diff options
Diffstat (limited to 'backend')
| -rw-r--r-- | backend/microservice/api/ml_service.py | 35 |
1 files changed, 34 insertions, 1 deletions
diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index 21ec8fa3..b264b428 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -21,6 +21,7 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from dataclasses import dataclass import statistics as s +from sklearn.metrics import roc_auc_score def returnColumnsInfo(dataset): dict=[] @@ -252,6 +253,26 @@ def train(dataset, params, callback): y_pred=y_pred.flatten() result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred}) classifier.save("temp/"+model_name, save_format='h5') + # ROC multi-klasifikacioni + def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): + + #creating a set of all the unique classes using the actual class list + unique_class = set(actual_class) + roc_auc_dict = {} + for per_class in unique_class: + + #creating a list of all the classes except the current class + other_class = [x for x in unique_class if x != per_class] + + #marking the current class as 1 and all other classes as 0 + new_actual_class = [0 if x in other_class else 1 for x in actual_class] + new_pred_class = [0 if x in other_class else 1 for x in pred_class] + + #using the sklearn metrics method to calculate the roc_auc_score + roc_auc = roc_auc_score(new_actual_class, new_pred_class, average = average) + roc_auc_dict[per_class] = roc_auc + + return roc_auc_dict # # Metrike # @@ -301,5 +322,17 @@ def train(dataset, params, callback): "r2" : r2, "adj_r2" : adj_r2 } + elif(problem_type=="multi-klasifikacioni"): + # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook + 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') + roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred) + + # TODO upload trenirani model nazad na backend -#return TrainingResult(metrics)
\ No newline at end of file + #return TrainingResult(metrics)
\ No newline at end of file |