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69 lines
2.6 KiB
69 lines
2.6 KiB
#!/usr/bin/env python
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from sklearn.tree import DecisionTreeClassifier as _DecisionTreeClassifier
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from base import ClassifierMixin, BaseAlgo
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from codec import codecs_manager
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from util.param_util import convert_params
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from util.algo_util import tree_summary
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class DecisionTreeClassifier(ClassifierMixin, BaseAlgo):
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def __init__(self, options):
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self.handle_options(options)
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out_params = convert_params(
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options.get('params', {}),
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ints=['random_state', 'max_depth', 'min_samples_split', 'max_leaf_nodes'],
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strs=['criterion', 'splitter', 'max_features'],
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)
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# whitelist valid values for criterion, as error raised by sklearn for invalid values is uninformative
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if 'criterion' in out_params:
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try:
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assert out_params['criterion'] in ['gini', 'entropy']
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except AssertionError:
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raise RuntimeError(
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'Invalid value for option criterion: "%s"' % out_params['criterion']
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)
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# whitelist valid values for splitter, as error raised by sklearn for invalid values is uninformative
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if 'splitter' in out_params:
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try:
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assert out_params['splitter'] in ['best', 'random']
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except AssertionError:
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raise RuntimeError(
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'Invalid value for option splitter: "%s"' % out_params['splitter']
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)
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if 'max_depth' not in out_params:
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out_params.setdefault('max_leaf_nodes', 2000)
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# EAFP... convert max_features to int or float if it is a number.
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try:
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out_params['max_features'] = float(out_params['max_features'])
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max_features_int = int(out_params['max_features'])
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if out_params['max_features'] == max_features_int:
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out_params['max_features'] = max_features_int
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except:
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pass
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self.estimator = _DecisionTreeClassifier(class_weight='balanced', **out_params)
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def summary(self, options):
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if 'args' in options:
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raise RuntimeError('Summarization does not take values other than parameters')
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return tree_summary(self, options)
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@staticmethod
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def register_codecs():
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from codec.codecs import SimpleObjectCodec, TreeCodec
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codecs_manager.add_codec(
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'algos.DecisionTreeClassifier', 'DecisionTreeClassifier', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.tree._classes', 'DecisionTreeClassifier', SimpleObjectCodec
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)
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codecs_manager.add_codec('sklearn.tree._tree', 'Tree', TreeCodec)
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