shahriar May 24, 2020
fa1f17e
Added test for new code
Shihab-Shahriar May 24, 2020
b99fb0f
Corrected some pep8 related errors
Shihab-Shahriar May 24, 2020
f1d9a69
Merge remote-tracking branch 'upstream/master'
Shihab-Shahriar May 24, 2020
e0aaef2
small error: missed the i in import
Shihab-Shahriar May 24, 2020
0e10dab
Used effective n_jobs inside fit instead of user-supplied one
Shihab-Shahriar May 25, 2020
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Corrected some pep8 related errors
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@Shihab-Shahriar
Shihab-Shahriar committed May 24, 2020
commit b99fb0f9f76b0fe0beb0eb944d2985253f1b9b3b
1 change: 0 additions & 1 deletion pyod/models/feature_bagging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -308,7 +308,6 @@ def fit(self, X, y=None):

return self


def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.
The anomaly score of an input sample is computed based on different
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22 changes: 13 additions & 9 deletions pyod/test/test_feature_bagging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
from __future__ import division
from __future__ import print_function

import os
mport os
import sys

import unittest
Expand Down Expand Up @@ -127,8 +127,9 @@ def test_predict_rank_normalized(self):
assert_array_less(-0.1, pred_ranks)

def test_parallel(self):
feat_bag = FeatureBagging(n_jobs=3,
random_state=42).fit(self.X_train, self.y_train)
feat_bag = FeatureBagging(
n_jobs=3,
random_state=42).fit(self.X_train, self.y_train)

# predict_proba
feat_bag.set_params(n_jobs=1)
Expand All @@ -137,24 +138,27 @@ def test_parallel(self):
y2 = feat_bag.predict_proba(self.X_test)
assert_array_almost_equal(y1, y2)

feat_bag = FeatureBagging(n_jobs=1,
random_state=42).fit(self.X_train, self.y_train)
feat_bag = FeatureBagging(
n_jobs=1,
random_state=42).fit(self.X_train, self.y_train)

y3 = feat_bag.predict_proba(self.X_test)
assert_array_almost_equal(y1, y3)

# decision_function
feat_bag = FeatureBagging(n_jobs=3,
random_state=42).fit(self.X_train, self.y_train)
feat_bag = FeatureBagging(
n_jobs=3,
random_state=42).fit(self.X_train, self.y_train)

feat_bag.set_params(n_jobs=1)
decisions1 = feat_bag.decision_function(self.X_test)
feat_bag.set_params(n_jobs=2)
decisions2 = feat_bag.decision_function(self.X_test)
assert_array_almost_equal(decisions1, decisions2)

feat_bag = FeatureBagging(n_jobs=1,
random_state=42).fit(self.X_train, self.y_train)
feat_bag = FeatureBagging(
n_jobs=1,
random_state=42).fit(self.X_train, self.y_train)

decisions3 = feat_bag.decision_function(self.X_test)
assert_array_almost_equal(decisions1, decisions3)
Expand Down