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DOC Ensures that sklearn.cluster._kmeans.k_means passes numpydoc validation #21423

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Oct 23, 2021
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DOC Ensures that sklearn.cluster._kmeans.k_means passes numpydoc validation #21423

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09b53d2
Fix_numpydoc_kmeans
gpablo6 Oct 23, 2021
d7fb117
Fix rendering issue
gpablo6 Oct 23, 2021
2ae9b52
Merge branch 'main' into fix_numpydoc_kmeans
glemaitre Oct 23, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,6 @@
"sklearn._config.get_config",
"sklearn.base.clone",
"sklearn.cluster._affinity_propagation.affinity_propagation",
"sklearn.cluster._kmeans.k_means",
"sklearn.cluster._kmeans.kmeans_plusplus",
"sklearn.cluster._mean_shift.estimate_bandwidth",
"sklearn.cluster._mean_shift.get_bin_seeds",
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43 changes: 20 additions & 23 deletions sklearn/cluster/_kmeans.py
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Expand Up @@ -269,7 +269,7 @@ def k_means(
algorithm="auto",
return_n_iter=False,
):
"""K-means clustering algorithm.
"""Perform K-means clustering algorithm.

Read more in the :ref:`User Guide <k_means>`.

Expand All @@ -285,30 +285,27 @@ def k_means(
centroids to generate.

sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
The weights for each observation in `X`. If `None`, all observations
are assigned equal weight.

init : {'k-means++', 'random'}, callable or array-like of shape \
(n_clusters, n_features), default='k-means++'
Method for initialization:

'k-means++' : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.

'random': choose `n_clusters` observations (rows) at random from data
for the initial centroids.

If an array is passed, it should be of shape (n_clusters, n_features)
and gives the initial centers.

If a callable is passed, it should take arguments X, n_clusters and a
random state and return an initialization.
- `'k-means++'` : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.
- `'random'`: choose `n_clusters` observations (rows) at random from data
for the initial centroids.
- If an array is passed, it should be of shape `(n_clusters, n_features)`
and gives the initial centers.
- If a callable is passed, it should take arguments `X`, `n_clusters` and a
random state and return an initialization.

n_init : int, default=10
Number of time the k-means algorithm will be run with different
centroid seeds. The final results will be the best output of
n_init consecutive runs in terms of inertia.
`n_init` consecutive runs in terms of inertia.

max_iter : int, default=300
Maximum number of iterations of the k-means algorithm to run.
Expand All @@ -328,22 +325,22 @@ def k_means(

copy_x : bool, default=True
When pre-computing distances it is more numerically accurate to center
the data first. If copy_x is True (default), then the original data is
the data first. If `copy_x` is True (default), then the original data is
not modified. If False, the original data is modified, and put back
before the function returns, but small numerical differences may be
introduced by subtracting and then adding the data mean. Note that if
the original data is not C-contiguous, a copy will be made even if
copy_x is False. If the original data is sparse, but not in CSR format,
a copy will be made even if copy_x is False.
`copy_x` is False. If the original data is sparse, but not in CSR format,
a copy will be made even if `copy_x` is False.

algorithm : {"auto", "full", "elkan"}, default="auto"
K-means algorithm to use. The classical EM-style algorithm is "full".
The "elkan" variation is more efficient on data with well-defined
K-means algorithm to use. The classical EM-style algorithm is `"full"`.
The `"elkan"` variation is more efficient on data with well-defined
clusters, by using the triangle inequality. However it's more memory
intensive due to the allocation of an extra array of shape
(n_samples, n_clusters).
`(n_samples, n_clusters)`.

For now "auto" (kept for backward compatibility) chooses "elkan" but it
For now `"auto"` (kept for backward compatibility) chooses `"elkan"` but it
might change in the future for a better heuristic.

return_n_iter : bool, default=False
Expand All @@ -355,7 +352,7 @@ def k_means(
Centroids found at the last iteration of k-means.

label : ndarray of shape (n_samples,)
label[i] is the code or index of the centroid the
The `label[i]` is the code or index of the centroid the
i'th observation is closest to.

inertia : float
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