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DOC Replace the phrase "where n_samples in the number" with "where n_samples is the number" #20914

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Sep 2, 2021
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DOC Replace the phrase "where n_samples in the number" with "where n_samples is the number" #20914

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f6d9f0d
Add backticks to mentions of "n_sample" (and other nearby parameters)…
genvalen Aug 27, 2021
039f909
Edit instances captured by `git grep` with the typo: 'where `n_sample…
genvalen Sep 1, 2021
f095554
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
genvalen Sep 1, 2021
bc78677
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
genvalen Sep 1, 2021
de0417d
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
genvalen Sep 2, 2021
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4 changes: 2 additions & 2 deletions sklearn/compose/_target.py
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Expand Up @@ -187,8 +187,8 @@ def fit(self, X, y, **fit_params):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : array-like of shape (n_samples,)
Target values.
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10 changes: 5 additions & 5 deletions sklearn/covariance/_empirical_covariance.py
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Expand Up @@ -213,8 +213,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
Expand All @@ -240,9 +240,9 @@ def score(self, X_test, y=None):
Parameters
----------
X_test : array-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is
the number of samples and n_features is the number of features.
X_test is assumed to be drawn from the same distribution than
Test data of which we compute the likelihood, where `n_samples` is
the number of samples and `n_features` is the number of features.
`X_test` is assumed to be drawn from the same distribution than
the data used in fit (including centering).

y : Ignored
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4 changes: 2 additions & 2 deletions sklearn/covariance/_shrunk_covariance.py
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Expand Up @@ -157,8 +157,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
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8 changes: 4 additions & 4 deletions sklearn/cross_decomposition/_pls.py
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Expand Up @@ -462,12 +462,12 @@ def fit_transform(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of predictors.

y : array-like of shape (n_samples, n_targets), default=None
Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.
Target vectors, where `n_samples` is the number of samples and
`n_targets` is the number of response variables.

Returns
-------
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4 changes: 2 additions & 2 deletions sklearn/datasets/_svmlight_format_io.py
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Expand Up @@ -438,8 +438,8 @@ def dump_svmlight_file(
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : {array-like, sparse matrix}, shape = [n_samples (, n_labels)]
Target values. Class labels must be an
Expand Down
20 changes: 10 additions & 10 deletions sklearn/decomposition/_base.py
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Expand Up @@ -85,8 +85,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.

Returns
-------
Expand All @@ -103,14 +103,14 @@ def transform(self, X):
Parameters
----------
X : array-like of shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_features is the number of features.
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.

Returns
-------
X_new : array-like of shape (n_samples, n_components)
Projection of X in the first principal components, where n_samples
is the number of samples and n_components is the number of the components.
Projection of X in the first principal components, where `n_samples`
is the number of samples and `n_components` is the number of the components.
"""
check_is_fitted(self)

Expand All @@ -130,14 +130,14 @@ def inverse_transform(self, X):
Parameters
----------
X : array-like of shape (n_samples, n_components)
New data, where n_samples is the number of samples
and n_components is the number of components.
New data, where `n_samples` is the number of samples
and `n_components` is the number of components.

Returns
-------
X_original array-like of shape (n_samples, n_features)
Original data, where n_samples is the number of samples
and n_features is the number of features.
Original data, where `n_samples` is the number of samples
and `n_features` is the number of features.

Notes
-----
Expand Down
2 changes: 1 addition & 1 deletion sklearn/decomposition/_dict_learning.py
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Expand Up @@ -1546,7 +1546,7 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` in the number of samples
Training vector, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Expand Down
24 changes: 12 additions & 12 deletions sklearn/decomposition/_fastica.py
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Expand Up @@ -172,8 +172,8 @@ def fastica(
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.

n_components : int, default=None
Number of components to extract. If None no dimension reduction
Expand Down Expand Up @@ -464,8 +464,8 @@ def _fit(self, X, compute_sources=False):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

compute_sources : bool, default=False
If False, sources are not computes but only the rotation matrix.
Expand Down Expand Up @@ -600,8 +600,8 @@ def fit_transform(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
Expand All @@ -620,8 +620,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
Expand All @@ -640,8 +640,8 @@ def transform(self, X, copy=True):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data to transform, where n_samples is the number of samples
and n_features is the number of features.
Data to transform, where `n_samples` is the number of samples
and `n_features` is the number of features.

copy : bool, default=True
If False, data passed to fit can be overwritten. Defaults to True.
Expand All @@ -668,8 +668,8 @@ def inverse_transform(self, X, copy=True):
Parameters
----------
X : array-like of shape (n_samples, n_components)
Sources, where n_samples is the number of samples
and n_components is the number of components.
Sources, where `n_samples` is the number of samples
and `n_components` is the number of components.
copy : bool, default=True
If False, data passed to fit are overwritten. Defaults to True.

Expand Down
12 changes: 6 additions & 6 deletions sklearn/decomposition/_incremental_pca.py
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Expand Up @@ -190,8 +190,8 @@ def fit(self, X, y=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
Expand Down Expand Up @@ -239,8 +239,8 @@ def partial_fit(self, X, y=None, check_input=True):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : Ignored
Not used, present for API consistency by convention.
Expand Down Expand Up @@ -360,8 +360,8 @@ def transform(self, X):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_features is the number of features.
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.

Returns
-------
Expand Down
8 changes: 4 additions & 4 deletions sklearn/decomposition/_pca.py
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Expand Up @@ -368,8 +368,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Ignored.
Expand All @@ -388,8 +388,8 @@ def fit_transform(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Ignored.
Expand Down
4 changes: 2 additions & 2 deletions sklearn/decomposition/_sparse_pca.py
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Expand Up @@ -157,8 +157,8 @@ def fit(self, X, y=None):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples
and n_features is the number of features.
Training vector, where `n_samples` is the number of samples
and `n_features` is the number of features.

y : Ignored
Not used, present here for API consistency by convention.
Expand Down
4 changes: 2 additions & 2 deletions sklearn/discriminant_analysis.py
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Expand Up @@ -828,8 +828,8 @@ def fit(self, X, y):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : array-like of shape (n_samples,)
Target values (integers)
Expand Down
20 changes: 10 additions & 10 deletions sklearn/ensemble/_stacking.py
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Expand Up @@ -248,8 +248,8 @@ def predict(self, X, **predict_params):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

**predict_params : dict of str -> obj
Parameters to the `predict` called by the `final_estimator`. Note
Expand Down Expand Up @@ -490,8 +490,8 @@ def predict(self, X, **predict_params):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

**predict_params : dict of str -> obj
Parameters to the `predict` called by the `final_estimator`. Note
Expand All @@ -515,8 +515,8 @@ def predict_proba(self, X):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

Returns
-------
Expand All @@ -535,8 +535,8 @@ def decision_function(self, X):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

Returns
-------
Expand Down Expand Up @@ -734,8 +734,8 @@ def fit(self, X, y, sample_weight=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : array-like of shape (n_samples,)
Target values.
Expand Down
12 changes: 6 additions & 6 deletions sklearn/ensemble/_voting.py
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Expand Up @@ -288,8 +288,8 @@ def fit(self, X, y, sample_weight=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : array-like of shape (n_samples,)
Target values.
Expand Down Expand Up @@ -390,8 +390,8 @@ def transform(self, X):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

Returns
-------
Expand Down Expand Up @@ -510,8 +510,8 @@ def fit(self, X, y, sample_weight=None):
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of features.

y : array-like of shape (n_samples,)
Target values.
Expand Down
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