10000 API Rename force_all_finite into ensure_all_finite (#29404) · scikit-learn/scikit-learn@1c47967 · GitHub
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API Rename force_all_finite into ensure_all_finite (#29404)
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
1 parent 0a2ca15 commit 1c47967

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+354
-164
lines changed

‎doc/whats_new/v1.6.rst

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@@ -231,6 +231,11 @@ Changelog
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scoring="max_error" which is now deprecated.
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:pr:`29462` by :user:`Farid "Freddie" Taba <artificialfintelligence>`.
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- |API| the `assert_all_finite` parameter of functions
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:func:`metrics.pairwise.check_pairwise_arrays` and :func:`metrics.pairwise_distances`
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is renamed into `ensure_all_finite`. `force_all_finite` will be removed in 1.8.
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:pr:`29404` by :user:`Jérémie du Boisberranger <jeremiedb>`.
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:mod:`sklearn.model_selection`
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..............................
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@@ -272,6 +277,14 @@ Changelog
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traversed.
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:pr:`27966` by :user:`Adam Li <adam2392>`.
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:mod:`sklearn.utils`
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....................
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- |API| the `assert_all_finite` parameter of functions :func:`utils.check_array`,
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:func:`utils.check_X_y`, :func:`utils.as_float_array` is renamed into
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`ensure_all_finite`. `force_all_finite` will be removed in 1.8.
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:pr:`29404` by :user:`Jérémie du Boisberranger <jeremiedb>`.
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.. rubric:: Code and documentation contributors
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Thanks to everyone who has contributed to the maintenance and improvement of

‎sklearn/cluster/_hdbscan/hdbscan.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -737,7 +737,7 @@ def fit(self, X, y=None):
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X = self._validate_data(
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X,
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accept_sparse=["csr", "lil"],
740-
force_all_finite=False,
740+
ensure_all_finite=False,
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dtype=np.float64,
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)
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self._raw_data = X
@@ -782,7 +782,7 @@ def fit(self, X, y=None):
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# Perform data validation after removing infinite values (numpy.inf)
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# from the given distance matrix.
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X = self._validate_data(
785-
X, force_all_finite=False, dtype=np.float64, force_writeable=True
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X, ensure_all_finite=False, dtype=np.float64, force_writeable=True
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)
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if np.isnan(X).any():
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# TODO: Support np.nan in Cython implementation for precomputed

‎sklearn/compose/_column_transformer.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1137,7 +1137,7 @@ def _hstack(self, Xs, *, n_samples):
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# in a sparse matrix, `check_array` is used for the
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# dtype conversion if necessary.
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converted_Xs = [
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check_array(X, accept_sparse=True, force_all_finite=False)
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check_array(X, accept_sparse=True, ensure_all_finite=False)
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for X in Xs
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]
11431143
except ValueError as e:
@@ -1325,7 +1325,7 @@ def _check_X(X):
13251325
"""Use check_array only when necessary, e.g. on lists and other non-array-likes."""
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if hasattr(X, "__array__") or hasattr(X, "__dataframe__") or sparse.issparse(X):
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return X
1328-
return check_array(X, force_all_finite="allow-nan", dtype=object)
1328+
return check_array(X, ensure_all_finite="allow-nan", dtype=object)
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def _is_empty_column_selection(column):

‎sklearn/compose/_target.py

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@@ -262,7 +262,7 @@ def fit(self, X, y, **fit_params):
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y,
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input_name="y",
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accept_sparse=False,
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force_all_finite=True,
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ensure_all_finite=True,
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ensure_2d=False,
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dtype="numeric",
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allow_nd=True,

‎sklearn/covariance/_empirical_covariance.py

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Original file line numberDiff line numberDiff line change
@@ -90,7 +90,7 @@ def empirical_covariance(X, *, assume_centered=False):
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[0.25, 0.25, 0.25],
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[0.25, 0.25, 0.25]])
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"""
93-
X = check_array(X, ensure_2d=False, force_all_finite=False)
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X = check_array(X, ensure_2d=False, ensure_all_finite=False)
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if X.ndim == 1:
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X = np.reshape(X, (1, -1))

