from sklearn.linear_model.ridge import RidgeCV as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class RidgeCVImpl():
def __init__(self, alphas=[0.1, 1.0, 10.0], fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False):
self._hyperparams = {
'alphas': alphas,
'fit_intercept': fit_intercept,
'normalize': normalize,
'scoring': scoring,
'cv': cv,
'gcv_mode': gcv_mode,
'store_cv_values': store_cv_values}
[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)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for RidgeCV Ridge regression with built-in cross-validation.',
'allOf': [{
'type': 'object',
'required': ['alphas', 'fit_intercept', 'normalize', 'scoring', 'cv', 'gcv_mode', 'store_cv_values'],
'relevantToOptimizer': ['fit_intercept', 'normalize', 'cv', 'gcv_mode', 'store_cv_values'],
'additionalProperties': False,
'properties': {
'alphas': {
'type': 'array',
'items': {
'type': 'number'},
'default': [0.1, 1.0, 10.0],
'description': 'Array of alpha values to try.'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'Whether to calculate the intercept for this model. If set'},
'normalize': {
'type': 'boolean',
'default': False,
'description': 'This parameter is ignored when ``fit_intercept`` is set to False.'},
'scoring': {
'anyOf': [{
'type': 'string'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'A string (see model evaluation documentation) or'},
'cv': {
'XXX TODO XXX': 'int, cross-validation generator or an iterable, optional',
'description': 'Determines the cross-validation splitting strategy.',
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 4,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None},
'gcv_mode': {
'enum': [None, 'auto', 'svd', 'eigen'],
'default': None,
'description': 'Flag indicating which strategy to use when performing'},
'store_cv_values': {
'type': 'boolean',
'default': False,
'description': 'Flag indicating if the cross-validation values corresponding to'},
}}, {
'XXX TODO XXX': 'Parameter: store_cv_values > only compatible with cv=none (i'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit Ridge regression model',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data'},
'y': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': "Target values. Will be cast to X's dtype if necessary"},
'sample_weight': {
'anyOf': [{
'type': 'number'}, {
'type': 'array',
'items': {
'type': 'number'},
}],
'description': 'Sample weight'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array_like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Returns predicted values.',
'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},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
RidgeCV = lale.operators.make_operator(RidgeCVImpl, _combined_schemas)