from sklearn.linear_model.bayes import BayesianRidge as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class BayesianRidgeImpl():
def __init__(self, n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, fit_intercept=True, normalize=False, copy_X=True, verbose=False):
self._hyperparams = {
'n_iter': n_iter,
'tol': tol,
'alpha_1': alpha_1,
'alpha_2': alpha_2,
'lambda_1': lambda_1,
'lambda_2': lambda_2,
'compute_score': compute_score,
'fit_intercept': fit_intercept,
'normalize': normalize,
'copy_X': copy_X,
'verbose': verbose}
[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 BayesianRidge Bayesian ridge regression',
'allOf': [{
'type': 'object',
'required': ['n_iter', 'tol', 'alpha_1', 'alpha_2', 'lambda_1', 'lambda_2', 'compute_score', 'fit_intercept', 'normalize', 'copy_X', 'verbose'],
'relevantToOptimizer': ['n_iter', 'tol', 'compute_score', 'fit_intercept', 'normalize', 'copy_X'],
'additionalProperties': False,
'properties': {
'n_iter': {
'type': 'integer',
'minimumForOptimizer': 5,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 300,
'description': 'Maximum number of iterations. Default is 300.'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.001,
'description': 'Stop the algorithm if w has converged. Default is 1.e-3.'},
'alpha_1': {
'type': 'number',
'default': 1e-06,
'description': 'Hyper-parameter : shape parameter for the Gamma distribution prior'},
'alpha_2': {
'type': 'number',
'default': 1e-06,
'description': 'Hyper-parameter : inverse scale parameter (rate parameter) for the'},
'lambda_1': {
'type': 'number',
'default': 1e-06,
'description': 'Hyper-parameter : shape parameter for the Gamma distribution prior'},
'lambda_2': {
'type': 'number',
'default': 1e-06,
'description': 'Hyper-parameter : inverse scale parameter (rate parameter) for the'},
'compute_score': {
'type': 'boolean',
'default': False,
'description': 'If True, compute the objective function at each step of the model.'},
'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.'},
'verbose': {
'type': 'boolean',
'default': False,
'description': 'Verbose mode when fitting the model.'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the 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. Will be cast to X's dtype if necessary"},
'sample_weight': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Individual weights for each sample'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Samples.'},
'return_std': {
'anyOf': [{
'type': 'boolean'}, {
'enum': [None]}],
'default': None,
'description': 'Whether to return the standard deviation of posterior prediction.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model.',
}
_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)
BayesianRidge = lale.operators.make_operator(BayesianRidgeImpl, _combined_schemas)