diff options
Diffstat (limited to 'backend/microservice/api/newmlservice.py')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 41 |
1 files changed, 16 insertions, 25 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index bcff5a33..85be0c2f 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -303,7 +303,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ###OPTIMIZATORI print(paramsModel['optimizer']) if(paramsModel['optimizer']=='Adam'): - opt=tf.keras.optimizers.Adam(learning_rate=3) + opt=tf.keras.optimizers.Adam(learning_rate=float(paramsModel['learningRate'])) elif(paramsModel['optimizer']=='Adadelta'): opt=tf.keras.optimizers.Adadelta(learning_rate=float(paramsModel['learningRate'])) @@ -370,7 +370,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): - classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['accuracy','mae','mse']) history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) @@ -383,9 +383,9 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): scores = classifier.evaluate(x_test, y_test) #print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100)) - + ''' classifier.save(filepath, save_format='h5') - 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') @@ -401,12 +401,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): {"Name":"macro_averaged_f1","JsonValue": str(macro_averaged_f1)}, {"Name":"micro_averaged_f1", "JsonValue": str(micro_averaged_f1)} ] - + ''' #vizuelizacija u python-u #from ann_visualizer.visualize import ann_viz; #ann_viz(classifier, title="My neural network") - return filepath,hist,metrics + return filepath,[hist['loss'],hist['val_loss'],hist['accuracy'],hist['val_accuracy'],hist['mae'],hist['val_mae'],hist['mse'],hist['val_mse']] elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') @@ -444,6 +444,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history + y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -452,7 +453,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # ann_viz(classifier, title="My neural network") classifier.save(filepath, save_format='h5') - + """ accuracy = float(sm.accuracy_score(y_test,y_pred)) precision = float(sm.precision_score(y_test,y_pred)) recall = float(sm.recall_score(y_test,y_pred)) @@ -461,22 +462,9 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): f1 = float(sm.f1_score(y_test,y_pred)) fpr, tpr, _ = sm.roc_curve(y_test,y_pred) logloss = float(sm.log_loss(y_test, y_pred)) - metrics= [ - {"Name":"accuracy" ,"JsonValue": str(accuracy)}, - {"Name":"precision","JsonValue": str(precision)}, - {"Name":"recall" , "JsonValue":str(recall)}, - {"Name":"specificity" ,"JsonValue":str(specificity)}, - {"Name":"f1" ,"JsonValue": str(f1)}, - {"Name":"tn" , "JsonValue":str(tn)}, - {"Name":"fp" , "JsonValue":str(fp)}, - {"Name":"fn" , "JsonValue":str(fn)}, - {"Name":"tp" , "JsonValue":str(tp)}, - {"Name":"fpr" ,"JsonValue": str(fpr.tolist())}, - {"Name":"tpr" , "JsonValue":str(tpr.tolist())}, - {"Name":"logloss" , "JsonValue":str(logloss)} - ] + """ - return filepath,hist,metrics + return filepath,[hist['loss'],hist['val_loss'],hist['accuracy'],hist['val_accuracy'],hist['mae'],hist['val_mae'],hist['mse'],hist['val_mse']] elif(problem_type=='regresioni'): reg=paramsModel['layers'][0]['regularisation'] @@ -519,8 +507,10 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #print(classifier.evaluate(x_test, y_test)) classifier.save(filepath, save_format='h5') - + ''' + mse = float(sm.mean_squared_error(y_test,y_pred)) + mae = float(sm.mean_absolute_error(y_test,y_pred)) mape = float(sm.mean_absolute_percentage_error(y_test,y_pred)) rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred))) @@ -531,6 +521,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): n = 40 k = 2 adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1))) + metrics= [ {"Name":"mse","JsonValue":str(mse)}, @@ -541,8 +532,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): {"Name":"r2","JsonValue":str( r2)}, {"Name":"adj_r2","JsonValue":str(adj_r2)} ] - - return filepath,hist,metrics + ''' + return filepath,[hist['loss'],hist['val_loss'],[],[],hist['mae'],hist['val_mae'],hist['mse'],hist['val_mse']] def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): |