text: from sklearn import linear_model
  import argparse
  
  from .base import LinearModel
  from .options.learning_rate import LearningRateOptions
  from .options.loss_function import LossFunctionOptions
  from .options.optimizer import OptimizerOptions
  from .options.regularization import RegularizationOptions
  
  
  class SGDRegressor(LinearModel, linear_model.SGDRegressor):
  
      @staticmethod
      def parse_options(options):
          parser = argparse.ArgumentParser(description='Hivemall options for GeneralRegressor')
          parser = LearningRateOptions.setup(parser)
          parser = LossFunctionOptions.setup(parser)
          parser = OptimizerOptions.setup(parser)
          parser = RegularizationOptions.setup(parser)
          opts = vars(parser.parse_known_args(options.split())[0])
          SGDRegressor.validate_options(opts)
          if opts['loss'] == 'squared':
              opts['loss'] = 'squared_loss'
          if opts['learning_rate'] == 'fixed':
              opts['learning_rate'] = 'constant'
          elif opts['learning_rate'] == 'inverse':
              opts['learning_rate'] = 'invscaling'
          return opts
  
      @staticmethod
      def load(conn, table, feature_column='feature', weight_column='weight', bias_feature=None, options=''):
          coef, intercept, vectorizer = super(SGDRegressor, SGDRegressor).load(
              conn, table, feature_column, weight_column, bias_feature)
  
          opts = SGDRegressor.parse_options(options)
          clf = SGDRegressor(**opts)
  
          clf.fit_intercept = bias_feature is not None
  
          clf.intercept_ = intercept
          clf.coef_ = coef
  
          return clf, vectorizer
  