8000 FIX Fix handling of binary_only tag in check_estimator by brcharron · Pull Request #17812 · scikit-learn/scikit-learn · GitHub
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FIX Fix handling of binary_only tag in check_estimator #17812

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merged 2 commits into from
Jul 6, 2020
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FIX Fix handling of binary_only tag in check_estimator #17812

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636eab8
Fix handling of binary_only tag in check_estimator
brcharron Jul 1, 2020
524717d
Add entry to What's New for #17812
brcharron Jul 6, 2020
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7 changes: 7 additions & 0 deletions doc/whats_new/v0.24.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -286,6 +286,13 @@ Changelog
:meth:`tree.DecisionTreeRegressor.fit`, and has not effect.
:pr:`17614` by :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.

:mod:`sklearn.utils`
.........................

- |Fix| Fix :func:`utils.estimator_checks.check_estimator` so that all test
cases support the `binary_only` estimator tag.
:pr:`17812` by :user:`Bruno Charron <brcharron>`.

Code and Documentation Contributors
-----------------------------------

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56 changes: 15 additions & 41 deletions sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -701,15 +701,12 @@ def check_estimator_sparse_data(name, estimator_orig):
X[X < .8] = 0
X = _pairwise_estimator_convert_X(X, estimator_orig)
X_csr = sparse.csr_matrix(X)
tags = estimator_orig._get_tags()
if tags['binary_only']:
y = (2 * rng.rand(40)).astype(int)
else:
y = (4 * rng.rand(40)).astype(int)
y = (4 * rng.rand(40)).astype(int)
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
tags = estimator_orig._get_tags()
for matrix_format, X in _generate_sparse_matrix(X_csr):
# catch deprecation warnings
with ignore_warnings(category=FutureWarning):
Expand Down Expand Up @@ -807,10 +804,7 @@ def check_sample_weights_list(name, estimator_orig):
n_samples = 30
X = _pairwise_estimator_convert_X(rnd.uniform(size=(n_samples, 3)),
estimator_orig)
if estimator._get_tags()['binary_only']:
y = np.arange(n_samples) % 2
else:
y = np.arange(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
Expand Down Expand Up @@ -901,10 +895,7 @@ def check_dtype_object(name, estimator_orig):
X = _pairwise_estimator_convert_X(rng.rand(40, 10), estimator_orig)
X = X.astype(object)
tags = estimator_orig._get_tags()
if tags['binary_only']:
y = (X[:, 0] * 2).astype(int)
else:
y = (X[:, 0] * 4).astype(int)
y = (X[:, 0] * 4).astype(int)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)

Expand Down Expand Up @@ -998,8 +989,6 @@ def check_dont_overwrite_parameters(name, estimator_orig):
X = 3 * rnd.uniform(size=(20, 3))
X = _pairwise_estimator_convert_X(X, estimator_orig)
y = X[:, 0].astype(int)
if estimator._get_tags()['binary_only']:
y[y == 2] = 1
y = _enforce_estimator_tags_y(estimator, y)

if hasattr(estimator, "n_components"):
Expand Down Expand Up @@ -1050,8 +1039,6 @@ def check_fit2d_predict1d(name, estimator_orig):
X = _pairwise_estimator_convert_X(X, estimator_orig)
y = X[:, 0].astype(int)
tags = estimator_orig._get_tags()
if tags['binary_only']:
y[y == 2] = 1
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)

Expand Down Expand Up @@ -1100,8 +1087,6 @@ def check_methods_subset_invariance(name, estimator_orig):
X = 3 * rnd.uniform(size=(20, 3))
X = _pairwise_estimator_convert_X(X, estimator_orig)
y = X[:, 0].astype(int)
if estimator_orig._get_tags()['binary_only']:
y[y == 2] = 1
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)

