from sklearn.ensemble.weight_boosting import AdaBoostRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class AdaBoostRegressorImpl():
def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None):
self._hyperparams = {
'base_estimator': base_estimator,
'n_estimators': n_estimators,
'learning_rate': learning_rate,
'loss': loss,
'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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for AdaBoostRegressor An AdaBoost regressor.',
'allOf': [{
'type': 'object',
'required': ['base_estimator', 'n_estimators', 'learning_rate', 'loss', 'random_state'],
'relevantToOptimizer': ['n_estimators', 'loss'],
'additionalProperties': False,
'properties': {
'base_estimator': {
'anyOf': [{
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The base estimator from which the boosted ensemble is built.'},
'n_estimators': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 100,
'distribution': 'uniform',
'default': 50,
'description': 'The maximum number of estimators at which boosting is terminated.'},
'learning_rate': {
'type': 'number',
'default': 1.0,
'description': 'Learning rate shrinks the contribution of each regressor by'},
'loss': {
'enum': ['linear', 'square', 'exponential'],
'default': 'linear',
'description': 'The loss function to use when updating the weights after each'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'If int, random_state is the seed used by the random number generator;'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Build a boosted regressor from the training set (X, y).',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The training input samples. Sparse matrix can be CSC, CSR, COO,'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'The target values (real numbers).'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Sample weights. If None, the sample weights are initialized to'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict regression value for X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The training input samples. Sparse matrix can be CSC, CSR, COO,'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The predicted regression 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)
AdaBoostRegressor = lale.operators.make_operator(AdaBoostRegressorImpl, _combined_schemas)