DOC Ensures that incr_mean_variance_axis passes numpydoc validation (scikit-learn#24477)

yuanx749 · web-flow · commit 02e36b4d866d · 2022-09-20T11:12:47.000+02:00
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -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",
 ]
diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
@@ -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.
@@ -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

Original file line number	Diff line number	Diff line change
`@@ -35,7 +35,6 @@`
`35`	`35`	`"sklearn.utils.metaestimators.available_if",`
`36`	`36`	`"sklearn.utils.metaestimators.if_delegate_has_method",`
`37`	`37`	`"sklearn.utils.multiclass.unique_labels",`
`38`		`- "sklearn.utils.sparsefuncs.incr_mean_variance_axis",`
`39`	`38`	`"sklearn.utils.sparsefuncs.inplace_swap_row_csc",`
`40`	`39`	`"sklearn.utils.sparsefuncs.inplace_swap_row_csr",`
`41`	`40`	`]`