# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sklearn.ensemble.gradient_boosting
import lale.helpers
import lale.operators
[docs]class GradientBoostingClassifierImpl():
def __init__(self, loss='deviance', 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, verbose=0, max_leaf_nodes=None, warm_start=False, presort=None, 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,
'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, **fit_params):
self._sklearn_model = sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(**self._hyperparams)
if fit_params is None:
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X, y, **fit_params)
return self
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
[docs] def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Gradient Boosting for classification.',
'allOf': [{
'type': 'object',
'required': ['init', 'presort'],
'relevantToOptimizer': ['loss', 'learning_rate', 'n_estimators', 'subsample', 'min_samples_split', 'min_samples_leaf', 'max_depth', 'max_features', 'presort', 'n_iter_no_change', 'tol'],
'additionalProperties': False,
'properties': {
'loss': {
'enum': ['deviance', 'exponential'],
'default': 'deviance',
'description': "loss function to be optimized. 'deviance' refers to"},
'learning_rate': {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'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',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 1.0,
'description': 'The fraction of samples to be used for fitting the individual base'},
'criterion': {
'enum': ['friedman_mse', 'mse', 'mae'],
'default': 'friedman_mse',
'description': 'The function to measure the quality of a split. Supported criteria'},
'min_samples_split': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 20,
'distribution': 'uniform'}, {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5}],
'default': 2,
'description': 'The minimum number of samples required to split an internal node:'},
'min_samples_leaf': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 1,
'maximumForOptimizer': 20,
'distribution': 'uniform'}, {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5}],
'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,
'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': {
'anyOf': [{
'type': 'object'}, {
'enum': ['zero', None]}],
'default': None,
'description': 'An estimator object that is used to compute the initial'},
'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',
'minimum': 0.0,
'exclusiveMinimum': True,
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform'}, {
'enum': ['auto', 'sqrt', 'log2', None]}],
'default': None,
'description': 'The number of features to consider when looking for the best split:'},
'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': {
'anyOf': [{
'type': 'boolean'}, {
'enum': ['auto']}],
'default': 'auto',
'description': 'Whether to presort the data to speed up the finding of best splits in'},
'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',
'minimumForOptimizer': 5,
'maximumForOptimizer': 10}, {
'enum': [None]}],
'default': None,
'description': '``n_iter_no_change`` is used to decide if early stopping will be used'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Tolerance for the early stopping. When the loss is not improving'},
}}, {
'description': 'validation_fraction, only used if n_iter_no_change is set to an integer',
'anyOf': [
{ 'type': 'object',
'properties': {
'n_iter_no_change': {'not': {'enum': ['None']}}}},
{ 'type': 'object',
'properties': {
'validation_fraction':{'enum':[0.1]}}}]}]
}
_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': {
'anyOf':[
{'type': 'number'},
{'type': 'string'}]},
'description': 'Target values (strings or integers in classification, real numbers'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Sample weights. If None, then samples are equally weighted. Splits'},
'monitor': {
'type': 'object', #callable, optional
'description': 'The monitor is called after each iteration with the current'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict class for X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input samples. Internally, it will be converted to'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The predicted values.',
'type': 'array',
'items': {
'anyOf':[
{'type': 'number'},
{'type': 'string'}]},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict class probabilities for X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input samples. Internally, it will be converted to'},
},
}
_output_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The class probabilities of the input samples. The order of the',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'documentation_url': 'https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html',
'type': 'object',
'tags': {
'pre': [],
'op': ['estimator'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output': _output_predict_schema,
'input_predict_proba': _input_predict_proba_schema,
'output_predict_proba': _output_predict_proba_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
GradientBoostingClassifier = lale.operators.make_operator(GradientBoostingClassifierImpl, _combined_schemas)