}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Apply the dimension reduction learned on the train data.",
"laleType": "Any",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.cross_decomposition.PLSRegression#sklearn-cross_decomposition-plsregression",
"import_from": "sklearn.cross_decomposition",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer", "estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
PLSRegression = make_operator(_PLSRegressionImpl, _combined_schemas)
set_docstrings(PLSRegression)
}]
}
_combined_schemas = {
'$schema':
'http://json-schema.org/draft-04/schema#',
'description':
'Combined schema for expected data and hyperparameters for a transformer for'
' a text data transformer based on pre-trained BERT model '
'(https://github.com/huggingface/pytorch-pretrained-BERT).',
'type':
'object',
'tags': {
'pre': ['text'],
'op': ['transformer', '~interpretable'],
'post': ['embedding']
},
'properties': {
'input_fit': _input_schema_fit,
'input_predict': _input_schema_predict,
'output': _output_schema,
'hyperparams': _hyperparams_schema
}
}
if __name__ == "__main__":
lale.helpers.validate_is_schema(_combined_schemas)
BertPretrainedEncoder = make_operator(BertPretrainedEncoderImpl,
_combined_schemas)
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Returns predicted values.",
"type": "array",
"items": {
"type": "number"
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV#sklearn-linear_model-orthogonalmatchingpursuitcv",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
OrthogonalMatchingPursuitCV = make_operator(_OrthogonalMatchingPursuitCVImpl,
_combined_schemas)
set_docstrings(OrthogonalMatchingPursuitCV)
"laleType":
"Any",
"XXX TODO XXX":
"array of shape = [n_samples, n_classes], or a list of n_outputs",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.neighbors.KNeighborsClassifier#sklearn-neighbors-kneighborsclassifier",
"import_from": "sklearn.neighbors",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator", "classifier"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_predict_proba": _input_predict_proba_schema,
"output_predict_proba": _output_predict_proba_schema,
},
}
KNeighborsClassifier = make_operator(_KNeighborsClassifierImpl,
_combined_schemas)
set_docstrings(KNeighborsClassifier)
"properties": {
"X": {
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": "New data, where n_samples in the number of samples and n_features is the number of features",
}
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Apply the approximate feature map to X.",
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler#sklearn-kernel_approximation-skewedchi2sampler",
"import_from": "sklearn.kernel_approximation",
"type": "object",
"tags": {"pre": [], "op": ["transformer"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(SkewedChi2SamplerImpl, _combined_schemas)
SkewedChi2Sampler = make_operator(SkewedChi2SamplerImpl, _combined_schemas)
"description": "Copy the input X or not.",
},
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Perform standardization by centering and scaling",
"laleType": "Any",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.preprocessing.StandardScaler#sklearn-preprocessing-standardscaler",
"import_from": "sklearn.preprocessing",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(StandardScalerImpl, _combined_schemas)
StandardScaler = make_operator(StandardScalerImpl, _combined_schemas)
"laleType":
"Any",
"XXX TODO XXX":
"array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LogisticRegression#sklearn-linear_model-logisticregression",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_predict_proba": _input_predict_proba_schema,
"output_predict_proba": _output_predict_proba_schema,
"input_decision_function": _input_decision_function_schema,
"output_decision_function": _output_decision_function_schema,
},
}
set_docstrings(LogisticRegressionImpl, _combined_schemas)
LogisticRegression = make_operator(LogisticRegressionImpl, _combined_schemas)
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": "Samples",
}
},
}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Returns predicted values.",
"anyOf": [
{"type": "array", "items": {"type": "number"}},
{"type": "array", "items": {"type": "array", "items": {"type": "number"}}},
],
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.kernel_ridge.KernelRidge#sklearn-kernel_ridge-kernelridge",
"import_from": "sklearn.kernel_ridge",
