from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class GradientBoostingRegressorImpl():
def __init__(self, loss='ls', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto', validation_fraction=0.1, n_iter_no_change=None, tol=0.0001):
self._hyperparams = {
'loss': loss,
'learning_rate': learning_rate,
'n_estimators': n_estimators,
'subsample': subsample,
'criterion': criterion,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'min_weight_fraction_leaf': min_weight_fraction_leaf,
'max_depth': max_depth,
'min_impurity_decrease': min_impurity_decrease,
'min_impurity_split': min_impurity_split,
'init': init,
'random_state': random_state,
'max_features': max_features,
'alpha': alpha,
'verbose': verbose,
'max_leaf_nodes': max_leaf_nodes,
'warm_start': warm_start,
'presort': presort,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'tol': tol}
[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 GradientBoostingRegressor Gradient Boosting for regression.',
'allOf': [{
'type': 'object',
'required': ['loss', 'learning_rate', 'n_estimators', 'subsample', 'criterion', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_depth', 'min_impurity_decrease', 'min_impurity_split', 'init', 'random_state', 'max_features', 'alpha', 'verbose', 'max_leaf_nodes', 'warm_start', 'presort', 'validation_fraction', 'n_iter_no_change', 'tol'],
'relevantToOptimizer': ['loss', 'n_estimators', 'min_samples_split', 'min_samples_leaf', 'max_depth', 'max_features', 'alpha', 'presort'],
'additionalProperties': False,
'properties': {
'loss': {
'enum': ['ls', 'lad', 'huber', 'quantile'],
'default': 'ls',
'description': "loss function to be optimized. 'ls' refers to least squares"},
'learning_rate': {
'type': 'number',
'default': 0.1,
'description': 'learning rate shrinks the contribution of each tree by `learning_rate`.'},
'n_estimators': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 100,
'distribution': 'uniform',
'default': 100,
'description': 'The number of boosting stages to perform. Gradient boosting'},
'subsample': {
'type': 'number',
'default': 1.0,
'description': 'The fraction of samples to be used for fitting the individual base'},
'criterion': {
'type': 'string',
'default': 'friedman_mse',
'description': 'The function to measure the quality of a split. Supported criteria'},
'min_samples_split': {
'anyOf': [{
'type': 'integer',
'forOptimizer': False}, {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5,
'distribution': 'uniform'}],
'default': 2,
'description': 'The minimum number of samples required to split an internal node:'},
'min_samples_leaf': {
'anyOf': [{
'type': 'integer',
'forOptimizer': False}, {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5,
'distribution': 'uniform'}],
'default': 1,
'description': 'The minimum number of samples required to be at a leaf node.'},
'min_weight_fraction_leaf': {
'type': 'number',
'default': 0.0,
'description': 'The minimum weighted fraction of the sum total of weights (of all'},
'max_depth': {
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 5,
'distribution': 'uniform',
'default': 3,
'description': 'maximum depth of the individual regression estimators. The maximum'},
'min_impurity_decrease': {
'type': 'number',
'default': 0.0,
'description': 'A node will be split if this split induces a decrease of the impurity'},
'min_impurity_split': {
'anyOf': [{
'type': 'number'}, {
'enum': [None]}],
'default': None,
'description': 'Threshold for early stopping in tree growth. A node will split'},
'init': {
'XXX TODO XXX': 'estimator, optional (default=None)',
'description': 'An estimator object that is used to compute the initial',
'enum': [None],
'default': None},
'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;'},
'max_features': {
'anyOf': [{
'type': 'integer',
'forOptimizer': False}, {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 1.0,
'distribution': 'uniform'}, {
'type': 'string',
'forOptimizer': False}, {
'enum': [None]}],
'default': None,
'description': 'The number of features to consider when looking for the best split:'},
'alpha': {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 0.9,
'description': 'The alpha-quantile of the huber loss function and the quantile'},
'verbose': {
'type': 'integer',
'default': 0,
'description': 'Enable verbose output. If 1 then it prints progress and performance'},
'max_leaf_nodes': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'Grow trees with ``max_leaf_nodes`` in best-first fashion.'},
'warm_start': {
'type': 'boolean',
'default': False,
'description': 'When set to ``True``, reuse the solution of the previous call to fit'},
'presort': {
'XXX TODO XXX': "bool or 'auto', optional (default='auto')",
'description': 'Whether to presort the data to speed up the finding of best splits in',
'enum': ['auto'],
'default': 'auto'},
'validation_fraction': {
'type': 'number',
'default': 0.1,
'description': 'The proportion of training data to set aside as validation set for'},
'n_iter_no_change': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': '``n_iter_no_change`` is used to decide if early stopping will be used'},
'tol': {
'type': 'number',
'default': 0.0001,
'description': 'Tolerance for the early stopping. When the loss is not improving'},
}}, {
'XXX TODO XXX': 'Parameter: min_samples_leaf > only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches'}, {
'description': "alpha, only if loss='huber' or loss='quantile'",
'anyOf': [{
'type': 'object',
'properties': {
'alpha': {
'enum': [0.9]},
}}, {
'type': 'object',
'properties': {
'loss': {
'enum': ['huber', 'quantile']},
}}]}, {
'XXX TODO XXX': 'Parameter: validation_fraction > only used if n_iter_no_change is set to an integer'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the gradient boosting model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input samples. Internally, it will be converted to'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values (strings or integers in classification, real numbers'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'description': 'Sample weights. If None, then samples are equally weighted. Splits'},
'monitor': {
'anyOf': [{
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The monitor is called after each iteration with the current'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict regression target for X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input samples. Internally, it will be converted to'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The 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)
GradientBoostingRegressor = lale.operators.make_operator(GradientBoostingRegressorImpl, _combined_schemas)