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3RNN/Lib/site-packages/sklearn/utils/estimator_checks.py

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2024-05-26 19:49:15 +02:00
"""
The :mod:`sklearn.utils.estimator_checks` module includes various utilities to
check the compatibility of estimators with the scikit-learn API.
"""
import pickle
import re
import warnings
from contextlib import nullcontext
from copy import deepcopy
from functools import partial, wraps
from inspect import isfunction, signature
from numbers import Integral, Real
import joblib
import numpy as np
from scipy import sparse
from scipy.stats import rankdata
from .. import config_context
from ..base import (
ClusterMixin,
RegressorMixin,
clone,
is_classifier,
is_outlier_detector,
is_regressor,
)
from ..datasets import (
load_iris,
make_blobs,
make_classification,
make_multilabel_classification,
make_regression,
)
from ..exceptions import DataConversionWarning, NotFittedError, SkipTestWarning
from ..feature_selection import SelectFromModel, SelectKBest
from ..linear_model import (
LinearRegression,
LogisticRegression,
RANSACRegressor,
Ridge,
SGDRegressor,
)
from ..metrics import accuracy_score, adjusted_rand_score, f1_score
from ..metrics.pairwise import linear_kernel, pairwise_distances, rbf_kernel
from ..model_selection import ShuffleSplit, train_test_split
from ..model_selection._validation import _safe_split
from ..pipeline import make_pipeline
from ..preprocessing import StandardScaler, scale
from ..random_projection import BaseRandomProjection
from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
from ..utils._array_api import (
_atol_for_type,
_convert_to_numpy,
get_namespace,
yield_namespace_device_dtype_combinations,
)
from ..utils._array_api import device as array_device
from ..utils._param_validation import (
InvalidParameterError,
generate_invalid_param_val,
make_constraint,
)
from . import shuffle
from ._missing import is_scalar_nan
from ._param_validation import Interval
from ._tags import (
_DEFAULT_TAGS,
_safe_tags,
)
from ._testing import (
SkipTest,
_array_api_for_tests,
_get_args,
assert_allclose,
assert_allclose_dense_sparse,
assert_array_almost_equal,
assert_array_equal,
assert_array_less,
assert_raise_message,
create_memmap_backed_data,
ignore_warnings,
raises,
set_random_state,
)
from .fixes import _IS_PYPY, SPARSE_ARRAY_PRESENT, parse_version, sp_version
from .validation import _num_samples, check_is_fitted, has_fit_parameter
REGRESSION_DATASET = None
CROSS_DECOMPOSITION = ["PLSCanonical", "PLSRegression", "CCA", "PLSSVD"]
def _yield_checks(estimator):
name = estimator.__class__.__name__
tags = _safe_tags(estimator)
yield check_no_attributes_set_in_init
yield check_estimators_dtypes
yield check_fit_score_takes_y
if has_fit_parameter(estimator, "sample_weight"):
yield check_sample_weights_pandas_series
yield check_sample_weights_not_an_array
yield check_sample_weights_list
if not tags["pairwise"]:
# We skip pairwise because the data is not pairwise
yield check_sample_weights_shape
yield check_sample_weights_not_overwritten
yield partial(check_sample_weights_invariance, kind="ones")
yield partial(check_sample_weights_invariance, kind="zeros")
yield check_estimators_fit_returns_self
yield partial(check_estimators_fit_returns_self, readonly_memmap=True)
# Check that all estimator yield informative messages when
# trained on empty datasets
if not tags["no_validation"]:
yield check_complex_data
yield check_dtype_object
yield check_estimators_empty_data_messages
if name not in CROSS_DECOMPOSITION:
# cross-decomposition's "transform" returns X and Y
yield check_pipeline_consistency
if not tags["allow_nan"] and not tags["no_validation"]:
# Test that all estimators check their input for NaN's and infs
yield check_estimators_nan_inf
if tags["pairwise"]:
# Check that pairwise estimator throws error on non-square input
yield check_nonsquare_error
yield check_estimators_overwrite_params
if hasattr(estimator, "sparsify"):
yield check_sparsify_coefficients
yield check_estimator_sparse_array
yield check_estimator_sparse_matrix
# Test that estimators can be pickled, and once pickled
# give the same answer as before.
yield check_estimators_pickle
yield partial(check_estimators_pickle, readonly_memmap=True)
yield check_estimator_get_tags_default_keys
if tags["array_api_support"]:
for check in _yield_array_api_checks(estimator):
yield check
def _yield_classifier_checks(classifier):
tags = _safe_tags(classifier)
# test classifiers can handle non-array data and pandas objects
yield check_classifier_data_not_an_array
# test classifiers trained on a single label always return this label
yield check_classifiers_one_label
yield check_classifiers_one_label_sample_weights
yield check_classifiers_classes
yield check_estimators_partial_fit_n_features
if tags["multioutput"]:
yield check_classifier_multioutput
# basic consistency testing
yield check_classifiers_train
yield partial(check_classifiers_train, readonly_memmap=True)
yield partial(check_classifiers_train, readonly_memmap=True, X_dtype="float32")
yield check_classifiers_regression_target
if tags["multilabel"]:
yield check_classifiers_multilabel_representation_invariance
yield check_classifiers_multilabel_output_format_predict
yield check_classifiers_multilabel_output_format_predict_proba
yield check_classifiers_multilabel_output_format_decision_function
if not tags["no_validation"]:
yield check_supervised_y_no_nan
if not tags["multioutput_only"]:
yield check_supervised_y_2d
if tags["requires_fit"]:
yield check_estimators_unfitted
if "class_weight" in classifier.get_params().keys():
yield check_class_weight_classifiers
yield check_non_transformer_estimators_n_iter
# test if predict_proba is a monotonic transformation of decision_function
yield check_decision_proba_consistency
@ignore_warnings(category=FutureWarning)
def check_supervised_y_no_nan(name, estimator_orig):
# Checks that the Estimator targets are not NaN.
estimator = clone(estimator_orig)
rng = np.random.RandomState(888)
X = rng.standard_normal(size=(10, 5))
for value in [np.nan, np.inf]:
y = np.full(10, value)
y = _enforce_estimator_tags_y(estimator, y)
module_name = estimator.__module__
if module_name.startswith("sklearn.") and not (
"test_" in module_name or module_name.endswith("_testing")
):
# In scikit-learn we want the error message to mention the input
# name and be specific about the kind of unexpected value.
if np.isinf(value):
match = (
r"Input (y|Y) contains infinity or a value too large for"
r" dtype\('float64'\)."
)
else:
match = r"Input (y|Y) contains NaN."
else:
# Do not impose a particular error message to third-party libraries.
match = None
err_msg = (
f"Estimator {name} should have raised error on fitting array y with inf"
" value."
)
with raises(ValueError, match=match, err_msg=err_msg):
estimator.fit(X, y)
def _yield_regressor_checks(regressor):
tags = _safe_tags(regressor)
# TODO: test with intercept
# TODO: test with multiple responses
# basic testing
yield check_regressors_train
yield partial(check_regressors_train, readonly_memmap=True)
yield partial(check_regressors_train, readonly_memmap=True, X_dtype="float32")
yield check_regressor_data_not_an_array
yield check_estimators_partial_fit_n_features
if tags["multioutput"]:
yield check_regressor_multioutput
yield check_regressors_no_decision_function
if not tags["no_validation"] and not tags["multioutput_only"]:
yield check_supervised_y_2d
yield check_supervised_y_no_nan
name = regressor.__class__.__name__
if name != "CCA":
# check that the regressor handles int input
yield check_regressors_int
if tags["requires_fit"]:
yield check_estimators_unfitted
yield check_non_transformer_estimators_n_iter
def _yield_transformer_checks(transformer):
tags = _safe_tags(transformer)
# All transformers should either deal with sparse data or raise an
# exception with type TypeError and an intelligible error message
if not tags["no_validation"]:
yield check_transformer_data_not_an_array
# these don't actually fit the data, so don't raise errors
yield check_transformer_general
if tags["preserves_dtype"]:
yield check_transformer_preserve_dtypes
yield partial(check_transformer_general, readonly_memmap=True)
if not _safe_tags(transformer, key="stateless"):
yield check_transformers_unfitted
else:
yield check_transformers_unfitted_stateless
# Dependent on external solvers and hence accessing the iter
# param is non-trivial.
external_solver = [
"Isomap",
"KernelPCA",
"LocallyLinearEmbedding",
"RandomizedLasso",
"LogisticRegressionCV",
"BisectingKMeans",
]
name = transformer.__class__.__name__
if name not in external_solver:
yield check_transformer_n_iter
def _yield_clustering_checks(clusterer):
yield check_clusterer_compute_labels_predict
name = clusterer.__class__.__name__
if name not in ("WardAgglomeration", "FeatureAgglomeration"):
# this is clustering on the features
# let's not test that here.
yield check_clustering
yield partial(check_clustering, readonly_memmap=True)
yield check_estimators_partial_fit_n_features
if not hasattr(clusterer, "transform"):
yield check_non_transformer_estimators_n_iter
def _yield_outliers_checks(estimator):
# checks for the contamination parameter
if hasattr(estimator, "contamination"):
yield check_outlier_contamination
# checks for outlier detectors that have a fit_predict method
if hasattr(estimator, "fit_predict"):
yield check_outliers_fit_predict
# checks for estimators that can be used on a test set
if hasattr(estimator, "predict"):
yield check_outliers_train
yield partial(check_outliers_train, readonly_memmap=True)
# test outlier detectors can handle non-array data
yield check_classifier_data_not_an_array
# test if NotFittedError is raised
if _safe_tags(estimator, key="requires_fit"):
yield check_estimators_unfitted
yield check_non_transformer_estimators_n_iter
def _yield_array_api_checks(estimator):
for (
array_namespace,
device,
dtype_name,
) in yield_namespace_device_dtype_combinations():
yield partial(
check_array_api_input,
array_namespace=array_namespace,
dtype_name=dtype_name,
device=device,
)
def _yield_all_checks(estimator):
name = estimator.__class__.__name__
tags = _safe_tags(estimator)
if "2darray" not in tags["X_types"]:
warnings.warn(
"Can't test estimator {} which requires input of type {}".format(
name, tags["X_types"]
),
SkipTestWarning,
)
return
if tags["_skip_test"]:
warnings.warn(
"Explicit SKIP via _skip_test tag for estimator {}.".format(name),
SkipTestWarning,
)
return
for check in _yield_checks(estimator):
yield check
if is_classifier(estimator):
for check in _yield_classifier_checks(estimator):
yield check
if is_regressor(estimator):
for check in _yield_regressor_checks(estimator):
yield check
if hasattr(estimator, "transform"):
for check in _yield_transformer_checks(estimator):
yield check
if isinstance(estimator, ClusterMixin):
for check in _yield_clustering_checks(estimator):
yield check
if is_outlier_detector(estimator):
for check in _yield_outliers_checks(estimator):
yield check
yield check_parameters_default_constructible
if not tags["non_deterministic"]:
yield check_methods_sample_order_invariance
yield check_methods_subset_invariance
yield check_fit2d_1sample
yield check_fit2d_1feature
yield check_get_params_invariance
yield check_set_params
yield check_dict_unchanged
yield check_dont_overwrite_parameters
yield check_fit_idempotent
yield check_fit_check_is_fitted
if not tags["no_validation"]:
yield check_n_features_in
yield check_fit1d
yield check_fit2d_predict1d
if tags["requires_y"]:
yield check_requires_y_none
if tags["requires_positive_X"]:
yield check_fit_non_negative
def _get_check_estimator_ids(obj):
"""Create pytest ids for checks.
When `obj` is an estimator, this returns the pprint version of the
estimator (with `print_changed_only=True`). When `obj` is a function, the
name of the function is returned with its keyword arguments.
`_get_check_estimator_ids` is designed to be used as the `id` in
`pytest.mark.parametrize` where `check_estimator(..., generate_only=True)`
is yielding estimators and checks.
Parameters
----------
obj : estimator or function
Items generated by `check_estimator`.
Returns
-------
id : str or None
See Also
--------
check_estimator
"""
if isfunction(obj):
return obj.__name__
if isinstance(obj, partial):
if not obj.keywords:
return obj.func.__name__
kwstring = ",".join(["{}={}".format(k, v) for k, v in obj.keywords.items()])
return "{}({})".format(obj.func.__name__, kwstring)
if hasattr(obj, "get_params"):
with config_context(print_changed_only=True):
return re.sub(r"\s", "", str(obj))
def _construct_instance(Estimator):
"""Construct Estimator instance if possible."""
required_parameters = getattr(Estimator, "_required_parameters", [])
if len(required_parameters):
if required_parameters in (["estimator"], ["base_estimator"]):
# `RANSACRegressor` will raise an error with any model other
# than `LinearRegression` if we don't fix `min_samples` parameter.
# For common test, we can enforce using `LinearRegression` that
# is the default estimator in `RANSACRegressor` instead of `Ridge`.
if issubclass(Estimator, RANSACRegressor):
estimator = Estimator(LinearRegression())
elif issubclass(Estimator, RegressorMixin):
estimator = Estimator(Ridge())
elif issubclass(Estimator, SelectFromModel):
# Increases coverage because SGDRegressor has partial_fit
estimator = Estimator(SGDRegressor(random_state=0))
else:
estimator = Estimator(LogisticRegression(C=1))
elif required_parameters in (["estimators"],):
# Heterogeneous ensemble classes (i.e. stacking, voting)
if issubclass(Estimator, RegressorMixin):
estimator = Estimator(
estimators=[
("est1", DecisionTreeRegressor(max_depth=3, random_state=0)),
("est2", DecisionTreeRegressor(max_depth=3, random_state=1)),
]
)
else:
estimator = Estimator(
estimators=[
("est1", DecisionTreeClassifier(max_depth=3, random_state=0)),
("est2", DecisionTreeClassifier(max_depth=3, random_state=1)),
]
)
else:
msg = (
f"Can't instantiate estimator {Estimator.__name__} "
f"parameters {required_parameters}"
)
# raise additional warning to be shown by pytest
warnings.warn(msg, SkipTestWarning)
raise SkipTest(msg)
else:
estimator = Estimator()
return estimator
def _maybe_mark_xfail(estimator, check, pytest):
# Mark (estimator, check) pairs as XFAIL if needed (see conditions in
# _should_be_skipped_or_marked())
# This is similar to _maybe_skip(), but this one is used by
# @parametrize_with_checks() instead of check_estimator()
should_be_marked, reason = _should_be_skipped_or_marked(estimator, check)
if not should_be_marked:
return estimator, check
else:
return pytest.param(estimator, check, marks=pytest.mark.xfail(reason=reason))
def _maybe_skip(estimator, check):
# Wrap a check so that it's skipped if needed (see conditions in
# _should_be_skipped_or_marked())
# This is similar to _maybe_mark_xfail(), but this one is used by
# check_estimator() instead of @parametrize_with_checks which requires
# pytest
should_be_skipped, reason = _should_be_skipped_or_marked(estimator, check)
if not should_be_skipped:
return check
check_name = check.func.__name__ if isinstance(check, partial) else check.__name__
@wraps(check)
def wrapped(*args, **kwargs):
raise SkipTest(
f"Skipping {check_name} for {estimator.__class__.__name__}: {reason}"
)
return wrapped
def _should_be_skipped_or_marked(estimator, check):
# Return whether a check should be skipped (when using check_estimator())
# or marked as XFAIL (when using @parametrize_with_checks()), along with a
# reason.
# Currently, a check should be skipped or marked if
# the check is in the _xfail_checks tag of the estimator
check_name = check.func.__name__ if isinstance(check, partial) else check.__name__
xfail_checks = _safe_tags(estimator, key="_xfail_checks") or {}
if check_name in xfail_checks:
return True, xfail_checks[check_name]
return False, "placeholder reason that will never be used"
def parametrize_with_checks(estimators):
"""Pytest specific decorator for parametrizing estimator checks.
The `id` of each check is set to be a pprint version of the estimator
and the name of the check with its keyword arguments.
This allows to use `pytest -k` to specify which tests to run::
pytest test_check_estimators.py -k check_estimators_fit_returns_self
Parameters
----------
estimators : list of estimators instances
Estimators to generated checks for.
.. versionchanged:: 0.24
Passing a class was deprecated in version 0.23, and support for
classes was removed in 0.24. Pass an instance instead.
.. versionadded:: 0.24
Returns
-------
decorator : `pytest.mark.parametrize`
See Also
--------
check_estimator : Check if estimator adheres to scikit-learn conventions.
Examples
--------
>>> from sklearn.utils.estimator_checks import parametrize_with_checks
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.tree import DecisionTreeRegressor
>>> @parametrize_with_checks([LogisticRegression(),
... DecisionTreeRegressor()])
... def test_sklearn_compatible_estimator(estimator, check):
... check(estimator)
"""
import pytest
if any(isinstance(est, type) for est in estimators):
msg = (
"Passing a class was deprecated in version 0.23 "
"and isn't supported anymore from 0.24."
"Please pass an instance instead."
)
raise TypeError(msg)
def checks_generator():
for estimator in estimators:
name = type(estimator).__name__
for check in _yield_all_checks(estimator):
check = partial(check, name)
yield _maybe_mark_xfail(estimator, check, pytest)
return pytest.mark.parametrize(
"estimator, check", checks_generator(), ids=_get_check_estimator_ids
)
def check_estimator(estimator=None, generate_only=False):
"""Check if estimator adheres to scikit-learn conventions.
