diff options
| -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), |