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117 lines
4.2 KiB
117 lines
4.2 KiB
#!/usr/bin/env python
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from pandas import DataFrame
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from sklearn.ensemble import GradientBoostingClassifier as _GradientBoostingClassifier
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from cexc import get_messages_logger
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from base import ClassifierMixin, BaseAlgo
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from util.param_util import convert_params
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from util.algo_util import handle_max_features
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from codec import codecs_manager
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from codec.codecs import SimpleObjectCodec
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messages = get_messages_logger()
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class GradientBoostingClassifier(ClassifierMixin, BaseAlgo):
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def __init__(self, options):
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self.handle_options(options)
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params = options.get('params', {})
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out_params = convert_params(
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params,
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strs=['loss', 'max_features'],
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floats=['learning_rate', 'min_weight_fraction_leaf'],
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ints=[
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'n_estimators',
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'max_depth',
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'min_samples_split',
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'min_samples_leaf',
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'max_leaf_nodes',
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'random_state',
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],
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)
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valid_loss = ['deviance', 'exponential']
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if 'loss' in out_params:
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if out_params['loss'] not in valid_loss:
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msg = "loss must be one of: {}".format(', '.join(valid_loss))
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raise RuntimeError(msg)
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if 'max_features' in out_params:
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out_params['max_features'] = handle_max_features(out_params['max_features'])
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if 'max_leaf_nodes' in out_params and 'max_depth' in out_params:
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messages.warn('max_depth ignored when max_leaf_nodes is set')
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self.estimator = _GradientBoostingClassifier(**out_params)
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def apply(self, df, options):
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# needed for backward compatibility with sklearn 0.17
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# since n_features_ was added in version 0.18
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self.estimator.n_features_ = len(self.columns)
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return super(GradientBoostingClassifier, self).apply(df, options)
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def summary(self, options):
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if len(options) != 2: # only model name and mlspl_limits
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msg = '"%s" models do not take options for summarization' % self.__class__.__name__
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raise RuntimeError(msg)
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df = DataFrame(
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{'feature': self.columns, 'importance': self.estimator.feature_importances_.ravel()}
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)
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return df
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@staticmethod
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def register_codecs():
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from codec.codecs import TreeCodec
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codecs_manager.add_codec(
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'algos.GradientBoostingClassifier', 'GradientBoostingClassifier', SimpleObjectCodec
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)
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codecs_manager.add_codec('sklearn.ensemble._gb', 'GradientBoostingClassifier', GBTCodec)
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codecs_manager.add_codec(
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'sklearn.ensemble._gb_losses', 'BinomialDeviance', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble._gb_losses', 'MultinomialDeviance', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble._gb_losses', 'ExponentialLoss', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble.gradient_boosting', 'LogOddsEstimator', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble.gradient_boosting', 'ScaledLogOddsEstimator', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble.gradient_boosting', 'BinomialDeviance', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.ensemble.gradient_boosting', 'PriorProbabilityEstimator', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.tree._classes', 'DecisionTreeRegressor', SimpleObjectCodec
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)
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codecs_manager.add_codec('sklearn.tree._tree', 'Tree', TreeCodec)
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class GBTCodec(SimpleObjectCodec):
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@classmethod
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def encode(cls, obj):
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import sklearn.ensemble
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is_clf = type(obj) == sklearn.ensemble.GradientBoostingClassifier
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is_reg = type(obj) == sklearn.ensemble.GradientBoostingRegressor
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assert is_clf or is_reg
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obj.estimators_ = obj.estimators_.tolist()
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return SimpleObjectCodec.encode(obj)
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@classmethod
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def decode(cls, obj):
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import numpy as np
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obj = SimpleObjectCodec.decode(obj)
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obj.estimators_ = np.array(obj.estimators_)
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return obj
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