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Add curated schema for CalibratedClassifierCV
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| import typing | ||
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| import sklearn | ||
| from sklearn.calibration import CalibratedClassifierCV as Op | ||
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| import lale.operators | ||
| from lale.docstrings import set_docstrings | ||
| from lale.operators import make_operator | ||
| from lale.schemas import AnyOf, Bool, Int, Null | ||
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| _hyperparams_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "Probability calibration with isotonic regression or sigmoid.", | ||
| "allOf": [ | ||
| { | ||
| "type": "object", | ||
| "required": ["base_estimator", "method", "cv"], | ||
| "relevantToOptimizer": ["method", "cv"], | ||
| "additionalProperties": False, | ||
| "properties": { | ||
| "base_estimator": { | ||
| "description": "The classifier whose output decision function needs to be calibrated to offer more accurate predict_proba outputs", | ||
| "default": None, | ||
| "anyOf": [ | ||
| { | ||
| "description": "None uses the default classifier, LinearSVC.", | ||
| "enum": [None], | ||
| }, | ||
| {"laleType": "operator"}, | ||
| ], | ||
| }, | ||
| "method": { | ||
| "description": "The method to use for calibration. Can be ‘sigmoid’ which corresponds to Platt’s method (i.e. a logistic regression model) or ‘isotonic’ which is a non-parametric approach. It is not advised to use isotonic calibration with too few calibration samples (<<1000) since it tends to overfit.", | ||
| "enum": ["sigmoid", "isotonic"], | ||
| "default": "sigmoid", | ||
| }, | ||
| "cv": { | ||
| "description": "Determines the cross-validation splitting strategy", | ||
| "default": None, | ||
| "anyOf": [ | ||
| { | ||
| "description": "use the default 5-fold cross-validation", | ||
| "enum": [None], | ||
| }, | ||
| { | ||
| "type": "integer", | ||
| "minimumForOptimizer": 3, | ||
| "maximumForOptimizer": 4, | ||
| "distribution": "uniform", | ||
| }, | ||
| { | ||
| "laleType": "Any", | ||
| "description": "CV splitter or an iterable yielding (train, test) splits as arrays of indices.", | ||
| }, | ||
| ], | ||
| }, | ||
| }, | ||
| } | ||
| ], | ||
| } | ||
| _input_fit_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "Fit the calibrated model", | ||
| "type": "object", | ||
| "required": ["X", "y"], | ||
| "properties": { | ||
| "X": { | ||
| "type": "array", | ||
| "items": {"type": "array", "items": {"type": "number"}}, | ||
| "description": "Training data.", | ||
| }, | ||
| "y": { | ||
| "type": "array", | ||
| "items": {"type": "number"}, | ||
| "description": "Target values.", | ||
| }, | ||
| "sample_weight": { | ||
| "anyOf": [{"type": "array", "items": {"type": "number"}}, {"enum": [None]}], | ||
| "description": "Sample weights", | ||
| }, | ||
| }, | ||
| } | ||
| _input_predict_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "Predict the target of new samples. Can be different from the", | ||
| "type": "object", | ||
| "required": ["X"], | ||
| "properties": { | ||
| "X": { | ||
| "type": "array", | ||
| "items": {"type": "array", "items": {"type": "number"}}, | ||
| "description": "The samples.", | ||
| } | ||
| }, | ||
| } | ||
| _output_predict_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "The predicted class.", | ||
| "type": "array", | ||
| "items": {"type": "number"}, | ||
| } | ||
| _input_predict_proba_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "Posterior probabilities of classification", | ||
| "type": "object", | ||
| "required": ["X"], | ||
| "properties": { | ||
| "X": { | ||
| "type": "array", | ||
| "items": {"type": "array", "items": {"type": "number"}}, | ||
| "description": "The samples.", | ||
| } | ||
| }, | ||
| } | ||
| _output_predict_proba_schema = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": "The predicted probas.", | ||
| "type": "array", | ||
| "items": {"type": "array", "items": {"type": "number"}}, | ||
| } | ||
| _combined_schemas = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": """`CalibratedClassifierCV`_ : Probability calibration with isotonic regression or sigmoid. | ||
| .. _`CalibratedClassifierCV`: https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV | ||
| """, | ||
| "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.calibrated_classifier_cv.html", | ||
| "import_from": "sklearn.calibration", | ||
| "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, | ||
| }, | ||
| } | ||
| CalibratedClassifierCV = make_operator(Op, _combined_schemas) | ||
|
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||
| if sklearn.__version__ >= "0.24": | ||
| # old: https://scikit-learn.org/0.23/modules/generated/sklearn.calibration.CalibratedClassifierCV.html | ||
| # new: https://scikit-learn.org/0.24/modules/generated/sklearn.calibration.CalibratedClassifierCV.html | ||
| CalibratedClassifierCV = typing.cast( | ||
| lale.operators.PlannedIndividualOp, | ||
| CalibratedClassifierCV.customize_schema( | ||
| n_jobs=AnyOf( | ||
| types=[ | ||
| Int(minimum=1), | ||
| Int(minimum=-1, maximum=-1, desc="Use all the processors"), | ||
| Null(), | ||
| ], | ||
| desc="Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.", | ||
| default=None, | ||
| ), | ||
| ensemble=Bool( | ||
| default=True, desc="Determines how the calibrator is fitted." | ||
| ), | ||
| ), | ||
| ) | ||
|
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| set_docstrings(CalibratedClassifierCV) |
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