ai-content-maker/.venv/Lib/site-packages/sklearn/ensemble/tests/test_iforest.py

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12 KiB
Python

"""
Testing for Isolation Forest algorithm (sklearn.ensemble.iforest).
"""
# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import warnings
from unittest.mock import Mock, patch
import numpy as np
import pytest
from sklearn.datasets import load_diabetes, load_iris, make_classification
from sklearn.ensemble import IsolationForest
from sklearn.ensemble._iforest import _average_path_length
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import ParameterGrid, train_test_split
from sklearn.utils import check_random_state
from sklearn.utils._testing import (
assert_allclose,
assert_array_almost_equal,
assert_array_equal,
ignore_warnings,
)
from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
# load iris & diabetes dataset
iris = load_iris()
diabetes = load_diabetes()
def test_iforest(global_random_seed):
"""Check Isolation Forest for various parameter settings."""
X_train = np.array([[0, 1], [1, 2]])
X_test = np.array([[2, 1], [1, 1]])
grid = ParameterGrid(
{"n_estimators": [3], "max_samples": [0.5, 1.0, 3], "bootstrap": [True, False]}
)
with ignore_warnings():
for params in grid:
IsolationForest(random_state=global_random_seed, **params).fit(
X_train
).predict(X_test)
@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_iforest_sparse(global_random_seed, sparse_container):
"""Check IForest for various parameter settings on sparse input."""
rng = check_random_state(global_random_seed)
X_train, X_test = train_test_split(diabetes.data[:50], random_state=rng)
grid = ParameterGrid({"max_samples": [0.5, 1.0], "bootstrap": [True, False]})
X_train_sparse = sparse_container(X_train)
X_test_sparse = sparse_container(X_test)
for params in grid:
# Trained on sparse format
sparse_classifier = IsolationForest(
n_estimators=10, random_state=global_random_seed, **params
).fit(X_train_sparse)
sparse_results = sparse_classifier.predict(X_test_sparse)
# Trained on dense format
dense_classifier = IsolationForest(
n_estimators=10, random_state=global_random_seed, **params
).fit(X_train)
dense_results = dense_classifier.predict(X_test)
assert_array_equal(sparse_results, dense_results)
def test_iforest_error():
"""Test that it gives proper exception on deficient input."""
X = iris.data
# The dataset has less than 256 samples, explicitly setting
# max_samples > n_samples should result in a warning. If not set
# explicitly there should be no warning
warn_msg = "max_samples will be set to n_samples for estimation"
with pytest.warns(UserWarning, match=warn_msg):
IsolationForest(max_samples=1000).fit(X)
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
IsolationForest(max_samples="auto").fit(X)
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
IsolationForest(max_samples=np.int64(2)).fit(X)
# test X_test n_features match X_train one:
with pytest.raises(ValueError):
IsolationForest().fit(X).predict(X[:, 1:])
def test_recalculate_max_depth():
"""Check max_depth recalculation when max_samples is reset to n_samples"""
X = iris.data
clf = IsolationForest().fit(X)
for est in clf.estimators_:
assert est.max_depth == int(np.ceil(np.log2(X.shape[0])))
def test_max_samples_attribute():
X = iris.data
clf = IsolationForest().fit(X)
assert clf.max_samples_ == X.shape[0]
clf = IsolationForest(max_samples=500)
warn_msg = "max_samples will be set to n_samples for estimation"
with pytest.warns(UserWarning, match=warn_msg):
clf.fit(X)
assert clf.max_samples_ == X.shape[0]
clf = IsolationForest(max_samples=0.4).fit(X)
assert clf.max_samples_ == 0.4 * X.shape[0]
def test_iforest_parallel_regression(global_random_seed):
"""Check parallel regression."""
