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test_feature_significance.py
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# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
import numpy as np
import pandas as pd
from unittest import TestCase
import tsfresh.feature_selection.feature_selector
from tsfresh.feature_selection.settings import FeatureSignificanceTestsSettings
class FeatureSignificanceTestCase(TestCase):
"""Test cases for the whole feature selection algorithm."""
def setUp(self):
"""Fix the random seed."""
np.random.seed(seed=42)
self.settings = FeatureSignificanceTestsSettings()
def test_binary_target_mixed_case(self):
# Mixed case with binomial target
y = pd.Series(np.random.binomial(1, 0.5, 1000))
X = pd.DataFrame(index=range(1000))
z = y - np.random.binomial(1, 0.1, 1000) + np.random.binomial(1, 0.1, 1000)
z[z == -1] = 0
z[z == 2] = 1
X["rel1"] = z
X["rel2"] = y * np.random.normal(0, 1, 1000) + np.random.normal(0, 1, 1000)
X["rel3"] = y + np.random.normal(0, 1, 1000)
X["rel4"] = y ** 2 + np.random.normal(0, 1, 1000)
X["rel5"] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)
X["irr_constant"] = 1.113344
X["irr1"] = np.random.normal(0, 1, 1000)
X["irr2"] = np.random.poisson(1, 1000)
X["irr3"] = np.random.binomial(1, 0.3, 1000)
X["irr4"] = np.random.normal(0, 1, 1000)
X["irr5"] = np.random.poisson(1, 1000)
X["irr6"] = np.random.binomial(1, 0.3, 1000)
X["irr7"] = np.random.normal(0, 1, 1000)
X["irr8"] = np.random.poisson(1, 1000)
X["irr9"] = np.random.binomial(1, 0.3, 1000)
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2', 'rel3', 'rel4', 'rel5'])
self.assertGreater(len(feat_rej), 0)
# Test type outputs
for i in xrange(1, 6):
row = df_bh.loc["rel{}".format(i)]
self.assertEqual(row.Feature, "rel{}".format(i))
if i == 1:
self.assertEqual(row.type, "binary")
else:
self.assertEqual(row.type, "real")
for i in xrange(1, 10):
row = df_bh.loc["irr{}".format(i)]
self.assertEqual(row.Feature, "irr{}".format(i))
if i not in [3, 6, 9]:
self.assertEqual(row.type, "real")
else:
self.assertEqual(row.type, "binary")
self.assertEqual(row.rejected, False)
# Assert that all of the relevant features are kept.
# THIS FAILS!
# self.assertEqual(len(kept_feature), 5)
def test_binary_target_binary_features(self):
# Binomial random variables and binomial target
y = pd.Series(np.random.binomial(1, 0.5, 5000))
X = pd.DataFrame(index=range(5000))
for i in range(10):
X["irr{}".format(i)] = np.random.binomial(1, 0.1, 5000)
for i in range(10, 20):
X["irr{}".format(i)] = np.random.binomial(1, 0.8, 5000)
z = y - np.random.binomial(1, 0.01, 5000) + np.random.binomial(1, 0.01, 5000)
z[z == -1] = 0
z[z == 2] = 1
X["rel1"] = z
z = y - np.random.binomial(1, 0.05, 5000) + np.random.binomial(1, 0.05, 5000)
z[z == -1] = 0
z[z == 2] = 1
X["rel2"] = z
z = y - np.random.binomial(1, 0.10, 5000) + np.random.binomial(1, 0.10, 5000)
z[z == -1] = 0
z[z == 2] = 1
X["rel3"] = z
z = y - np.random.binomial(1, 0.15, 5000) + np.random.binomial(1, 0.15, 5000)
z[z == -1] = 0
z[z == 2] = 1
X["rel4"] = z
z = y - np.random.binomial(1, 0.20, 5000) + np.random.binomial(1, 0.20, 5000)
z[z == -1] = 0
z[z == 2] = 1
X["rel5"] = z
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2', 'rel3', 'rel4', 'rel5'])
self.assertGreater(len(feat_rej), 0)
# Test type outputs
for i in xrange(1, 6):
row = df_bh.loc["rel{}".format(i)]
self.assertEqual(row.Feature, "rel{}".format(i))
self.assertEqual(row.type, "binary")
for i in xrange(1, 20):
row = df_bh.loc["irr{}".format(i)]
self.assertEqual(row.Feature, "irr{}".format(i))
self.assertEqual(row.type, "binary")
self.assertEqual(row.rejected, False)
def test_binomial_target_realvalued_features(self):
# Real valued random variables and binomial target
y = pd.Series(np.random.binomial(1, 0.5, 5000))
X = pd.DataFrame(index=range(5000))
for i in range(10):
X["irr{}".format(i)] = np.random.normal(1, 0.3, 5000)
for i in range(10, 20):
X["irr{}".format(i)] = np.random.normal(1, 0.5, 5000)
for i in range(20, 30):
X["irr{}".format(i)] = np.random.normal(1, 0.8, 5000)
X["rel1"] = y * np.random.normal(0, 1, 5000) + np.random.normal(0, 1, 5000)
