# -*- coding: utf-8 -*-
import os
import sys
import numpy as np
import unittest
# noinspection PyProtectedMember
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_greater_equal
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.estimator_checks import check_estimator
from sklearn.metrics import roc_auc_score
from scipy.stats import rankdata
from pyod.utils.data import generate_data
from pyod.models.knn import KNN
from pyod.models.lof import LOF
from pyod.models.ocsvm import OCSVM
# temporary solution for relative imports in case pyod is not installed
# if combo is installed, no need to use the following line
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from combo.models.detector_comb import SimpleDetectorAggregator
class TestAverage(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
detectors = [KNN(), LOF(), OCSVM()]
self.clf = SimpleDetectorAggregator(base_estimators=detectors,
method='average',
contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, proba_method='something')
def tearDown(self):
pass
class Maximization(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
detectors = [KNN(), LOF(), OCSVM()]
self.clf = SimpleDetectorAggregator(base_estimators=detectors,
method='maximization',
contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, proba_method='something')
def tearDown(self):
pass
class TestMedian(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
detectors = [KNN(), LOF(), OCSVM()]
self.clf = SimpleDetectorAggregator(base_estimators=detectors,
method='median',
contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, proba_method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, proba_method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def tearDown(self):
pass
if __name__ == '__main__':
unittest.main()
