8000 DOC docstring (shape= -> of shape) (#14640) · scikit-learn/scikit-learn@0eebade · GitHub
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DOC docstring (shape= -> of shape) (#14640)
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‎sklearn/base.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -337,13 +337,13 @@ def score(self, X, y, sample_weight=None):
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Parameters
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----------
340-
X : array-like, shape = (n_samples, n_features)
340+
X : array-like of shape (n_samples, n_features)
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Test samples.
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y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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True labels for X.
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sample_weight : array-like, shape = [n_samples], optional
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
@@ -373,16 +373,16 @@ def score(self, X, y, sample_weight=None):
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Parameters
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----------
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X : array-like, shape = (n_samples, n_features)
376+
X : array-like of shape (n_samples, n_features)
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Test samples. For some estimators this may be a
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precomputed kernel matrix instead, shape = (n_samples,
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n_samples_fitted], where n_samples_fitted is the number of
380380
samples used in the fitting for the estimator.
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382-
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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True values for X.
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sample_weight : array-like, shape = [n_samples], optional
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
@@ -565,7 +565,7 @@ def score(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = (n_samples, n_features)
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X : array-like of shape (n_samples, n_features)
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Returns
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-------

‎sklearn/calibration.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -122,7 +122,7 @@ def fit(self, X, y, sample_weight=None):
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y : array-like, shape (n_samples,)
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Target values.
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sample_weight : array-like, shape = [n_samples] or None
125+
sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Returns
@@ -326,7 +326,7 @@ def fit(self, X, y, sample_weight=None):
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y : array-like, shape (n_samples,)
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Target values.
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sample_weight : array-like, shape = [n_samples] or None
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Returns
@@ -413,7 +413,7 @@ def _sigmoid_calibration(df, y, sample_weight=None):
413413
y : ndarray, shape (n_samples,)
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The targets.
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416-
sample_weight : array-like, shape = [n_samples] or None
416+
sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Returns
@@ -487,7 +487,7 @@ def fit(self, X, y, sample_weight=None):
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y : array-like, shape (n_samples,)
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Training target.
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sample_weight : array-like, shape = [n_samples] or None
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If None, then samples are equally weighted.
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Returns

‎sklearn/cluster/_feature_agglomeration.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -27,7 +27,7 @@ def transform(self, X):
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Parameters
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----------
30-
X : array-like, shape = [n_samples, n_features] or [n_features]
30+
X : array-like of shape (n_samples, n_features) or (n_samples,)
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A M by N array of M observations in N dimensions or a length
3232
M array of M one-dimensional observations.
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@@ -62,7 +62,7 @@ def inverse_transform(self, Xred):
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Parameters
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----------
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Xred : array-like, shape=[n_samples, n_clusters] or [n_clusters,]
65+
Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
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The values to be assigned to each cluster of samples
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Returns

‎sklearn/cluster/hierarchical.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1025,7 +1025,7 @@ def fit(self, X, y=None, **params):
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Parameters
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----------
1028-
X : array-like, shape = [n_samples, n_features]
1028+
X : array-like of shape (n_samples, n_features)
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The data
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y : Ignored

‎sklearn/cluster/k_means_.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -987,7 +987,7 @@ def fit_predict(self, X, y=None, sample_weight=None):
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to transform.
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y : Ignored
@@ -1011,7 +1011,7 @@ def fit_transform(self, X, y=None, sample_weight=None):
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Parameters
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----------
1014-
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
1014+
X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to transform.
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y : Ignored
@@ -1041,7 +1041,7 @@ def transform(self, X):
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to transform.
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Returns
@@ -1067,7 +1067,7 @@ def predict(self, X, sample_weight=None):
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to predict.
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sample_weight : array-like, shape (n_samples,), optional
@@ -1091,7 +1091,7 @@ def score(self, X, y=None, sample_weight=None):
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Parameters
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----------
1094-
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data.
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y : Ignored
@@ -1666,7 +1666,7 @@ def partial_fit(self, X, y=None, sample_weight=None):
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Parameters
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----------
1669-
X : array-like, shape = [n_samples, n_features]
1669+
X : array-like of shape (n_samples, n_features)
16701670
Coordinates of the data points to cluster. It must be noted that
16711671
X will be copied if it is not C-contiguous.
16721672
@@ -1737,7 +1737,7 @@ def predict(self, X, sample_weight=None):
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Parameters
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----------
1740-
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
1740+
X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data to predict.
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sample_weight : array-like, shape (n_samples,), optional

