MAINT parameter validation for sklearn.datasets.dump_svmlight_file (s…

…cikit-learn#25726)

Co-authored-by: jeremiedbb <[email protected]>

sklearn/datasets/_svmlight_format_io.py

@@ -25,6 +25,7 @@
 from .. import __version__
 
 from ..utils import check_array, IS_PYPY
+from ..utils._param_validation import validate_params, HasMethods
 
 if not IS_PYPY:
  from ._svmlight_format_fast import (
@@ -404,6 +405,17 @@ def _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id):
  )
 
 
+@validate_params(
+ {
+ "X": ["array-like", "sparse matrix"],
+ "y": ["array-like", "sparse matrix"],
+ "f": [str, HasMethods(["write"])],
+ "zero_based": ["boolean"],
+ "comment": [str, bytes, None],
+ "query_id": ["array-like", None],
+ "multilabel": ["boolean"],
+ }
+)
 def dump_svmlight_file(
  X,
  y,
@@ -428,7 +440,7 @@ def dump_svmlight_file(
  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)]
+ y : {array-like, sparse matrix}, shape = (n_samples,) or (n_samples, n_labels)
  Target values. Class labels must be an
  integer or float, or array-like objects of integer or float for
  multilabel classifications.
@@ -442,7 +454,7 @@ def dump_svmlight_file(
  Whether column indices should be written zero-based (True) or one-based
  (False).
 
- comment : str, default=None
+ comment : str or bytes, default=None
  Comment to insert at the top of the file. This should be either a
  Unicode string, which will be encoded as UTF-8, or an ASCII byte
  string.
@@ -459,7 +471,7 @@ def dump_svmlight_file(
  https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html).
 
  .. versionadded:: 0.17
- parameter *multilabel* to support multilabel datasets.
+ parameter `multilabel` to support multilabel datasets.
  """
  if comment is not None:
  # Convert comment string to list of lines in UTF-8.

sklearn/tests/test_public_functions.py

@@ -102,6 +102,7 @@ def _check_function_param_validation(
  "sklearn.cluster.ward_tree",
  "sklearn.covariance.empirical_covariance",
  "sklearn.covariance.shrunk_covariance",
+ "sklearn.datasets.dump_svmlight_file",
  "sklearn.datasets.fetch_california_housing",
  "sklearn.datasets.fetch_kddcup99",
  "sklearn.datasets.make_classification",