347 lines
12 KiB
Python
347 lines
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 pytest
|
|
|
|
import numpy as np
|
|
|
|
from sklearn.utils._testing import assert_array_equal
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
from sklearn.utils._testing import assert_raises
|
|
from sklearn.utils._testing import assert_warns_message
|
|
from sklearn.utils._testing import ignore_warnings
|
|
from sklearn.utils._testing import assert_allclose
|
|
|
|
from sklearn.model_selection import ParameterGrid
|
|
from sklearn.ensemble import IsolationForest
|
|
from sklearn.ensemble._iforest import _average_path_length
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.datasets import load_diabetes, load_iris
|
|
from sklearn.utils import check_random_state
|
|
from sklearn.metrics import roc_auc_score
|
|
|
|
from scipy.sparse import csc_matrix, csr_matrix
|
|
from unittest.mock import Mock, patch
|
|
|
|
rng = check_random_state(0)
|
|
|
|
# load the iris dataset
|
|
# and randomly permute it
|
|
iris = load_iris()
|
|
perm = rng.permutation(iris.target.size)
|
|
iris.data = iris.data[perm]
|
|
iris.target = iris.target[perm]
|
|
|
|
# also load the diabetes dataset
|
|
# and randomly permute it
|
|
diabetes = load_diabetes()
|
|
perm = rng.permutation(diabetes.target.size)
|
|
diabetes.data = diabetes.data[perm]
|
|
diabetes.target = diabetes.target[perm]
|
|
|
|
|
|
def test_iforest():
|
|
"""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=rng,
|
|
**params).fit(X_train).predict(X_test)
|
|
|
|
|
|
def test_iforest_sparse():
|
|
"""Check IForest for various parameter settings on sparse input."""
|
|
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)
|
|
grid = ParameterGrid({"max_samples": [0.5, 1.0],
|
|
"bootstrap": [True, False]})
|
|
|
|
for sparse_format in [csc_matrix, csr_matrix]:
|
|
X_train_sparse = sparse_format(X_train)
|
|
X_test_sparse = sparse_format(X_test)
|
|
|
|
for params in grid:
|
|
# Trained on sparse format
|
|
sparse_classifier = IsolationForest(
|
|
n_estimators=10, random_state=1, **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=1, **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
|
|
|
|
# Test max_samples
|
|
assert_raises(ValueError,
|
|
IsolationForest(max_samples=-1).fit, X)
|
|
assert_raises(ValueError,
|
|
IsolationForest(max_samples=0.0).fit, X)
|
|
assert_raises(ValueError,
|
|
IsolationForest(max_samples=2.0).fit, X)
|
|
# 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
|
|
assert_warns_message(UserWarning,
|
|
"max_samples will be set to n_samples for estimation",
|
|
IsolationForest(max_samples=1000).fit, X)
|
|
# note that assert_no_warnings does not apply since it enables a
|
|
# PendingDeprecationWarning triggered by scipy.sparse's use of
|
|
# np.matrix. See issue #11251.
|
|
with pytest.warns(None) as record:
|
|
IsolationForest(max_samples='auto').fit(X)
|
|
user_warnings = [each for each in record
|
|
if issubclass(each.category, UserWarning)]
|
|
assert len(user_warnings) == 0
|
|
with pytest.warns(None) as record:
|
|
IsolationForest(max_samples=np.int64(2)).fit(X)
|
|
user_warnings = [each for each in record
|
|
if issubclass(each.category, UserWarning)]
|
|
assert len(user_warnings) == 0
|
|
|
|
assert_raises(ValueError, IsolationForest(max_samples='foobar').fit, X)
|
|
assert_raises(ValueError, IsolationForest(max_samples=1.5).fit, X)
|
|
|
|
# test X_test n_features match X_train one:
|
|
assert_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)
|
|
assert_warns_message(UserWarning,
|
|
"max_samples will be set to n_samples for estimation",
|
|
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():
|
|
"""Check parallel regression."""
|
|
rng = check_random_state(0)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(diabetes.data,
|
|
diabetes.target,
|
|
random_state=rng)
|
|
|
|
ensemble = IsolationForest(n_jobs=3,
|
|
random_state=0).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=0).fit(X_train)
|
|
|
|
y3 = ensemble.predict(X_test)
|
|
assert_array_almost_equal(y1, y3)
|
|
|
|
|
|
def test_iforest_performance():
|
|
"""Test Isolation Forest performs well"""
|
|
|
|
# Generate train/test data
|
|
rng = check_random_state(2)
|
|
X = 0.3 * rng.randn(120, 2)
|
|
X_train = np.r_[X + 2, X - 2]
|
|
X_train = X[:100]
|
|
|
|
# Generate some abnormal novel observations
|
|
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
|
|
X_test = np.r_[X[100:], X_outliers]
|
|
y_test = np.array([0] * 20 + [1] * 20)
|
|
|
|
# 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):
|
|
# toy sample (the last two samples are outliers)
|
|
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [6, 3], [-4, 7]]
|
|
|
|
# Test IsolationForest
|
|
clf = IsolationForest(random_state=rng, 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., 2.]]),
|
|
clf1.decision_function([[2., 2.]]) + clf1.offset_)
|
|
assert_array_equal(clf2.score_samples([[2., 2.]]),
|
|
clf2.decision_function([[2., 2.]]) + clf2.offset_)
|
|
assert_array_equal(clf1.score_samples([[2., 2.]]),
|
|
clf2.score_samples([[2., 2.]]))
|
|
|
|
|
|
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 = 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
|
|
):
|
|
test_iforest_works(contamination)
|
|
assert mocked_get_chunk.call_count == n_predict_calls
|
|
|
|
|
|
# idem with chunk_size = 5 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
|
|
):
|
|
test_iforest_works(contamination)
|
|
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)
|