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DOC Ensures that incr_mean_variance_axis passes numpydoc validation #24477

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Sep 20, 2022
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DOC Ensures that incr_mean_variance_axis passes numpydoc validation #24477

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1 change: 0 additions & 1 deletion sklearn/tests/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,6 @@
"sklearn.utils.metaestimators.available_if",
"sklearn.utils.metaestimators.if_delegate_has_method",
"sklearn.utils.multiclass.unique_labels",
"sklearn.utils.sparsefuncs.incr_mean_variance_axis",
"sklearn.utils.sparsefuncs.inplace_swap_row_csc",
"sklearn.utils.sparsefuncs.inplace_swap_row_csr",
]
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7 changes: 3 additions & 4 deletions sklearn/utils/sparsefuncs.py
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Expand Up @@ -126,8 +126,7 @@ def mean_variance_axis(X, axis, weights=None, return_sum_weights=False):


def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
"""Compute incremental mean and variance along an axis on a CSR or
CSC matrix.
"""Compute incremental mean and variance along an axis on a CSR or CSC matrix.

last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0-arrays of the proper size, i.e.
Expand All @@ -153,8 +152,8 @@ def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=Non
last_n : float or ndarray of shape (n_features,) or (n_samples,), \
dtype=floating
Sum of the weights seen so far, excluding the current weights
If not float, it should be of shape (n_samples,) if
axis=0 or (n_features,) if axis=1. If float it corresponds to
If not float, it should be of shape (n_features,) if
axis=0 or (n_samples,) if axis=1. If float it corresponds to
having same weights for all samples (or features).

weights : ndarray of shape (n_samples,) or (n_features,), default=None
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