Expand Down Expand Up @@ -1373,10 +1358,7 @@ def check_fit_score_takes_y(name, estimator_orig):
n_samples = 30
X = rnd.uniform(size=(n_samples, 3))
X = _pairwise_estimator_convert_X(X, estimator_orig)
if estimator_orig._get_tags()['binary_only']:
y = np.arange(n_samples) % 2
else:
y = np.arange(n_samples) % 3
y = np.arange(n_samples) % 3
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_random_state(estimator)
Expand Down Expand Up @@ -1406,8 +1388,6 @@ def check_estimators_dtypes(name, estimator_orig):
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]
if estimator_orig._get_tags()['binary_only']:
y[y == 2] = 1
y = _enforce_estimator_tags_y(estimator_orig, y)

methods = ["predict", "transform", "decision_function", "predict_proba"]
Expand Down Expand Up @@ -1581,6 +1561,7 @@ def check_estimators_partial_fit_n_features(name, estimator_orig):
estimator = clone(estimator_orig)
X, y = make_blobs(n_samples=50, random_state=1)
X -= X.min()
y = _enforce_estimator_tags_y(estimator_orig, y)

try:
if is_classifier(estimator):
Expand Down Expand Up @@ -2047,11 +2028,7 @@ def check_classifiers_multilabel_representation_invariance(name,
def check_estimators_fit_returns_self(name, estimator_orig,
readonly_memmap=False):
"""Check if self is returned when calling fit"""
if estimator_orig._get_tags()['binary_only']:
n_centers = 2
else:
n_centers = 3
X, y = make_blobs(random_state=0, n_samples=21, centers=n_centers)
X, y = make_blobs(random_state=0, n_samples=21)
# some want non-negative input
X -= X.min()
X = _pairwise_estimator_convert_X(X, estimator_orig)
Expand Down Expand Up @@ -2093,10 +2070,7 @@ def check_supervised_y_2d(name, estimator_orig):
X = _pairwise_estimator_convert_X(
rnd.uniform(size=(n_samples, 3)), estimator_orig
)
if tags['binary_only']:
y = np.arange(n_samples) % 2
else:
y = np.arange(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)
Expand Down Expand Up @@ -2414,11 +2388,7 @@ def check_class_weight_balanced_linear_classifier(name, Classifier):

@ignore_warnings(category=FutureWarning)
def check_estimators_overwrite_params(name, estimator_orig):
if estimator_orig._get_tags()['binary_only']:
n_centers = 2
else:
n_centers = 3
X, y = make_blobs(random_state=0, n_samples=21, centers=n_centers)
X, y = make_blobs(random_state=0, n_samples=21)
# some want non-negative input
X -= X.min()
X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
Expand Down Expand Up @@ -2489,7 +2459,8 @@ def check_no_attributes_set_in_init(name, estimator_orig):
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 = [1, 1, 1, 2, 2, 2, 3, 3, 3]
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)
Expand All @@ -2513,7 +2484,7 @@ 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 = _pairwise_estimator_convert_X(X, estimator_orig)
y = [1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2]
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,
Expand Down Expand Up @@ -2649,6 +2620,9 @@ def _enforce_estimator_tags_y(estimator, y):
# Create strictly positive y. The minimal increment above 0 is 1, as
# y could be of integer dtype.
y += 1 + abs(y.min())
# Estimators with a `binary_only` tag only accept up to two unique y values
if estimator._get_tags()["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 estimator._get_tags()["multioutput_only"]:
Expand Down
17 changes: 12 additions & 5 deletions sklearn/utils/tests/test_estimator_checks.py
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< 75D1 td class="blob-code blob-code-context js-file-line"> return np.array([self.value_] * X.shape[0])
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,6 @@
from sklearn.linear_model import MultiTaskElasticNet, LogisticRegression
from sklearn.svm import SVC, NuSVC
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils.validation import check_array
from sklearn.utils import all_estimators
from sklearn.exceptions import SkipTestWarning
Expand Down Expand Up @@ -307,11 +306,19 @@ def predict(self, X):


class UntaggedBinaryClassifier(DecisionTreeClassifier):
class UntaggedBinaryClassifier(SGDClassifier):
# Toy classifier that only supports binary classification, will fail tests.
def fit(self, X, y, sample_weight=None):
super().fit(X, y, sample_weight)
if np.all(self.n_classes_ > 2):
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
super().fit(X, y, coef_init, intercept_init, sample_weight)
if len(self.classes_) > 2:
raise ValueError('Only 2 classes are supported')
return self

def partial_fit(self, X, y, classes=None, sample_weight=None):
super().partial_fit(X=X, y=y, classes=classes,
sample_weight=sample_weight)
if len(self.classes_) > 2:
raise ValueError('Only 2 classes are supported')
return self

Expand Down
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