MAINT Param validation: decorate all estimators with _fit_context (scikit-learn#26473)

Author: jeremiedbb

sklearn/base.py

@@ -27,7 +27,7 @@
 from .utils.validation import _num_features
 from .utils.validation import _check_feature_names_in
 from .utils.validation import _generate_get_feature_names_out
-from .utils.validation import check_is_fitted
+from .utils.validation import _is_fitted, check_is_fitted
 from .utils._metadata_requests import _MetadataRequester
 from .utils.validation import _get_feature_names
 from .utils._estimator_html_repr import estimator_html_repr
@@ -1131,7 +1131,13 @@ def decorator(fit_method):
         @functools.wraps(fit_method)
         def wrapper(estimator, *args, **kwargs):
             global_skip_validation = get_config()["skip_parameter_validation"]
-            if not global_skip_validation:
+
+            # we don't want to validate again for each call to partial_fit
+            partial_fit_and_fitted = (
+                fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
+            )
+
+            if not global_skip_validation and not partial_fit_and_fitted:
                 estimator._validate_params()
 
             with config_context(

sklearn/calibration.py

@@ -25,6 +25,7 @@
     RegressorMixin,
     clone,
     MetaEstimatorMixin,
+    _fit_context,
 )
 from .preprocessing import label_binarize, LabelEncoder
 from .utils import (
@@ -318,6 +319,10 @@ def _get_estimator(self):
 
         return estimator
 
+    @_fit_context(
+        # CalibratedClassifierCV.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
     def fit(self, X, y, sample_weight=None, **fit_params):
         """Fit the calibrated model.
 
@@ -341,8 +346,6 @@ def fit(self, X, y, sample_weight=None, **fit_params):
         self : object
             Returns an instance of self.
         """
-        self._validate_params()
-
         check_classification_targets(y)
         X, y = indexable(X, y)
         if sample_weight is not None:

sklearn/cluster/_affinity_propagation.py

@@ -12,6 +12,7 @@
 
 from ..exceptions import ConvergenceWarning
 from ..base import BaseEstimator, ClusterMixin
+from ..base import _fit_context
 from ..utils import check_random_state
 from ..utils._param_validation import Interval, StrOptions, validate_params
 from ..utils.validation import check_is_fitted
@@ -469,6 +470,7 @@ def __init__(
     def _more_tags(self):
         return {"pairwise": self.affinity == "precomputed"}
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """Fit the clustering from features, or affinity matrix.
 
@@ -488,8 +490,6 @@ def fit(self, X, y=None):
         self
             Returns the instance itself.
         """
-        self._validate_params()
-
         if self.affinity == "precomputed":
             accept_sparse = False
         else:

sklearn/cluster/_agglomerative.py

@@ -16,6 +16,7 @@
 from scipy.sparse.csgraph import connected_components
 
 from ..base import BaseEstimator, ClusterMixin, ClassNamePrefixFeaturesOutMixin
+from ..base import _fit_context
 from ..metrics.pairwise import paired_distances
 from ..metrics.pairwise import _VALID_METRICS
 from ..metrics import DistanceMetric
@@ -950,6 +951,7 @@ def __init__(
         self.metric = metric
         self.compute_distances = compute_distances
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """Fit the hierarchical clustering from features, or distance matrix.
 
@@ -968,7 +970,6 @@ def fit(self, X, y=None):
         self : object
             Returns the fitted instance.
         """
-        self._validate_params()
         X = self._validate_data(X, ensure_min_samples=2)
         return self._fit(X)
 
@@ -1324,6 +1325,7 @@ def __init__(
         )
         self.pooling_func = pooling_func
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """Fit the hierarchical clustering on the data.
 
@@ -1340,7 +1342,6 @@ def fit(self, X, y=None):
         self : object
             Returns the transformer.
         """
-        self._validate_params()
         X = self._validate_data(X, ensure_min_features=2)
         super()._fit(X.T)
         self._n_features_out = self.n_clusters_

sklearn/cluster/_bicluster.py

@@ -13,6 +13,7 @@
 
 from . import KMeans, MiniBatchKMeans
 from ..base import BaseEstimator, BiclusterMixin
+from ..base import _fit_context
 from ..utils import check_random_state
 from ..utils import check_scalar
 
@@ -118,6 +119,7 @@ def __init__(
     def _check_parameters(self, n_samples):
         """Validate parameters depending on the input data."""
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """Create a biclustering for X.
 
