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print("============ Relative error in total energy: {}".format(metrics.relative_error_total_energy(predicted, ground_truth)))
print("============ Mean absolute error(in Watts): {}".format(metrics.mean_absolute_error(predicted, ground_truth)))
The text was updated successfully, but these errors were encountered:
You are right. Same for the rest of the ipynd. If you have corrected this, feel free to submit a pull request. Otherwise, I am going to fix this in the following days.
Hi!
thanks for the prompt replay. Unfortunately I don't have access to the REDD database since I am not part of an academic institute so I cannot test my changes at that level of the notebook.
I am trying to re run the notebook with the UKDALE dataset but I am getting into another trouble with the metadata of the hdf5 file.
So if you like to make the change by your self feel free to do it; I am not ready yet.
Many thanks again :)
in DAE-example.ipynb the F1 score should be called with rpaf[3]? It should be .45701927 for the example given.
import metrics
rpaf = metrics.recall_precision_accuracy_f1(predicted, ground_truth)
print("============ Recall: {}".format(rpaf[0]))
print("============ Precision: {}".format(rpaf[1]))
print("============ Accuracy: {}".format(rpaf[2]))
print("============ F1 Score: {}".format(rpaf[2]))
print("============ Relative error in total energy: {}".format(metrics.relative_error_total_energy(predicted, ground_truth)))
print("============ Mean absolute error(in Watts): {}".format(metrics.mean_absolute_error(predicted, ground_truth)))
The text was updated successfully, but these errors were encountered: