# 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.tree.tree
import lale.helpers
import lale.operators
[docs]class DecisionTreeRegressorImpl():
def __init__(self, criterion='mse', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, presort=False):
self._hyperparams = {
'criterion': criterion,
'splitter': splitter,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'min_weight_fraction_leaf': min_weight_fraction_leaf,
'max_features': max_features,
'random_state': random_state,
'max_leaf_nodes': max_leaf_nodes,
'min_impurity_decrease': min_impurity_decrease,
'min_impurity_split': min_impurity_split,
'presort': presort}
[docs] def fit(self, X, y, **fit_params):
self._sklearn_model = sklearn.tree.tree.DecisionTreeRegressor(**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)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'A decision tree regressor.',
'allOf': [
{ 'type': 'object',
'required': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features'],
'relevantToOptimizer': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features'],
'additionalProperties': False,
'properties': {
'criterion': {
'description': 'Function to measure the quality of a split.',
'enum': ['mse', 'friedman_mse', 'mae'],
'default': 'mse'},
'splitter': {
'enum': ['best', 'random'],
'default': 'best',
'description': 'Strategy to choose the split at each node.'},
'max_depth': {
'description': 'Maximum depth of the tree.',
'default': None,
'anyOf': [
{ 'type': 'integer',
'minimum': 1,
'minimumForOptimizer': 3,
'maximumForOptimizer': 5},
{ 'enum': [None],
'description': 'If None, then nodes are expanded until all leaves are pure, or until all leaves contain less than min_samples_split samples.'}]},
'min_samples_split': {
'description': 'Minimum number of samples required to split an internal node.',
'anyOf': [
{ 'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 20,
'distribution': 'uniform'},
{ 'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5}],
'default': 2},
'min_samples_leaf': {
'description': 'Minimum number of samples required to be at a leaf node.',
'anyOf': [
{ 'type': 'integer',
'minimumForOptimizer': 1,
'maximumForOptimizer': 20,
'distribution': 'uniform'},
{ 'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 0.5}],
'default': 1},
'min_weight_fraction_leaf': {
'type': 'number',
'default': 0.0,
'description': 'Minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.'},
'max_features': {
'description': 'The number of features to consider when looking for the best split.',
'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},
'random_state': {
'anyOf': [
{ 'type': 'integer'},
{ 'type': 'object'},
{ 'enum': [None]}],
'default': None},
'max_leaf_nodes': {
'anyOf': [
{ 'type': 'integer'},
{ 'enum': [None]}],
'default': None,
'description': 'Grow a tree with `max_leaf_nodes` in best-first fashion.'},
'min_impurity_decrease': {
'type': 'number',
'default': 0.0,
'description': 'A node will be split if this split induces a decrease of the impurity greater than or equal to this value.'},
'min_impurity_split': {
'anyOf':[
{'type': 'number'},{
'enum': [None]
}],
'default': None,
'description': 'Threshold for early stopping in tree growth.'},
'presort': {
'type': 'boolean',
'default': False,
'description': 'Whether to presort the data to speed up the finding of best splits in fitting.'},
}}]}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Build a decision tree regressor from the training set (X, y).',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'The training input samples. Internally, it will be converted to'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'The target values (real numbers). Use ``dtype=np.float64`` and'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'description': 'Sample weights. If None, then samples are equally weighted. Splits'},
'check_input': {
'type': 'boolean',
'default': True,
'description': 'Allow to bypass several input checking.'},
'X_idx_sorted': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}, {
'enum': [None]}],
'default': None,
'description': 'The indexes of the sorted training input samples. If many tree'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict class or regression value for X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input samples. Internally, its dtype will be converted to'},
'check_input': {
'type': 'boolean',
'default': True,
'description': 'Allow to bypass several input checking.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The predicted classes, or the predict values.',
'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.tree.DecisionTreeRegressor.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},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
DecisionTreeRegressor = lale.operators.make_operator(DecisionTreeRegressorImpl, _combined_schemas)