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347 refactor validation category #368
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…y array output to the classifier predicting tool
…sts for scoring, updated relevant tests and notebook
@msmiyels , do you have time to review this PR in the next couple of days? Ended up doing quite a lot of modeling related stuff, so thought it could be good to involve you. If not, I can ask someone else |
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Hey Nico,
nice work overall! 🐱👤 Left a view comments here and there, but nothing criticial.
I'm a bit torned whether it is better to have one classification_evaluation
rather than separate modules ...label_evaluation
and ...probability_evaluation.
, but it will not make any difference at least for the GUI.
If you have any questions, let me know 📯
eis_toolkit/validation/classification_probability_evaluation.py
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import sys
sys.path.append('..')
is missing to import the toolkit from the notebooks folder. Please also check for the other provided notebooks, I did not investigate all of them 😉
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I think these lines are only needed if files need to be opened in the notebook, which is currently not the case with testing_classifier_evaluation.ipynb
. Also, the other notebooks should be pretty much as they were before, just modified some cells slightly according to the general ML tool modifications.
Hey @msmiyels and thanks for the review! I made some changes according to your review and carried out the renaming |
Do you think this could me merged @msmiyels ? |
Hi Niko, yep, think this PR can be merged 👍 |
New tools added and old ones modified. Here is a brief list of the changes:
get_pa_intersection
toplot_prediction_area_curves.py
module, since it is needed only thereclassifier_probability_evaluation.py
that has the following tools:summarize_probability_metrics
to generate a report dictionary from probability arrayplot_roc_curve
plot_det_curve
plot_precision_recall_curve
plot_calibration_curve
plot_predicted_probability_distribution
classifier_label_evaluation.py
and the following toolssummarize_label_metrics_binary
confusion_matrix
(just direct import from Sklearn, no wrapper)testing_classifier_evaluation.ipynb
to demonstrate the two new modules abovescoring.py
plot_confusion_matrix
machine_learning_predicti.py
predict
tool for classifier and regressor models, so two new functions:predict_classifier
, this tool now can return the probability array based on parameter. NOTE: Testing for Keras models still not adequate enoughpredict_regressor
TODO: