358 lines
12 KiB
Python
358 lines
12 KiB
Python
"""
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Testing for Isolation Forest algorithm (sklearn.ensemble.iforest).
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"""
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# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
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# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
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# License: BSD 3 clause
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import pytest
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import numpy as np
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_raises
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from sklearn.utils._testing import assert_warns_message
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils._testing import assert_allclose
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from sklearn.model_selection import ParameterGrid
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from sklearn.ensemble import IsolationForest
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from sklearn.ensemble._iforest import _average_path_length
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_diabetes, load_iris
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from sklearn.utils import check_random_state
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from sklearn.metrics import roc_auc_score
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from scipy.sparse import csc_matrix, csr_matrix
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from unittest.mock import Mock, patch
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rng = check_random_state(0)
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# load the iris dataset
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# and randomly permute it
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iris = load_iris()
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perm = rng.permutation(iris.target.size)
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iris.data = iris.data[perm]
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iris.target = iris.target[perm]
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# also load the diabetes dataset
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# and randomly permute it
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diabetes = load_diabetes()
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perm = rng.permutation(diabetes.target.size)
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diabetes.data = diabetes.data[perm]
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diabetes.target = diabetes.target[perm]
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def test_iforest():
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"""Check Isolation Forest for various parameter settings."""
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X_train = np.array([[0, 1], [1, 2]])
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X_test = np.array([[2, 1], [1, 1]])
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grid = ParameterGrid({"n_estimators": [3],
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"max_samples": [0.5, 1.0, 3],
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"bootstrap": [True, False]})
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with ignore_warnings():
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for params in grid:
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IsolationForest(random_state=rng,
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**params).fit(X_train).predict(X_test)
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def test_iforest_sparse():
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"""Check IForest for various parameter settings on sparse input."""
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rng = check_random_state(0)
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X_train, X_test, y_train, y_test = train_test_split(diabetes.data[:50],
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diabetes.target[:50],
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random_state=rng)
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grid = ParameterGrid({"max_samples": [0.5, 1.0],
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"bootstrap": [True, False]})
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for sparse_format in [csc_matrix, csr_matrix]:
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X_train_sparse = sparse_format(X_train)
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X_test_sparse = sparse_format(X_test)
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for params in grid:
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# Trained on sparse format
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sparse_classifier = IsolationForest(
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n_estimators=10, random_state=1, **params).fit(X_train_sparse)
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sparse_results = sparse_classifier.predict(X_test_sparse)
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# Trained on dense format
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dense_classifier = IsolationForest(
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n_estimators=10, random_state=1, **params).fit(X_train)
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dense_results = dense_classifier.predict(X_test)
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assert_array_equal(sparse_results, dense_results)
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def test_iforest_error():
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"""Test that it gives proper exception on deficient input."""
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X = iris.data
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# Test max_samples
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assert_raises(ValueError,
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IsolationForest(max_samples=-1).fit, X)
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assert_raises(ValueError,
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IsolationForest(max_samples=0.0).fit, X)
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assert_raises(ValueError,
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IsolationForest(max_samples=2.0).fit, X)
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# The dataset has less than 256 samples, explicitly setting
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# max_samples > n_samples should result in a warning. If not set
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# explicitly there should be no warning
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assert_warns_message(UserWarning,
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"max_samples will be set to n_samples for estimation",
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IsolationForest(max_samples=1000).fit, X)
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# note that assert_no_warnings does not apply since it enables a
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# PendingDeprecationWarning triggered by scipy.sparse's use of
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# np.matrix. See issue #11251.
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with pytest.warns(None) as record:
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IsolationForest(max_samples='auto').fit(X)
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user_warnings = [each for each in record
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if issubclass(each.category, UserWarning)]
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assert len(user_warnings) == 0
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with pytest.warns(None) as record:
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IsolationForest(max_samples=np.int64(2)).fit(X)
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user_warnings = [each for each in record
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if issubclass(each.category, UserWarning)]
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assert len(user_warnings) == 0
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assert_raises(ValueError, IsolationForest(max_samples='foobar').fit, X)
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assert_raises(ValueError, IsolationForest(max_samples=1.5).fit, X)
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# test X_test n_features match X_train one:
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assert_raises(ValueError, IsolationForest().fit(X).predict, X[:, 1:])
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# test that behaviour='old' will raise an error
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msg = "The old behaviour of IsolationForest is not implemented anymore."
