laywerrobot/lib/python3.6/site-packages/sklearn/ensemble/tests/test_iforest.py
2020-08-27 21:55:39 +02:00

228 lines
8.2 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 numpy as np
from sklearn.utils.fixes import euler_gamma
from sklearn.utils.testing import assert_almost_equal
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 assert_equal
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import ignore_warnings
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_boston, load_iris
from sklearn.utils import check_random_state
from sklearn.metrics import roc_auc_score
from scipy.sparse import csc_matrix, csr_matrix
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 boston dataset
# and randomly permute it
boston = load_boston()
perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.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(boston.data[:50],
boston.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)
assert_no_warnings(IsolationForest(max_samples='auto').fit, X)
assert_no_warnings(IsolationForest(max_samples=np.int64(2)).fit, X)
assert_raises(ValueError, IsolationForest(max_samples='foobar').fit, X)
assert_raises(ValueError, IsolationForest(max_samples=1.5).fit, X)
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_equal(est.max_depth, int(np.ceil(np.log2(X.shape[0]))))
def test_max_samples_attribute():
X = iris.data
clf = IsolationForest().fit(X)
assert_equal(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_equal(clf.max_samples_, X.shape[0])
clf = IsolationForest(max_samples=0.4).fit(X)
assert_equal(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(boston.data,
boston.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_greater(roc_auc_score(y_test, y_pred), 0.98)
def test_iforest_works():
# 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 LOF
clf = IsolationForest(random_state=rng, contamination=0.25)
clf.fit(X)
decision_func = - clf.decision_function(X)
pred = clf.predict(X)
# assert detect outliers:
assert_greater(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_equal(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(boston.data[:50],
boston.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
result_one = 2. * (np.log(4.) + euler_gamma) - 2. * 4. / 5.
result_two = 2. * (np.log(998.) + euler_gamma) - 2. * 998. / 999.
assert_almost_equal(_average_path_length(1), 1., decimal=10)
assert_almost_equal(_average_path_length(5), result_one, decimal=10)
assert_almost_equal(_average_path_length(999), result_two, decimal=10)
assert_array_almost_equal(_average_path_length(np.array([1, 5, 999])),
[1., result_one, result_two], decimal=10)