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42 lines
1.3 KiB
42 lines
1.3 KiB
#!/usr/bin/env python
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import pandas as pd
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from sklearn.linear_model import LinearRegression as _LinearRegression
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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 LinearRegression(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(options.get('params', {}), bools=['fit_intercept'])
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self.estimator = _LinearRegression(**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(
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'algos.LinearRegression', 'LinearRegression', SimpleObjectCodec
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)
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codecs_manager.add_codec(
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'sklearn.linear_model._base', 'LinearRegression', SimpleObjectCodec
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)
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