This function will run an extensive test-suite for input validation,
shapes, etc, making sure that the estimator complies with `scikit-learn`
conventions as detailed in :ref:`rolling_your_own_estimator`.
Additional tests for classifiers, regressors, clustering or transformers
will be run if the Estimator class inherits from the corresponding mixin
from sklearn.base.
Setting `generate_only=True` returns a generator that yields (estimator,
check) tuples where the check can be called independently from each
other, i.e. `check(estimator)`. This allows all checks to be run
independently and report the checks that are failing.
scikit-learn provides a pytest specific decorator,
:func:`~sklearn.utils.estimator_checks.parametrize_with_checks`, making it
easier to test multiple estimators.
Parameters
----------
estimator : estimator object
Estimator instance to check.
.. versionadded:: 1.1
Passing a class was deprecated in version 0.23, and support for
classes was removed in 0.24.
generate_only : bool, default=False
When `False`, checks are evaluated when `check_estimator` is called.
When `True`, `check_estimator` returns a generator that yields
(estimator, check) tuples. The check is run by calling
`check(estimator)`.
.. versionadded:: 0.22
Returns
-------
checks_generator : generator
Generator that yields (estimator, check) tuples. Returned when
`generate_only=True`.
See Also
--------
parametrize_with_checks : Pytest specific decorator for parametrizing estimator
checks.
Examples
--------
>>> from sklearn.utils.estimator_checks import check_estimator
>>> from sklearn.linear_model import LogisticRegression
>>> check_estimator(LogisticRegression(), generate_only=True)
<generator object ...>
"""
if isinstance(estimator, type):
msg = (
"Passing a class was deprecated in version 0.23 "
"and isn't supported anymore from 0.24."
"Please pass an instance instead."
)
raise TypeError(msg)
name = type(estimator).__name__
def checks_generator():
for check in _yield_all_checks(estimator):
check = _maybe_skip(estimator, check)
yield estimator, partial(check, name)
if generate_only:
return checks_generator()
for estimator, check in checks_generator():
try:
check(estimator)
except SkipTest as exception:
# SkipTest is thrown when pandas can't be imported, or by checks
# that are in the xfail_checks tag
warnings.warn(str(exception), SkipTestWarning)
def _regression_dataset():
global REGRESSION_DATASET
if REGRESSION_DATASET is None:
X, y = make_regression(
n_samples=200,
n_features=10,
n_informative=1,
bias=5.0,
noise=20,
random_state=42,
)
X = StandardScaler().fit_transform(X)
REGRESSION_DATASET = X, y
return REGRESSION_DATASET
def _set_checking_parameters(estimator):
# set parameters to speed up some estimators and
# avoid deprecated behaviour
params = estimator.get_params()
name = estimator.__class__.__name__
if name == "TSNE":
estimator.set_params(perplexity=2)
if "n_iter" in params and name != "TSNE":
estimator.set_params(n_iter=5)
if "max_iter" in params:
if estimator.max_iter is not None:
estimator.set_params(max_iter=min(5, estimator.max_iter))
# LinearSVR, LinearSVC
if name in ["LinearSVR", "LinearSVC"]:
estimator.set_params(max_iter=20)
# NMF
if name == "NMF":
estimator.set_params(max_iter=500)
# DictionaryLearning
if name == "DictionaryLearning":
estimator.set_params(max_iter=20, transform_algorithm="lasso_lars")
# MiniBatchNMF
if estimator.__class__.__name__ == "MiniBatchNMF":
estimator.set_params(max_iter=20, fresh_restarts=True)
# MLP
if name in ["MLPClassifier", "MLPRegressor"]:
estimator.set_params(max_iter=100)
# MiniBatchDictionaryLearning
if name == "MiniBatchDictionaryLearning":
estimator.set_params(max_iter=5)
if "n_resampling" in params:
# randomized lasso
estimator.set_params(n_resampling=5)
if "n_estimators" in params:
estimator.set_params(n_estimators=min(5, estimator.n_estimators))
if "max_trials" in params:
# RANSAC
estimator.set_params(max_trials=10)
if "n_init" in params:
# K-Means
estimator.set_params(n_init=2)
if "batch_size" in params and not name.startswith("MLP"):
estimator.set_params(batch_size=10)
if name == "MeanShift":
# In the case of check_fit2d_1sample, bandwidth is set to None and
# is thus estimated. De facto it is 0.0 as a single sample is provided
# and this makes the test fails. Hence we give it a placeholder value.
estimator.set_params(bandwidth=1.0)
if name == "TruncatedSVD":
# TruncatedSVD doesn't run with n_components = n_features
# This is ugly :-/
estimator.n_components = 1
if name == "LassoLarsIC":
# Noise variance estimation does not work when `n_samples < n_features`.
# We need to provide the noise variance explicitly.
estimator.set_params(noise_variance=1.0)
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = min(estimator.n_clusters, 2)
if hasattr(estimator, "n_best"):
estimator.n_best = 1
if name == "SelectFdr":
# be tolerant of noisy datasets (not actually speed)
estimator.set_params(alpha=0.5)
if name == "TheilSenRegressor":
estimator.max_subpopulation = 100
if isinstance(estimator, BaseRandomProjection):
# Due to the jl lemma and often very few samples, the number
# of components of the random matrix projection will be probably
# greater than the number of features.
# So we impose a smaller number (avoid "auto" mode)
estimator.set_params(n_components=2)
if isinstance(estimator, SelectKBest):
# SelectKBest has a default of k=10
# which is more feature than we have in most case.
estimator.set_params(k=1)
if name in ("HistGradientBoostingClassifier", "HistGradientBoostingRegressor"):
# The default min_samples_leaf (20) isn't appropriate for small
# datasets (only very shallow trees are built) that the checks use.
estimator.set_params(min_samples_leaf=5)
if name == "DummyClassifier":
# the default strategy prior would output constant predictions and fail
# for check_classifiers_predictions
estimator.set_params(strategy="stratified")
# Speed-up by reducing the number of CV or splits for CV estimators
loo_cv = ["RidgeCV", "RidgeClassifierCV"]
if name not in loo_cv and hasattr(estimator, "cv"):
estimator.set_params(cv=3)
if hasattr(estimator, "n_splits"):
estimator.set_params(n_splits=3)
if name == "OneHotEncoder":
estimator.set_params(handle_unknown="ignore")
if name == "QuantileRegressor":
# Avoid warning due to Scipy deprecating interior-point solver
solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point"
estimator.set_params(solver=solver)
if name in CROSS_DECOMPOSITION:
estimator.set_params(n_components=1)
# Default "auto" parameter can lead to different ordering of eigenvalues on
# windows: #24105
if name == "SpectralEmbedding":
estimator.set_params(eigen_tol=1e-5)
if name == "HDBSCAN":
estimator.set_params(min_samples=1)
class _NotAnArray:
"""An object that is convertible to an array.
Parameters
----------
data : array-like
The data.
"""
def __init__(self, data):
self.data = np.asarray(data)
def __array__(self, dtype=None, copy=None):
return self.data
def __array_function__(self, func, types, args, kwargs):
if func.__name__ == "may_share_memory":
return True
raise TypeError("Don't want to call array_function {}!".format(func.__name__))
def _is_pairwise_metric(estimator):
"""Returns True if estimator accepts pairwise metric.
Parameters
----------
estimator : object
Estimator object to test.
Returns
-------
out : bool
True if _pairwise is set to True and False otherwise.
"""
metric = getattr(estimator, "metric", None)
return bool(metric == "precomputed")
def _generate_sparse_data(X_csr):
"""Generate sparse matrices or arrays with {32,64}bit indices of diverse format.
Parameters
----------
X_csr: scipy.sparse.csr_matrix or scipy.sparse.csr_array
Input in CSR format.
Returns
-------
out: iter(Matrices) or iter(Arrays)
In format['dok', 'lil', 'dia', 'bsr', 'csr', 'csc', 'coo',
'coo_64', 'csc_64', 'csr_64']
"""
assert X_csr.format == "csr"
yield "csr", X_csr.copy()
for sparse_format in ["dok", "lil", "dia", "bsr", "csc", "coo"]:
yield sparse_format, X_csr.asformat(sparse_format)
# Generate large indices matrix only if its supported by scipy
X_coo = X_csr.asformat("coo")
X_coo.row = X_coo.row.astype("int64")
X_coo.col = X_coo.col.astype("int64")
yield "coo_64", X_coo
for sparse_format in ["csc", "csr"]:
X = X_csr.asformat(sparse_format)
X.indices = X.indices.astype("int64")
X.indptr = X.indptr.astype("int64")
yield sparse_format + "_64", X
def check_array_api_input(
name,
estimator_orig,
array_namespace,
device=None,
dtype_name="float64",
check_values=False,
):
"""Check that the estimator can work consistently with the Array API
By default, this just checks that the types and shapes of the arrays are
consistent with calling the same estimator with numpy arrays.
When check_values is True, it also checks that calling the estimator on the
array_api Array gives the same results as ndarrays.
"""
xp = _array_api_for_tests(array_namespace, device)
X, y = make_classification(random_state=42)
X = X.astype(dtype_name, copy=False)
X = _enforce_estimator_tags_X(estimator_orig, X)
y = _enforce_estimator_tags_y(estimator_orig, y)
est = clone(estimator_orig)
X_xp = xp.asarray(X, device=device)
y_xp = xp.asarray(y, device=device)
est.fit(X, y)
array_attributes = {
key: value for key, value in vars(est).items() if isinstance(value, np.ndarray)
}
est_xp = clone(est)
with config_context(array_api_dispatch=True):
est_xp.fit(X_xp, y_xp)
input_ns = get_namespace(X_xp)[0].__name__
# Fitted attributes which are arrays must have the same
# namespace as the one of the training data.
for key, attribute in array_attributes.items():
est_xp_param = getattr(est_xp, key)
with config_context(array_api_dispatch=True):
attribute_ns = get_namespace(est_xp_param)[0].__name__
assert attribute_ns == input_ns, (
f"'{key}' attribute is in wrong namespace, expected {input_ns} "
f"got {attribute_ns}"
)
assert array_device(est_xp_param) == array_device(X_xp)
est_xp_param_np = _convert_to_numpy(est_xp_param, xp=xp)
if check_values:
assert_allclose(
attribute,
est_xp_param_np,
err_msg=f"{key} not the same",
atol=_atol_for_type(X.dtype),
)
else:
assert attribute.shape == est_xp_param_np.shape
assert attribute.dtype == est_xp_param_np.dtype
# Check estimator methods, if supported, give the same results
methods = (
"score",
"score_samples",
"decision_function",
"predict",
"predict_log_proba",
"predict_proba",
"transform",
)
for method_name in methods:
method = getattr(est, method_name, None)
if method is None:
continue
if method_name == "score":
result = method(X, y)
with config_context(array_api_dispatch=True):
result_xp = getattr(est_xp, method_name)(X_xp, y_xp)
# score typically returns a Python float
assert isinstance(result, float)
assert isinstance(result_xp, float)
if check_values:
assert abs(result - result_xp) < _atol_for_type(X.dtype)
continue
else:
result = method(X)
with config_context(array_api_dispatch=True):
result_xp = getattr(est_xp, method_name)(X_xp)
with config_context(array_api_dispatch=True):
result_ns = get_namespace(result_xp)[0].__name__
assert result_ns == input_ns, (
f"'{method}' output is in wrong namespace, expected {input_ns}, "
f"got {result_ns}."
)
assert array_device(result_xp) == array_device(X_xp)
result_xp_np = _convert_to_numpy(result_xp, xp=xp)
if check_values:
assert_allclose(
result,
result_xp_np,
err_msg=f"{method} did not the return the same result",
atol=_atol_for_type(X.dtype),
)
else:
if hasattr(result, "shape"):
assert result.shape == result_xp_np.shape
assert result.dtype == result_xp_np.dtype
if method_name == "transform" and hasattr(est, "inverse_transform"):
inverse_result = est.inverse_transform(result)
with config_context(array_api_dispatch=True):
invese_result_xp = est_xp.inverse_transform(result_xp)
inverse_result_ns = get_namespace(invese_result_xp)[0].__name__
assert inverse_result_ns == input_ns, (
"'inverse_transform' output is in wrong namespace, expected"
f" {input_ns}, got {inverse_result_ns}."
)
assert array_device(invese_result_xp) == array_device(X_xp)
invese_result_xp_np = _convert_to_numpy(invese_result_xp, xp=xp)
if check_values:
assert_allclose(
inverse_result,
invese_result_xp_np,
err_msg="inverse_transform did not the return the same result",
atol=_atol_for_type(X.dtype),
)
else:
assert inverse_result.shape == invese_result_xp_np.shape
assert inverse_result.dtype == invese_result_xp_np.dtype
def check_array_api_input_and_values(
name,
estimator_orig,
array_namespace,
device=None,
dtype_name="float64",
):
return check_array_api_input(
name,
estimator_orig,
array_namespace=array_namespace,
device=device,
dtype_name=dtype_name,
check_values=True,
)
def _check_estimator_sparse_container(name, estimator_orig, sparse_type):
rng = np.random.RandomState(0)
X = rng.uniform(size=(40, 3))
X[X < 0.8] = 0
X = _enforce_estimator_tags_X(estimator_orig, X)
y = (4 * rng.uniform(size=40)).astype(int)
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
tags = _safe_tags(estimator_orig)
for matrix_format, X in _generate_sparse_data(sparse_type(X)):
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
estimator = clone(estimator_orig)
if name in ["Scaler", "StandardScaler"]:
estimator.set_params(with_mean=False)
# fit and predict
if "64" in matrix_format:
err_msg = (
f"Estimator {name} doesn't seem to support {matrix_format} "
"matrix, and is not failing gracefully, e.g. by using "
"check_array(X, accept_large_sparse=False)."
)
else:
err_msg = (
f"Estimator {name} doesn't seem to fail gracefully on sparse "
"data: error message should state explicitly that sparse "
"input is not supported if this is not the case, e.g. by using "
"check_array(X, accept_sparse=False)."
)
with raises(
(TypeError, ValueError),
match=["sparse", "Sparse"],
may_pass=True,
err_msg=err_msg,
):
with ignore_warnings(category=FutureWarning):
estimator.fit(X, y)
if hasattr(estimator, "predict"):
pred = estimator.predict(X)
if tags["multioutput_only"]:
assert pred.shape == (X.shape[0], 1)
else:
assert pred.shape == (X.shape[0],)
if hasattr(estimator, "predict_proba"):
probs = estimator.predict_proba(X)
if tags["binary_only"]:
expected_probs_shape = (X.shape[0], 2)
else:
expected_probs_shape = (X.shape[0], 4)
assert probs.shape == expected_probs_shape
def check_estimator_sparse_matrix(name, estimator_orig):
_check_estimator_sparse_container(name, estimator_orig, sparse.csr_matrix)
def check_estimator_sparse_array(name, estimator_orig):
if SPARSE_ARRAY_PRESENT:
_check_estimator_sparse_container(name, estimator_orig, sparse.csr_array)
@ignore_warnings(category=FutureWarning)
def check_sample_weights_pandas_series(name, estimator_orig):
# check that estimators will accept a 'sample_weight' parameter of
# type pandas.Series in the 'fit' function.
estimator = clone(estimator_orig)
try:
import pandas as pd
X = np.array(
[
[1, 1],
[1, 2],
[1, 3],
[1, 4],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[3, 1],
[3, 2],
[3, 3],
[3, 4],
]
)
X = pd.DataFrame(_enforce_estimator_tags_X(estimator_orig, X), copy=False)
y = pd.Series([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2])
weights = pd.Series([1] * 12)
if _safe_tags(estimator, key="multioutput_only"):
y = pd.DataFrame(y, copy=False)
try:
estimator.fit(X, y, sample_weight=weights)
except ValueError:
raise ValueError(
"Estimator {0} raises error if "
"'sample_weight' parameter is of "
"type pandas.Series".format(name)
)
except ImportError:
raise SkipTest(
"pandas is not installed: not testing for "
"input of type pandas.Series to class weight."