‎sklearn/cluster/mean_shift_.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -35,7 +35,7 @@ def estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0,
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Parameters
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----------
38-
X : array-like, shape=[n_samples, n_features]
38+
X : array-like of shape (n_samples, n_features)
3939
Input points.
4040
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quantile : float, default 0.3
@@ -115,7 +115,7 @@ def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
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Parameters
116116
----------
117117
118-
X : array-like, shape=[n_samples, n_features]
118+
X : array-like of shape (n_samples, n_features)
119119
Input data.
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bandwidth : float, optional
@@ -126,7 +126,7 @@ def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
126126
the number of samples. The sklearn.cluster.estimate_bandwidth function
127127
can be used to do this more efficiently.
128128
129-
seeds : array-like, shape=[n_seeds, n_features] or None
129+
seeds : array-like of shape (n_seeds, n_features) or None
130130
Point used as initial kernel locations. If None and bin_seeding=False,
131131
each data point is used as a seed. If None and bin_seeding=True,
132132
see bin_seeding.
@@ -256,7 +256,7 @@ def get_bin_seeds(X, bin_size, min_bin_freq=1):
256256
Parameters
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----------
258258
259-
X : array-like, shape=[n_samples, n_features]
259+
X : array-like of shape (n_samples, n_features)
260260
Input points, the same points that will be used in mean_shift.
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bin_size : float
@@ -272,7 +272,7 @@ def get_bin_seeds(X, bin_size, min_bin_freq=1):
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Returns
274274
-------
275-
bin_seeds : array-like, shape=[n_samples, n_features]
275+
bin_seeds : array-like of shape (n_samples, n_features)
276276
Points used as initial kernel positions in clustering.mean_shift.
277277
"""
278278

@@ -408,7 +408,7 @@ def fit(self, X, y=None):
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Parameters
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----------
411-
X : array-like, shape=[n_samples, n_features]
411+
X : array-like of shape (n_samples, n_features)
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Samples to cluster.
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y : Ignored

‎sklearn/compose/_target.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -210,7 +210,7 @@ def predict(self, X):
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Parameters
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----------
213-
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
213+
X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Samples.
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Returns

‎sklearn/covariance/empirical_covariance_.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -179,7 +179,7 @@ def fit(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
182+
X : array-like of shape (n_samples, n_features)
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Training data, where n_samples is the number of samples and
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n_features is the number of features.
185185
@@ -208,7 +208,7 @@ def score(self, X_test, y=None):
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Parameters
210210
----------
211-
X_test : array-like, shape = [n_samples, n_features]
211+
X_test : array-like of shape (n_samples, n_features)
212212
Test data of which we compute the likelihood, where n_samples is
213213
the number of samples and n_features is the number of features.
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X_test is assumed to be drawn from the same distribution than
@@ -239,7 +239,7 @@ def error_norm(self, comp_cov, norm='frobenius', scaling=True,
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Parameters
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----------
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comp_cov : array-like, shape = [n_features, n_features]
242+
comp_cov : array-like of shape (n_features, n_features)
243243
The covariance to compare with.
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norm : str
@@ -289,7 +289,7 @@ def mahalanobis(self, X):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
293293
The observations, the Mahalanobis distances of the which we
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compute. Observations are assumed to be drawn from the same
295295
distribution than the data used in fit.

‎sklearn/covariance/robust_covariance.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -624,7 +624,7 @@ def fit(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
627+
X : array-like of shape (n_samples, n_features)
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Training data, where n_samples is the number of samples
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and n_features is the number of features.
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‎sklearn/covariance/shrunk_covariance_.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -131,7 +131,7 @@ def fit(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
134+
X : array-like of shape (n_samples, n_features)
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Training data, where n_samples is the number of samples
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and n_features is the number of features.
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@@ -406,7 +406,7 @@ def fit(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training data, where n_samples is the number of samples
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and n_features is the number of features.
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y
@@ -561,7 +561,7 @@ def fit(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training data, where n_samples is the number of samples
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and n_features is the number of features.
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y

‎sklearn/cross_decomposition/pls_.py

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -241,11 +241,11 @@ def fit(self, X, Y):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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Y : array-like, shape = [n_samples, n_targets]
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Y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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"""
@@ -387,11 +387,11 @@ def transform(self, X, Y=None, copy=True):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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Y : array-like, shape = [n_samples, n_targets]
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Y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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@@ -450,11 +450,11 @@ def fit_transform(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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y : array-like, shape = [n_samples, n_targets]
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y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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@@ -818,11 +818,11 @@ def fit(self, X, Y):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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Y : array-like, shape = [n_samples, n_targets]
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Y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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"""
@@ -868,11 +868,11 @@ def transform(self, X, Y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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Y : array-like, shape = [n_samples, n_targets]
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Y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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"""
@@ -893,11 +893,11 @@ def fit_transform(self, X, y=None):
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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X : array-like of shape (n_samples, n_features)
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Training vectors, where n_samples is the number of samples and
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n_features is the number of predictors.
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y : array-like, shape = [n_samples, n_targets]
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y : array-like of shape (n_samples, n_targets)
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Target vectors, where n_samples is the number of samples and
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n_targets is the number of response variables.
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