from sklearn.linear_model.ridge import Ridge as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class RidgeImpl():
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None):
self._hyperparams = {
'alpha': alpha,
'fit_intercept': fit_intercept,
'normalize': normalize,
'copy_X': copy_X,
'max_iter': max_iter,
'tol': tol,
'solver': solver,
'random_state': random_state}
[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 Ridge Linear least squares with l2 regularization.',
'allOf': [{
'type': 'object',
'required': ['alpha', 'fit_intercept', 'normalize', 'copy_X', 'max_iter', 'tol', 'solver', 'random_state'],
'relevantToOptimizer': ['alpha', 'fit_intercept', 'normalize', 'copy_X', 'max_iter', 'tol', 'solver'],
'additionalProperties': False,
'properties': {
'alpha': {
'XXX TODO XXX': '{float, array-like}, shape (n_targets)',
'description': 'Regularization strength; must be a positive float. Regularization',
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 1.0},
'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.'},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If True, X will be copied; else, it may be overwritten.'},
'max_iter': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'Maximum number of iterations for conjugate gradient solver.'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.001,
'description': 'Precision of the solution.'},
'solver': {
'enum': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'],
'default': 'auto',
'description': 'Solver to use in the computational routines:'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The seed of the pseudo random number generator to use when shuffling'},
}}, {
'XXX TODO XXX': 'Parameter: solver > only guaranteed on features with approximately the same scale'}],
}
_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'},
'sample_weight': {
'anyOf': [{
'type': 'number'}, {
'type': 'array',
'items': {
'type': 'number'},
}],
'description': 'Individual weights for each sample'},
},
}
_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)
Ridge = lale.operators.make_operator(RidgeImpl, _combined_schemas)