)
@ignore_warnings(category=(FutureWarning))
def check_sample_weights_not_an_array(name, estimator_orig):
# check that estimators will accept a 'sample_weight' parameter of
# type _NotAnArray in the 'fit' function.
estimator = clone(estimator_orig)
X = np.array(
[
[1, 1],
[1, 2],
[1, 3],
[1, 4],
[2, 1],
[2, 2],
[2, 3],
[2, 4],
[3, 1],
[3, 2],
[3, 3],
[3, 4],
]
)
X = _NotAnArray(_enforce_estimator_tags_X(estimator_orig, X))
y = _NotAnArray([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2])
weights = _NotAnArray([1] * 12)
if _safe_tags(estimator, key="multioutput_only"):
y = _NotAnArray(y.data.reshape(-1, 1))
estimator.fit(X, y, sample_weight=weights)
@ignore_warnings(category=(FutureWarning))
def check_sample_weights_list(name, estimator_orig):
# check that estimators will accept a 'sample_weight' parameter of
# type list in the 'fit' function.
estimator = clone(estimator_orig)
rnd = np.random.RandomState(0)
n_samples = 30
X = _enforce_estimator_tags_X(estimator_orig, rnd.uniform(size=(n_samples, 3)))
y = np.arange(n_samples) % 3
y = _enforce_estimator_tags_y(estimator, y)
sample_weight = [3] * n_samples
# Test that estimators don't raise any exception
estimator.fit(X, y, sample_weight=sample_weight)
@ignore_warnings(category=FutureWarning)
def check_sample_weights_shape(name, estimator_orig):
# check that estimators raise an error if sample_weight
# shape mismatches the input
estimator = clone(estimator_orig)
X = np.array(
[
[1, 3],
[1, 3],
[1, 3],
[1, 3],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[3, 3],
[3, 3],
[3, 3],
[3, 3],
[4, 1],
[4, 1],
[4, 1],
[4, 1],
]
)
y = np.array([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2])
y = _enforce_estimator_tags_y(estimator, y)
estimator.fit(X, y, sample_weight=np.ones(len(y)))
with raises(ValueError):
estimator.fit(X, y, sample_weight=np.ones(2 * len(y)))
with raises(ValueError):
estimator.fit(X, y, sample_weight=np.ones((len(y), 2)))
@ignore_warnings(category=FutureWarning)
def check_sample_weights_invariance(name, estimator_orig, kind="ones"):
# For kind="ones" check that the estimators yield same results for
# unit weights and no weights
# For kind="zeros" check that setting sample_weight to 0 is equivalent
# to removing corresponding samples.
estimator1 = clone(estimator_orig)
estimator2 = clone(estimator_orig)
set_random_state(estimator1, random_state=0)
set_random_state(estimator2, random_state=0)
X1 = np.array(
[
[1, 3],
[1, 3],
[1, 3],
[1, 3],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[3, 3],
[3, 3],
[3, 3],
[3, 3],
[4, 1],
[4, 1],
[4, 1],
[4, 1],
],
dtype=np.float64,
)
y1 = np.array([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int)
if kind == "ones":
X2 = X1
y2 = y1
sw2 = np.ones(shape=len(y1))
err_msg = (
f"For {name} sample_weight=None is not equivalent to sample_weight=ones"
)
elif kind == "zeros":
# Construct a dataset that is very different to (X, y) if weights
# are disregarded, but identical to (X, y) given weights.
X2 = np.vstack([X1, X1 + 1])
y2 = np.hstack([y1, 3 - y1])
sw2 = np.ones(shape=len(y1) * 2)
sw2[len(y1) :] = 0
X2, y2, sw2 = shuffle(X2, y2, sw2, random_state=0)
err_msg = (
f"For {name}, a zero sample_weight is not equivalent to removing the sample"
)
else: # pragma: no cover
raise ValueError
y1 = _enforce_estimator_tags_y(estimator1, y1)
y2 = _enforce_estimator_tags_y(estimator2, y2)
estimator1.fit(X1, y=y1, sample_weight=None)
estimator2.fit(X2, y=y2, sample_weight=sw2)
for method in ["predict", "predict_proba", "decision_function", "transform"]:
if hasattr(estimator_orig, method):
X_pred1 = getattr(estimator1, method)(X1)
X_pred2 = getattr(estimator2, method)(X1)
assert_allclose_dense_sparse(X_pred1, X_pred2, err_msg=err_msg)
def check_sample_weights_not_overwritten(name, estimator_orig):
# check that estimators don't override the passed sample_weight parameter
estimator = clone(estimator_orig)
set_random_state(estimator, random_state=0)
X = np.array(
[
[1, 3],
[1, 3],
[1, 3],
[1, 3],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[3, 3],
[3, 3],
[3, 3],
[3, 3],
[4, 1],
[4, 1],
[4, 1],
[4, 1],
],
dtype=np.float64,
)
y = np.array([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int)
y = _enforce_estimator_tags_y(estimator, y)
sample_weight_original = np.ones(y.shape[0])
sample_weight_original[0] = 10.0
sample_weight_fit = sample_weight_original.copy()
estimator.fit(X, y, sample_weight=sample_weight_fit)
err_msg = f"{name} overwrote the original `sample_weight` given during fit"
assert_allclose(sample_weight_fit, sample_weight_original, err_msg=err_msg)
@ignore_warnings(category=(FutureWarning, UserWarning))
def check_dtype_object(name, estimator_orig):
# check that estimators treat dtype object as numeric if possible
rng = np.random.RandomState(0)
X = _enforce_estimator_tags_X(estimator_orig, rng.uniform(size=(40, 10)))
X = X.astype(object)
tags = _safe_tags(estimator_orig)
y = (X[:, 0] * 4).astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
estimator.fit(X, y)
if hasattr(estimator, "predict"):
estimator.predict(X)
if hasattr(estimator, "transform"):
estimator.transform(X)
with raises(Exception, match="Unknown label type", may_pass=True):
estimator.fit(X, y.astype(object))
if "string" not in tags["X_types"]:
X[0, 0] = {"foo": "bar"}
# This error is raised by:
# - `np.asarray` in `check_array`
# - `_unique_python` for encoders
msg = "argument must be .* string.* number"
with raises(TypeError, match=msg):
estimator.fit(X, y)
else:
# Estimators supporting string will not call np.asarray to convert the
# data to numeric and therefore, the error will not be raised.
# Checking for each element dtype in the input array will be costly.
# Refer to #11401 for full discussion.
estimator.fit(X, y)
def check_complex_data(name, estimator_orig):
rng = np.random.RandomState(42)
# check that estimators raise an exception on providing complex data
X = rng.uniform(size=10) + 1j * rng.uniform(size=10)
X = X.reshape(-1, 1)
# Something both valid for classification and regression
y = rng.randint(low=0, high=2, size=10) + 1j
estimator = clone(estimator_orig)
set_random_state(estimator, random_state=0)
with raises(ValueError, match="Complex data not supported"):
estimator.fit(X, y)
@ignore_warnings
def check_dict_unchanged(name, estimator_orig):
# this estimator raises
# ValueError: Found array with 0 feature(s) (shape=(23, 0))
# while a minimum of 1 is required.
# error
if name in ["SpectralCoclustering"]:
return
rnd = np.random.RandomState(0)
if name in ["RANSACRegressor"]:
X = 3 * rnd.uniform(size=(20, 3))
else:
X = 2 * rnd.uniform(size=(20, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
if hasattr(estimator, "n_best"):
estimator.n_best = 1
set_random_state(estimator, 1)
estimator.fit(X, y)
for method in ["predict", "transform", "decision_function", "predict_proba"]:
if hasattr(estimator, method):
dict_before = estimator.__dict__.copy()
getattr(estimator, method)(X)
assert estimator.__dict__ == dict_before, (
"Estimator changes __dict__ during %s" % method
)
def _is_public_parameter(attr):
return not (attr.startswith("_") or attr.endswith("_"))
@ignore_warnings(category=FutureWarning)
def check_dont_overwrite_parameters(name, estimator_orig):
# check that fit method only changes or sets private attributes
if hasattr(estimator_orig.__init__, "deprecated_original"):
# to not check deprecated classes
return
estimator = clone(estimator_orig)
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
dict_before_fit = estimator.__dict__.copy()
estimator.fit(X, y)
dict_after_fit = estimator.__dict__
public_keys_after_fit = [
key for key in dict_after_fit.keys() if _is_public_parameter(key)
]
attrs_added_by_fit = [
key for key in public_keys_after_fit if key not in dict_before_fit.keys()
]
# check that fit doesn't add any public attribute
assert not attrs_added_by_fit, (
"Estimator adds public attribute(s) during"
" the fit method."
" Estimators are only allowed to add private attributes"
" either started with _ or ended"
" with _ but %s added" % ", ".join(attrs_added_by_fit)
)
# check that fit doesn't change any public attribute
attrs_changed_by_fit = [
key
for key in public_keys_after_fit
if (dict_before_fit[key] is not dict_after_fit[key])
]
assert not attrs_changed_by_fit, (
"Estimator changes public attribute(s) during"
" the fit method. Estimators are only allowed"
" to change attributes started"
" or ended with _, but"
" %s changed" % ", ".join(attrs_changed_by_fit)
)
@ignore_warnings(category=FutureWarning)
def check_fit2d_predict1d(name, estimator_orig):
# check by fitting a 2d array and predicting with a 1d array
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
estimator.fit(X, y)
for method in ["predict", "transform", "decision_function", "predict_proba"]:
if hasattr(estimator, method):
assert_raise_message(
ValueError, "Reshape your data", getattr(estimator, method), X[0]
)
def _apply_on_subsets(func, X):
# apply function on the whole set and on mini batches
result_full = func(X)
n_features = X.shape[1]
result_by_batch = [func(batch.reshape(1, n_features)) for batch in X]
# func can output tuple (e.g. score_samples)
if type(result_full) == tuple:
result_full = result_full[0]
result_by_batch = list(map(lambda x: x[0], result_by_batch))
if sparse.issparse(result_full):
result_full = result_full.toarray()
result_by_batch = [x.toarray() for x in result_by_batch]
return np.ravel(result_full), np.ravel(result_by_batch)
@ignore_warnings(category=FutureWarning)
def check_methods_subset_invariance(name, estimator_orig):
# check that method gives invariant results if applied
# on mini batches or the whole set
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
estimator.fit(X, y)
for method in [
"predict",
"transform",
"decision_function",
"score_samples",
"predict_proba",
]:
msg = ("{method} of {name} is not invariant when applied to a subset.").format(
method=method, name=name
)
if hasattr(estimator, method):
result_full, result_by_batch = _apply_on_subsets(
getattr(estimator, method), X
)
assert_allclose(result_full, result_by_batch, atol=1e-7, err_msg=msg)
@ignore_warnings(category=FutureWarning)
def check_methods_sample_order_invariance(name, estimator_orig):
# check that method gives invariant results if applied
# on a subset with different sample order
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(np.int64)
if _safe_tags(estimator_orig, key="binary_only"):
y[y == 2] = 1
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 2
set_random_state(estimator, 1)
estimator.fit(X, y)
idx = np.random.permutation(X.shape[0])
for method in [
"predict",
"transform",
"decision_function",
"score_samples",
"predict_proba",
]:
msg = (
"{method} of {name} is not invariant when applied to a dataset"
"with different sample order."
).format(method=method, name=name)
if hasattr(estimator, method):
assert_allclose_dense_sparse(
getattr(estimator, method)(X)[idx],
getattr(estimator, method)(X[idx]),
atol=1e-9,
err_msg=msg,
)
@ignore_warnings
def check_fit2d_1sample(name, estimator_orig):
# Check that fitting a 2d array with only one sample either works or
# returns an informative message. The error message should either mention
# the number of samples or the number of classes.
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(1, 10))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
# min_cluster_size cannot be less than the data size for OPTICS.
if name == "OPTICS":
estimator.set_params(min_samples=1.0)
# perplexity cannot be more than the number of samples for TSNE.
if name == "TSNE":
estimator.set_params(perplexity=0.5)
msgs = [
"1 sample",
"n_samples = 1",
"n_samples=1",
"one sample",
"1 class",
"one class",
]
with raises(ValueError, match=msgs, may_pass=True):
estimator.fit(X, y)
@ignore_warnings
def check_fit2d_1feature(name, estimator_orig):
# check fitting a 2d array with only 1 feature either works or returns
# informative message
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(10, 1))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = X[:, 0].astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
# ensure two labels in subsample for RandomizedLogisticRegression
if name == "RandomizedLogisticRegression":
estimator.sample_fraction = 1
# ensure non skipped trials for RANSACRegressor
if name == "RANSACRegressor":
estimator.residual_threshold = 0.5
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator, 1)
msgs = [r"1 feature\(s\)", "n_features = 1", "n_features=1"]
with raises(ValueError, match=msgs, may_pass=True):
estimator.fit(X, y)
@ignore_warnings
def check_fit1d(name, estimator_orig):
# check fitting 1d X array raises a ValueError
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20))
y = X.astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
with raises(ValueError):
estimator.fit(X, y)
@ignore_warnings(category=FutureWarning)
def check_transformer_general(name, transformer, readonly_memmap=False):
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = StandardScaler().fit_transform(X)
X = _enforce_estimator_tags_X(transformer, X)
if readonly_memmap:
X, y = create_memmap_backed_data([X, y])
_check_transformer(name, transformer, X, y)
@ignore_warnings(category=FutureWarning)
def check_transformer_data_not_an_array(name, transformer):
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = StandardScaler().fit_transform(X)
X = _enforce_estimator_tags_X(transformer, X)
this_X = _NotAnArray(X)
this_y = _NotAnArray(np.asarray(y))
_check_transformer(name, transformer, this_X, this_y)
# try the same with some list
_check_transformer(name, transformer, X.tolist(), y.tolist())
@ignore_warnings(category=FutureWarning)
def check_transformers_unfitted(name, transformer):
X, y = _regression_dataset()
transformer = clone(transformer)
with raises(
(AttributeError, ValueError),
err_msg=(
"The unfitted "
f"transformer {name} does not raise an error when "
"transform is called. Perhaps use "
"check_is_fitted in transform."
),
):
transformer.transform(X)
@ignore_warnings(category=FutureWarning)
def check_transformers_unfitted_stateless(name, transformer):
"""Check that using transform without prior fitting
doesn't raise a NotFittedError for stateless transformers.
"""
rng = np.random.RandomState(0)
X = rng.uniform(size=(20, 5))
X = _enforce_estimator_tags_X(transformer, X)
transformer = clone(transformer)
X_trans = transformer.transform(X)
assert X_trans.shape[0] == X.shape[0]
def _check_transformer(name, transformer_orig, X, y):
n_samples, n_features = np.asarray(X).shape
transformer = clone(transformer_orig)
set_random_state(transformer)
# fit
if name in CROSS_DECOMPOSITION:
y_ = np.c_[np.asarray(y), np.asarray(y)]
y_[::2, 1] *= 2
if isinstance(X, _NotAnArray):
y_ = _NotAnArray(y_)
else:
y_ = y
transformer.fit(X, y_)
# fit_transform method should work on non fitted estimator
transformer_clone = clone(transformer)
X_pred = transformer_clone.fit_transform(X, y=y_)
if isinstance(X_pred, tuple):
for x_pred in X_pred:
assert x_pred.shape[0] == n_samples
else:
# check for consistent n_samples
assert X_pred.shape[0] == n_samples
if hasattr(transformer, "transform"):
if name in CROSS_DECOMPOSITION:
X_pred2 = transformer.transform(X, y_)
X_pred3 = transformer.fit_transform(X, y=y_)
else:
X_pred2 = transformer.transform(X)
X_pred3 = transformer.fit_transform(X, y=y_)
if _safe_tags(transformer_orig, key="non_deterministic"):
msg = name + " is non deterministic"
raise SkipTest(msg)
if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple):
for x_pred, x_pred2, x_pred3 in zip(X_pred, X_pred2, X_pred3):
assert_allclose_dense_sparse(
x_pred,
x_pred2,
atol=1e-2,
err_msg="fit_transform and transform outcomes not consistent in %s"
% transformer,
)
assert_allclose_dense_sparse(
x_pred,
x_pred3,
atol=1e-2,
err_msg="consecutive fit_transform outcomes not consistent in %s"
% transformer,
)
else:
assert_allclose_dense_sparse(
X_pred,
X_pred2,
err_msg="fit_transform and transform outcomes not consistent in %s"
% transformer,
atol=1e-2,
)
assert_allclose_dense_sparse(
X_pred,
X_pred3,
atol=1e-2,
err_msg="consecutive fit_transform outcomes not consistent in %s"
% transformer,
)
assert _num_samples(X_pred2) == n_samples
assert _num_samples(X_pred3) == n_samples
# raises error on malformed input for transform
if (
hasattr(X, "shape")
and not _safe_tags(transformer, key="stateless")
and X.ndim == 2
and X.shape[1] > 1
):
# If it's not an array, it does not have a 'T' property
with raises(
ValueError,
err_msg=(
f"The transformer {name} does not raise an error "
"when the number of features in transform is different from "
"the number of features in fit."
),
):
transformer.transform(X[:, :-1])
@ignore_warnings
def check_pipeline_consistency(name, estimator_orig):
if _safe_tags(estimator_orig, key="non_deterministic"):
msg = name + " is non deterministic"
raise SkipTest(msg)
# check that make_pipeline(est) gives same score as est
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = _enforce_estimator_tags_X(estimator_orig, X, kernel=rbf_kernel)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator)
pipeline = make_pipeline(estimator)
estimator.fit(X, y)
pipeline.fit(X, y)
funcs = ["score", "fit_transform"]
for func_name in funcs:
func = getattr(estimator, func_name, None)
if func is not None:
func_pipeline = getattr(pipeline, func_name)
result = func(X, y)
result_pipe = func_pipeline(X, y)
assert_allclose_dense_sparse(result, result_pipe)
@ignore_warnings
def check_fit_score_takes_y(name, estimator_orig):
# check that all estimators accept an optional y
# in fit and score so they can be used in pipelines
rnd = np.random.RandomState(0)
n_samples = 30
X = rnd.uniform(size=(n_samples, 3))
X = _enforce_estimator_tags_X(estimator_orig, X)
y = np.arange(n_samples) % 3
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator)
funcs = ["fit", "score", "partial_fit", "fit_predict", "fit_transform"]
for func_name in funcs:
func = getattr(estimator, func_name, None)
if func is not None:
func(X, y)
args = [p.name for p in signature(func).parameters.values()]
if args[0] == "self":
# available_if makes methods into functions
# with an explicit "self", so need to shift arguments
args = args[1:]
assert args[1] in ["y", "Y"], (
"Expected y or Y as second argument for method "
"%s of %s. Got arguments: %r."
