# 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 import lale.docstrings import lale.operators class DecisionTreeRegressorImpl(): def __init__(self, **hyperparams): self._hyperparams = hyperparams self._wrapped_model = sklearn.tree.DecisionTreeRegressor(**self._hyperparams) def fit(self, X, y, **fit_params): self._wrapped_model.fit(X, y, **fit_params) return self def predict(self, X): return self._wrapped_model.predict(X) _hyperparams_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': { 'anyOf': [ { 'type': 'integer', 'minimum': 2, 'forOptimizer': False, 'description': 'Consider min_samples_split as the minimum number.'}, { 'type': 'number', 'minimum': 0.0, 'exclusiveMinimum': True, 'maximum': 1.0, 'minimumForOptimizer': 0.01, 'maximumForOptimizer': 0.5, 'description': 'min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.'}], 'default': 2, 'description': 'The minimum number of samples required to split an internal node.'}, 'min_samples_leaf': { 'anyOf': [ { 'type': 'integer', 'minimum': 1, 'forOptimizer': False, 'description': 'Consider min_samples_leaf as the minimum number.'}, { 'type': 'number', 'minimum': 0.0, 'exclusiveMinimum': True, 'maximum': 0.5, 'description': 'min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.'}], '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': 'Minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.'}, 'max_features': { 'anyOf': [ { 'type': 'integer', 'minimum': 2, 'forOptimizer': False, 'description': 'Consider max_features features at each split.'}, { 'type': 'number', 'minimum': 0.0, 'exclusiveMinimum': True, 'maximum': 1.0, 'distribution': 'uniform', 'description': 'max_features is a fraction and int(max_features * n_features) features are considered at each split.'}, { 'enum': ['auto', 'sqrt', 'log2', None]}], 'default': 'auto', 'description': 'The number of features to consider when looking for the best split.'}, 'random_state': { 'description': 'Seed of pseudo-random number generator.', 'anyOf': [ { 'laleType': 'numpy.random.RandomState'}, { 'description': 'RandomState used by np.random', 'enum': [None]}, { 'description': 'Explicit seed.', 'type': 'integer'}], 'default': None}, 'max_leaf_nodes': { 'anyOf': [ { 'type': 'integer'}, { 'enum': [None], 'description': 'Unlimited number of leaf nodes.'}], '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', 'minimum': 0.0}, { '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 = { 'type': 'object', 'required': ['X', 'y'], 'properties': { 'X': { 'type': 'array', 'description': 'The outer array is over samples aka rows.', 'items': { 'type': 'array', 'description': 'The inner array is over features aka columns.', 'items': { 'type': 'number'}}}, 'y': { 'type': 'array', 'items': { 'type': 'number'}, 'description': 'The target values (real numbers).'}, 'sample_weight': { 'anyOf': [ { 'type': 'array', 'items': {'type': 'number'}}, { 'enum': [None], 'description': 'Samples are equally weighted.'}], 'description': 'Sample weights.'}, '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 = { 'type': 'object', 'properties': { 'X': { 'type': 'array', 'description': 'The outer array is over samples aka rows.', 'items': { 'type': 'array', 'description': 'The inner array is over features aka columns.', 'items': { 'type': 'number'}}}, 'check_input': { 'type': 'boolean', 'default': True, 'description': 'Allow to bypass several input checking.'}}} _output_predict_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': """`Decision tree regressor`_ from scikit-learn. .. _`Decision tree regressor`: https://scikit-learn.org/0.20/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn-tree-decisiontreeregressor """, 'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.decision_tree_regressor.html', 'import_from': 'sklearn.tree', 'type': 'object', 'tags': { 'pre': [], 'op': ['estimator', 'regressor'], 'post': []}, 'properties': { 'hyperparams': _hyperparams_schema, 'input_fit': _input_fit_schema, 'input_predict': _input_predict_schema, 'output_predict': _output_predict_schema}} DecisionTreeRegressor : lale.operators.IndividualOp DecisionTreeRegressor = lale.operators.make_operator(DecisionTreeRegressorImpl, _combined_schemas) if sklearn.__version__ >= '0.22': # old: https://scikit-learn.org/0.20/modules/generated/sklearn.tree.DecisionTreeRegressor.html # new: https://scikit-learn.org/0.23/modules/generated/sklearn.tree.DecisionTreeRegressor.html from lale.schemas import AnyOf, Bool, Enum, Float DecisionTreeRegressor = DecisionTreeRegressor.customize_schema( presort=AnyOf( types=[Bool(), Enum(['deprecated'])], desc='This parameter is deprecated and will be removed in v0.24.', default='deprecated'), ccp_alpha=Float( desc='Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed.', default=0.0, forOptimizer=True, min=0.0, maxForOptimizer=0.1)) lale.docstrings.set_docstrings(DecisionTreeRegressorImpl, DecisionTreeRegressor._schemas)