‎sklearn/ensemble/_bagging.py

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -391,7 +391,7 @@ def fit(self, X, y, *, sample_weight=None, **fit_params):
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y,
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accept_sparse=["csr", "csc"],
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dtype=None,
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force_all_finite=False,
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ensure_all_finite=False,
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multi_output=True,
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)
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@@ -941,7 +941,7 @@ def predict_proba(self, X):
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X,
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accept_sparse=["csr", "csc"],
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dtype=None,
944-
force_all_finite=False,
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ensure_all_finite=False,
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reset=False,
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)
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@@ -991,7 +991,7 @@ def predict_log_proba(self, X):
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X,
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accept_sparse=["csr", "csc"],
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dtype=None,
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force_all_finite=False,
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ensure_all_finite=False,
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reset=False,
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)
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@@ -1046,7 +1046,7 @@ def decision_function(self, X):
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X,
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accept_sparse=["csr", "csc"],
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dtype=None,
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force_all_finite=False,
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ensure_all_finite=False,
10501050
reset=False,
10511051
)
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@@ -1279,7 +1279,7 @@ def predict(self, X):
12791279
X,
12801280
accept_sparse=["csr", "csc"],
12811281
dtype=None,
1282-
force_all_finite=False,
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ensure_all_finite=False,
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reset=False,
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)
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‎sklearn/ensemble/_forest.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -362,7 +362,7 @@ def fit(self, X, y, sample_weight=None):
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multi_output=True,
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accept_sparse="csc",
364364
dtype=DTYPE,
365-
force_all_finite=False,
365+
ensure_all_finite=False,
366366
)
367367
# _compute_missing_values_in_feature_mask checks if X has missing values and
368368
# will raise an error if the underlying tree base estimator can't handle missing
@@ -630,16 +630,16 @@ def _validate_X_predict(self, X):
630630
Validate X whenever one tries to predict, apply, predict_proba."""
631631
check_is_fitted(self)
632632
if self.estimators_[0]._support_missing_values(X):
633-
force_all_finite = "allow-nan"
633+
ensure_all_finite = "allow-nan"
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else:
635-
force_all_finite = True
635+
ensure_all_finite = True
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637637
X = self._validate_data(
638638
X,
639639
dtype=DTYPE,
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accept_sparse="csr",
641641
reset=False,
642-
force_all_finite=force_all_finite,
642+
ensure_all_finite=ensure_all_finite,
643643
)
644644
if issparse(X) and (X.indices.dtype != np.intc or X.indptr.dtype != np.intc):
645645
raise ValueError("No support for np.int64 index based sparse matrices")

‎sklearn/ensemble/_gb.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -773,7 +773,7 @@ def fit(self, X, y, sample_weight=None, monitor=None):
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dtype=DTYPE,
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order="C",
775775
accept_sparse="csr",
776-
force_all_finite=False,
776+
ensure_all_finite=False,
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)
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raw_predictions = self._raw_predict(X_train)
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self._resize_state()

‎sklearn/ensemble/_hist_gradient_boosting/binning.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -194,7 +194,7 @@ def fit(self, X, y=None):
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)
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)
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197-
X = check_array(X, dtype=[X_DTYPE], force_all_finite=False)
197+
X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)
198198
max_bins = self.n_bins - 1
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200200
rng = check_random_state(self.random_state)
@@ -275,7 +275,7 @@ def transform(self, X):
275275
X_binned : array-like of shape (n_samples, n_features)
276276
The binned data (fortran-aligned).
277277
"""
278-
X = check_array(X, dtype=[X_DTYPE], force_all_finite=False)
278+
X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)
279279
check_is_fitted(self)
280280
if X.shape[1] != self.n_bins_non_missing_.shape[0]:
281281
raise ValueError(

‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

Lines changed: 1 addition & 1 deletion
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@@ -263,7 +263,7 @@ def _preprocess_X(self, X, *, reset):
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"""
264264
# If there is a preprocessor, we let the preprocessor handle the validation.
265265
# Otherwise, we validate the data ourselves.
266-
check_X_kwargs = dict(dtype=[X_DTYPE], force_all_finite=False)
266+
check_X_kwargs = dict(dtype=[X_DTYPE], ensure_all_finite=False)
267267
if not reset:
268268
if self._preprocessor is None:
269269
return self._validate_data(X, reset=False, **check_X_kwargs)

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