% (func_name, type(estimator).__name__, args)
)
@ignore_warnings
def check_estimators_dtypes(name, estimator_orig):
rnd = np.random.RandomState(0)
X_train_32 = 3 * rnd.uniform(size=(20, 5)).astype(np.float32)
X_train_32 = _enforce_estimator_tags_X(estimator_orig, X_train_32)
X_train_64 = X_train_32.astype(np.float64)
X_train_int_64 = X_train_32.astype(np.int64)
X_train_int_32 = X_train_32.astype(np.int32)
y = X_train_int_64[:, 0]
y = _enforce_estimator_tags_y(estimator_orig, y)
methods = ["predict", "transform", "decision_function", "predict_proba"]
for X_train in [X_train_32, X_train_64, X_train_int_64, X_train_int_32]:
estimator = clone(estimator_orig)
set_random_state(estimator, 1)
estimator.fit(X_train, y)
for method in methods:
if hasattr(estimator, method):
getattr(estimator, method)(X_train)
def check_transformer_preserve_dtypes(name, transformer_orig):
# check that dtype are preserved meaning if input X is of some dtype
# X_transformed should be from the same dtype.
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
cluster_std=0.1,
)
X = StandardScaler().fit_transform(X)
X = _enforce_estimator_tags_X(transformer_orig, X)
for dtype in _safe_tags(transformer_orig, key="preserves_dtype"):
X_cast = X.astype(dtype)
transformer = clone(transformer_orig)
set_random_state(transformer)
X_trans1 = transformer.fit_transform(X_cast, y)
X_trans2 = transformer.fit(X_cast, y).transform(X_cast)
for Xt, method in zip([X_trans1, X_trans2], ["fit_transform", "transform"]):
if isinstance(Xt, tuple):
# cross-decompostion returns a tuple of (x_scores, y_scores)
# when given y with fit_transform; only check the first element
Xt = Xt[0]
# check that the output dtype is preserved
assert Xt.dtype == dtype, (
f"{name} (method={method}) does not preserve dtype. "
f"Original/Expected dtype={dtype.__name__}, got dtype={Xt.dtype}."
)
@ignore_warnings(category=FutureWarning)
def check_estimators_empty_data_messages(name, estimator_orig):
e = clone(estimator_orig)
set_random_state(e, 1)
X_zero_samples = np.empty(0).reshape(0, 3)
# The precise message can change depending on whether X or y is
# validated first. Let us test the type of exception only:
err_msg = (
f"The estimator {name} does not raise a ValueError when an "
"empty data is used to train. Perhaps use check_array in train."
)
with raises(ValueError, err_msg=err_msg):
e.fit(X_zero_samples, [])
X_zero_features = np.empty(0).reshape(12, 0)
# the following y should be accepted by both classifiers and regressors
# and ignored by unsupervised models
y = _enforce_estimator_tags_y(e, np.array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]))
msg = r"0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* " "is required."
with raises(ValueError, match=msg):
e.fit(X_zero_features, y)
@ignore_warnings(category=FutureWarning)
def check_estimators_nan_inf(name, estimator_orig):
# Checks that Estimator X's do not contain NaN or inf.
rnd = np.random.RandomState(0)
X_train_finite = _enforce_estimator_tags_X(
estimator_orig, rnd.uniform(size=(10, 3))
)
X_train_nan = rnd.uniform(size=(10, 3))
X_train_nan[0, 0] = np.nan
X_train_inf = rnd.uniform(size=(10, 3))
X_train_inf[0, 0] = np.inf
y = np.ones(10)
y[:5] = 0
y = _enforce_estimator_tags_y(estimator_orig, y)
error_string_fit = f"Estimator {name} doesn't check for NaN and inf in fit."
error_string_predict = f"Estimator {name} doesn't check for NaN and inf in predict."
error_string_transform = (
f"Estimator {name} doesn't check for NaN and inf in transform."
)
for X_train in [X_train_nan, X_train_inf]:
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
estimator = clone(estimator_orig)
set_random_state(estimator, 1)
# try to fit
with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit):
estimator.fit(X_train, y)
# actually fit
estimator.fit(X_train_finite, y)
# predict
if hasattr(estimator, "predict"):
with raises(
ValueError,
match=["inf", "NaN"],
err_msg=error_string_predict,
):
estimator.predict(X_train)
# transform
if hasattr(estimator, "transform"):
with raises(
ValueError,
match=["inf", "NaN"],
err_msg=error_string_transform,
):
estimator.transform(X_train)
@ignore_warnings
def check_nonsquare_error(name, estimator_orig):
"""Test that error is thrown when non-square data provided."""
X, y = make_blobs(n_samples=20, n_features=10)
estimator = clone(estimator_orig)
with raises(
ValueError,
err_msg=(
f"The pairwise estimator {name} does not raise an error on non-square data"
),
):
estimator.fit(X, y)
@ignore_warnings
def check_estimators_pickle(name, estimator_orig, readonly_memmap=False):
"""Test that we can pickle all estimators."""
check_methods = ["predict", "transform", "decision_function", "predict_proba"]
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = _enforce_estimator_tags_X(estimator_orig, X, kernel=rbf_kernel)
tags = _safe_tags(estimator_orig)
# include NaN values when the estimator should deal with them
if tags["allow_nan"]:
# set randomly 10 elements to np.nan
rng = np.random.RandomState(42)
mask = rng.choice(X.size, 10, replace=False)
X.reshape(-1)[mask] = np.nan
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator)
estimator.fit(X, y)
if readonly_memmap:
unpickled_estimator = create_memmap_backed_data(estimator)
else:
# No need to touch the file system in that case.
pickled_estimator = pickle.dumps(estimator)
module_name = estimator.__module__
if module_name.startswith("sklearn.") and not (
"test_" in module_name or module_name.endswith("_testing")
):
# strict check for sklearn estimators that are not implemented in test
# modules.
assert b"_sklearn_version" in pickled_estimator
unpickled_estimator = pickle.loads(pickled_estimator)
result = dict()
for method in check_methods:
if hasattr(estimator, method):
result[method] = getattr(estimator, method)(X)
for method in result:
unpickled_result = getattr(unpickled_estimator, method)(X)
assert_allclose_dense_sparse(result[method], unpickled_result)
@ignore_warnings(category=FutureWarning)
def check_estimators_partial_fit_n_features(name, estimator_orig):
# check if number of features changes between calls to partial_fit.
if not hasattr(estimator_orig, "partial_fit"):
return
estimator = clone(estimator_orig)
X, y = make_blobs(n_samples=50, random_state=1)
X = _enforce_estimator_tags_X(estimator_orig, X)
y = _enforce_estimator_tags_y(estimator_orig, y)
try:
if is_classifier(estimator):
classes = np.unique(y)
estimator.partial_fit(X, y, classes=classes)
else:
estimator.partial_fit(X, y)
except NotImplementedError:
return
with raises(
ValueError,
err_msg=(
f"The estimator {name} does not raise an error when the "
"number of features changes between calls to partial_fit."
),
):
estimator.partial_fit(X[:, :-1], y)
@ignore_warnings(category=FutureWarning)
def check_classifier_multioutput(name, estimator):
n_samples, n_labels, n_classes = 42, 5, 3
tags = _safe_tags(estimator)
estimator = clone(estimator)
X, y = make_multilabel_classification(
random_state=42, n_samples=n_samples, n_labels=n_labels, n_classes=n_classes
)
estimator.fit(X, y)
y_pred = estimator.predict(X)
assert y_pred.shape == (n_samples, n_classes), (
"The shape of the prediction for multioutput data is "
"incorrect. Expected {}, got {}.".format((n_samples, n_labels), y_pred.shape)
)
assert y_pred.dtype.kind == "i"
if hasattr(estimator, "decision_function"):
decision = estimator.decision_function(X)
assert isinstance(decision, np.ndarray)
assert decision.shape == (n_samples, n_classes), (
"The shape of the decision function output for "
"multioutput data is incorrect. Expected {}, got {}.".format(
(n_samples, n_classes), decision.shape
)
)
dec_pred = (decision > 0).astype(int)
dec_exp = estimator.classes_[dec_pred]
assert_array_equal(dec_exp, y_pred)
if hasattr(estimator, "predict_proba"):
y_prob = estimator.predict_proba(X)
if isinstance(y_prob, list) and not tags["poor_score"]:
for i in range(n_classes):
assert y_prob[i].shape == (n_samples, 2), (
"The shape of the probability for multioutput data is"
" incorrect. Expected {}, got {}.".format(
(n_samples, 2), y_prob[i].shape
)
)
assert_array_equal(
np.argmax(y_prob[i], axis=1).astype(int), y_pred[:, i]
)
elif not tags["poor_score"]:
assert y_prob.shape == (n_samples, n_classes), (
"The shape of the probability for multioutput data is"
" incorrect. Expected {}, got {}.".format(
(n_samples, n_classes), y_prob.shape
)
)
assert_array_equal(y_prob.round().astype(int), y_pred)
if hasattr(estimator, "decision_function") and hasattr(estimator, "predict_proba"):
for i in range(n_classes):
y_proba = estimator.predict_proba(X)[:, i]
y_decision = estimator.decision_function(X)
assert_array_equal(rankdata(y_proba), rankdata(y_decision[:, i]))
@ignore_warnings(category=FutureWarning)
def check_regressor_multioutput(name, estimator):
estimator = clone(estimator)
n_samples = n_features = 10
if not _is_pairwise_metric(estimator):
n_samples = n_samples + 1
X, y = make_regression(
random_state=42, n_targets=5, n_samples=n_samples, n_features=n_features
)
X = _enforce_estimator_tags_X(estimator, X)
estimator.fit(X, y)
y_pred = estimator.predict(X)
assert y_pred.dtype == np.dtype("float64"), (
"Multioutput predictions by a regressor are expected to be"
" floating-point precision. Got {} instead".format(y_pred.dtype)
)
assert y_pred.shape == y.shape, (
"The shape of the prediction for multioutput data is incorrect."
" Expected {}, got {}."
)
@ignore_warnings(category=FutureWarning)
def check_clustering(name, clusterer_orig, readonly_memmap=False):
clusterer = clone(clusterer_orig)
X, y = make_blobs(n_samples=50, random_state=1)
X, y = shuffle(X, y, random_state=7)
X = StandardScaler().fit_transform(X)
rng = np.random.RandomState(7)
X_noise = np.concatenate([X, rng.uniform(low=-3, high=3, size=(5, 2))])
if readonly_memmap:
X, y, X_noise = create_memmap_backed_data([X, y, X_noise])
n_samples, n_features = X.shape
# catch deprecation and neighbors warnings
if hasattr(clusterer, "n_clusters"):
clusterer.set_params(n_clusters=3)
set_random_state(clusterer)
if name == "AffinityPropagation":
clusterer.set_params(preference=-100)
clusterer.set_params(max_iter=100)
# fit
clusterer.fit(X)
# with lists
clusterer.fit(X.tolist())
pred = clusterer.labels_
assert pred.shape == (n_samples,)
assert adjusted_rand_score(pred, y) > 0.4
if _safe_tags(clusterer, key="non_deterministic"):
return
set_random_state(clusterer)
with warnings.catch_warnings(record=True):
pred2 = clusterer.fit_predict(X)
assert_array_equal(pred, pred2)
# fit_predict(X) and labels_ should be of type int
assert pred.dtype in [np.dtype("int32"), np.dtype("int64")]
assert pred2.dtype in [np.dtype("int32"), np.dtype("int64")]
# Add noise to X to test the possible values of the labels
labels = clusterer.fit_predict(X_noise)
# There should be at least one sample in every cluster. Equivalently
# labels_ should contain all the consecutive values between its
# min and its max.
labels_sorted = np.unique(labels)
assert_array_equal(
labels_sorted, np.arange(labels_sorted[0], labels_sorted[-1] + 1)
)
# Labels are expected to start at 0 (no noise) or -1 (if noise)
assert labels_sorted[0] in [0, -1]
# Labels should be less than n_clusters - 1
if hasattr(clusterer, "n_clusters"):
n_clusters = getattr(clusterer, "n_clusters")
assert n_clusters - 1 >= labels_sorted[-1]
# else labels should be less than max(labels_) which is necessarily true
@ignore_warnings(category=FutureWarning)
def check_clusterer_compute_labels_predict(name, clusterer_orig):
"""Check that predict is invariant of compute_labels."""
X, y = make_blobs(n_samples=20, random_state=0)
clusterer = clone(clusterer_orig)
set_random_state(clusterer)
if hasattr(clusterer, "compute_labels"):
# MiniBatchKMeans
X_pred1 = clusterer.fit(X).predict(X)
clusterer.set_params(compute_labels=False)
X_pred2 = clusterer.fit(X).predict(X)
assert_array_equal(X_pred1, X_pred2)
@ignore_warnings(category=FutureWarning)
def check_classifiers_one_label(name, classifier_orig):
error_string_fit = "Classifier can't train when only one class is present."
error_string_predict = "Classifier can't predict when only one class is present."
rnd = np.random.RandomState(0)
X_train = rnd.uniform(size=(10, 3))
X_test = rnd.uniform(size=(10, 3))
y = np.ones(10)
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
classifier = clone(classifier_orig)
with raises(
ValueError, match="class", may_pass=True, err_msg=error_string_fit
) as cm:
classifier.fit(X_train, y)
if cm.raised_and_matched:
# ValueError was raised with proper error message
return
assert_array_equal(classifier.predict(X_test), y, err_msg=error_string_predict)
@ignore_warnings(category=FutureWarning)
def check_classifiers_one_label_sample_weights(name, classifier_orig):
"""Check that classifiers accepting sample_weight fit or throws a ValueError with
an explicit message if the problem is reduced to one class.
"""
error_fit = (
f"{name} failed when fitted on one label after sample_weight trimming. Error "
"message is not explicit, it should have 'class'."
)
error_predict = f"{name} prediction results should only output the remaining class."
rnd = np.random.RandomState(0)
# X should be square for test on SVC with precomputed kernel
X_train = rnd.uniform(size=(10, 10))
X_test = rnd.uniform(size=(10, 10))
y = np.arange(10) % 2
sample_weight = y.copy() # select a single class
classifier = clone(classifier_orig)
if has_fit_parameter(classifier, "sample_weight"):
match = [r"\bclass(es)?\b", error_predict]
err_type, err_msg = (AssertionError, ValueError), error_fit
else:
match = r"\bsample_weight\b"
err_type, err_msg = (TypeError, ValueError), None
with raises(err_type, match=match, may_pass=True, err_msg=err_msg) as cm:
classifier.fit(X_train, y, sample_weight=sample_weight)
if cm.raised_and_matched:
# raise the proper error type with the proper error message
return
# for estimators that do not fail, they should be able to predict the only
# class remaining during fit
assert_array_equal(
classifier.predict(X_test), np.ones(10), err_msg=error_predict
)
@ignore_warnings # Warnings are raised by decision function
def check_classifiers_train(
name, classifier_orig, readonly_memmap=False, X_dtype="float64"
):
X_m, y_m = make_blobs(n_samples=300, random_state=0)
X_m = X_m.astype(X_dtype)
X_m, y_m = shuffle(X_m, y_m, random_state=7)
X_m = StandardScaler().fit_transform(X_m)
# generate binary problem from multi-class one
y_b = y_m[y_m != 2]
X_b = X_m[y_m != 2]
if name in ["BernoulliNB", "MultinomialNB", "ComplementNB", "CategoricalNB"]:
X_m -= X_m.min()
X_b -= X_b.min()
if readonly_memmap:
X_m, y_m, X_b, y_b = create_memmap_backed_data([X_m, y_m, X_b, y_b])
problems = [(X_b, y_b)]
tags = _safe_tags(classifier_orig)
if not tags["binary_only"]:
problems.append((X_m, y_m))
for X, y in problems:
classes = np.unique(y)
n_classes = len(classes)
n_samples, n_features = X.shape
classifier = clone(classifier_orig)
X = _enforce_estimator_tags_X(classifier, X)
y = _enforce_estimator_tags_y(classifier, y)
set_random_state(classifier)
# raises error on malformed input for fit
if not tags["no_validation"]:
with raises(
ValueError,
err_msg=(
f"The classifier {name} does not raise an error when "
"incorrect/malformed input data for fit is passed. The number "
"of training examples is not the same as the number of "
"labels. Perhaps use check_X_y in fit."
),
):
classifier.fit(X, y[:-1])
# fit
classifier.fit(X, y)
# with lists
classifier.fit(X.tolist(), y.tolist())
assert hasattr(classifier, "classes_")
y_pred = classifier.predict(X)
assert y_pred.shape == (n_samples,)
# training set performance
if not tags["poor_score"]:
assert accuracy_score(y, y_pred) > 0.83
# raises error on malformed input for predict
msg_pairwise = (
"The classifier {} does not raise an error when shape of X in "
" {} is not equal to (n_test_samples, n_training_samples)"
)
msg = (
"The classifier {} does not raise an error when the number of "
"features in {} is different from the number of features in "
"fit."
)
if not tags["no_validation"]:
if tags["pairwise"]:
with raises(
ValueError,
err_msg=msg_pairwise.format(name, "predict"),
):
classifier.predict(X.reshape(-1, 1))
else:
with raises(ValueError, err_msg=msg.format(name, "predict")):
classifier.predict(X.T)
if hasattr(classifier, "decision_function"):
try:
# decision_function agrees with predict
decision = classifier.decision_function(X)
if n_classes == 2:
if not tags["multioutput_only"]:
assert decision.shape == (n_samples,)
else:
assert decision.shape == (n_samples, 1)
dec_pred = (decision.ravel() > 0).astype(int)
assert_array_equal(dec_pred, y_pred)
else:
assert decision.shape == (n_samples, n_classes)
assert_array_equal(np.argmax(decision, axis=1), y_pred)
# raises error on malformed input for decision_function
if not tags["no_validation"]:
if tags["pairwise"]:
with raises(
ValueError,
err_msg=msg_pairwise.format(name, "decision_function"),
):
classifier.decision_function(X.reshape(-1, 1))
else:
with raises(
ValueError,
err_msg=msg.format(name, "decision_function"),
):
classifier.decision_function(X.T)
except NotImplementedError:
pass
if hasattr(classifier, "predict_proba"):
# predict_proba agrees with predict
y_prob = classifier.predict_proba(X)
assert y_prob.shape == (n_samples, n_classes)
assert_array_equal(np.argmax(y_prob, axis=1), y_pred)
# check that probas for all classes sum to one
assert_array_almost_equal(np.sum(y_prob, axis=1), np.ones(n_samples))
if not tags["no_validation"]:
# raises error on malformed input for predict_proba
if tags["pairwise"]:
with raises(
ValueError,
err_msg=msg_pairwise.format(name, "predict_proba"),
):
classifier.predict_proba(X.reshape(-1, 1))
else:
with raises(
ValueError,
err_msg=msg.format(name, "predict_proba"),
):
classifier.predict_proba(X.T)
if hasattr(classifier, "predict_log_proba"):
# predict_log_proba is a transformation of predict_proba
y_log_prob = classifier.predict_log_proba(X)
assert_allclose(y_log_prob, np.log(y_prob), 8, atol=1e-9)
assert_array_equal(np.argsort(y_log_prob), np.argsort(y_prob))
def check_outlier_corruption(num_outliers, expected_outliers, decision):
# Check for deviation from the precise given contamination level that may
# be due to ties in the anomaly scores.
if num_outliers < expected_outliers:
start = num_outliers
end = expected_outliers + 1
else:
start = expected_outliers
end = num_outliers + 1
# ensure that all values in the 'critical area' are tied,
# leading to the observed discrepancy between provided
# and actual contamination levels.
sorted_decision = np.sort(decision)
msg = (
"The number of predicted outliers is not equal to the expected "
"number of outliers and this difference is not explained by the "
"number of ties in the decision_function values"
)
assert len(np.unique(sorted_decision[start:end])) == 1, msg
def check_outliers_train(name, estimator_orig, readonly_memmap=True):
n_samples = 300
X, _ = make_blobs(n_samples=n_samples, random_state=0)
X = shuffle(X, random_state=7)
if readonly_memmap:
X = create_memmap_backed_data(X)
n_samples, n_features = X.shape
estimator = clone(estimator_orig)
set_random_state(estimator)
# fit
estimator.fit(X)
# with lists
estimator.fit(X.tolist())
y_pred = estimator.predict(X)
assert y_pred.shape == (n_samples,)
assert y_pred.dtype.kind == "i"
assert_array_equal(np.unique(y_pred), np.array([-1, 1]))
decision = estimator.decision_function(X)
scores = estimator.score_samples(X)
for output in [decision, scores]:
assert output.dtype == np.dtype("float")
assert output.shape == (n_samples,)
# raises error on malformed input for predict
with raises(ValueError):
estimator.predict(X.T)
# decision_function agrees with predict
dec_pred = (decision >= 0).astype(int)
dec_pred[dec_pred == 0] = -1
assert_array_equal(dec_pred, y_pred)
# raises error on malformed input for decision_function
with raises(ValueError):
estimator.decision_function(X.T)
# decision_function is a translation of score_samples
y_dec = scores - estimator.offset_
assert_allclose(y_dec, decision)
# raises error on malformed input for score_samples
with raises(ValueError):
estimator.score_samples(X.T)
# contamination parameter (not for OneClassSVM which has the nu parameter)
if hasattr(estimator, "contamination") and not hasattr(estimator, "novelty"):
# proportion of outliers equal to contamination parameter when not
# set to 'auto'. This is true for the training set and cannot thus be
# checked as follows for estimators with a novelty parameter such as
# LocalOutlierFactor (tested in check_outliers_fit_predict)
expected_outliers = 30
contamination = expected_outliers / n_samples
estimator.set_params(contamination=contamination)
estimator.fit(X)
y_pred = estimator.predict(X)
num_outliers = np.sum(y_pred != 1)
# num_outliers should be equal to expected_outliers unless
# there are ties in the decision_function values. this can
# only be tested for estimators with a decision_function
# method, i.e. all estimators except LOF which is already
# excluded from this if branch.
if num_outliers != expected_outliers:
decision = estimator.decision_function(X)
check_outlier_corruption(num_outliers, expected_outliers, decision)
def check_outlier_contamination(name, estimator_orig):
# Check that the contamination parameter is in (0.0, 0.5] when it is an
# interval constraint.
if not hasattr(estimator_orig, "_parameter_constraints"):
# Only estimator implementing parameter constraints will be checked
return
if "contamination" not in estimator_orig._parameter_constraints:
return
contamination_constraints = estimator_orig._parameter_constraints["contamination"]
if not any([isinstance(c, Interval) for c in contamination_constraints]):
raise AssertionError(
"contamination constraints should contain a Real Interval constraint."
)
for constraint in contamination_constraints:
if isinstance(constraint, Interval):
assert (
constraint.type == Real
and constraint.left >= 0.0
and constraint.right <= 0.5
and (constraint.left > 0 or constraint.closed in {"right", "neither"})
), "contamination constraint should be an interval in (0, 0.5]"
@ignore_warnings(category=FutureWarning)
def check_classifiers_multilabel_representation_invariance(name, classifier_orig):
X, y = make_multilabel_classification(
n_samples=100,
n_features=2,
n_classes=5,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0,
)
X = scale(X)
X_train, y_train = X[:80], y[:80]
X_test = X[80:]
y_train_list_of_lists = y_train.tolist()
y_train_list_of_arrays = list(y_train)
classifier = clone(classifier_orig)
set_random_state(classifier)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
y_pred_list_of_lists = classifier.fit(X_train, y_train_list_of_lists).predict(
X_test
)
y_pred_list_of_arrays = classifier.fit(X_train, y_train_list_of_arrays).predict(
X_test
)
assert_array_equal(y_pred, y_pred_list_of_arrays)
assert_array_equal(y_pred, y_pred_list_of_lists)
assert y_pred.dtype == y_pred_list_of_arrays.dtype
assert y_pred.dtype == y_pred_list_of_lists.dtype
assert type(y_pred) == type(y_pred_list_of_arrays)
assert type(y_pred) == type(y_pred_list_of_lists)
@ignore_warnings(category=FutureWarning)
def check_classifiers_multilabel_output_format_predict(name, classifier_orig):
"""Check the output of the `predict` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs = 100, 25, 5
X, y = make_multilabel_classification(
n_samples=n_samples,
n_features=2,
n_classes=n_outputs,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0,
)
X = scale(X)
X_train, X_test = X[:-test_size], X[-test_size:]
y_train, y_test = y[:-test_size], y[-test_size:]
classifier.fit(X_train, y_train)
response_method_name = "predict"
predict_method = getattr(classifier, response_method_name, None)
if predict_method is None:
raise SkipTest(f"{name} does not have a {response_method_name} method.")
y_pred = predict_method(X_test)
# y_pred.shape -> y_test.shape with the same dtype
assert isinstance(y_pred, np.ndarray), (
f"{name}.predict is expected to output a NumPy array. Got "
f"{type(y_pred)} instead."
)
assert y_pred.shape == y_test.shape, (
f"{name}.predict outputs a NumPy array of shape {y_pred.shape} "
f"instead of {y_test.shape}."
)
assert y_pred.dtype == y_test.dtype, (
f"{name}.predict does not output the same dtype than the targets. "
f"Got {y_pred.dtype} instead of {y_test.dtype}."
)
@ignore_warnings(category=FutureWarning)
def check_classifiers_multilabel_output_format_predict_proba(name, classifier_orig):
"""Check the output of the `predict_proba` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs = 100, 25, 5
X, y = make_multilabel_classification(
n_samples=n_samples,
n_features=2,
n_classes=n_outputs,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0,
)
X = scale(X)
X_train, X_test = X[:-test_size], X[-test_size:]
y_train = y[:-test_size]
classifier.fit(X_train, y_train)
response_method_name = "predict_proba"
predict_proba_method = getattr(classifier, response_method_name, None)
if predict_proba_method is None:
raise SkipTest(f"{name} does not have a {response_method_name} method.")
y_pred = predict_proba_method(X_test)
# y_pred.shape -> 2 possibilities:
# - list of length n_outputs of shape (n_samples, 2);
# - ndarray of shape (n_samples, n_outputs).
# dtype should be floating
if isinstance(y_pred, list):
assert len(y_pred) == n_outputs, (
f"When {name}.predict_proba returns a list, the list should "
"be of length n_outputs and contain NumPy arrays. Got length "
f"of {len(y_pred)} instead of {n_outputs}."
)
for pred in y_pred:
assert pred.shape == (test_size, 2), (
f"When {name}.predict_proba returns a list, this list "
"should contain NumPy arrays of shape (n_samples, 2). Got "
f"NumPy arrays of shape {pred.shape} instead of "
f"{(test_size, 2)}."
)
assert pred.dtype.kind == "f", (
f"When {name}.predict_proba returns a list, it should "
"contain NumPy arrays with floating dtype. Got "
f"{pred.dtype} instead."
)
# check that we have the correct probabilities
err_msg = (
f"When {name}.predict_proba returns a list, each NumPy "
"array should contain probabilities for each class and "
"thus each row should sum to 1 (or close to 1 due to "
"numerical errors)."
)
assert_allclose(pred.sum(axis=1), 1, err_msg=err_msg)
elif isinstance(y_pred, np.ndarray):
assert y_pred.shape == (test_size, n_outputs), (
f"When {name}.predict_proba returns a NumPy array, the "
f"expected shape is (n_samples, n_outputs). Got {y_pred.shape}"
f" instead of {(test_size, n_outputs)}."
)
assert y_pred.dtype.kind == "f", (
f"When {name}.predict_proba returns a NumPy array, the "
f"expected data type is floating. Got {y_pred.dtype} instead."
)
err_msg = (
f"When {name}.predict_proba returns a NumPy array, this array "
"is expected to provide probabilities of the positive class "
"and should therefore contain values between 0 and 1."
)
assert_array_less(0, y_pred, err_msg=err_msg)
assert_array_less(y_pred, 1, err_msg=err_msg)
else:
raise ValueError(
f"Unknown returned type {type(y_pred)} by {name}."
"predict_proba. A list or a Numpy array is expected."
)
@ignore_warnings(category=FutureWarning)
def check_classifiers_multilabel_output_format_decision_function(name, classifier_orig):
"""Check the output of the `decision_function` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs = 100, 25, 5
X, y = make_multilabel_classification(
n_samples=n_samples,
n_features=2,
n_classes=n_outputs,
n_labels=3,
length=50,
allow_unlabeled=True,
random_state=0,
)
X = scale(X)
X_train, X_test = X[:-test_size], X[-test_size:]
y_train = y[:-test_size]
classifier.fit(X_train, y_train)
response_method_name = "decision_function"
decision_function_method = getattr(classifier, response_method_name, None)
if decision_function_method is None:
raise SkipTest(f"{name} does not have a {response_method_name} method.")
y_pred = decision_function_method(X_test)
# y_pred.shape -> y_test.shape with floating dtype
assert isinstance(y_pred, np.ndarray), (
f"{name}.decision_function is expected to output a NumPy array."
f" Got {type(y_pred)} instead."
)
assert y_pred.shape == (test_size, n_outputs), (
f"{name}.decision_function is expected to provide a NumPy array "
f"of shape (n_samples, n_outputs). Got {y_pred.shape} instead of "
f"{(test_size, n_outputs)}."
)
assert y_pred.dtype.kind == "f", (
f"{name}.decision_function is expected to output a floating dtype."
f" Got {y_pred.dtype} instead."
)
@ignore_warnings(category=FutureWarning)
def check_get_feature_names_out_error(name, estimator_orig):
"""Check the error raised by get_feature_names_out when called before fit.
Unfitted estimators with get_feature_names_out should raise a NotFittedError.
"""
estimator = clone(estimator_orig)
err_msg = (
f"Estimator {name} should have raised a NotFitted error when fit is called"
" before get_feature_names_out"
)
with raises(NotFittedError, err_msg=err_msg):
estimator.get_feature_names_out()
@ignore_warnings(category=FutureWarning)
def check_estimators_fit_returns_self(name, estimator_orig, readonly_memmap=False):
"""Check if self is returned when calling fit."""
X, y = make_blobs(random_state=0, n_samples=21)
X = _enforce_estimator_tags_X(estimator_orig, X)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
if readonly_memmap:
X, y = create_memmap_backed_data([X, y])
set_random_state(estimator)
assert estimator.fit(X, y) is estimator
@ignore_warnings
def check_estimators_unfitted(name, estimator_orig):
"""Check that predict raises an exception in an unfitted estimator.
Unfitted estimators should raise a NotFittedError.
"""
# Common test for Regressors, Classifiers and Outlier detection estimators
X, y = _regression_dataset()
estimator = clone(estimator_orig)
for method in (
"decision_function",
"predict",
"predict_proba",
"predict_log_proba",
):
if hasattr(estimator, method):
with raises(NotFittedError):
getattr(estimator, method)(X)
@ignore_warnings(category=FutureWarning)
def check_supervised_y_2d(name, estimator_orig):
tags = _safe_tags(estimator_orig)
rnd = np.random.RandomState(0)
n_samples = 30
X = _enforce_estimator_tags_X(estimator_orig, rnd.uniform(size=(n_samples, 3)))
y = np.arange(n_samples) % 3
y = _enforce_estimator_tags_y(estimator_orig, y)
estimator = clone(estimator_orig)
set_random_state(estimator)
# fit
estimator.fit(X, y)
y_pred = estimator.predict(X)
set_random_state(estimator)
# Check that when a 2D y is given, a DataConversionWarning is
# raised
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DataConversionWarning)
warnings.simplefilter("ignore", RuntimeWarning)
estimator.fit(X, y[:, np.newaxis])
y_pred_2d = estimator.predict(X)
msg = "expected 1 DataConversionWarning, got: %s" % ", ".join(
[str(w_x) for w_x in w]
)
if not tags["multioutput"]:
# check that we warned if we don't support multi-output
assert len(w) > 0, msg
assert (
"DataConversionWarning('A column-vector y"
" was passed when a 1d array was expected" in msg
)
assert_allclose(y_pred.ravel(), y_pred_2d.ravel())
@ignore_warnings
def check_classifiers_predictions(X, y, name, classifier_orig):
classes = np.unique(y)
classifier = clone(classifier_orig)
if name == "BernoulliNB":
X = X > X.mean()
set_random_state(classifier)
classifier.fit(X, y)
y_pred = classifier.predict(X)
if hasattr(classifier, "decision_function"):
decision = classifier.decision_function(X)
assert isinstance(decision, np.ndarray)
if len(classes) == 2:
dec_pred = (decision.ravel() > 0).astype(int)
dec_exp = classifier.classes_[dec_pred]
assert_array_equal(
dec_exp,
y_pred,
err_msg=(
"decision_function does not match "
"classifier for %r: expected '%s', got '%s'"
)
% (
classifier,
", ".join(map(str, dec_exp)),
", ".join(map(str, y_pred)),
),
)
elif getattr(classifier, "decision_function_shape", "ovr") == "ovr":
decision_y = np.argmax(decision, axis=1).astype(int)
y_exp = classifier.classes_[decision_y]
assert_array_equal(
y_exp,
y_pred,
err_msg=(
"decision_function does not match "
"classifier for %r: expected '%s', got '%s'"
)
% (
classifier,
", ".join(map(str, y_exp)),
", ".join(map(str, y_pred)),
),
)
# training set performance
if name != "ComplementNB":
# This is a pathological data set for ComplementNB.
# For some specific cases 'ComplementNB' predicts less classes
# than expected
assert_array_equal(np.unique(y), np.unique(y_pred))
assert_array_equal(
classes,
classifier.classes_,
err_msg="Unexpected classes_ attribute for %r: expected '%s', got '%s'"
% (
classifier,
", ".join(map(str, classes)),
", ".join(map(str, classifier.classes_)),
),
)
def _choose_check_classifiers_labels(name, y, y_names):
# Semisupervised classifiers use -1 as the indicator for an unlabeled
# sample.
return (
y
if name in ["LabelPropagation", "LabelSpreading", "SelfTrainingClassifier"]
else y_names
)
def check_classifiers_classes(name, classifier_orig):
X_multiclass, y_multiclass = make_blobs(
n_samples=30, random_state=0, cluster_std=0.1
)
X_multiclass, y_multiclass = shuffle(X_multiclass, y_multiclass, random_state=7)
X_multiclass = StandardScaler().fit_transform(X_multiclass)
X_binary = X_multiclass[y_multiclass != 2]
y_binary = y_multiclass[y_multiclass != 2]
X_multiclass = _enforce_estimator_tags_X(classifier_orig, X_multiclass)
X_binary = _enforce_estimator_tags_X(classifier_orig, X_binary)
labels_multiclass = ["one", "two", "three"]
labels_binary = ["one", "two"]
y_names_multiclass = np.take(labels_multiclass, y_multiclass)
y_names_binary = np.take(labels_binary, y_binary)
problems = [(X_binary, y_binary, y_names_binary)]
if not _safe_tags(classifier_orig, key="binary_only"):
problems.append((X_multiclass, y_multiclass, y_names_multiclass))
for X, y, y_names in problems:
for y_names_i in [y_names, y_names.astype("O")]:
y_ = _choose_check_classifiers_labels(name, y, y_names_i)
check_classifiers_predictions(X, y_, name, classifier_orig)
labels_binary = [-1, 1]
y_names_binary = np.take(labels_binary, y_binary)
y_binary = _choose_check_classifiers_labels(name, y_binary, y_names_binary)
check_classifiers_predictions(X_binary, y_binary, name, classifier_orig)
@ignore_warnings(category=FutureWarning)
def check_regressors_int(name, regressor_orig):
X, _ = _regression_dataset()
X = _enforce_estimator_tags_X(regressor_orig, X[:50])
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
y = _enforce_estimator_tags_y(regressor_orig, y)
rnd = np.random.RandomState(0)
# separate estimators to control random seeds
regressor_1 = clone(regressor_orig)
regressor_2 = clone(regressor_orig)
set_random_state(regressor_1)
set_random_state(regressor_2)
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
# fit
regressor_1.fit(X, y_)
pred1 = regressor_1.predict(X)
regressor_2.fit(X, y_.astype(float))
pred2 = regressor_2.predict(X)
assert_allclose(pred1, pred2, atol=1e-2, err_msg=name)
@ignore_warnings(category=FutureWarning)
def check_regressors_train(
name, regressor_orig, readonly_memmap=False, X_dtype=np.float64
):
X, y = _regression_dataset()
X = X.astype(X_dtype)
y = scale(y) # X is already scaled
regressor = clone(regressor_orig)
X = _enforce_estimator_tags_X(regressor, X)
y = _enforce_estimator_tags_y(regressor, y)
if name in CROSS_DECOMPOSITION:
rnd = np.random.RandomState(0)
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
if readonly_memmap:
X, y, y_ = create_memmap_backed_data([X, y, y_])
if not hasattr(regressor, "alphas") and hasattr(regressor, "alpha"):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
if name == "PassiveAggressiveRegressor":
regressor.C = 0.01
# raises error on malformed input for fit
with raises(
ValueError,
err_msg=(
f"The classifier {name} does not raise an error when "
"incorrect/malformed input data for fit is passed. The number of "
"training examples is not the same as the number of labels. Perhaps "
"use check_X_y in fit."
),
):
regressor.fit(X, y[:-1])
# fit
set_random_state(regressor)
regressor.fit(X, y_)
regressor.fit(X.tolist(), y_.tolist())
y_pred = regressor.predict(X)
assert y_pred.shape == y_.shape
# TODO: find out why PLS and CCA fail. RANSAC is random
# and furthermore assumes the presence of outliers, hence
# skipped
if not _safe_tags(regressor, key="poor_score"):
assert regressor.score(X, y_) > 0.5
@ignore_warnings
def check_regressors_no_decision_function(name, regressor_orig):
# check that regressors don't have a decision_function, predict_proba, or
# predict_log_proba method.
rng = np.random.RandomState(0)
regressor = clone(regressor_orig)
X = rng.normal(size=(10, 4))
X = _enforce_estimator_tags_X(regressor_orig, X)
y = _enforce_estimator_tags_y(regressor, X[:, 0])
regressor.fit(X, y)
funcs = ["decision_function", "predict_proba", "predict_log_proba"]
for func_name in funcs:
assert not hasattr(regressor, func_name)
@ignore_warnings(category=FutureWarning)
def check_class_weight_classifiers(name, classifier_orig):
if _safe_tags(classifier_orig, key="binary_only"):
problems = [2]
else:
problems = [2, 3]
for n_centers in problems:
# create a very noisy dataset
X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0
)
# can't use gram_if_pairwise() here, setting up gram matrix manually
if _safe_tags(classifier_orig, key="pairwise"):
X_test = rbf_kernel(X_test, X_train)
X_train = rbf_kernel(X_train, X_train)
n_centers = len(np.unique(y_train))
if n_centers == 2:
class_weight = {0: 1000, 1: 0.0001}
else:
class_weight = {0: 1000, 1: 0.0001, 2: 0.0001}
classifier = clone(classifier_orig).set_params(class_weight=class_weight)
if hasattr(classifier, "n_iter"):
classifier.set_params(n_iter=100)
if hasattr(classifier, "max_iter"):
classifier.set_params(max_iter=1000)
if hasattr(classifier, "min_weight_fraction_leaf"):
classifier.set_params(min_weight_fraction_leaf=0.01)
if hasattr(classifier, "n_iter_no_change"):
classifier.set_params(n_iter_no_change=20)
set_random_state(classifier)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
# XXX: Generally can use 0.89 here. On Windows, LinearSVC gets
# 0.88 (Issue #9111)
if not _safe_tags(classifier_orig, key="poor_score"):
assert np.mean(y_pred == 0) > 0.87
@ignore_warnings(category=FutureWarning)
def check_class_weight_balanced_classifiers(
name, classifier_orig, X_train, y_train, X_test, y_test, weights
):
classifier = clone(classifier_orig)
if hasattr(classifier, "n_iter"):
classifier.set_params(n_iter=100)
if hasattr(classifier, "max_iter"):
classifier.set_params(max_iter=1000)
set_random_state(classifier)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
classifier.set_params(class_weight="balanced")
classifier.fit(X_train, y_train)
y_pred_balanced = classifier.predict(X_test)
assert f1_score(y_test, y_pred_balanced, average="weighted") > f1_score(
y_test, y_pred, average="weighted"
)
@ignore_warnings(category=FutureWarning)
def check_class_weight_balanced_linear_classifier(name, Classifier):
"""Test class weights with non-contiguous class labels."""
# this is run on classes, not instances, though this should be changed
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
y = np.array([1, 1, 1, -1, -1])
classifier = Classifier()
if hasattr(classifier, "n_iter"):
# This is a very small dataset, default n_iter are likely to prevent
# convergence
classifier.set_params(n_iter=1000)
if hasattr(classifier, "max_iter"):
classifier.set_params(max_iter=1000)
if hasattr(classifier, "cv"):
classifier.set_params(cv=3)
set_random_state(classifier)
# Let the model compute the class frequencies
classifier.set_params(class_weight="balanced")
coef_balanced = classifier.fit(X, y).coef_.copy()
# Count each label occurrence to reweight manually
n_samples = len(y)
n_classes = float(len(np.unique(y)))
class_weight = {
1: n_samples / (np.sum(y == 1) * n_classes),
-1: n_samples / (np.sum(y == -1) * n_classes),
}
classifier.set_params(class_weight=class_weight)
coef_manual = classifier.fit(X, y).coef_.copy()
assert_allclose(
coef_balanced,
coef_manual,
err_msg="Classifier %s is not computing class_weight=balanced properly." % name,
)
@ignore_warnings(category=FutureWarning)
def check_estimators_overwrite_params(name, estimator_orig):
X, y = make_blobs(random_state=0, n_samples=21)
X = _enforce_estimator_tags_X(estimator_orig, X, kernel=rbf_kernel)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator)
# Make a physical copy of the original estimator parameters before fitting.
params = estimator.get_params()
original_params = deepcopy(params)
# Fit the model
estimator.fit(X, y)
# Compare the state of the model parameters with the original parameters
new_params = estimator.get_params()
for param_name, original_value in original_params.items():
new_value = new_params[param_name]
# We should never change or mutate the internal state of input
# parameters by default. To check this we use the joblib.hash function
# that introspects recursively any subobjects to compute a checksum.
# The only exception to this rule of immutable constructor parameters
# is possible RandomState instance but in this check we explicitly
# fixed the random_state params recursively to be integer seeds.
assert joblib.hash(new_value) == joblib.hash(original_value), (
"Estimator %s should not change or mutate "
" the parameter %s from %s to %s during fit."
% (name, param_name, original_value, new_value)
)
@ignore_warnings(category=FutureWarning)
def check_no_attributes_set_in_init(name, estimator_orig):
"""Check setting during init."""
try:
# Clone fails if the estimator does not store
# all parameters as an attribute during init
estimator = clone(estimator_orig)
except AttributeError:
raise AttributeError(
f"Estimator {name} should store all parameters as an attribute during init."
)
if hasattr(type(estimator).__init__, "deprecated_original"):
return
init_params = _get_args(type(estimator).__init__)
if _IS_PYPY:
# __init__ signature has additional objects in PyPy
for key in ["obj"]:
if key in init_params:
init_params.remove(key)
parents_init_params = [
param
for params_parent in (_get_args(parent) for parent in type(estimator).__mro__)
for param in params_parent
]
# Test for no setting apart from parameters during init
invalid_attr = set(vars(estimator)) - set(init_params) - set(parents_init_params)
# Ignore private attributes
invalid_attr = set([attr for attr in invalid_attr if not attr.startswith("_")])
assert not invalid_attr, (
"Estimator %s should not set any attribute apart"
" from parameters during init. Found attributes %s."
% (name, sorted(invalid_attr))
)
@ignore_warnings(category=FutureWarning)
def check_sparsify_coefficients(name, estimator_orig):
X = np.array(
[
[-2, -1],
[-1, -1],
[-1, -2],
[1, 1],
[1, 2],
[2, 1],
[-1, -2],
[2, 2],
[-2, -2],
]
)
y = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3])
y = _enforce_estimator_tags_y(estimator_orig, y)
est = clone(estimator_orig)
est.fit(X, y)
pred_orig = est.predict(X)
# test sparsify with dense inputs
est.sparsify()
assert sparse.issparse(est.coef_)
pred = est.predict(X)
assert_array_equal(pred, pred_orig)
# pickle and unpickle with sparse coef_
est = pickle.loads(pickle.dumps(est))
assert sparse.issparse(est.coef_)
pred = est.predict(X)
assert_array_equal(pred, pred_orig)
@ignore_warnings(category=FutureWarning)
def check_classifier_data_not_an_array(name, estimator_orig):
X = np.array(
[
[3, 0],
[0, 1],
[0, 2],
[1, 1],
[1, 2],
[2, 1],
[0, 3],
[1, 0],
[2, 0],
[4, 4],
[2, 3],
[3, 2],
]
)
X = _enforce_estimator_tags_X(estimator_orig, X)
y = np.array([1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2])
y = _enforce_estimator_tags_y(estimator_orig, y)
for obj_type in ["NotAnArray", "PandasDataframe"]:
check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type)
@ignore_warnings(category=FutureWarning)
def check_regressor_data_not_an_array(name, estimator_orig):
X, y = _regression_dataset()
X = _enforce_estimator_tags_X(estimator_orig, X)
y = _enforce_estimator_tags_y(estimator_orig, y)
for obj_type in ["NotAnArray", "PandasDataframe"]:
check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type)
@ignore_warnings(category=FutureWarning)
def check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type):
if name in CROSS_DECOMPOSITION:
raise SkipTest(
"Skipping check_estimators_data_not_an_array "
"for cross decomposition module as estimators "
"are not deterministic."
)
# separate estimators to control random seeds
estimator_1 = clone(estimator_orig)
estimator_2 = clone(estimator_orig)
set_random_state(estimator_1)
set_random_state(estimator_2)
if obj_type not in ["NotAnArray", "PandasDataframe"]:
raise ValueError("Data type {0} not supported".format(obj_type))
if obj_type == "NotAnArray":
y_ = _NotAnArray(np.asarray(y))
X_ = _NotAnArray(np.asarray(X))
else:
# Here pandas objects (Series and DataFrame) are tested explicitly
# because some estimators may handle them (especially their indexing)
# specially.
try:
import pandas as pd
y_ = np.asarray(y)
if y_.ndim == 1:
y_ = pd.Series(y_, copy=False)
else:
y_ = pd.DataFrame(y_, copy=False)
X_ = pd.DataFrame(np.asarray(X), copy=False)
except ImportError:
raise SkipTest(
"pandas is not installed: not checking estimators for pandas objects."
)
# fit
estimator_1.fit(X_, y_)
pred1 = estimator_1.predict(X_)
estimator_2.fit(X, y)
pred2 = estimator_2.predict(X)
assert_allclose(pred1, pred2, atol=1e-2, err_msg=name)
def check_parameters_default_constructible(name, Estimator):
# test default-constructibility
# get rid of deprecation warnings
Estimator = Estimator.__class__
with ignore_warnings(category=FutureWarning):
estimator = _construct_instance(Estimator)
# test cloning
clone(estimator)
# test __repr__
repr(estimator)
# test that set_params returns self
assert estimator.set_params() is estimator
# test if init does nothing but set parameters
# this is important for grid_search etc.
# We get the default parameters from init and then
# compare these against the actual values of the attributes.
# this comes from getattr. Gets rid of deprecation decorator.
init = getattr(estimator.__init__, "deprecated_original", estimator.__init__)
try:
def param_filter(p):
"""Identify hyper parameters of an estimator."""
return (
p.name != "self"
and p.kind != p.VAR_KEYWORD
and p.kind != p.VAR_POSITIONAL
)
init_params = [
p for p in signature(init).parameters.values() if param_filter(p)
]
except (TypeError, ValueError):
# init is not a python function.
# true for mixins
return
params = estimator.get_params()
# they can need a non-default argument
init_params = init_params[len(getattr(estimator, "_required_parameters", [])) :]
for init_param in init_params:
assert (
init_param.default != init_param.empty
), "parameter %s for %s has no default value" % (
init_param.name,
type(estimator).__name__,
)
allowed_types = {
str,
int,
float,
bool,
tuple,
type(None),
type,
}
# Any numpy numeric such as np.int32.
allowed_types.update(np.sctypeDict.values())
allowed_value = (
type(init_param.default) in allowed_types
or
# Although callables are mutable, we accept them as argument
# default value and trust that neither the implementation of
# the callable nor of the estimator changes the state of the
# callable.
callable(init_param.default)
)
assert allowed_value, (
f"Parameter '{init_param.name}' of estimator "
f"'{Estimator.__name__}' is of type "
f"{type(init_param.default).__name__} which is not allowed. "
f"'{init_param.name}' must be a callable or must be of type "
f"{set(type.__name__ for type in allowed_types)}."
)
if init_param.name not in params.keys():
# deprecated parameter, not in get_params
assert init_param.default is None, (
f"Estimator parameter '{init_param.name}' of estimator "
f"'{Estimator.__name__}' is not returned by get_params. "
"If it is deprecated, set its default value to None."
)
continue
param_value = params[init_param.name]
if isinstance(param_value, np.ndarray):
assert_array_equal(param_value, init_param.default)
else:
failure_text = (
f"Parameter {init_param.name} was mutated on init. All "
"parameters must be stored unchanged."
)
if is_scalar_nan(param_value):
# Allows to set default parameters to np.nan
assert param_value is init_param.default, failure_text
else:
assert param_value == init_param.default, failure_text
def _enforce_estimator_tags_y(estimator, y):
# Estimators with a `requires_positive_y` tag only accept strictly positive
# data
if _safe_tags(estimator, key="requires_positive_y"):
# Create strictly positive y. The minimal increment above 0 is 1, as
# y could be of integer dtype.
y += 1 + abs(y.min())
if _safe_tags(estimator, key="binary_only") and y.size > 0:
y = np.where(y == y.flat[0], y, y.flat[0] + 1)
# Estimators in mono_output_task_error raise ValueError if y is of 1-D
# Convert into a 2-D y for those estimators.
if _safe_tags(estimator, key="multioutput_only"):
return np.reshape(y, (-1, 1))
return y
def _enforce_estimator_tags_X(estimator, X, kernel=linear_kernel):
# Estimators with `1darray` in `X_types` tag only accept
# X of shape (`n_samples`,)
if "1darray" in _safe_tags(estimator, key="X_types"):
X = X[:, 0]
# Estimators with a `requires_positive_X` tag only accept
# strictly positive data
if _safe_tags(estimator, key="requires_positive_X"):
X = X - X.min()
if "categorical" in _safe_tags(estimator, key="X_types"):
dtype = np.float64 if _safe_tags(estimator, key="allow_nan") else np.int32
X = np.round((X - X.min())).astype(dtype)
if estimator.__class__.__name__ == "SkewedChi2Sampler":
# SkewedChi2Sampler requires X > -skewdness in transform
X = X - X.min()
# Pairwise estimators only accept
# X of shape (`n_samples`, `n_samples`)
if _is_pairwise_metric(estimator):
X = pairwise_distances(X, metric="euclidean")
elif _safe_tags(estimator, key="pairwise"):
X = kernel(X, X)
return X
@ignore_warnings(category=FutureWarning)
def check_non_transformer_estimators_n_iter(name, estimator_orig):
# Test that estimators that are not transformers with a parameter
# max_iter, return the attribute of n_iter_ at least 1.
# These models are dependent on external solvers like
# libsvm and accessing the iter parameter is non-trivial.
# SelfTrainingClassifier does not perform an iteration if all samples are
# labeled, hence n_iter_ = 0 is valid.
not_run_check_n_iter = [
"Ridge",
"RidgeClassifier",
"RandomizedLasso",
"LogisticRegressionCV",
"LinearSVC",
"LogisticRegression",
"SelfTrainingClassifier",
]
# Tested in test_transformer_n_iter
not_run_check_n_iter += CROSS_DECOMPOSITION
if name in not_run_check_n_iter:
return
# LassoLars stops early for the default alpha=1.0 the iris dataset.
if name == "LassoLars":
estimator = clone(estimator_orig).set_params(alpha=0.0)
else:
estimator = clone(estimator_orig)
if hasattr(estimator, "max_iter"):
iris = load_iris()
X, y_ = iris.data, iris.target
y_ = _enforce_estimator_tags_y(estimator, y_)
set_random_state(estimator, 0)
X = _enforce_estimator_tags_X(estimator_orig, X)
estimator.fit(X, y_)
assert np.all(estimator.n_iter_ >= 1)
@ignore_warnings(category=FutureWarning)
def check_transformer_n_iter(name, estimator_orig):
# Test that transformers with a parameter max_iter, return the
# attribute of n_iter_ at least 1.
estimator = clone(estimator_orig)
if hasattr(estimator, "max_iter"):
if name in CROSS_DECOMPOSITION:
# Check using default data
X = [[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [2.0, 2.0, 2.0], [2.0, 5.0, 4.0]]
y_ = [[0.1, -0.2], [0.9, 1.1], [0.1, -0.5], [0.3, -0.2]]
else:
X, y_ = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = _enforce_estimator_tags_X(estimator_orig, X)
set_random_state(estimator, 0)
estimator.fit(X, y_)
# These return a n_iter per component.
if name in CROSS_DECOMPOSITION:
for iter_ in estimator.n_iter_:
assert iter_ >= 1
else:
assert estimator.n_iter_ >= 1
@ignore_warnings(category=FutureWarning)
def check_get_params_invariance(name, estimator_orig):
# Checks if get_params(deep=False) is a subset of get_params(deep=True)
e = clone(estimator_orig)
shallow_params = e.get_params(deep=False)
deep_params = e.get_params(deep=True)
assert all(item in deep_params.items() for item in shallow_params.items())
@ignore_warnings(category=FutureWarning)
def check_set_params(name, estimator_orig):
# Check that get_params() returns the same thing
# before and after set_params() with some fuzz
estimator = clone(estimator_orig)
orig_params = estimator.get_params(deep=False)
msg = "get_params result does not match what was passed to set_params"
estimator.set_params(**orig_params)
curr_params = estimator.get_params(deep=False)
assert set(orig_params.keys()) == set(curr_params.keys()), msg
for k, v in curr_params.items():
assert orig_params[k] is v, msg
# some fuzz values
test_values = [-np.inf, np.inf, None]
test_params = deepcopy(orig_params)
for param_name in orig_params.keys():
default_value = orig_params[param_name]
for value in test_values:
test_params[param_name] = value
try:
estimator.set_params(**test_params)
except (TypeError, ValueError) as e:
e_type = e.__class__.__name__
# Exception occurred, possibly parameter validation
warnings.warn(
"{0} occurred during set_params of param {1} on "
"{2}. It is recommended to delay parameter "
"validation until fit.".format(e_type, param_name, name)
)
change_warning_msg = (
"Estimator's parameters changed after set_params raised {}".format(
e_type
)
)
params_before_exception = curr_params
curr_params = estimator.get_params(deep=False)
try:
assert set(params_before_exception.keys()) == set(
curr_params.keys()
)
for k, v in curr_params.items():
assert params_before_exception[k] is v
except AssertionError:
warnings.warn(change_warning_msg)
else:
curr_params = estimator.get_params(deep=False)
assert set(test_params.keys()) == set(curr_params.keys()), msg
for k, v in curr_params.items():
assert test_params[k] is v, msg
test_params[param_name] = default_value
@ignore_warnings(category=FutureWarning)
def check_classifiers_regression_target(name, estimator_orig):
# Check if classifier throws an exception when fed regression targets
X, y = _regression_dataset()
X = _enforce_estimator_tags_X(estimator_orig, X)
e = clone(estimator_orig)
msg = "Unknown label type: "
if not _safe_tags(e, key="no_validation"):
with raises(ValueError, match=msg):
e.fit(X, y)
@ignore_warnings(category=FutureWarning)
def check_decision_proba_consistency(name, estimator_orig):
# Check whether an estimator having both decision_function and
# predict_proba methods has outputs with perfect rank correlation.
centers = [(2, 2), (4, 4)]
X, y = make_blobs(
n_samples=100,
random_state=0,
n_features=4,
centers=centers,
cluster_std=1.0,
shuffle=True,
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0
)
estimator = clone(estimator_orig)
if hasattr(estimator, "decision_function") and hasattr(estimator, "predict_proba"):
estimator.fit(X_train, y_train)
# Since the link function from decision_function() to predict_proba()
# is sometimes not precise enough (typically expit), we round to the
# 10th decimal to avoid numerical issues: we compare the rank
# with deterministic ties rather than get platform specific rank
# inversions in case of machine level differences.
a = estimator.predict_proba(X_test)[:, 1].round(decimals=10)
b = estimator.decision_function(X_test).round(decimals=10)
rank_proba, rank_score = rankdata(a), rankdata(b)
try:
assert_array_almost_equal(rank_proba, rank_score)
except AssertionError:
# Sometimes, the rounding applied on the probabilities will have
# ties that are not present in the scores because it is
# numerically more precise. In this case, we relax the test by
# grouping the decision function scores based on the probability
# rank and check that the score is monotonically increasing.
grouped_y_score = np.array(
[b[rank_proba == group].mean() for group in np.unique(rank_proba)]
)
sorted_idx = np.argsort(grouped_y_score)
assert_array_equal(sorted_idx, np.arange(len(sorted_idx)))
def check_outliers_fit_predict(name, estimator_orig):
# Check fit_predict for outlier detectors.
n_samples = 300
X, _ = make_blobs(n_samples=n_samples, random_state=0)
X = shuffle(X, random_state=7)
n_samples, n_features = X.shape
estimator = clone(estimator_orig)
set_random_state(estimator)
y_pred = estimator.fit_predict(X)
assert y_pred.shape == (n_samples,)
assert y_pred.dtype.kind == "i"
assert_array_equal(np.unique(y_pred), np.array([-1, 1]))
# check fit_predict = fit.predict when the estimator has both a predict and
# a fit_predict method. recall that it is already assumed here that the
# estimator has a fit_predict method
if hasattr(estimator, "predict"):
y_pred_2 = estimator.fit(X).predict(X)
assert_array_equal(y_pred, y_pred_2)
if hasattr(estimator, "contamination"):
# proportion of outliers equal to contamination parameter when not
# set to 'auto'
expected_outliers = 30
contamination = float(expected_outliers) / n_samples
estimator.set_params(contamination=contamination)
y_pred = estimator.fit_predict(X)
num_outliers = np.sum(y_pred != 1)
# num_outliers should be equal to expected_outliers unless
# there are ties in the decision_function values. this can
# only be tested for estimators with a decision_function
# method
if num_outliers != expected_outliers and hasattr(
estimator, "decision_function"
):
decision = estimator.decision_function(X)
check_outlier_corruption(num_outliers, expected_outliers, decision)
def check_fit_non_negative(name, estimator_orig):
# Check that proper warning is raised for non-negative X
# when tag requires_positive_X is present
X = np.array([[-1.0, 1], [-1.0, 1]])
y = np.array([1, 2])
estimator = clone(estimator_orig)
with raises(ValueError):
estimator.fit(X, y)
def check_fit_idempotent(name, estimator_orig):
# Check that est.fit(X) is the same as est.fit(X).fit(X). Ideally we would
# check that the estimated parameters during training (e.g. coefs_) are
# the same, but having a universal comparison function for those
# attributes is difficult and full of edge cases. So instead we check that
# predict(), predict_proba(), decision_function() and transform() return
# the same results.
check_methods = ["predict", "transform", "decision_function", "predict_proba"]
rng = np.random.RandomState(0)
estimator = clone(estimator_orig)
set_random_state(estimator)
if "warm_start" in estimator.get_params().keys():
estimator.set_params(warm_start=False)
n_samples = 100
X = rng.normal(loc=100, size=(n_samples, 2))
X = _enforce_estimator_tags_X(estimator, X)
if is_regressor(estimator_orig):
y = rng.normal(size=n_samples)
else:
y = rng.randint(low=0, high=2, size=n_samples)
y = _enforce_estimator_tags_y(estimator, y)
train, test = next(ShuffleSplit(test_size=0.2, random_state=rng).split(X))
X_train, y_train = _safe_split(estimator, X, y, train)
X_test, y_test = _safe_split(estimator, X, y, test, train)
# Fit for the first time
estimator.fit(X_train, y_train)
result = {
method: getattr(estimator, method)(X_test)
for method in check_methods
if hasattr(estimator, method)
}
# Fit again
set_random_state(estimator)
estimator.fit(X_train, y_train)
for method in check_methods:
if hasattr(estimator, method):
new_result = getattr(estimator, method)(X_test)
if np.issubdtype(new_result.dtype, np.floating):
tol = 2 * np.finfo(new_result.dtype).eps
else:
tol = 2 * np.finfo(np.float64).eps
assert_allclose_dense_sparse(
result[method],
new_result,
atol=max(tol, 1e-9),
rtol=max(tol, 1e-7),
err_msg="Idempotency check failed for method {}".format(method),
)
def check_fit_check_is_fitted(name, estimator_orig):
# Make sure that estimator doesn't pass check_is_fitted before calling fit
# and that passes check_is_fitted once it's fit.
rng = np.random.RandomState(42)
estimator = clone(estimator_orig)
set_random_state(estimator)
if "warm_start" in estimator.get_params():
estimator.set_params(warm_start=False)
n_samples = 100
X = rng.normal(loc=100, size=(n_samples, 2))
X = _enforce_estimator_tags_X(estimator, X)
if is_regressor(estimator_orig):
y = rng.normal(size=n_samples)
else:
y = rng.randint(low=0, high=2, size=n_samples)
y = _enforce_estimator_tags_y(estimator, y)
if not _safe_tags(estimator).get("stateless", False):
# stateless estimators (such as FunctionTransformer) are always "fit"!
try:
check_is_fitted(estimator)
raise AssertionError(
f"{estimator.__class__.__name__} passes check_is_fitted before being"
" fit!"
)
except NotFittedError:
pass
estimator.fit(X, y)
try:
check_is_fitted(estimator)
except NotFittedError as e:
raise NotFittedError(
"Estimator fails to pass `check_is_fitted` even though it has been fit."
) from e
def check_n_features_in(name, estimator_orig):
# Make sure that n_features_in_ attribute doesn't exist until fit is
# called, and that its value is correct.
rng = np.random.RandomState(0)
estimator = clone(estimator_orig)
set_random_state(estimator)
if "warm_start" in estimator.get_params():
estimator.set_params(warm_start=False)
n_samples = 100
X = rng.normal(loc=100, size=(n_samples, 2))
X = _enforce_estimator_tags_X(estimator, X)
if is_regressor(estimator_orig):
y = rng.normal(size=n_samples)
else:
y = rng.randint(low=0, high=2, size=n_samples)
y = _enforce_estimator_tags_y(estimator, y)
assert not hasattr(estimator, "n_features_in_")
estimator.fit(X, y)
assert hasattr(estimator, "n_features_in_")
assert estimator.n_features_in_ == X.shape[1]
def check_requires_y_none(name, estimator_orig):
# Make sure that an estimator with requires_y=True fails gracefully when
# given y=None
rng = np.random.RandomState(0)
estimator = clone(estimator_orig)
set_random_state(estimator)
n_samples = 100
X = rng.normal(loc=100, size=(n_samples, 2))
X = _enforce_estimator_tags_X(estimator, X)
expected_err_msgs = (
"requires y to be passed, but the target y is None",
"Expected array-like (array or non-string sequence), got None",
"y should be a 1d array",
)
try:
estimator.fit(X, None)
except ValueError as ve:
if not any(msg in str(ve) for msg in expected_err_msgs):
raise ve
@ignore_warnings(category=FutureWarning)
def check_n_features_in_after_fitting(name, estimator_orig):
# Make sure that n_features_in are checked after fitting
tags = _safe_tags(estimator_orig)
is_supported_X_types = (
"2darray" in tags["X_types"] or "categorical" in tags["X_types"]
)
if not is_supported_X_types or tags["no_validation"]:
return
rng = np.random.RandomState(0)
estimator = clone(estimator_orig)
set_random_state(estimator)
if "warm_start" in estimator.get_params():
estimator.set_params(warm_start=False)
n_samples = 10
X = rng.normal(size=(n_samples, 4))
X = _enforce_estimator_tags_X(estimator, X)
if is_regressor(estimator):
y = rng.normal(size=n_samples)
else:
y = rng.randint(low=0, high=2, size=n_samples)
y = _enforce_estimator_tags_y(estimator, y)
estimator.fit(X, y)
assert estimator.n_features_in_ == X.shape[1]
# check methods will check n_features_in_
check_methods = [
"predict",
"transform",
"decision_function",
"predict_proba",
"score",
]
X_bad = X[:, [1]]
msg = f"X has 1 features, but \\w+ is expecting {X.shape[1]} features as input"
for method in check_methods:
if not hasattr(estimator, method):
continue
callable_method = getattr(estimator, method)
if method == "score":
callable_method = partial(callable_method, y=y)
with raises(ValueError, match=msg):
callable_method(X_bad)
# partial_fit will check in the second call
if not hasattr(estimator, "partial_fit"):
return
estimator = clone(estimator_orig)
if is_classifier(estimator):
estimator.partial_fit(X, y, classes=np.unique(y))
else:
estimator.partial_fit(X, y)
assert estimator.n_features_in_ == X.shape[1]
with raises(ValueError, match=msg):
estimator.partial_fit(X_bad, y)
def check_estimator_get_tags_default_keys(name, estimator_orig):
# check that if _get_tags is implemented, it contains all keys from
# _DEFAULT_KEYS
estimator = clone(estimator_orig)
if not hasattr(estimator, "_get_tags"):
return
tags_keys = set(estimator._get_tags().keys())
default_tags_keys = set(_DEFAULT_TAGS.keys())
assert tags_keys.intersection(default_tags_keys) == default_tags_keys, (
f"{name}._get_tags() is missing entries for the following default tags"
f": {default_tags_keys - tags_keys.intersection(default_tags_keys)}"
)
def check_dataframe_column_names_consistency(name, estimator_orig):
try:
import pandas as pd
except ImportError:
raise SkipTest(
"pandas is not installed: not checking column name consistency for pandas"
)
tags = _safe_tags(estimator_orig)
is_supported_X_types = (
"2darray" in tags["X_types"] or "categorical" in tags["X_types"]
)
if not is_supported_X_types or tags["no_validation"]:
return
rng = np.random.RandomState(0)
estimator = clone(estimator_orig)
set_random_state(estimator)
X_orig = rng.normal(size=(150, 8))
X_orig = _enforce_estimator_tags_X(estimator, X_orig)
n_samples, n_features = X_orig.shape
names = np.array([f"col_{i}" for i in range(n_features)])
X = pd.DataFrame(X_orig, columns=names, copy=False)
if is_regressor(estimator):
y = rng.normal(size=n_samples)
else:
y = rng.randint(low=0, high=2, size=n_samples)
y = _enforce_estimator_tags_y(estimator, y)
# Check that calling `fit` does not raise any warnings about feature names.
with warnings.catch_warnings():
warnings.filterwarnings(
"error",
message="X does not have valid feature names",
category=UserWarning,
module="sklearn",
)
estimator.fit(X, y)
if not hasattr(estimator, "feature_names_in_"):
raise ValueError(
"Estimator does not have a feature_names_in_ "
"attribute after fitting with a dataframe"
)
assert isinstance(estimator.feature_names_in_, np.ndarray)
assert estimator.feature_names_in_.dtype == object
assert_array_equal(estimator.feature_names_in_, names)
# Only check sklearn estimators for feature_names_in_ in docstring
module_name = estimator_orig.__module__
if (
module_name.startswith("sklearn.")
and not ("test_" in module_name or module_name.endswith("_testing"))
and ("feature_names_in_" not in (estimator_orig.__doc__))
):
raise ValueError(
f"Estimator {name} does not document its feature_names_in_ attribute"
)
check_methods = []
for method in (
"predict",
"transform",
"decision_function",
"predict_proba",
"score",
"score_samples",
"predict_log_proba",
):
if not hasattr(estimator, method):
continue
callable_method = getattr(estimator, method)
if method == "score":
callable_method = partial(callable_method, y=y)
check_methods.append((method, callable_method))
for _, method in check_methods:
with warnings.catch_warnings():
warnings.filterwarnings(
"error",
message="X does not have valid feature names",
category=UserWarning,
module="sklearn",
)
method(X) # works without UserWarning for valid features
invalid_names = [
(names[::-1], "Feature names must be in the same order as they were in fit."),
(
[f"another_prefix_{i}" for i in range(n_features)],
(
"Feature names unseen at fit time:\n- another_prefix_0\n-"
" another_prefix_1\n"
),
),
(
names[:3],
f"Feature names seen at fit time, yet now missing:\n- {min(names[3:])}\n",
),
]
params = {
key: value
for key, value in estimator.get_params().items()
if "early_stopping" in key
}
early_stopping_enabled = any(value is True for value in params.values())
for invalid_name, additional_message in invalid_names:
X_bad = pd.DataFrame(X, columns=invalid_name, copy=False)
expected_msg = re.escape(
"The feature names should match those that were passed during fit.\n"
f"{additional_message}"
)
for name, method in check_methods:
with raises(
ValueError, match=expected_msg, err_msg=f"{name} did not raise"
):
method(X_bad)
# partial_fit checks on second call
# Do not call partial fit if early_stopping is on
if not hasattr(estimator, "partial_fit") or early_stopping_enabled:
continue
estimator = clone(estimator_orig)
if is_classifier(estimator):
classes = np.unique(y)
estimator.partial_fit(X, y, classes=classes)
else:
estimator.partial_fit(X, y)
with raises(ValueError, match=expected_msg):
estimator.partial_fit(X_bad, y)
def check_transformer_get_feature_names_out(name, transformer_orig):
tags = transformer_orig._get_tags()
if "2darray" not in tags["X_types"] or tags["no_validation"]:
return
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = StandardScaler().fit_transform(X)
transformer = clone(transformer_orig)
X = _enforce_estimator_tags_X(transformer, X)
n_features = X.shape[1]
set_random_state(transformer)
y_ = y
if name in CROSS_DECOMPOSITION:
y_ = np.c_[np.asarray(y), np.asarray(y)]
y_[::2, 1] *= 2
X_transform = transformer.fit_transform(X, y=y_)
input_features = [f"feature{i}" for i in range(n_features)]
# input_features names is not the same length as n_features_in_
with raises(ValueError, match="input_features should have length equal"):
transformer.get_feature_names_out(input_features[::2])
feature_names_out = transformer.get_feature_names_out(input_features)
assert feature_names_out is not None
assert isinstance(feature_names_out, np.ndarray)
assert feature_names_out.dtype == object
assert all(isinstance(name, str) for name in feature_names_out)
if isinstance(X_transform, tuple):
n_features_out = X_transform[0].shape[1]
else:
n_features_out = X_transform.shape[1]
assert (
len(feature_names_out) == n_features_out
), f"Expected {n_features_out} feature names, got {len(feature_names_out)}"
def check_transformer_get_feature_names_out_pandas(name, transformer_orig):
try:
import pandas as pd
except ImportError:
raise SkipTest(
"pandas is not installed: not checking column name consistency for pandas"
)
tags = transformer_orig._get_tags()
if "2darray" not in tags["X_types"] or tags["no_validation"]:
return
X, y = make_blobs(
n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1,
)
X = StandardScaler().fit_transform(X)
transformer = clone(transformer_orig)
X = _enforce_estimator_tags_X(transformer, X)
n_features = X.shape[1]
set_random_state(transformer)
y_ = y
if name in CROSS_DECOMPOSITION:
y_ = np.c_[np.asarray(y), np.asarray(y)]
y_[::2, 1] *= 2
feature_names_in = [f"col{i}" for i in range(n_features)]
df = pd.DataFrame(X, columns=feature_names_in, copy=False)
X_transform = transformer.fit_transform(df, y=y_)
# error is raised when `input_features` do not match feature_names_in
invalid_feature_names = [f"bad{i}" for i in range(n_features)]
with raises(ValueError, match="input_features is not equal to feature_names_in_"):
transformer.get_feature_names_out(invalid_feature_names)
feature_names_out_default = transformer.get_feature_names_out()
feature_names_in_explicit_names = transformer.get_feature_names_out(
feature_names_in
)
assert_array_equal(feature_names_out_default, feature_names_in_explicit_names)
if isinstance(X_transform, tuple):
n_features_out = X_transform[0].shape[1]
else:
n_features_out = X_transform.shape[1]
assert (
len(feature_names_out_default) == n_features_out
), f"Expected {n_features_out} feature names, got {len(feature_names_out_default)}"
def check_param_validation(name, estimator_orig):
# Check that an informative error is raised when the value of a constructor
# parameter does not have an appropriate type or value.
rng = np.random.RandomState(0)
X = rng.uniform(size=(20, 5))
y = rng.randint(0, 2, size=20)
y = _enforce_estimator_tags_y(estimator_orig, y)
estimator_params = estimator_orig.get_params(deep=False).keys()
# check that there is a constraint for each parameter
if estimator_params:
validation_params = estimator_orig._parameter_constraints.keys()
unexpected_params = set(validation_params) - set(estimator_params)
missing_params = set(estimator_params) - set(validation_params)
err_msg = (
f"Mismatch between _parameter_constraints and the parameters of {name}."
f"\nConsider the unexpected parameters {unexpected_params} and expected but"
f" missing parameters {missing_params}"
)
assert validation_params == estimator_params, err_msg
# this object does not have a valid type for sure for all params
param_with_bad_type = type("BadType", (), {})()
fit_methods = ["fit", "partial_fit", "fit_transform", "fit_predict"]
for param_name in estimator_params:
constraints = estimator_orig._parameter_constraints[param_name]
if constraints == "no_validation":
# This parameter is not validated
continue
# Mixing an interval of reals and an interval of integers must be avoided.
if any(
isinstance(constraint, Interval) and constraint.type == Integral
for constraint in constraints
) and any(
isinstance(constraint, Interval) and constraint.type == Real
for constraint in constraints
):
raise ValueError(
f"The constraint for parameter {param_name} of {name} can't have a mix"
" of intervals of Integral and Real types. Use the type RealNotInt"
" instead of Real."
)
match = rf"The '{param_name}' parameter of {name} must be .* Got .* instead."
err_msg = (
f"{name} does not raise an informative error message when the "
f"parameter {param_name} does not have a valid type or value."
)
estimator = clone(estimator_orig)
# First, check that the error is raised if param doesn't match any valid type.
estimator.set_params(**{param_name: param_with_bad_type})
for method in fit_methods:
if not hasattr(estimator, method):
# the method is not accessible with the current set of parameters
continue
err_msg = (
f"{name} does not raise an informative error message when the parameter"
f" {param_name} does not have a valid type. If any Python type is"
" valid, the constraint should be 'no_validation'."
)
with raises(InvalidParameterError, match=match, err_msg=err_msg):
if any(
isinstance(X_type, str) and X_type.endswith("labels")
for X_type in _safe_tags(estimator, key="X_types")
):
# The estimator is a label transformer and take only `y`
getattr(estimator, method)(y)
else:
getattr(estimator, method)(X, y)
# Then, for constraints that are more than a type constraint, check that the
# error is raised if param does match a valid type but does not match any valid
# value for this type.
constraints = [make_constraint(constraint) for constraint in constraints]
for constraint in constraints:
try:
bad_value = generate_invalid_param_val(constraint)
except NotImplementedError:
continue
estimator.set_params(**{param_name: bad_value})
for method in fit_methods:
if not hasattr(estimator, method):
# the method is not accessible with the current set of parameters
continue
err_msg = (
f"{name} does not raise an informative error message when the "
f"parameter {param_name} does not have a valid value.\n"
"Constraints should be disjoint. For instance "
"[StrOptions({'a_string'}), str] is not a acceptable set of "
"constraint because generating an invalid string for the first "
"constraint will always produce a valid string for the second "
"constraint."
)
with raises(InvalidParameterError, match=match, err_msg=err_msg):
if any(
X_type.endswith("labels")
for X_type in _safe_tags(estimator, key="X_types")
):
# The estimator is a label transformer and take only `y`
getattr(estimator, method)(y)
else:
getattr(estimator, method)(X, y)
def check_set_output_transform(name, transformer_orig):
# Check transformer.set_output with the default configuration does not
# change the transform output.
tags = transformer_orig._get_tags()
if "2darray" not in tags["X_types"] or tags["no_validation"]:
return
rng = np.random.RandomState(0)
transformer = clone(transformer_orig)
X = rng.uniform(size=(20, 5))
X = _enforce_estimator_tags_X(transformer_orig, X)
y = rng.randint(0, 2, size=20)
y = _enforce_estimator_tags_y(transformer_orig, y)
set_random_state(transformer)
def fit_then_transform(est):
if name in CROSS_DECOMPOSITION:
return est.fit(X, y).transform(X, y)
return est.fit(X, y).transform(X)
def fit_transform(est):
return est.fit_transform(X, y)
transform_methods = {
"transform": fit_then_transform,
"fit_transform": fit_transform,
}
for name, transform_method in transform_methods.items():
transformer = clone(transformer)
if not hasattr(transformer, name):
continue
X_trans_no_setting = transform_method(transformer)
# Auto wrapping only wraps the first array
if name in CROSS_DECOMPOSITION:
X_trans_no_setting = X_trans_no_setting[0]
transformer.set_output(transform="default")
X_trans_default = transform_method(transformer)
if name in CROSS_DECOMPOSITION:
X_trans_default = X_trans_default[0]
# Default and no setting -> returns the same transformation
assert_allclose_dense_sparse(X_trans_no_setting, X_trans_default)
def _output_from_fit_transform(transformer, name, X, df, y):
"""Generate output to test `set_output` for different configuration:
- calling either `fit.transform` or `fit_transform`;
- passing either a dataframe or a numpy array to fit;
- passing either a dataframe or a numpy array to transform.
"""
outputs = {}
# fit then transform case:
cases = [
("fit.transform/df/df", df, df),
("fit.transform/df/array", df, X),
("fit.transform/array/df", X, df),
("fit.transform/array/array", X, X),
]
if all(hasattr(transformer, meth) for meth in ["fit", "transform"]):
for (
case,
data_fit,
data_transform,
) in cases:
transformer.fit(data_fit, y)
if name in CROSS_DECOMPOSITION:
X_trans, _ = transformer.transform(data_transform, y)
else:
X_trans = transformer.transform(data_transform)
outputs[case] = (X_trans, transformer.get_feature_names_out())
# fit_transform case:
cases = [
("fit_transform/df", df),
("fit_transform/array", X),
]
if hasattr(transformer, "fit_transform"):
for case, data in cases:
if name in CROSS_DECOMPOSITION:
X_trans, _ = transformer.fit_transform(data, y)
else:
X_trans = transformer.fit_transform(data, y)
outputs[case] = (X_trans, transformer.get_feature_names_out())
return outputs
def _check_generated_dataframe(
name,
case,
index,
outputs_default,
outputs_dataframe_lib,
is_supported_dataframe,
create_dataframe,
assert_frame_equal,
):
"""Check if the generated DataFrame by the transformer is valid.
The DataFrame implementation is specified through the parameters of this function.
Parameters
----------
name : str
The name of the transformer.
case : str
A single case from the cases generated by `_output_from_fit_transform`.
index : index or None
The index of the DataFrame. `None` if the library does not implement a DataFrame
with an index.
outputs_default : tuple
A tuple containing the output data and feature names for the default output.
outputs_dataframe_lib : tuple
A tuple containing the output data and feature names for the pandas case.
is_supported_dataframe : callable
A callable that takes a DataFrame instance as input and return whether or
E.g. `lambda X: isintance(X, pd.DataFrame)`.
create_dataframe : callable
A callable taking as parameters `data`, `columns`, and `index` and returns
a callable. Be aware that `index` can be ignored. For example, polars dataframes
would ignore the idnex.
assert_frame_equal : callable
A callable taking 2 dataframes to compare if they are equal.
"""
X_trans, feature_names_default = outputs_default
df_trans, feature_names_dataframe_lib = outputs_dataframe_lib
assert is_supported_dataframe(df_trans)
# We always rely on the output of `get_feature_names_out` of the
# transformer used to generate the dataframe as a ground-truth of the
# columns.
# If a dataframe is passed into transform, then the output should have the same
# index
expected_index = index if case.endswith("df") else None
expected_dataframe = create_dataframe(
X_trans, columns=feature_names_dataframe_lib, index=expected_index
)
try:
assert_frame_equal(df_trans, expected_dataframe)
except AssertionError as e:
raise AssertionError(
f"{name} does not generate a valid dataframe in the {case} "
"case. The generated dataframe is not equal to the expected "
f"dataframe. The error message is: {e}"
) from e
def _check_set_output_transform_dataframe(
name,
transformer_orig,
*,
dataframe_lib,
is_supported_dataframe,
create_dataframe,
assert_frame_equal,
context,
):
"""Check that a transformer can output a DataFrame when requested.
The DataFrame implementation is specified through the parameters of this function.
Parameters
----------
name : str
The name of the transformer.
transformer_orig : estimator
The original transformer instance.
dataframe_lib : str
The name of the library implementing the DataFrame.
is_supported_dataframe : callable
A callable that takes a DataFrame instance as input and returns whether or
not it is supported by the dataframe library.
E.g. `lambda X: isintance(X, pd.DataFrame)`.
create_dataframe : callable
A callable taking as parameters `data`, `columns`, and `index` and returns
a callable. Be aware that `index` can be ignored. For example, polars dataframes
will ignore the index.
assert_frame_equal : callable
A callable taking 2 dataframes to compare if they are equal.
context : {"local", "global"}
Whether to use a local context by setting `set_output(...)` on the transformer
or a global context by using the `with config_context(...)`
"""
# Check transformer.set_output configures the output of transform="pandas".
tags = transformer_orig._get_tags()
if "2darray" not in tags["X_types"] or tags["no_validation"]:
return
rng = np.random.RandomState(0)
transformer = clone(transformer_orig)
X = rng.uniform(size=(20, 5))
X = _enforce_estimator_tags_X(transformer_orig, X)
y = rng.randint(0, 2, size=20)
y = _enforce_estimator_tags_y(transformer_orig, y)
set_random_state(transformer)
feature_names_in = [f"col{i}" for i in range(X.shape[1])]
index = [f"index{i}" for i in range(X.shape[0])]
df = create_dataframe(X, columns=feature_names_in, index=index)
transformer_default = clone(transformer).set_output(transform="default")
outputs_default = _output_from_fit_transform(transformer_default, name, X, df, y)
if context == "local":
transformer_df = clone(transformer).set_output(transform=dataframe_lib)
context_to_use = nullcontext()
else: # global
transformer_df = clone(transformer)
context_to_use = config_context(transform_output=dataframe_lib)
try:
with context_to_use:
outputs_df = _output_from_fit_transform(transformer_df, name, X, df, y)
except ValueError as e:
# transformer does not support sparse data
capitalized_lib = dataframe_lib.capitalize()
error_message = str(e)
assert (
f"{capitalized_lib} output does not support sparse data." in error_message
or "The transformer outputs a scipy sparse matrix." in error_message
), e
return
for case in outputs_default:
_check_generated_dataframe(
name,
case,
index,
outputs_default[case],
outputs_df[case],
is_supported_dataframe,
create_dataframe,
assert_frame_equal,
)
def _check_set_output_transform_pandas_context(name, transformer_orig, context):
try:
import pandas as pd
except ImportError: # pragma: no cover
raise SkipTest("pandas is not installed: not checking set output")
_check_set_output_transform_dataframe(
name,
transformer_orig,
dataframe_lib="pandas",
is_supported_dataframe=lambda X: isinstance(X, pd.DataFrame),
create_dataframe=lambda X, columns, index: pd.DataFrame(
X, columns=columns, copy=False, index=index
),
assert_frame_equal=pd.testing.assert_frame_equal,
context=context,
)
def check_set_output_transform_pandas(name, transformer_orig):
_check_set_output_transform_pandas_context(name, transformer_orig, "local")
def check_global_output_transform_pandas(name, transformer_orig):
_check_set_output_transform_pandas_context(name, transformer_orig, "global")
def _check_set_output_transform_polars_context(name, transformer_orig, context):
try:
import polars as pl
from polars.testing import assert_frame_equal
except ImportError: # pragma: no cover
raise SkipTest("polars is not installed: not checking set output")
def create_dataframe(X, columns, index):
if isinstance(columns, np.ndarray):
columns = columns.tolist()
return pl.DataFrame(X, schema=columns, orient="row")
_check_set_output_transform_dataframe(
name,
transformer_orig,
dataframe_lib="polars",
is_supported_dataframe=lambda X: isinstance(X, pl.DataFrame),
create_dataframe=create_dataframe,
assert_frame_equal=assert_frame_equal,
context=context,
)
def check_set_output_transform_polars(name, transformer_orig):
_check_set_output_transform_polars_context(name, transformer_orig, "local")
def check_global_set_output_transform_polars(name, transformer_orig):
_check_set_output_transform_polars_context(name, transformer_orig, "global")
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