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with pytest.raises(NotImplementedError, match=msg):
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IsolationForest(behaviour='old').fit(X)
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def test_recalculate_max_depth():
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"""Check max_depth recalculation when max_samples is reset to n_samples"""
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X = iris.data
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clf = IsolationForest().fit(X)
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for est in clf.estimators_:
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assert est.max_depth == int(np.ceil(np.log2(X.shape[0])))
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def test_max_samples_attribute():
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X = iris.data
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clf = IsolationForest().fit(X)
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assert clf.max_samples_ == X.shape[0]
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clf = IsolationForest(max_samples=500)
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assert_warns_message(UserWarning,
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"max_samples will be set to n_samples for estimation",
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clf.fit, X)
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assert clf.max_samples_ == X.shape[0]
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clf = IsolationForest(max_samples=0.4).fit(X)
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assert clf.max_samples_ == 0.4*X.shape[0]
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def test_iforest_parallel_regression():
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"""Check parallel regression."""
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rng = check_random_state(0)
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X_train, X_test, y_train, y_test = train_test_split(diabetes.data,
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diabetes.target,
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random_state=rng)
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ensemble = IsolationForest(n_jobs=3,
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random_state=0).fit(X_train)
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ensemble.set_params(n_jobs=1)
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y1 = ensemble.predict(X_test)
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ensemble.set_params(n_jobs=2)
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y2 = ensemble.predict(X_test)
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assert_array_almost_equal(y1, y2)
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ensemble = IsolationForest(n_jobs=1,
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random_state=0).fit(X_train)
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y3 = ensemble.predict(X_test)
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assert_array_almost_equal(y1, y3)
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def test_iforest_performance():
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"""Test Isolation Forest performs well"""
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# Generate train/test data
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rng = check_random_state(2)
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X = 0.3 * rng.randn(120, 2)
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X_train = np.r_[X + 2, X - 2]
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X_train = X[:100]
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# Generate some abnormal novel observations
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X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
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X_test = np.r_[X[100:], X_outliers]
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y_test = np.array([0] * 20 + [1] * 20)
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# fit the model
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clf = IsolationForest(max_samples=100, random_state=rng).fit(X_train)
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# predict scores (the lower, the more normal)
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y_pred = - clf.decision_function(X_test)
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# check that there is at most 6 errors (false positive or false negative)
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assert roc_auc_score(y_test, y_pred) > 0.98
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@pytest.mark.parametrize("contamination", [0.25, "auto"])
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def test_iforest_works(contamination):
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# toy sample (the last two samples are outliers)
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [6, 3], [-4, 7]]
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# Test IsolationForest
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clf = IsolationForest(random_state=rng, contamination=contamination)
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clf.fit(X)
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decision_func = -clf.decision_function(X)
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pred = clf.predict(X)
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# assert detect outliers:
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assert np.min(decision_func[-2:]) > np.max(decision_func[:-2])
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assert_array_equal(pred, 6 * [1] + 2 * [-1])
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def test_max_samples_consistency():
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# Make sure validated max_samples in iforest and BaseBagging are identical
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X = iris.data
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clf = IsolationForest().fit(X)
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assert clf.max_samples_ == clf._max_samples
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def test_iforest_subsampled_features():
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# It tests non-regression for #5732 which failed at predict.
