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DOC Ensures that load_svmlight_files passes numpydoc validation #24164

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Aug 10, 2022
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DOC Ensures that load_svmlight_files passes numpydoc validation #24164

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21 changes: 10 additions & 11 deletions sklearn/datasets/_svmlight_format_io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -230,7 +230,7 @@ def load_svmlight_files(
offset=0,
length=-1,
):
"""Load dataset from multiple files in SVMlight format
"""Load dataset from multiple files in SVMlight format.

This function is equivalent to mapping load_svmlight_file over a list of
files, except that the results are concatenated into a single, flat list
Expand Down Expand Up @@ -268,7 +268,7 @@ def load_svmlight_files(

multilabel : bool, default=False
Samples may have several labels each (see
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html).

zero_based : bool or "auto", default="auto"
Whether column indices in f are zero-based (True) or one-based
Expand Down Expand Up @@ -296,23 +296,22 @@ def load_svmlight_files(

Returns
-------
[X1, y1, ..., Xn, yn]
where each (Xi, yi) pair is the result from load_svmlight_file(files[i]).
[X1, y1, ..., Xn, yn] or [X1, y1, q1, ..., Xn, yn, qn]: list of arrays
Each (Xi, yi) pair is the result from load_svmlight_file(files[i]).
If query_id is set to True, this will return instead (Xi, yi, qi)
triplets.

If query_id is set to True, this will return instead [X1, y1, q1,
..., Xn, yn, qn] where (Xi, yi, qi) is the result from
load_svmlight_file(files[i])
See Also
--------
load_svmlight_file: Similar function for loading a single file in this
format.

Notes
-----
When fitting a model to a matrix X_train and evaluating it against a
matrix X_test, it is essential that X_train and X_test have the same
number of features (X_train.shape[1] == X_test.shape[1]). This may not
be the case if you load the files individually with load_svmlight_file.

See Also
--------
load_svmlight_file
"""
if (offset != 0 or length > 0) and zero_based == "auto":
# disable heuristic search to avoid getting inconsistent results on
Expand Down
1 change: 0 additions & 1 deletion sklearn/tests/test_docstrings.py
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Expand Up @@ -15,7 +15,6 @@
"sklearn.datasets._lfw.fetch_lfw_people",
"sklearn.datasets._species_distributions.fetch_species_distributions",
"sklearn.datasets._svmlight_format_io.load_svmlight_file",
"sklearn.datasets._svmlight_format_io.load_svmlight_files",
"sklearn.decomposition._dict_learning.dict_learning",
"sklearn.decomposition._dict_learning.dict_learning_online",
"sklearn.decomposition._nmf.non_negative_factorization",
Expand Down