"type": "object",
"tags": {"pre": [], "op": ["estimator"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
set_docstrings(KernelRidgeImpl, _combined_schemas)
KernelRidge = make_operator(KernelRidgeImpl, _combined_schemas)
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Projected array.",
"anyOf": [
{
"type": "array",
"items": {"laleType": "Any", "XXX TODO XXX": "item type"},
"XXX TODO XXX": "numpy array or scipy sparse of shape [n_samples, n_components]",
},
{"type": "array", "items": {"type": "array", "items": {"type": "number"}}},
],
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.random_projection.GaussianRandomProjection#sklearn-random_projection-gaussianrandomprojection",
"import_from": "sklearn.random_projection",
"type": "object",
"tags": {"pre": [], "op": ["transformer"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(GaussianRandomProjectionImpl, _combined_schemas)
GaussianRandomProjection = make_operator(
GaussianRandomProjectionImpl, _combined_schemas
)
},
],
"description": "The input samples",
}
},
}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "The predicted values.",
"anyOf": [
{"type": "array", "items": {"type": "number"}},
{"type": "array", "items": {"type": "array", "items": {"type": "number"}}},
],
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.RandomForestRegressor#sklearn-ensemble-randomforestregressor",
"import_from": "sklearn.ensemble",
"type": "object",
"tags": {"pre": [], "op": ["estimator", "regressor"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
set_docstrings(RandomForestRegressorImpl, _combined_schemas)
RandomForestRegressor = make_operator(RandomForestRegressorImpl, _combined_schemas)
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "The predicted values.",
"type": "array",
"items": {
"type": "number"
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.GradientBoostingRegressor#sklearn-ensemble-gradientboostingregressor",
"import_from": "sklearn.ensemble",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator", "regressor"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
set_docstrings(GradientBoostingRegressorImpl, _combined_schemas)
GradientBoostingRegressor = make_operator(GradientBoostingRegressorImpl,
_combined_schemas)
"such arrays if n_outputs > 1",
"laleType":
"Any",
"XXX TODO XXX":
"array of shape = [n_samples, n_classes], or a list of n_outputs",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.ExtraTreesClassifier#sklearn-ensemble-extratreesclassifier",
"import_from": "sklearn.ensemble",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator", "classifier"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_predict_proba": _input_predict_proba_schema,
"output_predict_proba": _output_predict_proba_schema,
},
}
set_docstrings(ExtraTreesClassifierImpl, _combined_schemas)
ExtraTreesClassifier = make_operator(ExtraTreesClassifierImpl,
_combined_schemas)
"documentation_url":
"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.k_means.html",
"import_from": "sklearn.cluster",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer", "clustering", "estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
KMeans = make_operator(SKLModel, _combined_schemas)
if sklearn.__version__ >= "1.0":
# old: https://scikit-learn.org/0.24/modules/generated/sklearn.cluster.KMeans.html
# new: https://scikit-learn.org/1.0/modules/generated/sklearn.cluster.KMeans.html
KMeans = KMeans.customize_schema(
precompute_distances=None,
n_jobs=None,
set_as_available=True,
)
set_docstrings(KMeans)
},
],
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis#sklearn-discriminant_analysis-quadraticdiscriminantanalysis",
"import_from": "sklearn.discriminant_analysis",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_predict_proba": _input_predict_proba_schema,
"output_predict_proba": _output_predict_proba_schema,
"input_decision_function": _input_decision_function_schema,
"output_decision_function": _output_decision_function_schema,
},
}
QuadraticDiscriminantAnalysis = make_operator(
_QuadraticDiscriminantAnalysisImpl, _combined_schemas)
set_docstrings(QuadraticDiscriminantAnalysis)
"X": {
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": "Data matrix to be transformed by the model",
}
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Transformed data",
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.decomposition.NMF#sklearn-decomposition-nmf",
