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Starting from version 1.1.0, scikit-learn class LassoLarsIC takes the keyword argument noise_variance. This causes an error with LEAR model if the calibration window is set to a relatively small value (e.g., 84 days, which is used in the paper). Here are the commands to reproduce the error:
conda create -n epf_env python=3.9
conda activate epf_env
git clone https://github.com/jeslago/epftoolbox.git
cd epftoolbox
pip install .
cd examples
python recalibrating_lear_flexible.py --dataset PJM --years_test 2 --calibration_window 84
ValueError: You are using LassoLarsIC in the case where the number of samples is smaller than the number of features. In this setting, getting a good estimate for the variance of the noise is not possible. Provide an estimate of the noise variance in the constructor.
I obtained the above error with scikit-learn version 1.1.3. When I use scikit-learn 1.0.1, the LEAR model works fine.
The text was updated successfully, but these errors were encountered:
Hi,
Starting from version 1.1.0, scikit-learn class LassoLarsIC takes the keyword argument noise_variance. This causes an error with LEAR model if the calibration window is set to a relatively small value (e.g., 84 days, which is used in the paper). Here are the commands to reproduce the error:
I obtained the above error with scikit-learn version 1.1.3. When I use scikit-learn 1.0.1, the LEAR model works fine.
The text was updated successfully, but these errors were encountered: