def train(self):
""""""
start = time.time()
print('size before truncated outliers is %d ' % len(self.TrainData))
self.TrainData = self.TrainData[
(self.TrainData['logerror'] > self._low) & (self.TrainData['logerror'] < self._up)]
print('size after truncated outliers is %d ' % len(self.TrainData))
X = self.TrainData.drop(self._l_drop_cols, axis=1)
Y = self.TrainData['logerror']
self._l_train_columns = X.columns
X = X.values.astype(np.float32, copy=False)
svr = SVR(C = self._C, epsilon= self._epsilon, tol= 1e-3, kernel= 'linear',max_iter= 100, verbose= True)
self._model = svr.fit(X, Y)
end = time.time()
print('time consumed %d ' % ((end - start)))
self._f_eval_train_model = '{0}/{1}_{2}.pkl'.format(self.OutputDir, self.__class__.__name__,
datetime.now().strftime('%Y%m%d-%H:%M:%S'))
# with open(self._f_eval_train_model, 'wb') as o_file:
# pickle.dump(self._model, o_file, -1)
# o_file.close()
self.TrainData = pd.concat([self.TrainData, self.ValidData[self.TrainData.columns]],
ignore_index=True) ## ignore_index will reset the index or index will be overlaped
return
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