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| author | Danijel Anđelković <adanijel99@gmail.com> | 2022-05-12 12:49:28 +0200 |
|---|---|---|
| committer | Danijel Anđelković <adanijel99@gmail.com> | 2022-05-12 12:49:28 +0200 |
| commit | 2ce380a489f0c7acda75b7fa659a8148935c7462 (patch) | |
| tree | 8fc0d163a609602f5ea51507dd9c33ce6dfc78e9 /backend/microservice/api/newmlservice.py | |
| parent | 2477f1796ba88ab1ae7d8aa869a55a8b37d1d8bb (diff) | |
| parent | 166bceb500bd3475c196b7aa9ad9f68c4f83cefc (diff) | |
Merge branch 'redesign' of http://gitlab.pmf.kg.ac.rs/igrannonica/neuronstellar into redesign
Diffstat (limited to 'backend/microservice/api/newmlservice.py')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 16 |
1 files changed, 7 insertions, 9 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index c401a3e6..6a863013 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -384,17 +384,15 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.save(filepath, save_format='h5') - - accuracy=metrics.accuracy_score(y_test, y_pred) - macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro') - micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro') - macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro') - micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro') - macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro') - micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') + metrics={} + macro_averaged_precision=sm.precision_score(y_test, y_pred, average = 'macro') + micro_averaged_precision=sm.precision_score(y_test, y_pred, average = 'micro') + macro_averaged_recall=sm.recall_score(y_test, y_pred, average = 'macro') + micro_averaged_recall=sm.recall_score(y_test, y_pred, average = 'micro') + macro_averaged_f1=sm.f1_score(y_test, y_pred, average = 'macro') + micro_averaged_f1=sm.f1_score(y_test, y_pred, average = 'micro') metrics= { - "accuracy" : float(accuracy), "macro_averaged_precision" :float(macro_averaged_precision), "micro_averaged_precision" : float(micro_averaged_precision), "macro_averaged_recall" : float(macro_averaged_recall), |