Merge branch 'redesign' of http://gitlab.pmf.kg.ac.rs/igrannonica/neuronstellar into redesign

1 files changed, 18 insertions, 22 deletions

diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index d84d9567..2f08d4b4 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -130,7 +130,7 @@ def returnColumnsInfo(dataset):
         #print(NullRows)
         #print(len(NullRows))
         allNullRows=len(NullRows)
-        print(cMatrix.to_json(orient='index'))
+        #print(cMatrix.to_json(orient='index'))
         #json.loads()['data']
     return {'columnInfo':dict,'allNullColl':int(allNullCols),'allNullRows':int(allNullRows),'rowCount':int(rowCount),'colCount':int(colCount),'cMatrix':json.loads(cMatrix.to_json(orient='split'))['data']}
 
@@ -180,17 +180,20 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
     kategorijskekolone=[]
     ###PRETVARANJE NUMERICKIH U KATREGORIJSKE AKO JE KORISNIK TAKO OZNACIO
     columnInfo=paramsDataset['columnInfo']
-    for col in columnInfo:
-        if(col['columnType']=="Kategorijski"):
+    columnTypes=paramsExperiment['columnTypes']
+    for i in range(len(columnInfo)):
+        col=columnInfo[i]
+        if(columnTypes[i]=="categorical" and col['columnName'] in paramsExperiment['inputColumns']):
             data[col['columnName']]=data[col['columnName']].apply(str)
-            kategorijskekolone.append(col['coumnName'])
-
+            kategorijskekolone.append(col['columnName'])
+    #kategorijskekolone=data.select_dtypes(include=['object']).columns
+    print(kategorijskekolone)
     ###NULL
     null_value_options = paramsExperiment["nullValues"]
     null_values_replacers = paramsExperiment["nullValuesReplacers"]
     
     if(null_value_options=='replace'):
-        #print("replace null") #
+        #print("replace null") 
         dict=null_values_replacers
         while(len(dict)>0):
             replace=dict.pop()
@@ -362,9 +365,9 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
         
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt, metrics =paramsModel['metrics'])
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt, metrics = ['accuracy','mae','mse'])
 
-        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
      
         hist=history.history
         #plt.plot(hist['accuracy'])
@@ -416,16 +419,9 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
         
         classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics'])
-
-        print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
-        print(x_train)
-        print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
-        print(y_train)
-        print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
-
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['accuracy','mae','mse'])
 
-        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
         y_pred=classifier.predict(x_test)
         y_pred=(y_pred>=0.5).astype('int')
@@ -470,11 +466,11 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
             classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][i+1]['neurons'], activation=paramsModel['layers'][i+1]['activationFunction'],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj
         
-        classifier.add(tf.keras.layers.Dense(units=1),activation=paramsModel['outputLayerActivationFunction'])
+        classifier.add(tf.keras.layers.Dense(units=1,activation=paramsModel['outputLayerActivationFunction']))
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics'])
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['accuracy','mae','mse'])
 
-        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
         y_pred=classifier.predict(x_test)
         #print(classifier.evaluate(x_test, y_test))
@@ -642,9 +638,9 @@ def manageH5(dataset,params,h5model):
     h5model.summary()
     #ann_viz(h5model, title="My neural network")
 
-    h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics'])
+    h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics = ['accuracy','mae','mse'])
 
-    history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize'])
+    history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=int(params['batchSize']))
     
     y_pred2=h5model.predict(x2)