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49 lines
1.5 KiB
49 lines
1.5 KiB
#!/usr/bin/env python
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import pandas as pd
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from sklearn.linear_model import ElasticNet as _ElasticNet
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from base import RegressorMixin, 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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class ElasticNet(RegressorMixin, 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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bools=['fit_intercept'],
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floats=['alpha', 'l1_ratio'],
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)
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if 'l1_ratio' in out_params:
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if out_params['l1_ratio'] < 0 or out_params['l1_ratio'] > 1:
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raise RuntimeError('l1_ratio must be >= 0 and <= 1')
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self.estimator = _ElasticNet(**out_params)
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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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raise RuntimeError(
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'"%s" models do not take options for summarization' % self.__class__.__name__
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)
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df = pd.DataFrame(
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{'feature': self.columns, 'coefficient': self.estimator.coef_.ravel()}
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)
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idf = pd.DataFrame(
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{'feature': ['_intercept'], 'coefficient': [self.estimator.intercept_]}
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)
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return pd.concat([df, idf])
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@staticmethod
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def register_codecs():
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from codec.codecs import SimpleObjectCodec
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codecs_manager.add_codec('algos.ElasticNet', 'ElasticNet', SimpleObjectCodec)
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codecs_manager.add_codec(
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'sklearn.linear_model._coordinate_descent', 'ElasticNet', SimpleObjectCodec
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)
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