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-rw-r--r--backend/microservice/api/controller.py35
-rw-r--r--backend/microservice/api/newmlservice.py75
2 files changed, 59 insertions, 51 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 7852b63d..c82634a2 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -69,22 +69,30 @@ def train():
#dataset, paramsModel, paramsExperiment, callback)
- filepath,result,finalMetrics= newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback)
+ filepath,histMetrics= newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback)
"""
f = request.json['filepath']
dataset = pd.read_csv(f)
filepath,result=newmlservice.train(dataset,request.json['model'],train_callback)
print(result)
"""
-
-
+ #m = []
+ #for attribute, value in result.items():
+ #m.append(histMetrics(attribute,str(value)).__dict__)
+ '''
+ m = []
+ for attribute, value in result.items():
+ m.append({"Name" : attribute, "JsonValue" : value}))
+
+ print("**************************************************************")
+ print(m)
+
+ print("**************************************************************")
+ '''
url = config.api_url + "/file/h5"
files = {'file': open(filepath, 'rb')}
r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']})
fileId=r.text
- m = []
- for attribute, value in result.items():
- m.append({"Name" : attribute, "JsonValue" : value})
predictor = {
"_id" : "",
"uploaderId" : paramsModel["uploaderId"],
@@ -95,14 +103,21 @@ def train():
"experimentId" : paramsExperiment["_id"],
"modelId" : paramsModel["_id"],
"h5FileId" : fileId,
- "metrics" : m,
- "finalMetrics":finalMetrics
-
+ "metricsLoss":histMetrics[0],
+ "metricsValLoss":histMetrics[1],
+ "metricsAcc":histMetrics[2],
+ "metricsValAcc":histMetrics[3],
+ "metricsMae":histMetrics[4],
+ "metricsValMae":histMetrics[5],
+ "metricsMse":histMetrics[6],
+ "metricsValMse":histMetrics[7]
}
#print(predictor)
+
url = config.api_url + "/Predictor/add"
r = requests.post(url, json=predictor).text
- print(r)
+
+ #print(r)
return r
@app.route('/predict', methods = ['POST'])
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index fd21f8ce..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')
@@ -393,20 +393,20 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
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= {
- "macro_averaged_precision" :float(macro_averaged_precision),
- "micro_averaged_precision" : float(micro_averaged_precision),
- "macro_averaged_recall" : float(macro_averaged_recall),
- "micro_averaged_recall" : float(micro_averaged_recall),
- "macro_averaged_f1" : float(macro_averaged_f1),
- "micro_averaged_f1" : float(micro_averaged_f1)
- }
-
+ metrics= [
+ {"Name":"macro_averaged_precision", "JsonValue":str(macro_averaged_precision)},
+ {"Name":"micro_averaged_precision" ,"JsonValue":str(micro_averaged_precision)},
+ {"Name":"macro_averaged_recall", "JsonValue":str(macro_averaged_recall)},
+ {"Name":"micro_averaged_recall" ,"JsonValue":str(micro_averaged_recall)},
+ {"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= {
- "accuracy" : accuracy,
- "precision" : precision,
- "recall" : recall,
- "specificity" : specificity,
- "f1" : f1,
- "tn" : float(tn),
- "fp" : float(fp),
- "fn" : float(fn),
- "tp" : float(tp),
- "fpr" : fpr.tolist(),
- "tpr" : tpr.tolist(),
- "logloss" : 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']
@@ -514,13 +502,15 @@ 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)
#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)))
@@ -531,16 +521,19 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
n = 40
k = 2
adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1)))
- metrics= {"mse" : mse,
- "mae" : mae,
- "mape" : mape,
- "rmse" : rmse,
- "rmsle" : rmsle,
- "r2" : r2,
- "adj_r2" : adj_r2
- }
-
- return filepath,hist,metrics
+
+ metrics= [
+ {"Name":"mse","JsonValue":str(mse)},
+
+ {"Name":"mae","JsonValue":str(mae)},
+ {"Name":"mape","JsonValue":str( mape)},
+ {"Name":"rmse","JsonValue":str(rmse)},
+ {"Name":"rmsle","JsonValue":str(rmsle)},
+ {"Name":"r2","JsonValue":str( r2)},
+ {"Name":"adj_r2","JsonValue":str(adj_r2)}
+ ]
+ '''
+ 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"):
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-20 00:48:31 +0000