X["rel2"] = y + np.random.normal(0, 1, 5000)
X["rel3"] = y ** 2 + np.random.normal(0, 1, 5000)
X["rel4"] = np.sqrt(y) + np.random.binomial(2, 0.1, 5000)
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2', 'rel3', 'rel4'])
self.assertGreater(len(feat_rej), 0)
# Test type outputs
for i in xrange(1, 5):
row = df_bh.loc["rel{}".format(i)]
self.assertEqual(row.Feature, "rel{}".format(i))
self.assertEqual(row.type, "real")
for i in xrange(1, 30):
row = df_bh.loc["irr{}".format(i)]
self.assertEqual(row.Feature, "irr{}".format(i))
self.assertEqual(row.type, "real")
self.assertEqual(row.rejected, False)
def test_real_target_mixed_case(self):
# Mixed case with real target
y = pd.Series(np.random.normal(0, 1, 5000))
X = pd.DataFrame(index=range(5000))
z = y.copy()
z[z <= 0] = 0
z[z > 0] = 1
X["rel1"] = z
X["rel2"] = y
X["rel3"] = y ** 2
X["rel4"] = np.sqrt(abs(y))
X["irr1"] = np.random.normal(0, 1, 5000)
X["irr2"] = np.random.poisson(1, 5000)
X["irr3"] = np.random.binomial(1, 0.1, 5000)
X["irr4"] = np.random.normal(0, 1, 5000)
X["irr5"] = np.random.poisson(1, 5000)
X["irr6"] = np.random.binomial(1, 0.05, 5000)
X["irr7"] = np.random.normal(0, 1, 5000)
X["irr8"] = np.random.poisson(1, 5000)
X["irr9"] = np.random.binomial(1, 0.2, 5000)
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2', 'rel3', 'rel4'])
self.assertGreater(len(feat_rej), 0)
# Test type outputs
for i in xrange(1, 5):
row = df_bh.loc["rel{}".format(i)]
self.assertEqual(row.Feature, "rel{}".format(i))
if i == 1:
self.assertEqual(row.type, "binary")
else:
self.assertEqual(row.type, "real")
for i in xrange(1, 10):
row = df_bh.loc["irr{}".format(i)]
self.assertEqual(row.Feature, "irr{}".format(i))
if i in [3, 6, 9]:
self.assertEqual(row.type, "binary")
else:
self.assertEqual(row.type, "real")
self.assertEqual(row.rejected, False)
def test_real_target_binary_features(self):
# Mixed case with real target
y = pd.Series(np.random.normal(0, 1, 1000))
X = pd.DataFrame(index=range(1000))
z = y - np.random.binomial(1, 0.20, 1000) + np.random.binomial(1, 0.20, 1000)
z[z == -1] = 0
z[z == 2] = 1
X["rel1"] = z
z = y - np.random.binomial(1, 0.10, 1000) + np.random.binomial(1, 0.10, 1000)
z[z == -1] = 0
z[z == 2] = 1
X["rel2"] = z
X["irr1"] = np.random.binomial(0, 0.1, 1000)
X["irr2"] = np.random.binomial(0, 0.15, 1000)
X["irr3"] = np.random.binomial(0, 0.05, 1000)
X["irr4"] = np.random.binomial(0, 0.2, 1000)
X["irr5"] = np.random.binomial(0, 0.25, 1000)
X["irr6"] = np.random.binomial(0, 0.01, 1000)
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2'])
self.assertGreater(len(feat_rej), 0)
def test_all_features_good(self):
# Mixed case with real target
y = pd.Series(np.random.normal(0, 1, 1000))
X = pd.DataFrame(index=range(1000))
z = y - np.random.binomial(1, 0.20, 1000) + np.random.binomial(1, 0.20, 1000)
z[z == -1] = 0
z[z == 2] = 1
X["rel1"] = z
z = y - np.random.binomial(1, 0.10, 1000) + np.random.binomial(1, 0.10, 1000)
z[z == -1] = 0
z[z == 2] = 1
X["rel2"] = z
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
# Make sure all selected variables are relevant
for kept_feature in feat_rej:
self.assertIn(kept_feature, ['rel1', 'rel2'])
self.assertGreater(len(feat_rej), 0)
def test_all_features_bad(self):
# Mixed case with real target
y = pd.Series(np.random.normal(0, 1, 1000))
X = pd.DataFrame(index=range(1000))
X["irr1"] = np.random.binomial(0, 0.1, 1000)
X["irr2"] = np.random.binomial(0, 0.15, 1000)
X["irr3"] = np.random.binomial(0, 0.05, 1000)
X["irr4"] = np.random.binomial(0, 0.2, 1000)
X["irr5"] = np.random.binomial(0, 0.25, 1000)
X["irr6"] = np.random.binomial(0, 0.01, 1000)
df_bh = tsfresh.feature_selection.feature_selector.check_fs_sig_bh(X, y, self.settings)
feat_rej = df_bh[df_bh.rejected].Feature
self.assertEqual(len(feat_rej), 0)
class FeatureSignificanceTestCaseWithOtherHypothesis(FeatureSignificanceTestCase):
"""
This class has the same tests as FeatureSignificanceTestCase,
but with feature _independent set to True, instead of false.
"""
def setUp(self):
FeatureSignificanceTestCase.setUp(self)
self.settings.hypotheses_independent = True