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-rw-r--r--backend/api/api/Models/Predictor.cs22
-rw-r--r--backend/api/api/Program.cs2
-rw-r--r--backend/api/api/appsettings.json10
-rw-r--r--backend/microservice/api/controller.py31
-rw-r--r--backend/microservice/api/newmlservice.py43
5 files changed, 64 insertions, 44 deletions
diff --git a/backend/api/api/Models/Predictor.cs b/backend/api/api/Models/Predictor.cs
index bfe95a0f..5a7b5eda 100644
--- a/backend/api/api/Models/Predictor.cs
+++ b/backend/api/api/Models/Predictor.cs
@@ -18,15 +18,25 @@ namespace api.Models
public string experimentId { get; set; }
public string modelId { get; set; }
public string h5FileId { get; set; }
- public Metric[] metrics { get; set; }
- public Metric[] finalMetrics { get; set; }
- }
- public class Metric
+ //public Metric[] metrics { get; set; }
+
+ public float[] metricsLoss { get; set; }
+ public float[] metricsValLoss { get; set; }
+ public float[] metricsAcc { get; set; }
+ public float[] metricsValAcc { get; set; }
+ public float[] metricsMae { get; set; }
+ public float[] metricsValMae { get; set; }
+ public float[] metricsMse { get; set; }
+ public float[] metricsValMse { get; set; }
+ //public Metric[] finalMetrics { get; set; }
+ }
+
+ /*public class Metric
{
string Name { get; set; }
string JsonValue { get; set; }
- }
-
+ }*/
+
} \ No newline at end of file
diff --git a/backend/api/api/Program.cs b/backend/api/api/Program.cs
index cf64d58d..5977e843 100644
--- a/backend/api/api/Program.cs
+++ b/backend/api/api/Program.cs
@@ -37,7 +37,7 @@ builder.Services.AddScoped<IFileService, FileService>();
builder.Services.AddScoped<IJwtToken, JwtToken>();
builder.Services.AddScoped<IExperimentService, ExperimentService>();
builder.Services.AddHostedService<TempFileService>();
-builder.Services.AddHostedService<FillAnEmptyDb>();
+//builder.Services.AddHostedService<FillAnEmptyDb>();
//Ml Api Ip Filter
builder.Services.AddScoped<MlApiCheckActionFilter>(container =>
diff --git a/backend/api/api/appsettings.json b/backend/api/api/appsettings.json
index 44d63ac3..b502efeb 100644
--- a/backend/api/api/appsettings.json
+++ b/backend/api/api/appsettings.json
@@ -16,22 +16,26 @@
"UserStoreDatabaseSettings": {
/* LocalHost*/
- /*"ConnectionString": "mongodb://127.0.0.1:27017/",
+ "ConnectionString": "mongodb://127.0.0.1:27017/",
"DatabaseName": "si_project",
"CollectionName": "users",
"DatasetCollectionName": "Dataset",
"ModelCollectionName": "Model",
"PredictorCollectionName": "Predictor",
"FilesCollectionName": "Files",
- "ExperimentCollectionName": "Experiment"*/
+ "ExperimentCollectionName": "Experiment"
+ /*
"ConnectionString": "mongodb+srv://si_user:si_user@sidatabase.twtfm.mongodb.net/myFirstDatabase?retryWrites=true&w=majority",
+
+ "ConnectionString": "mongodb+srv://si_user:si_user@sidatabase.twtfm.mongodb.net/myFirstDatabase?retryWrites=true&w=majority",
+ 529394dd526811e059dfb5f8f76597ffd90b2fea
"DatabaseName": "si_db",
"CollectionName": "users",
"DatasetCollectionName": "Dataset",
"ModelCollectionName": "Model",
"PredictorCollectionName": "Predictor",
"FilesCollectionName": "Files",
- "ExperimentCollectionName": "Experiment"
+ "ExperimentCollectionName": "Experiment" */
}
} \ No newline at end of file
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 6f483008..c82634a2 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -69,15 +69,26 @@ 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']})
@@ -92,15 +103,21 @@ def train():
"experimentId" : paramsExperiment["_id"],
"modelId" : paramsModel["_id"],
"h5FileId" : fileId,
- "metrics" : result,
- "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 427111a7..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']
@@ -514,15 +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
- print()
+
y_pred=classifier.predict(x_test)
#print(classifier.evaluate(x_test, y_test))
classifier.save(filepath, save_format='h5')
- metrics={}
+ '''
mse = float(sm.mean_squared_error(y_test,y_pred))
- metrics.append
+
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)))
@@ -533,6 +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)},
@@ -543,8 +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"):
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-19 18:53:25 +0000