[FIX] improve compliance with sklearn checks (nilearn#5061)

nilearn/_utils/class_inspect.py

@@ -26,6 +26,7 @@
     "check_get_params_invariance",
     "check_mixin_order",
     "check_non_transformer_estimators_n_iter",
+    "check_parameters_default_constructible",
     "check_set_params",
     "check_transformer_n_iter",
     "check_transformers_unfitted",

nilearn/_utils/docs.py

@@ -382,6 +382,21 @@ def custom_function(vertices):
 
 """
 
+# groups
+docdict["groups"] = """
+groups : None, default=None
+    Group labels for the samples used
+    while splitting the dataset into train/test set.
+
+    Note that this parameter must be specified in some scikit-learn
+    cross-validation generators to calculate the number of splits,
+    for example sklearn.model_selection.LeaveOneGroupOut or
+    sklearn.model_selection.LeavePGroupsOut.
+
+    For more details see
+    https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data
+"""
+
 # hemi
 docdict["hemi"] = """
 hemi : {"left", "right", "both"}, default="left"
@@ -980,6 +995,96 @@ def custom_function(vertices):
         Type of atlas.
         See :term:`Probabilistic atlas` and :term:`Deterministic atlas`."""
 
+docdict["base_decoder_fit_attributes"] = """
+        Attributes
+        ----------
+        masker_ : instance of NiftiMasker, MultiNiftiMasker, or SurfaceMasker
+            The masker used to mask the data.
+
+        mask_img_ : Nifti1Image or :obj:`~nilearn.surface.SurfaceImage`
+            Mask computed by the masker object.
+
+        classes_ : numpy.ndarray
+            Classes to predict. For classification only.
+
+        screening_percentile_ : :obj:`float`
+            Screening percentile corrected according to volume of mask,
+            relative to the volume of standard brain.
+
+        coef_ : numpy.ndarray, shape=(n_classes, n_features)
+            Contains the mean of the models weight vector across
+            fold for each class. Returns None for Dummy estimators.
+
+        coef_img_ : :obj:`dict` of Nifti1Image
+            Dictionary containing ``coef_`` with class names as keys,
+            and ``coef_`` transformed in Nifti1Images as values.
+            In the case of a regression,
+            it contains a single Nifti1Image at the key 'beta'.
+            Ignored if Dummy estimators are provided.
+
+        intercept_ : ndarray, shape (nclasses,)
+            Intercept (also known as bias) added to the decision function.
+            Ignored if Dummy estimators are provided.
+
+        cv_ : :obj:`list` of pairs of lists
+            List of the (n_folds,) folds.
+            For the corresponding fold,
+            each pair is composed of two lists of indices,
+            one for the train samples and one for the test samples.
+
+        std_coef_ : numpy.ndarray, shape=(n_classes, n_features)
+            Contains the standard deviation of the models weight vector across
+            fold for each class.
+            Note that folds are not independent,
+            see
+            https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data
+            Ignored if Dummy estimators are provided.
+
+        std_coef_img_ : :obj:`dict` of Nifti1Image
+            Dictionary containing `std_coef_` with class names as keys,
+            and `coef_` transformed in Nifti1Image as values.
+            In the case of a regression,
+            it contains a single Nifti1Image at the key 'beta'.
+            Ignored if Dummy estimators are provided.
+
+        cv_params_ : :obj:`dict` of :obj:`list`
+            Best point in the parameter grid for each tested fold
+            in the inner cross validation loop.
+            The grid is empty
+            when Dummy estimators are provided.
+
+            .. note::
+
+                If the estimator used its built-in cross-validation,
+                this will include an additional key
+                for the single best value estimated
+                by the built-in cross-validation
+                ('best_C' for LogisticRegressionCV
+                and 'best_alpha' for RidgeCV/RidgeClassifierCV/LassoCV),
+                in addition to the input list of values.
+
+        scorer_ : function
+            Scorer function used on the held out data to choose the best
+            parameters for the model.
+
+        cv_scores_ : :obj:`dict`, (classes, n_folds)
+            Scores (misclassification) for each parameter, and on each fold
+
+        n_outputs_ : :obj:`int`
+            Number of outputs (column-wise)
+
+        dummy_output_ : ndarray, shape=(n_classes, 2) \
+                       or shape=(1, 1) for regression
+            Contains dummy estimator attributes after class predictions
+            using strategies of :class:`sklearn.dummy.DummyClassifier`
+            (class_prior)
+            and  :class:`sklearn.dummy.DummyRegressor` (constant)
+            from scikit-learn.
+            This attribute is necessary for estimating class predictions
+            after fit.
+            Returns None if non-dummy estimators are provided.
+"""
+
 # dataset description
 docdict["description"] = """'description' : :obj:`str`
         Description of the dataset."""

nilearn/connectome/tests/test_connectivity_matrices.py

@@ -50,7 +50,6 @@ def test_check_estimator_cov_estimator(estimator):
 
 
 extra_valid_checks = [
-    "check_parameters_default_constructible",
     "check_no_attributes_set_in_init",
     "check_estimators_unfitted",
     "check_do_not_raise_errors_in_init_or_set_params",

nilearn/connectome/tests/test_group_sparse_cov.py

@@ -16,7 +16,6 @@
     "check_do_not_raise_errors_in_init_or_set_params",
     "check_fit1d",
     "check_no_attributes_set_in_init",
-    "check_parameters_default_constructible",
 ]
 
 

nilearn/decoding/decoder.py

@@ -622,6 +622,7 @@ def __init__(
         self.n_jobs = n_jobs
         self.verbose = verbose
 
+    @fill_doc
     def fit(self, X, y, groups=None):
         """Fit the decoder (learner).
 
@@ -637,94 +638,10 @@ def fit(self, X, y, groups=None):
             Target variable to predict. Must have exactly as many elements as
             3D images in niimg.
 
-        groups : None
-            Group labels for the samples used while splitting the dataset into
-            train/test set. Default None.
+        %(groups)s
 
-            Note that this parameter must be specified in some scikit-learn
-            cross-validation generators to calculate the number of splits, e.g.
-            sklearn.model_selection.LeaveOneGroupOut or
-            sklearn.model_selection.LeavePGroupsOut.
+        %(base_decoder_fit_attributes)s
 
-            For more details see
-            https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data
-
-        Attributes
-        ----------
-        masker_ : instance of NiftiMasker, MultiNiftiMasker, or SurfaceMasker
-            The masker used to mask the data.
-
-        mask_img_ : Nifti1Image or :obj:`~nilearn.surface.SurfaceImage`
-            Mask computed by the masker object.
-
-        classes_ : numpy.ndarray
-            Classes to predict. For classification only.
-
-        screening_percentile_ : float
-            Screening percentile corrected according to volume of mask,
-            relative to the volume of standard brain.
-
-        coef_ : numpy.ndarray, shape=(n_classes, n_features)
-            Contains the mean of the models weight vector across
-            fold for each class. Returns None for Dummy estimators.
-
-        coef_img_ : dict of Nifti1Image
-            Dictionary containing `coef_` with class names as keys,
-            and `coef_` transformed in Nifti1Images as values. In the case of
-            a regression, it contains a single Nifti1Image at the key 'beta'.
-            Ignored if Dummy estimators are provided.
-
-        intercept_ : ndarray, shape (nclasses,)
-            Intercept (a.k.a. bias) added to the decision function.
-            Ignored if Dummy estimators are provided.
-
-        cv_ : list of pairs of lists
-            List of the (n_folds,) folds. For the corresponding fold,
-            each pair is composed of two lists of indices,
-            one for the train samples and one for the test samples.
-
-        std_coef_ : numpy.ndarray, shape=(n_classes, n_features)
-            Contains the standard deviation of the models weight vector across
-            fold for each class. Note that folds are not independent, see
-            https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data
-            Ignored if Dummy estimators are provided.
-
-        std_coef_img_ : dict of Nifti1Image
-            Dictionary containing `std_coef_` with class names as keys,
-            and `coef_` transformed in Nifti1Image as values. In the case of
-            a regression, it contains a single Nifti1Image at the key 'beta'.
-            Ignored if Dummy estimators are provided.
-
-        cv_params_ : dict of lists
-            Best point in the parameter grid for each tested fold
-            in the inner cross validation loop. The grid is empty
-            when Dummy estimators are provided. Note: if the estimator used its
-            built-in cross-validation, this will include an additional key for
-            the single best value estimated by the built-in cross-validation
-            ('best_C' for LogisticRegressionCV and 'best_alpha' for
-            RidgeCV/RidgeClassifierCV/LassoCV), in addition to the input list
-            of values.
-
-        scorer_ : function
-            Scorer function used on the held out data to choose the best
-            parameters for the model.
-
-        cv_scores_ : dict, (classes, n_folds)
-            Scores (misclassification) for each parameter, and on each fold
-
-        n_outputs_ : int
-            Number of outputs (column-wise)
-
-        dummy_output_ : ndarray, shape=(n_classes, 2) \
-                       or shape=(1, 1) for regression
-            Contains dummy estimator attributes after class predictions
-            using strategies of :class:`sklearn.dummy.DummyClassifier`
-            (class_prior)
-            and  :class:`sklearn.dummy.DummyRegressor` (constant)
-            from scikit-learn.
-            This attribute is necessary for estimating class predictions
-            after fit.
-            Returns None if non-dummy estimators are provided.
         """
         self.estimator = _check_estimator(self.estimator)
         self.memory_ = check_memory(self.memory, self.verbose)
@@ -1466,8 +1383,6 @@ def __init__(
         n_jobs=1,
         verbose=0,
     ):
-        self.classes_ = ["beta"]
-
         super().__init__(
             estimator=estimator,
             mask=mask,
@@ -1514,6 +1429,30 @@ def __sklearn_tags__(self):
         tags.estimator_type = "regressor"
         return tags
 
