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Make all estimators use `_validate_params`

See original GitHub issue

PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.

Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR must mention which estimator it’s dealing with. We recommend using the following pattern for titles:

MAINT Parameters validation for <Estimator>

where <Estimator> is a placeholder to be replaced with the Estimator you chose.

The description of the PR must begin with Towards #23462 so that this issue and the PR are mutually crossed-linked.

Steps

The estimator must define a class attribute _parameter_constraints that defines the valid types and values for the parameters of the estimator. Do not rely only on the docstring of the estimator to define it: although it can help, it’s important to primarily rely on the implementation to find the valid values because the docstring might not be completely accurate. See how it’s done in KMeans for instance https://github.com/scikit-learn/scikit-learn/blob/02ebf9e68fe1fc7687d9e1047b9e465ae0fd945e/sklearn/cluster/_kmeans.py#L835-L847
If the estimator class inherits from a base class that already defines _parameter_constraints, we just need to extend it.
Then, the first thing that fit and partial_fit should do is call self._validate_params.
All existing simple param validation can now be removed. (simple means that does not depend on the input data or that does not depend on the value of another parameter for instance). Missing removal of such validation should be easy to spot with codecov since they become unreachable code.
Tests that checks error messages from simple param validation can also be removed (carefully: we need to keep the tests checking for more complex param validation !).
Finally, remove the estimator from the list of skipped estimators for the common param validation test https://github.com/scikit-learn/scikit-learn/blob/ec5d2ed9e5bfb6b0baff57ff1f994310e9a31ad9/sklearn/tests/test_common.py#L448 and make sure the test passes: pytest -vl sklearn/tests/test_common.py -k check_param_validation

Estimators to update

ARDRegression
AdaBoostClassifier
AdaBoostRegressor
AdditiveChi2Sampler
AffinityPropagation
AgglomerativeClustering
BaggingClassifier
BaggingRegressor
BayesianGaussianMixture
BayesianRidge
BernoulliNB
BernoulliRBM
Binarizer
Birch
CCA
CalibratedClassifierCV
CategoricalNB
ClassifierChain
ComplementNB
CountVectorizer
DBSCAN
DecisionTreeClassifier
DecisionTreeRegressor
DictVectorizer
DictionaryLearning
DummyClassifier
DummyRegressor
ElasticNet
ElasticNetCV
EllipticEnvelope
EmpiricalCovariance
ExtraTreeClassifier
ExtraTreeRegressor
ExtraTreesClassifier
ExtraTreesRegressor
FactorAnalysis
FastICA
FeatureAgglomeration
FeatureHasher
FunctionTransformer
GammaRegressor
GaussianMixture
GaussianNB
GaussianProcessClassifier
GaussianProcessRegressor
GaussianRandomProjection
GenericUnivariateSelect
GradientBoostingClassifier
GradientBoostingRegressor
GraphicalLasso
GraphicalLassoCV
HashingVectorizer
HistGradientBoostingClassifier
HistGradientBoostingRegressor
HuberRegressor
IncrementalPCA
IsolationForest
Isomap
IsotonicRegression
IterativeImputer
KBinsDiscretizer
KNNImputer
KNeighborsClassifier
KNeighborsRegressor
KNeighborsTransformer
KernelDensity
KernelPCA
KernelRidge
LabelBinarizer
LabelPropagation
LabelSpreading
Lars
LarsCV
Lasso
LassoCV
LassoLars
LassoLarsCV
LassoLarsIC
LatentDirichletAllocation
LedoitWolf
LinearDiscriminantAnalysis
LinearRegression
LinearSVC
LinearSVR
LocalOutlierFactor
LocallyLinearEmbedding
LogisticRegression
LogisticRegressionCV
MDS
MLPClassifier
MLPRegressor
MaxAbsScaler
MeanShift
MinCovDet
MinMaxScaler
MiniBatchDictionaryLearning
MiniBatchNMF
MiniBatchSparsePCA
MissingIndicator
MultiLabelBinarizer
MultiOutputClassifier
MultiOutputRegressor
MultiTaskElasticNet
MultiTaskElasticNetCV
MultiTaskLasso
MultiTaskLassoCV
MultinomialNB
NMF
NearestCentroid
NearestNeighbors
NeighborhoodComponentsAnalysis
Normalizer
NuSVC
NuSVR
Nystroem
OAS
OPTICS
OneClassSVM
OneHotEncoder
OneVsOneClassifier
OneVsRestClassifier
OrdinalEncoder
OrthogonalMatchingPursuit
OrthogonalMatchingPursuitCV
OutputCodeClassifier
PCA
PLSCanonical
PLSRegression
PLSSVD
PassiveAggressiveClassifier
PassiveAggressiveRegressor
PatchExtractor
Perceptron
PoissonRegressor
PolynomialCountSketch
PolynomialFeatures
PowerTransformer
QuadraticDiscriminantAnalysis
QuantileRegressor
QuantileTransformer
RANSACRegressor
RBFSampler
RFE
RFECV
RadiusNeighborsClassifier
RadiusNeighborsRegressor
RadiusNeighborsTransformer
RandomForestClassifier
RandomForestRegressor
RandomTreesEmbedding
RegressorChain
Ridge
RidgeCV
RidgeClassifier
RidgeClassifierCV
RobustScaler
SGDClassifier
SGDOneClassSVM
SGDRegressor
SVC
SVR
SelectFdr
SelectFpr
SelectFromModel
SelectFwe
SelectKBest
SelectPercentile
SelfTrainingClassifier
SequentialFeatureSelector
ShrunkCovariance
SimpleImputer
SkewedChi2Sampler
SparsePCA
SparseRandomProjection
SpectralBiclustering
SpectralClustering
SpectralCoclustering
SpectralEmbedding
SplineTransformer
StackingClassifier
StackingRegressor
StandardScaler
TSNE
TfidfTransformer
TfidfVectorizer
TheilSenRegressor
TransformedTargetRegressor
TruncatedSVD
TweedieRegressor
VarianceThreshold
VotingClassifier
VotingRegressor

Issue Analytics

State:
Created a year ago
Comments:118 (107 by maintainers)

Top GitHub Comments

12reactions

jeremiedbbcommented, Sep 2, 2022

Each estimator is done. We can now close this big meta-issue. Thanks to every contributor and reviewer who worked on this issue !

2reactions

devkaranjoshicommented, Jun 24, 2022

Thank you very much for solving my query. @Jitensid

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