MAINT add parameter validation framework (#955)

Author: glemaitre

doc/whats_new/v0.10.rst

@@ -19,6 +19,9 @@ Compatibility
 - Maintenance release for be compatible with scikit-learn >= 1.0.2.
   :pr:`946`, :pr:`947`, :pr:`949` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- Add support for automatic parameters validation as in scikit-learn >= 1.2.
+  :pr:`955` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Deprecation
 ...........
 

imblearn/base.py

@@ -12,6 +12,7 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 from .utils import check_sampling_strategy, check_target_type
+from .utils._param_validation import validate_parameter_constraints
 from .utils._validation import ArraysTransformer
 
 
@@ -113,7 +114,26 @@ def _fit_resample(self, X, y):
         pass
 
 
-class BaseSampler(SamplerMixin):
+class _ParamsValidationMixin:
+    """Mixin class to validate parameters."""
+
+    def _validate_params(self):
+        """Validate types and values of constructor parameters.
+
+        The expected type and values must be defined in the `_parameter_constraints`
+        class attribute, which is a dictionary `param_name: list of constraints`. See
+        the docstring of `validate_parameter_constraints` for a description of the
+        accepted constraints.
+        """
+        if hasattr(self, "_parameter_constraints"):
+            validate_parameter_constraints(
+                self._parameter_constraints,
+                self.get_params(deep=False),
+                caller_name=self.__class__.__name__,
+            )
+
+
+class BaseSampler(SamplerMixin, _ParamsValidationMixin):
     """Base class for sampling algorithms.
 
     Warning: This class should not be used directly. Use the derive classes
@@ -130,6 +150,52 @@ def _check_X_y(self, X, y, accept_sparse=None):
         X, y = self._validate_data(X, y, reset=True, accept_sparse=accept_sparse)
         return X, y, binarize_y
 
+    def fit(self, X, y):
+        """Check inputs and statistics of the sampler.
+
+        You should use ``fit_resample`` in all cases.
+
+        Parameters
+        ----------
+        X : {array-like, dataframe, sparse matrix} of shape \
+                (n_samples, n_features)
+            Data array.
+
+        y : array-like of shape (n_samples,)
+            Target array.
+
+        Returns
+        -------
+        self : object
+            Return the instance itself.
+        """
+        self._validate_params()
+        return super().fit(X, y)
+
+    def fit_resample(self, X, y):
+        """Resample the dataset.
+
+        Parameters
+        ----------
+        X : {array-like, dataframe, sparse matrix} of shape \
+                (n_samples, n_features)
+            Matrix containing the data which have to be sampled.
+
+        y : array-like of shape (n_samples,)
+            Corresponding label for each sample in X.
+
+        Returns
+        -------
+        X_resampled : {array-like, dataframe, sparse matrix} of shape \
+                (n_samples_new, n_features)
+            The array containing the resampled data.
+
+        y_resampled : array-like of shape (n_samples_new,)
+            The corresponding label of `X_resampled`.
+        """
+        self._validate_params()
+        return super().fit_resample(X, y)
+
     def _more_tags(self):
         return {"X_types": ["2darray", "sparse", "dataframe"]}
 
@@ -241,6 +307,13 @@ class FunctionSampler(BaseSampler):
 
     _sampling_type = "bypass"
 
+    _parameter_constraints: dict = {
+        "func": [callable, None],
+        "accept_sparse": ["boolean"],
+        "kw_args": [dict, None],
+        "validate": ["boolean"],
+    }
+
     def __init__(self, *, func=None, accept_sparse=True, kw_args=None, validate=True):
         super().__init__()
         self.func = func
@@ -267,6 +340,7 @@ def fit(self, X, y):
         self : object
             Return the instance itself.
         """
+        self._validate_params()
         # we need to overwrite SamplerMixin.fit to bypass the validation
         if self.validate:
             check_classification_targets(y)
@@ -298,6 +372,7 @@ def fit_resample(self, X, y):
         y_resampled : array-like of shape (n_samples_new,)
             The corresponding label of `X_resampled`.
         """
+        self._validate_params()
         arrays_transformer = ArraysTransformer(X, y)
 
         if self.validate:

imblearn/combine/_smote_enn.py

@@ -4,6 +4,8 @@
 #          Christos Aridas
 # License: MIT
 
+import numbers
+
 from sklearn.base import clone
 from sklearn.utils import check_X_y
 
@@ -102,6 +104,13 @@ class SMOTEENN(BaseSampler):
 
     _sampling_type = "over-sampling"
 
+    _parameter_constraints: dict = {
+        **BaseOverSampler._parameter_constraints,
+        "smote": [SMOTE, None],
+        "enn": [EditedNearestNeighbours, None],
+        "n_jobs": [numbers.Integral, None],
+    }
+
     def __init__(
         self,
         *,
@@ -121,14 +130,7 @@ def __init__(
     def _validate_estimator(self):
         "Private function to validate SMOTE and ENN objects"
         if self.smote is not None:
-            if isinstance(self.smote, SMOTE):
-                self.smote_ = clone(self.smote)
-            else:
-                raise ValueError(
-                    f"smote needs to be a SMOTE object."
-                    f"Got {type(self.smote)} instead."
-                )
-        # Otherwise create a default SMOTE
+            self.smote_ = clone(self.smote)
         else:
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
@@ -137,14 +139,7 @@ def _validate_estimator(self):
             )
 
         if self.enn is not None:
-            if isinstance(self.enn, EditedNearestNeighbours):
-                self.enn_ = clone(self.enn)
-            else:
-                raise ValueError(
-                    f"enn needs to be an EditedNearestNeighbours."
-                    f" Got {type(self.enn)} instead."
-                )
-        # Otherwise create a default EditedNearestNeighbours
+            self.enn_ = clone(self.enn)
         else:
             self.enn_ = EditedNearestNeighbours(
                 sampling_strategy="all", n_jobs=self.n_jobs

imblearn/combine/_smote_tomek.py

@@ -5,6 +5,8 @@
 #          Christos Aridas
 # License: MIT
 
+import numbers
+
 from sklearn.base import clone
 from sklearn.utils import check_X_y
 
@@ -100,6 +102,13 @@ class SMOTETomek(BaseSampler):
 
     _sampling_type = "over-sampling"
 
+    _parameter_constraints: dict = {
+        **BaseOverSampler._parameter_constraints,
+        "smote": [SMOTE, None],
+        "tomek": [TomekLinks, None],
+        "n_jobs": [numbers.Integral, None],
+    }
+
     def __init__(
         self,
         *,
@@ -120,14 +129,7 @@ def _validate_estimator(self):
         "Private function to validate SMOTE and ENN objects"
 
         if self.smote is not None:
-            if isinstance(self.smote, SMOTE):
-                self.smote_ = clone(self.smote)
-            else:
-                raise ValueError(
-                    f"smote needs to be a SMOTE object."
-                    f"Got {type(self.smote)} instead."
-                )
-        # Otherwise create a default SMOTE
+            self.smote_ = clone(self.smote)
         else:
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
@@ -136,14 +138,7 @@ def _validate_estimator(self):
             )
 
         if self.tomek is not None:
-            if isinstance(self.tomek, TomekLinks):
-                self.tomek_ = clone(self.tomek)
-            else:
-                raise ValueError(
-                    f"tomek needs to be a TomekLinks object."
-                    f"Got {type(self.tomek)} instead."
-                )
-        # Otherwise create a default TomekLinks
+            self.tomek_ = clone(self.tomek)
         else:
             self.tomek_ = TomekLinks(sampling_strategy="all", n_jobs=self.n_jobs)
 

imblearn/combine/tests/test_smote_enn.py

@@ -4,7 +4,6 @@
 # License: MIT
 
 import numpy as np
-import pytest
 from sklearn.utils._testing import assert_allclose, assert_array_equal
 
