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"""Common tests"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import warnings
from collections import OrderedDict
import numpy as np
import pytest
from sklearn.base import clone
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils._testing import SkipTest, ignore_warnings, set_random_state
from sklearn.utils.estimator_checks import _construct_instance, _get_check_estimator_ids
from sklearn.utils.estimator_checks import (
parametrize_with_checks as parametrize_with_checks_sklearn,
)
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import NearMiss, RandomUnderSampler
from imblearn.utils.estimator_checks import (
_set_checking_parameters,
check_dataframe_column_names_consistency,
check_param_validation,
parametrize_with_checks,
)
from imblearn.utils.testing import all_estimators
@pytest.mark.parametrize("name, Estimator", all_estimators())
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = f"Base estimators such as {name} should not be included" f" in all_estimators"
assert not name.lower().startswith("base"), msg
def _tested_estimators():
for name, Estimator in all_estimators():
try:
estimator = _construct_instance(Estimator)
set_random_state(estimator)
except SkipTest:
continue
if isinstance(estimator, NearMiss):
# For NearMiss, let's check the three algorithms
for version in (1, 2, 3):
yield clone(estimator).set_params(version=version)
else:
yield estimator
@parametrize_with_checks_sklearn(list(_tested_estimators()))
def test_estimators_compatibility_sklearn(estimator, check, request):
_set_checking_parameters(estimator)
check(estimator)
@parametrize_with_checks(list(_tested_estimators()))
def test_estimators_imblearn(estimator, check, request):
# Common tests for estimator instances
with ignore_warnings(
category=(
FutureWarning,
ConvergenceWarning,
UserWarning,
FutureWarning,
)
):
_set_checking_parameters(estimator)
check(estimator)
@pytest.mark.parametrize(
"estimator", _tested_estimators(), ids=_get_check_estimator_ids
)
def test_check_param_validation(estimator):
name = estimator.__class__.__name__
_set_checking_parameters(estimator)
check_param_validation(name, estimator)
@pytest.mark.parametrize("Sampler", [RandomOverSampler, RandomUnderSampler])
def test_strategy_as_ordered_dict(Sampler):
"""Check that it is possible to pass an `OrderedDict` as strategy."""
rng = np.random.RandomState(42)
X, y = rng.randn(30, 2), np.array([0] * 10 + [1] * 20)
sampler = Sampler(random_state=42)
if isinstance(sampler, RandomOverSampler):
strategy = OrderedDict({0: 20, 1: 20})
else:
strategy = OrderedDict({0: 10, 1: 10})
sampler.set_params(sampling_strategy=strategy)
X_res, y_res = sampler.fit_resample(X, y)
assert X_res.shape[0] == sum(strategy.values())
assert y_res.shape[0] == sum(strategy.values())
@pytest.mark.parametrize(
"estimator", _tested_estimators(), ids=_get_check_estimator_ids
)
def test_pandas_column_name_consistency(estimator):
_set_checking_parameters(estimator)
with ignore_warnings(category=(FutureWarning)):
with warnings.catch_warnings(record=True) as record:
check_dataframe_column_names_consistency(
estimator.__class__.__name__, estimator
)
for warning in record:
assert "was fitted without feature names" not in str(warning.message)
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