+    @fill_doc
+    def fit(self, X, y, groups=None):
+        """Fit the decoder (learner).
+
+        Parameters
+        ----------
+        X : list of Niimg-like or :obj:`~nilearn.surface.SurfaceImage` objects
+            See :ref:`extracting_data`.
+            Data on which model is to be fitted. If this is a list,
+            the affine is considered the same for all.
+
+        y : numpy.ndarray of shape=(n_samples) or list of length n_samples
+            The dependent variable (age, sex, IQ, yes/no, etc.).
+            Target variable to predict. Must have exactly as many elements as
+            3D images in niimg.
+
+        %(groups)s
+
+        %(base_decoder_fit_attributes)s
+
+        """
+        self.classes_ = ["beta"]
+        super().fit(X, y, groups=groups)
+
 
 @fill_doc
 class FREMRegressor(_BaseDecoder):
@@ -1645,11 +1584,6 @@ def __init__(
         n_jobs=1,
         verbose=0,
     ):
-        self.classes_ = ["beta"]
-
-        if isinstance(cv, int):
-            cv = ShuffleSplit(cv, random_state=0)
-
         super().__init__(
             estimator=estimator,
             mask=mask,
@@ -1673,6 +1607,32 @@ def __init__(
             n_jobs=n_jobs,
         )
 
+    @fill_doc
+    def fit(self, X, y, groups=None):
+        """Fit the decoder (learner).
+
+        Parameters
+        ----------
+        X : list of Niimg-like or :obj:`~nilearn.surface.SurfaceImage` objects
+            See :ref:`extracting_data`.
+            Data on which model is to be fitted. If this is a list,
+            the affine is considered the same for all.
+
+        y : numpy.ndarray of shape=(n_samples) or list of length n_samples
+            The dependent variable (age, sex, IQ, yes/no, etc.).
+            Target variable to predict. Must have exactly as many elements as
+            3D images in niimg.
+
+        %(groups)s
+
+        %(base_decoder_fit_attributes)s
+
+        """
+        self.classes_ = ["beta"]
+        if isinstance(self.cv, int):
+            self.cv = ShuffleSplit(self.cv, random_state=0)
+        super().fit(X, y, groups=groups)
+
 
 @fill_doc
 class FREMClassifier(_BaseDecoder):
@@ -1803,9 +1763,6 @@ def __init__(
         n_jobs=1,
         verbose=0,
     ):
-        if isinstance(cv, int):
-            cv = StratifiedShuffleSplit(cv, random_state=0)
-
         super().__init__(
             estimator=estimator,
             mask=mask,
@@ -1828,3 +1785,31 @@ def __init__(
             high_pass=high_pass,
             t_r=t_r,
         )
+
+    @fill_doc
+    def fit(self, X, y, groups=None):
+        """Fit the decoder (learner).
+
+        Parameters
+        ----------
+        X : :obj:`list` of Niimg-like \
+            or :obj:`~nilearn.surface.SurfaceImage` objects
+            See :ref:`extracting_data`.
+            Data on which model is to be fitted.
+            If this is a list,
+            the affine is considered the same for all.
+
+        y : numpy.ndarray of shape=(n_samples) \
+            or :obj:`list` of length n_samples
+            The dependent variable (age, sex, IQ, yes/no, etc.).
+            Target variable to predict. Must have exactly as many elements as
+            3D images in niimg.
+
+        %(groups)s
+
+        %(base_decoder_fit_attributes)s
+
+        """
+        if isinstance(self.cv, int):
+            self.cv = StratifiedShuffleSplit(self.cv, random_state=0)
+        super().fit(X, y, groups=groups)