 from imblearn.combine import SMOTEENN
@@ -156,16 +155,3 @@ def test_parallelisation():
     assert smt.n_jobs == 8
     assert smt.smote_.n_jobs == 8
     assert smt.enn_.n_jobs == 8
-
-
-@pytest.mark.parametrize(
-    "smote_params, err_msg",
-    [
-        ({"smote": "rnd"}, "smote needs to be a SMOTE"),
-        ({"enn": "rnd"}, "enn needs to be an "),
-    ],
-)
-def test_error_wrong_object(smote_params, err_msg):
-    smt = SMOTEENN(**smote_params)
-    with pytest.raises(ValueError, match=err_msg):
-        smt.fit_resample(X, Y)

imblearn/combine/tests/test_smote_tomek.py

@@ -4,7 +4,6 @@
 # License: MIT
 
 import numpy as np
-import pytest
 from sklearn.utils._testing import assert_allclose, assert_array_equal
 
 from imblearn.combine import SMOTETomek
@@ -166,16 +165,3 @@ def test_parallelisation():
     assert smt.n_jobs == 8
     assert smt.smote_.n_jobs == 8
     assert smt.tomek_.n_jobs == 8
-
-
-@pytest.mark.parametrize(
-    "smote_params, err_msg",
-    [
-        ({"smote": "rnd"}, "smote needs to be a SMOTE"),
-        ({"tomek": "rnd"}, "tomek needs to be a TomekLinks"),
-    ],
-)
-def test_error_wrong_object(smote_params, err_msg):
-    smt = SMOTETomek(**smote_params)
-    with pytest.raises(ValueError, match=err_msg):
-        smt.fit_resample(X, Y)

imblearn/ensemble/_bagging.py

@@ -4,6 +4,7 @@
 #          Christos Aridas
 # License: MIT
 
+import copy
 import inspect
 import numbers
 import warnings
@@ -18,21 +19,23 @@
 from sklearn.utils.fixes import delayed
 from sklearn.utils.validation import check_is_fitted
 
+from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._available_if import available_if
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
-from ._common import _estimator_has
+from ..utils._param_validation import HasMethods, Interval, StrOptions
+from ._common import _bagging_parameter_constraints, _estimator_has
 
 
 @Substitution(
     sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
     n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
-class BalancedBaggingClassifier(BaggingClassifier):
+class BalancedBaggingClassifier(BaggingClassifier, _ParamsValidationMixin):
     """A Bagging classifier with additional balancing.
 
     This implementation of Bagging is similar to the scikit-learn
@@ -252,6 +255,26 @@ class BalancedBaggingClassifier(BaggingClassifier):
      [  2 225]]
     """
 
+    # make a deepcopy to not modify the original dictionary
+    if hasattr(BaggingClassifier, "_parameter_constraints"):
+        # scikit-learn >= 1.2
+        _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
+    else:
+        _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)
+
+    _parameter_constraints.update(
+        {
+            "sampling_strategy": [
+                Interval(numbers.Real, 0, 1, closed="right"),
+                StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
+                dict,
+                callable,
+            ],
+            "replacement": ["boolean"],
+            "sampler": [HasMethods(["fit_resample"]), None],
+        }
+    )
+
     def __init__(
         self,
         estimator=None,
@@ -316,17 +339,7 @@ def _validate_y(self, y):
 
     def _validate_estimator(self, default=DecisionTreeClassifier()):
         """Check the estimator and the n_estimator attribute, set the
-        `base_estimator_` attribute."""
-        if not isinstance(self.n_estimators, (numbers.Integral, np.integer)):
-            raise ValueError(
-                f"n_estimators must be an integer, " f"got {type(self.n_estimators)}."
-            )
-
-        if self.n_estimators <= 0:
-            raise ValueError(
-                f"n_estimators must be greater than zero, " f"got {self.n_estimators}."
-            )
-
+        `estimator_` attribute."""
         if self.estimator is not None and (
             self.base_estimator not in [None, "deprecated"]
         ):
@@ -395,6 +408,7 @@ def fit(self, X, y):
             Fitted estimator.
         """
         # overwrite the base class method by disallowing `sample_weight`
+        self._validate_params()
         return super().fit(X, y)
 
     def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):