364 lines
12 KiB
Python
364 lines
12 KiB
Python
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"""
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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 warnings
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from unittest.mock import Mock, patch
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import numpy as np
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import pytest
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from sklearn.datasets import load_diabetes, load_iris, make_classification
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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.metrics import roc_auc_score
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from sklearn.model_selection import ParameterGrid, train_test_split
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import (
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assert_allclose,
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assert_array_almost_equal,
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assert_array_equal,
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ignore_warnings,
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)
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from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
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# load iris & diabetes dataset
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iris = load_iris()
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diabetes = load_diabetes()
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def test_iforest(global_random_seed):
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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(
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{"n_estimators": [3], "max_samples": [0.5, 1.0, 3], "bootstrap": [True, False]}
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)
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with ignore_warnings():
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for params in grid:
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IsolationForest(random_state=global_random_seed, **params).fit(
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X_train
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).predict(X_test)
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@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
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def test_iforest_sparse(global_random_seed, sparse_container):
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"""Check IForest for various parameter settings on sparse input."""
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rng = check_random_state(global_random_seed)
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X_train, X_test = train_test_split(diabetes.data[:50], random_state=rng)
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grid = ParameterGrid({"max_samples": [0.5, 1.0], "bootstrap": [True, False]})
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X_train_sparse = sparse_container(X_train)
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X_test_sparse = sparse_container(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=global_random_seed, **params
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).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=global_random_seed, **params
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).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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# 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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warn_msg = "max_samples will be set to n_samples for estimation"
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with pytest.warns(UserWarning, match=warn_msg):
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IsolationForest(max_samples=1000).fit(X)
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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IsolationForest(max_samples="auto").fit(X)
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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IsolationForest(max_samples=np.int64(2)).fit(X)
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# test X_test n_features match X_train one:
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with pytest.raises(ValueError):
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IsolationForest().fit(X).predict(X[:, 1:])
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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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warn_msg = "max_samples will be set to n_samples for estimation"
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with pytest.warns(UserWarning, match=warn_msg):
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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(global_random_seed):
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"""Check parallel regression."""
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rng = check_random_state(global_random_seed)
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X_train, X_test = train_test_split(diabetes.data, random_state=rng)
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ensemble = IsolationForest(n_jobs=3, random_state=global_random_seed).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, random_state=global_random_seed).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(global_random_seed):
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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(global_random_seed)
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X = 0.3 * rng.randn(600, 2)
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X = rng.permutation(np.vstack((X + 2, X - 2)))
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X_train = X[:1000]
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# Generate some abnormal novel observations
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X_outliers = rng.uniform(low=-1, high=1, size=(200, 2))
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X_test = np.vstack((X[1000:], X_outliers))
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y_test = np.array([0] * 200 + [1] * 200)
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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, global_random_seed):
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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], [7, 4], [-5, 9]]
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# Test IsolationForest
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clf = IsolationForest(random_state=global_random_seed, 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(
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diabetes.data[:50], diabetes.target[:50], random_state=rng
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)
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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(
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clf1.score_samples([[2.0, 2.0]]),
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clf1.decision_function([[2.0, 2.0]]) + clf1.offset_,
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)
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assert_array_equal(
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clf2.score_samples([[2.0, 2.0]]),
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clf2.decision_function([[2.0, 2.0]]) + clf2.offset_,
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)
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assert_array_equal(
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clf1.score_samples([[2.0, 2.0]]), clf2.score_samples([[2.0, 2.0]])
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)
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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(
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n_estimators=10, max_samples=20, random_state=rng, warm_start=True
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)
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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 has 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("contamination, n_predict_calls", [(0.25, 3), ("auto", 2)])
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def test_iforest_chunks_works1(
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mocked_get_chunk, contamination, n_predict_calls, global_random_seed
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):
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test_iforest_works(contamination, global_random_seed)
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assert mocked_get_chunk.call_count == n_predict_calls
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# idem with chunk_size = 10 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("contamination, n_predict_calls", [(0.25, 3), ("auto", 2)])
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def test_iforest_chunks_works2(
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mocked_get_chunk, contamination, n_predict_calls, global_random_seed
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):
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test_iforest_works(contamination, global_random_seed)
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assert mocked_get_chunk.call_count == n_predict_calls
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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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@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
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def test_iforest_with_n_jobs_does_not_segfault(csc_container):
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"""Check that Isolation Forest does not segfault with n_jobs=2
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Non-regression test for #23252
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"""
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X, _ = make_classification(n_samples=85_000, n_features=100, random_state=0)
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X = csc_container(X)
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IsolationForest(n_estimators=10, max_samples=256, n_jobs=2).fit(X)
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def test_iforest_preserve_feature_names():
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"""Check that feature names are preserved when contamination is not "auto".
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Feature names are required for consistency checks during scoring.
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Non-regression test for Issue #25844
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"""
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pd = pytest.importorskip("pandas")
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rng = np.random.RandomState(0)
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X = pd.DataFrame(data=rng.randn(4), columns=["a"])
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model = IsolationForest(random_state=0, contamination=0.05)
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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model.fit(X)
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@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
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def test_iforest_sparse_input_float_contamination(sparse_container):
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"""Check that `IsolationForest` accepts sparse matrix input and float value for
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contamination.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/27626
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"""
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X, _ = make_classification(n_samples=50, n_features=4, random_state=0)
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X = sparse_container(X)
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X.sort_indices()
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contamination = 0.1
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iforest = IsolationForest(
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n_estimators=5, contamination=contamination, random_state=0
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|
).fit(X)
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||
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X_decision = iforest.decision_function(X)
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assert (X_decision < 0).sum() / X.shape[0] == pytest.approx(contamination)
|