from sklearn.calibration import CalibratedClassifierCV as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class CalibratedClassifierCVImpl():
def __init__(self, base_estimator=None, method='sigmoid', cv=3):
self._hyperparams = {
'base_estimator': base_estimator,
'method': method,
'cv': cv}
[docs] def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
[docs] def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for CalibratedClassifierCV Probability calibration with isotonic regression or sigmoid.',
'allOf': [{
'type': 'object',
'required': ['base_estimator', 'method', 'cv'],
'relevantToOptimizer': ['method', 'cv'],
'additionalProperties': False,
'properties': {
'base_estimator': {
'XXX TODO XXX': 'instance BaseEstimator',
'description': 'The classifier whose output decision function needs to be calibrated',
'enum': [None],
'default': None},
'method': {
'XXX TODO XXX': "'sigmoid' or 'isotonic'",
'description': "The method to use for calibration. Can be 'sigmoid' which",
'enum': ['isotonic', 'sigmoid'],
'default': 'sigmoid'},
'cv': {
'XXX TODO XXX': 'integer, cross-validation generator, iterable or "prefit", optional',
'description': 'Determines the cross-validation splitting strategy.',
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 4,
'distribution': 'uniform',
'default': 3},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the calibrated model',
'type': 'object',
'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. If None, then samples are equally weighted.'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict the target of new samples. Can be different from the',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The predicted class.',
'type': 'array',
'items': {
'type': 'number'},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Posterior probabilities of classification',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The samples.'},
},
}
_output_predict_proba_schema = {
'$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': 'Combined schema for expected data and hyperparameters.',
'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},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
CalibratedClassifierCV = lale.operators.make_operator(CalibratedClassifierCVImpl, _combined_schemas)