"import_from": "sklearn.decomposition",
"type": "object",
"tags": {"pre": [], "op": ["transformer"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
NMF = make_operator(_NMFImpl, _combined_schemas)
set_docstrings(NMF)
"properties": {
"X": {
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": "New data.",
}
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Reduced version of X",
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.decomposition.TruncatedSVD#sklearn-decomposition-truncatedsvd",
"import_from": "sklearn.decomposition",
"type": "object",
"tags": {"pre": [], "op": ["transformer"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(TruncatedSVDImpl, _combined_schemas)
TruncatedSVD = make_operator(TruncatedSVDImpl, _combined_schemas)
"X": {
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": "Input array.",
},
"y": {"laleType": "Any", "XXX TODO XXX": "(ignored)", "description": ""},
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Transformed input.",
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.preprocessing.FunctionTransformer#sklearn-preprocessing-functiontransformer",
"import_from": "sklearn.preprocessing",
"type": "object",
"tags": {"pre": [], "op": ["transformer"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(FunctionTransformerImpl, _combined_schemas)
FunctionTransformer = make_operator(FunctionTransformerImpl, _combined_schemas)
"items": {
"type": "array",
"items": {
"type": "number"
}
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.naive_bayes.GaussianNB#sklearn-naive_bayes-gaussiannb",
"import_from": "sklearn.naive_bayes",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_predict_proba": _input_predict_proba_schema,
"output_predict_proba": _output_predict_proba_schema,
},
}
set_docstrings(GaussianNBImpl, _combined_schemas)
GaussianNB = make_operator(GaussianNBImpl, _combined_schemas)
"description":
"Confidence scores per (sample, class) combination",
"laleType":
"Any",
"XXX TODO XXX":
"array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.RidgeClassifierCV#sklearn-linear_model-ridgeclassifiercv",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
"input_decision_function": _input_decision_function_schema,
"output_decision_function": _output_decision_function_schema,
},
}
set_docstrings(RidgeClassifierCVImpl, _combined_schemas)
RidgeClassifierCV = make_operator(RidgeClassifierCVImpl, _combined_schemas)
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Document topic distribution for X.",
"laleType": "Any",
"XXX TODO XXX": "shape=(n_samples, n_components)",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.decomposition.LatentDirichletAllocation#sklearn-decomposition-latentdirichletallocation",
"import_from": "sklearn.decomposition",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(LatentDirichletAllocationImpl, _combined_schemas)
LatentDirichletAllocation = make_operator(LatentDirichletAllocationImpl,
_combined_schemas)
_user_validate_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"type": "object",
"tags": {"pre": [], "op": [""], "post": []},
"properties": {
"hyperparams": _user_validate_hyperparam_schema,
# "input_fit": None,
# "input_transform": ,
# "output_transform": _output_transform_schema,
},
}
UserValidatorOp = Ops.make_operator(UserValidatorImpl, _user_validate_combined_schemas)
class TestUserValidator(unittest.TestCase):
def test_validate_none(self):
import re
import jsonschema
self.assertRaisesRegex(
jsonschema.ValidationError,
re.compile(
r"invalid value validate=None.*boolean", re.MULTILINE | re.DOTALL
),
UserValidatorOp,
validate=None,
"http://json-schema.org/draft-04/schema#",
"description":
"Target values",
"laleType":
"Any",
"XXX TODO XXX":
"array of float, shape = [n_samples] or [n_samples, n_outputs]",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor#sklearn-neighbors-radiusneighborsregressor",
"import_from": "sklearn.neighbors",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator", "regressor"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
set_docstrings(RadiusNeighborsRegressorImpl, _combined_schemas)
RadiusNeighborsRegressor = make_operator(RadiusNeighborsRegressorImpl,
_combined_schemas)
"items": {"type": "array", "items": {"type": "number"}},
},
],
"description": "Samples.",
}
},
}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Returns predicted values.",
"type": "array",
"items": {"type": "number"},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LassoLarsCV#sklearn-linear_model-lassolarscv",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {"pre": [], "op": ["estimator", "regressor"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
LassoLarsCV = make_operator(_LassoLarsCVImpl, _combined_schemas)
set_docstrings(LassoLarsCV)
},
},
}
_output_transform_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Binarize each element of X",
"laleType": "Any",
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.preprocessing.Binarizer#sklearn-preprocessing-binarizer",
"import_from": "sklearn.preprocessing",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
Binarizer = make_operator(_BinarizerImpl, _combined_schemas)
set_docstrings(Binarizer)
}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Returns predicted values.",
"type": "array",
"items": {
"type": "number"
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.ElasticNet#sklearn-linear_model-elasticnet",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
set_docstrings(ElasticNetImpl, _combined_schemas)
ElasticNet = make_operator(ElasticNetImpl, _combined_schemas)
"description": "Apply dimensionality reduction to X.",
"type": "array",
"items": {
"type": "array",
"items": {
"type": "number"
}
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.decomposition.PCA#sklearn-decomposition-pca",
"import_from": "sklearn.decomposition",
"type": "object",
"tags": {
"pre": [],
"op": ["transformer"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_transform": _input_transform_schema,
"output_transform": _output_transform_schema,
},
}
set_docstrings(PCAImpl, _combined_schemas)
PCA = make_operator(PCAImpl, _combined_schemas)
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Returns predicted values.",
"type": "array",
"items": {
"type": "number"
},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url":
"https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.ElasticNetCV#sklearn-linear_model-elasticnetcv",
"import_from": "sklearn.linear_model",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
ElasticNetCV = make_operator(_ElasticNetCVImpl, _combined_schemas)
set_docstrings(ElasticNetCV)
"X": {
"type": "array",
"items": {"type": "array", "items": {"type": "number"}},
"description": 'For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train).',
}
},
}
_output_predict_schema = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Perform regression on samples in X.",
"type": "array",
"items": {"type": "number"},
}
_combined_schemas = {
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Combined schema for expected data and hyperparameters.",
"documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.svm.NuSVR#sklearn-svm-nusvr",
"import_from": "sklearn.svm",
"type": "object",
"tags": {"pre": [], "op": ["estimator"], "post": []},
"properties": {
"hyperparams": _hyperparams_schema,
"input_fit": _input_fit_schema,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
NuSVR = make_operator(_NuSVRImpl, _combined_schemas)
set_docstrings(NuSVR)
"description": "Combined schema for expected data and hyperparameters.",
"type": "object",
"tags": {
"pre": [],
"op": ["estimator"],
"post": []
},
"properties": {
"hyperparams": _hyperparam_schema,
"input_fit": _input_schema_fit,
"input_predict": _input_predict_schema,
"output_predict": _output_predict_schema,
},
}
MutatingOp = make_operator(MutatingOpImpl, _combined_schemas)
def fit_clone_fit(op):
op1 = make_sklearn_compat(op)
op1.fit(X=[1, 2], y=[1, 2])
op2 = clone(op1)
fit2 = op2.fit(X=[3, 4], y=[3, 4])
print(fit2)
class TestClone(unittest.TestCase):
def test_clone_clones_op(self):
op = MutatingOp(k=1)
fit_clone_fit(op)
'description': 'Combined schema for expected data and hyperparameters.',
'type': 'object',
'tags': {
'pre': [],
'op': ['estimator'],
'post': []
},
'properties': {
'hyperparams': _hyperparam_schema,
'input_fit': _input_schema_fit,
'input_predict': _input_schema_predict,
'output': _output_schema
}
}
IncreaseRows = make_operator(IncreaseRowsImpl, _combined_schemas)
import sklearn.linear_model
class MyLRImpl:
def __init__(self, penalty='l2', solver='liblinear', C=1.0):
self.penalty = penalty
self.solver = solver
self.C = C
def fit(self, X, y):
result = MyLRImpl(self.penalty, self.solver, self.C)
result._sklearn_model = sklearn.linear_model.LogisticRegression(
penalty=self.penalty, solver=self.solver, C=self.C)
result._sklearn_model.fit(X, y)