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rng = check_random_state(0)
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X_train, X_test, y_train, y_test = train_test_split(diabetes.data[:50],
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diabetes.target[:50],
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random_state=rng)
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clf = IsolationForest(max_features=0.8)
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clf.fit(X_train, y_train)
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clf.predict(X_test)
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def test_iforest_average_path_length():
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# It tests non-regression for #8549 which used the wrong formula
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# for average path length, strictly for the integer case
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# Updated to check average path length when input is <= 2 (issue #11839)
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result_one = 2.0 * (np.log(4.0) + np.euler_gamma) - 2.0 * 4.0 / 5.0
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result_two = 2.0 * (np.log(998.0) + np.euler_gamma) - 2.0 * 998.0 / 999.0
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assert_allclose(_average_path_length([0]), [0.0])
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assert_allclose(_average_path_length([1]), [0.0])
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assert_allclose(_average_path_length([2]), [1.0])
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assert_allclose(_average_path_length([5]), [result_one])
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assert_allclose(_average_path_length([999]), [result_two])
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assert_allclose(
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_average_path_length(np.array([1, 2, 5, 999])),
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[0.0, 1.0, result_one, result_two],
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)
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# _average_path_length is increasing
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avg_path_length = _average_path_length(np.arange(5))
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assert_array_equal(avg_path_length, np.sort(avg_path_length))
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def test_score_samples():
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X_train = [[1, 1], [1, 2], [2, 1]]
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clf1 = IsolationForest(contamination=0.1).fit(X_train)
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clf2 = IsolationForest().fit(X_train)
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assert_array_equal(clf1.score_samples([[2., 2.]]),
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clf1.decision_function([[2., 2.]]) + clf1.offset_)
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assert_array_equal(clf2.score_samples([[2., 2.]]),
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clf2.decision_function([[2., 2.]]) + clf2.offset_)
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assert_array_equal(clf1.score_samples([[2., 2.]]),
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clf2.score_samples([[2., 2.]]))
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def test_iforest_warm_start():
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"""Test iterative addition of iTrees to an iForest """
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rng = check_random_state(0)
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X = rng.randn(20, 2)
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# fit first 10 trees
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clf = IsolationForest(n_estimators=10, max_samples=20,
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random_state=rng, warm_start=True)
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clf.fit(X)
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# remember the 1st tree
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tree_1 = clf.estimators_[0]
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# fit another 10 trees
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clf.set_params(n_estimators=20)
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clf.fit(X)
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# expecting 20 fitted trees and no overwritten trees
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assert len(clf.estimators_) == 20
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assert clf.estimators_[0] is tree_1
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# mock get_chunk_n_rows to actually test more than one chunk (here one
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# chunk = 3 rows:
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@patch(
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"sklearn.ensemble._iforest.get_chunk_n_rows",
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side_effect=Mock(**{"return_value": 3}),
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)
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@pytest.mark.parametrize(
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"contamination, n_predict_calls", [(0.25, 3), ("auto", 2)]
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)
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def test_iforest_chunks_works1(
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mocked_get_chunk, contamination, n_predict_calls
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):
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test_iforest_works(contamination)
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assert mocked_get_chunk.call_count == n_predict_calls
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# idem with chunk_size = 5 rows
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@patch(
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"sklearn.ensemble._iforest.get_chunk_n_rows",
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side_effect=Mock(**{"return_value": 10}),
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)
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@pytest.mark.parametrize(
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"contamination, n_predict_calls", [(0.25, 3), ("auto", 2)]
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)
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def test_iforest_chunks_works2(
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mocked_get_chunk, contamination, n_predict_calls
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):
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test_iforest_works(contamination)
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assert mocked_get_chunk.call_count == n_predict_calls
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def test_iforest_deprecation():
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iforest = IsolationForest(behaviour='new')
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warn_msg = "'behaviour' is deprecated in 0.22 and will be removed in 0.24"
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with pytest.warns(FutureWarning, match=warn_msg):
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iforest.fit(iris.data)
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def test_iforest_with_uniform_data():
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"""Test whether iforest predicts inliers when using uniform data"""
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# 2-d array of all 1s
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X = np.ones((100, 10))
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iforest = IsolationForest()
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iforest.fit(X)
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rng = np.random.RandomState(0)
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assert all(iforest.predict(X) == 1)
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assert all(iforest.predict(rng.randn(100, 10)) == 1)
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assert all(iforest.predict(X + 1) == 1)
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assert all(iforest.predict(X - 1) == 1)
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# 2-d array where columns contain the same value across rows
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X = np.repeat(rng.randn(1, 10), 100, 0)
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iforest = IsolationForest()
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iforest.fit(X)
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assert all(iforest.predict(X) == 1)
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assert all(iforest.predict(rng.randn(100, 10)) == 1)
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assert all(iforest.predict(np.ones((100, 10))) == 1)
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# Single row
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X = rng.randn(1, 10)
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iforest = IsolationForest()
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iforest.fit(X)
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assert all(iforest.predict(X) == 1)
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assert all(iforest.predict(rng.randn(100, 10)) == 1)
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assert all(iforest.predict(np.ones((100, 10))) == 1)
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