rng = check_random_state(global_random_seed)
X_train, X_test = train_test_split(diabetes.data, random_state=rng)
ensemble = IsolationForest(n_jobs=3, random_state=global_random_seed).fit(X_train)
ensemble.set_params(n_jobs=1)
y1 = ensemble.predict(X_test)
ensemble.set_params(n_jobs=2)
y2 = ensemble.predict(X_test)
assert_array_almost_equal(y1, y2)
ensemble = IsolationForest(n_jobs=1, random_state=global_random_seed).fit(X_train)
y3 = ensemble.predict(X_test)
assert_array_almost_equal(y1, y3)
def test_iforest_performance(global_random_seed):
"""Test Isolation Forest performs well"""
# Generate train/test data
rng = check_random_state(global_random_seed)
X = 0.3 * rng.randn(600, 2)
X = rng.permutation(np.vstack((X + 2, X - 2)))
X_train = X[:1000]
# Generate some abnormal novel observations
X_outliers = rng.uniform(low=-1, high=1, size=(200, 2))
X_test = np.vstack((X[1000:], X_outliers))
y_test = np.array([0] * 200 + [1] * 200)
# fit the model
clf = IsolationForest(max_samples=100, random_state=rng).fit(X_train)
# predict scores (the lower, the more normal)
y_pred = -clf.decision_function(X_test)
# check that there is at most 6 errors (false positive or false negative)
assert roc_auc_score(y_test, y_pred) > 0.98
@pytest.mark.parametrize("contamination", [0.25, "auto"])
def test_iforest_works(contamination, global_random_seed):
# toy sample (the last two samples are outliers)
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [7, 4], [-5, 9]]
# Test IsolationForest
clf = IsolationForest(random_state=global_random_seed, contamination=contamination)
clf.fit(X)
decision_func = -clf.decision_function(X)
pred = clf.predict(X)
# assert detect outliers:
assert np.min(decision_func[-2:]) > np.max(decision_func[:-2])
assert_array_equal(pred, 6 * [1] + 2 * [-1])
def test_max_samples_consistency():
# Make sure validated max_samples in iforest and BaseBagging are identical
X = iris.data
clf = IsolationForest().fit(X)
assert clf.max_samples_ == clf._max_samples
def test_iforest_subsampled_features():
# It tests non-regression for #5732 which failed at predict.
rng = check_random_state(0)
X_train, X_test, y_train, y_test = train_test_split(
diabetes.data[:50], diabetes.target[:50], random_state=rng
)
clf = IsolationForest(max_features=0.8)
clf.fit(X_train, y_train)
clf.predict(X_test)
def test_iforest_average_path_length():
# It tests non-regression for #8549 which used the wrong formula
# for average path length, strictly for the integer case
# Updated to check average path length when input is <= 2 (issue #11839)
result_one = 2.0 * (np.log(4.0) + np.euler_gamma) - 2.0 * 4.0 / 5.0
result_two = 2.0 * (np.log(998.0) + np.euler_gamma) - 2.0 * 998.0 / 999.0
assert_allclose(_average_path_length([0]), [0.0])
assert_allclose(_average_path_length([1]), [0.0])
assert_allclose(_average_path_length([2]), [1.0])
assert_allclose(_average_path_length([5]), [result_one])
assert_allclose(_average_path_length([999]), [result_two])
assert_allclose(
_average_path_length(np.array([1, 2, 5, 999])),
[0.0, 1.0, result_one, result_two],
)
# _average_path_length is increasing
avg_path_length = _average_path_length(np.arange(5))
assert_array_equal(avg_path_length, np.sort(avg_path_length))
def test_score_samples():
X_train = [[1, 1], [1, 2], [2, 1]]
clf1 = IsolationForest(contamination=0.1).fit(X_train)
clf2 = IsolationForest().fit(X_train)
assert_array_equal(
clf1.score_samples([[2.0, 2.0]]),
clf1.decision_function([[2.0, 2.0]]) + clf1.offset_,
)
assert_array_equal(
clf2.score_samples([[2.0, 2.0]]),
clf2.decision_function([[2.0, 2.0]]) + clf2.offset_,
)
assert_array_equal(
clf1.score_samples([[2.0, 2.0]]), clf2.score_samples([[2.0, 2.0]])
)
def test_iforest_warm_start():
"""Test iterative addition of iTrees to an iForest"""