@@ -134,8 +136,6 @@ def fit(self, X, y=None):
         self : object
             SpectralBiclustering instance.
         """
-        self._validate_params()
-
         X = self._validate_data(X, accept_sparse="csr", dtype=np.float64)
         self._check_parameters(X.shape[0])
         self._fit(X)

sklearn/cluster/_birch.py

@@ -16,6 +16,7 @@
     ClusterMixin,
     BaseEstimator,
     ClassNamePrefixFeaturesOutMixin,
+    _fit_context,
 )
 from ..utils.extmath import row_norms
 from ..utils._param_validation import Interval
@@ -501,6 +502,7 @@ def __init__(
         self.compute_labels = compute_labels
         self.copy = copy
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """
         Build a CF Tree for the input data.
@@ -518,9 +520,6 @@ def fit(self, X, y=None):
         self
             Fitted estimator.
         """
-
-        self._validate_params()
-
         return self._fit(X, partial=False)
 
     def _fit(self, X, partial):
@@ -610,6 +609,7 @@ def _get_leaves(self):
             leaf_ptr = leaf_ptr.next_leaf_
         return leaves
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def partial_fit(self, X=None, y=None):
         """
         Online learning. Prevents rebuilding of CFTree from scratch.
@@ -629,8 +629,6 @@ def partial_fit(self, X=None, y=None):
         self
             Fitted estimator.
         """
-        self._validate_params()
-
         if X is None:
             # Perform just the final global clustering step.
             self._global_clustering()

sklearn/cluster/_bisect_k_means.py

@@ -6,6 +6,7 @@
 import numpy as np
 import scipy.sparse as sp
 
+from ..base import _fit_context
 from ._kmeans import _BaseKMeans
 from ._kmeans import _kmeans_single_elkan
 from ._kmeans import _kmeans_single_lloyd
@@ -347,6 +348,7 @@ def _bisect(self, X, x_squared_norms, sample_weight, cluster_to_bisect):
 
         cluster_to_bisect.split(best_labels, best_centers, scores)
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None, sample_weight=None):
         """Compute bisecting k-means clustering.
 
@@ -373,8 +375,6 @@ def fit(self, X, y=None, sample_weight=None):
         self
             Fitted estimator.
         """
-        self._validate_params()
-
         X = self._validate_data(
             X,
             accept_sparse="csr",

sklearn/cluster/_dbscan.py

@@ -16,6 +16,7 @@
 
 from ..metrics.pairwise import _VALID_METRICS
 from ..base import BaseEstimator, ClusterMixin
+from ..base import _fit_context
 from ..utils.validation import _check_sample_weight
 from ..utils._param_validation import Interval, StrOptions
 from ..neighbors import NearestNeighbors
@@ -338,6 +339,10 @@ def __init__(
         self.p = p
         self.n_jobs = n_jobs
 
+    @_fit_context(
+        # DBSCAN.metric is not validated yet
+        prefer_skip_nested_validation=False
+    )
     def fit(self, X, y=None, sample_weight=None):
         """Perform DBSCAN clustering from features, or distance matrix.
 
@@ -363,8 +368,6 @@ def fit(self, X, y=None, sample_weight=None):
         self : object
             Returns a fitted instance of self.
         """
-        self._validate_params()
-
         X = self._validate_data(X, accept_sparse="csr")
 
         if sample_weight is not None:

sklearn/cluster/_kmeans.py

@@ -23,6 +23,7 @@
     ClusterMixin,
     TransformerMixin,
     ClassNamePrefixFeaturesOutMixin,
+    _fit_context,
 )
 from ..metrics.pairwise import euclidean_distances
 from ..metrics.pairwise import _euclidean_distances
@@ -1448,6 +1449,7 @@ def _warn_mkl_vcomp(self, n_active_threads):
             f" variable OMP_NUM_THREADS={n_active_threads}."
         )
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None, sample_weight=None):
         """Compute k-means clustering.
 
@@ -1475,8 +1477,6 @@ def fit(self, X, y=None, sample_weight=None):
         self : object
             Fitted estimator.
         """
-        self._validate_params()
-
         X = self._validate_data(
             X,
             accept_sparse="csr",
@@ -2057,6 +2057,7 @@ def _random_reassign(self):
             return True
         return False
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None, sample_weight=None):
         """Compute the centroids on X by chunking it into mini-batches.
 
@@ -2084,8 +2085,6 @@ def fit(self, X, y=None, sample_weight=None):
         self : object
             Fitted estimator.
         """
-        self._validate_params()
-
         X = self._validate_data(
             X,
             accept_sparse="csr",
@@ -2214,6 +2213,7 @@ def fit(self, X, y=None, sample_weight=None):
 
         return self
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def partial_fit(self, X, y=None, sample_weight=None):
         """Update k means estimate on a single mini-batch X.
 
@@ -2241,9 +2241,6 @@ def partial_fit(self, X, y=None, sample_weight=None):
         """
         has_centers = hasattr(self, "cluster_centers_")
 
-        if not has_centers:
-            self._validate_params()
-
         X = self._validate_data(
             X,
             accept_sparse="csr",

sklearn/cluster/_mean_shift.py

@@ -24,6 +24,7 @@
 from ..utils.parallel import delayed, Parallel
 from ..utils import check_random_state, gen_batches, check_array
 from ..base import BaseEstimator, ClusterMixin
+from ..base import _fit_context
 from ..neighbors import NearestNeighbors
 from ..metrics.pairwise import pairwise_distances_argmin
 from .._config import config_context
@@ -435,6 +436,7 @@ def __init__(
         self.n_jobs = n_jobs
         self.max_iter = max_iter
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y=None):
         """Perform clustering.
 
@@ -451,7 +453,6 @@ def fit(self, X, y=None):
         self : object
                Fitted instance.
         """
-        self._validate_params()
         X = self._validate_data(X)
         bandwidth = self.bandwidth
         if bandwidth is None: