8000 WIP Replace boston in estimator_checks.py by lucyleeow · Pull Request #16897 · scikit-learn/scikit-learn · GitHub
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WIP Replace boston in estimator_checks.py #16897

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WIP Replace boston in estimator_checks.py #16897

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28 changes: 14 additions & 14 deletions sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,11 +51,11 @@
from .import deprecated
from .validation import has_fit_parameter, _num_samples
from ..preprocessing import StandardScaler
from ..datasets import (load_iris, load_boston, make_blobs,
from ..datasets import (load_iris, load_diabetes, make_blobs,
make_multilabel_classification, make_regression)


BOSTON = None
DIABETES = None
CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD']

def _safe_tags(estimator, key=None):
Expand Down Expand Up @@ -470,15 +470,15 @@ def check_estimator(Estimator, generate_only=False):
warnings.warn(str(exception), SkipTestWarning)


def _boston_subset(n_samples=200):
global BOSTON
if BOSTON is None:
X, y = load_boston(return_X_y=True)
def _diabetes_subset(n_samples=200):
global DIABETES
if DIABETES is None:
X, y = load_diabetes(return_X_y=True)
X, y = shuffle(X, y, random_state=0)
X, y = X[:n_samples], y[:n_samples]
X = StandardScaler().fit_transform(X)
BOSTON = X, y
return BOSTON
DIABETES = X, y
return DIABETES


@deprecated("set_checking_parameters is deprecated in version "
Expand Down Expand Up @@ -1222,7 +1222,7 @@ def check_transformer_data_not_an_array(name, transformer):

@ignore_warnings(category=FutureWarning)
def check_transformers_unfitted(name, transformer):
X, y = _boston_subset()
X, y = _diabetes_subset()

transformer = clone(transformer)
with assert_raises((AttributeError, ValueError), msg="The unfitted "
Expand Down Expand Up @@ -2052,7 +2052,7 @@ def check_estimators_unfitted(name, estimator_orig):
Unfitted estimators should raise a NotFittedError.
"""
# Common test for Regressors, Classifiers and Outlier detection estimators
X, y = _boston_subset()
X, y = _diabetes_subset()

estimator = clone(estimator_orig)
for method in ('decision_function', 'predict', 'predict_proba',
Expand Down Expand Up @@ -2195,7 +2195,7 @@ def check_classifiers_classes(name, classifier_orig):

@ignore_warnings(category=FutureWarning)
def check_regressors_int(name, regressor_orig):
X, _ = _boston_subset()
X, _ = _diabetes_subset()
X = _pairwise_estimator_convert_X(X[:50], regressor_orig)
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
Expand Down Expand Up @@ -2224,7 +2224,7 @@ def check_regressors_int(name, regressor_orig):
@ignore_warnings(category=FutureWarning)
def check_regressors_train(name, regressor_orig, readonly_memmap=False,
X_dtype=np.float64):
X, y = _boston_subset()
X, y = _diabetes_subset()
X = X.astype(X_dtype)
X = _pairwise_estimator_convert_X(X, regressor_orig)
y = StandardScaler().fit_transform(y.reshape(-1, 1)) # X is already scaled
Expand Down Expand Up @@ -2508,7 +2508,7 @@ def check_classifier_data_not_an_array(name, estimator_orig):

@ignore_warnings(category=FutureWarning)
def check_regressor_data_not_an_array(name, estimator_orig):
X, y = _boston_subset(n_samples=50)
X, y = _diabetes_subset(n_samples=50)
X = _pairwise_estimator_convert_X(X, estimator_orig)
y = _enforce_estimator_tags_y(estimator_orig, y)
for obj_type in ["NotAnArray", "PandasDataframe"]:
Expand Down Expand Up @@ -2793,7 +2793,7 @@ def check_set_params(name, estimator_orig):
def check_classifiers_regression_target(name, estimator_orig):
# Check if classifier throws an exception when fed regression targets

X, y = load_boston(return_X_y=True)
X, y = load_diabetes(return_X_y=True)
e = clone(estimator_orig)
msg = 'Unknown label type: '
if not _safe_tags(e, "no_validation"):
Expand Down
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