Update _sklearn.py #67
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check for horizon before X_fit is transformed
| if self.optimize_for_horizon: | ||
| lenX=len(X) | ||
| else: | ||
| lenX=0 | ||
| X_fit, y_fit = self._transform_data_to_tsmodel_input_format(self._X, self._y, lenX) |
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| if self.optimize_for_horizon: | |
| lenX=len(X) | |
| else: | |
| lenX=0 | |
| X_fit, y_fit = self._transform_data_to_tsmodel_input_format(self._X, self._y, lenX) | |
| X_fit, y_fit = self._transform_data_to_tsmodel_input_format(self._X, self._y, len(X) if self.optimize_for_horizon else 0) |
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@ambader Could you bit more explain the idea behind the MR in the description? |
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Sure, I tried to solve the issue raised by @lucheroni (see). It been a while, so Im not totally sure about the details, but as far as i remember, the problem was that hcrystalball sets the length of periods to predict (the horizon) automatically. What I added is a way to check if the users tries to set another length and hands it over to the sklearn function, which can handle the new parameter. |
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Yes, sorry for this big delay. Would you mind addind a test for this part? |
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Please see this notebook showing why we believe it is better idea to keep it as is. 02_ar_modelling_in_sklearn_patched.ipynb.zip FYI |
check for horizon before X_fit is transformed