rng = check_random_state(0)
X = rng.randn(20, 2)
# fit first 10 trees
clf = IsolationForest(
n_estimators=10, max_samples=20, random_state=rng, warm_start=True
)
clf.fit(X)
# remember the 1st tree
tree_1 = clf.estimators_[0]
# fit another 10 trees
clf.set_params(n_estimators=20)
clf.fit(X)
# expecting 20 fitted trees and no overwritten trees
assert len(clf.estimators_) == 20
assert clf.estimators_[0] is tree_1
# mock get_chunk_n_rows to actually test more than one chunk (here one
# chunk has 3 rows):
@patch(
"sklearn.ensemble._iforest.get_chunk_n_rows",
side_effect=Mock(**{"return_value": 3}),
)
@pytest.mark.parametrize("contamination, n_predict_calls", [(0.25, 3), ("auto", 2)])
def test_iforest_chunks_works1(
mocked_get_chunk, contamination, n_predict_calls, global_random_seed
):
test_iforest_works(contamination, global_random_seed)
assert mocked_get_chunk.call_count == n_predict_calls
# idem with chunk_size = 10 rows
@patch(
"sklearn.ensemble._iforest.get_chunk_n_rows",
side_effect=Mock(**{"return_value": 10}),
)
@pytest.mark.parametrize("contamination, n_predict_calls", [(0.25, 3), ("auto", 2)])
def test_iforest_chunks_works2(
mocked_get_chunk, contamination, n_predict_calls, global_random_seed
):
test_iforest_works(contamination, global_random_seed)
assert mocked_get_chunk.call_count == n_predict_calls
def test_iforest_with_uniform_data():
"""Test whether iforest predicts inliers when using uniform data"""
# 2-d array of all 1s
X = np.ones((100, 10))
iforest = IsolationForest()
iforest.fit(X)
rng = np.random.RandomState(0)
assert all(iforest.predict(X) == 1)
assert all(iforest.predict(rng.randn(100, 10)) == 1)
assert all(iforest.predict(X + 1) == 1)
assert all(iforest.predict(X - 1) == 1)
# 2-d array where columns contain the same value across rows
X = np.repeat(rng.randn(1, 10), 100, 0)
iforest = IsolationForest()
iforest.fit(X)
assert all(iforest.predict(X) == 1)
assert all(iforest.predict(rng.randn(100, 10)) == 1)
assert all(iforest.predict(np.ones((100, 10))) == 1)
# Single row
X = rng.randn(1, 10)
iforest = IsolationForest()
iforest.fit(X)
assert all(iforest.predict(X) == 1)
assert all(iforest.predict(rng.randn(100, 10)) == 1)
assert all(iforest.predict(np.ones((100, 10))) == 1)
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
def test_iforest_with_n_jobs_does_not_segfault(csc_container):
"""Check that Isolation Forest does not segfault with n_jobs=2
Non-regression test for #23252
"""
X, _ = make_classification(n_samples=85_000, n_features=100, random_state=0)
X = csc_container(X)
IsolationForest(n_estimators=10, max_samples=256, n_jobs=2).fit(X)
def test_iforest_preserve_feature_names():
"""Check that feature names are preserved when contamination is not "auto".
Feature names are required for consistency checks during scoring.
Non-regression test for Issue #25844
"""
pd = pytest.importorskip("pandas")
rng = np.random.RandomState(0)
X = pd.DataFrame(data=rng.randn(4), columns=["a"])
model = IsolationForest(random_state=0, contamination=0.05)
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
model.fit(X)
@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
def test_iforest_sparse_input_float_contamination(sparse_container):
"""Check that `IsolationForest` accepts sparse matrix input and float value for
contamination.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/27626
"""
X, _ = make_classification(n_samples=50, n_features=4, random_state=0)
X = sparse_container(X)
X.sort_indices()
contamination = 0.1
iforest = IsolationForest(
n_estimators=5, contamination=contamination, random_state=0
).fit(X)
X_decision = iforest.decision_function(X)
assert (X_decision < 0).sum() / X.shape[0] == pytest.approx(contamination)