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-rw-r--r--backend/api/api/Controllers/DatasetController.cs20
-rw-r--r--backend/api/api/Controllers/ModelController.cs40
-rw-r--r--backend/api/api/Models/Model.cs6
-rw-r--r--backend/api/api/Models/Predictor.cs47
-rw-r--r--backend/api/api/Program.cs2
-rw-r--r--backend/api/api/Services/DatasetService.cs12
-rw-r--r--backend/api/api/Services/FillAnEmptyDb.cs13
-rw-r--r--backend/api/api/Services/MlConnectionService.cs13
-rw-r--r--backend/api/api/appsettings.json10
-rw-r--r--backend/microservice/api/controller.py35
-rw-r--r--backend/microservice/api/newmlservice.py75
11 files changed, 134 insertions, 139 deletions
diff --git a/backend/api/api/Controllers/DatasetController.cs b/backend/api/api/Controllers/DatasetController.cs
index e4741412..849d9884 100644
--- a/backend/api/api/Controllers/DatasetController.cs
+++ b/backend/api/api/Controllers/DatasetController.cs
@@ -144,7 +144,6 @@ namespace api.Controllers
[Authorize(Roles = "User,Guest")]
public async Task<ActionResult<Dataset>> Post([FromBody] Dataset dataset)
{
- Console.WriteLine("PROBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA");
string uploaderId = getUserId();
dataset.uploaderId = uploaderId;
@@ -196,7 +195,7 @@ namespace api.Controllers
string ext = ".csv";
- //nesto
+
//Check Directory
if (!Directory.Exists(folderPath))
@@ -279,19 +278,4 @@ namespace api.Controllers
}
}
-}
-
-/*
-{
- "_id": "",
- "name": "name",
- "description": "description",
- "header" : ["ag","rt"],
- "fileId" : "652",
- "extension": "csb",
- "isPublic" : true,
- "accessibleByLink": true,
- "dateCreated": "dateCreated",
- "lastUpdated" : "proba12"
-}
-*/ \ No newline at end of file
+} \ No newline at end of file
diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs
index 39eb7830..be30ae6f 100644
--- a/backend/api/api/Controllers/ModelController.cs
+++ b/backend/api/api/Controllers/ModelController.cs
@@ -211,6 +211,8 @@ namespace api.Controllers
return BadRequest("Bad parameters!");*/
model.uploaderId = getUserId();
+ model.dateCreated = DateTime.Now;
+ model.lastUpdated = DateTime.Now;
var existingModel = _modelService.GetOneModel(model.uploaderId, model.name);
@@ -232,6 +234,44 @@ namespace api.Controllers
}
}
+ // POST api/<ModelController>/stealModel
+ [HttpPost("stealModel")]
+ [Authorize(Roles = "User,Guest")]
+ public ActionResult<Model> StealModel([FromBody] Model model)//, bool overwrite)
+ {
+ bool overwrite = false;
+ //username="" ako je GUEST
+ //Experiment e = _experimentService.Get(model.experimentId); umesto 1 ide e.inputColumns.Length TODO!!!!!!!!!!!!!!!!!
+ //model.inputNeurons = e.inputColumns.Length;
+ /*if (_modelService.CheckHyperparameters(1, model.hiddenLayerNeurons, model.hiddenLayers, model.outputNeurons) == false)
+ return BadRequest("Bad parameters!");*/
+
+ model.uploaderId = getUserId();
+ model._id = "";
+ model.dateCreated = DateTime.Now;
+ model.lastUpdated = DateTime.Now;
+ model.isPublic = false;
+
+ var existingModel = _modelService.GetOneModel(model.uploaderId, model.name);
+
+
+ if (existingModel != null && !overwrite && model.validationSize < 1 && model.validationSize > 0)
+ return NotFound($"Model already exisits or validation size is not between 0-1");
+ else
+ {
+ //_modelService.Create(model);
+ //return Ok();
+ if (existingModel == null)
+ _modelService.Create(model);
+ else
+ {
+ _modelService.Replace(model);
+ }
+
+ return CreatedAtAction(nameof(Get), new { id = model._id }, model);
+ }
+ }
+
// PUT api/<ModelController>/{name}
[HttpPut("{name}")]
[Authorize(Roles = "User,Guest")]
diff --git a/backend/api/api/Models/Model.cs b/backend/api/api/Models/Model.cs
index a807316f..bbbf201e 100644
--- a/backend/api/api/Models/Model.cs
+++ b/backend/api/api/Models/Model.cs
@@ -14,18 +14,14 @@ namespace api.Models
public string name { get; set; }
public string description { get; set; }
- //datetime
public DateTime dateCreated { get; set; }
public DateTime lastUpdated { get; set; }
- //proveriti id
- //public string experimentId { get; set; }
//Neural net training
public string type { get; set; }
public string optimizer { get; set; }
public string lossFunction { get; set; }
- //public int inputNeurons { get; set; }
public int hiddenLayers { get; set; }
public string batchSize { get; set; }
public string learningRate { get; set; }
@@ -36,8 +32,6 @@ namespace api.Models
public string[] metrics { get; set; }
public int epochs { get; set; }
- //public bool isTrained { get; set; }
- //public NullValues[] nullValues { get; set; }
public bool randomOrder { get; set; }
public bool randomTestSet { get; set; }
public float randomTestSetDistribution { get; set; }
diff --git a/backend/api/api/Models/Predictor.cs b/backend/api/api/Models/Predictor.cs
index 530257b2..5a7b5eda 100644
--- a/backend/api/api/Models/Predictor.cs
+++ b/backend/api/api/Models/Predictor.cs
@@ -18,38 +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; }
- }
-
-}
-
-/**
-* Paste one or more documents here
-
-{
- "_id": {
- "$oid": "625dc348b7856ace8a6f8702"
-
- },
- "uploaderId" : "6242ea59486c664208d4255c",
- "inputs": ["proba",
- "proba2",
- "proba3"
- ],
- "output" : "izlaz",
- "isPublic" : true,
- "accessibleByLink" : true,
- "dateCreated" : "2022-04-11T20:33:26.937+00:00",
- "experimentId" : "Neki id eksperimenta",
- "modelId" : "Neki id eksperimenta",
- "h5FileId" : "Neki id eksperimenta",
- "metrics" : [{ }]
-}*/ \ No newline at end of file
+ }*/
+
+} \ 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/Services/DatasetService.cs b/backend/api/api/Services/DatasetService.cs
index f38a363b..0b84721e 100644
--- a/backend/api/api/Services/DatasetService.cs
+++ b/backend/api/api/Services/DatasetService.cs
@@ -104,16 +104,6 @@ namespace api.Services
return dataset._id;
}
- /*
-public bool CheckDb()
-{
- Dataset? dataset = null;
- dataset = _dataset.Find(dataset => dataset.username == "igrannonica").FirstOrDefault();
-
- if (dataset != null)
- return false;
- else
- return true;
-}*/
+
}
}
diff --git a/backend/api/api/Services/FillAnEmptyDb.cs b/backend/api/api/Services/FillAnEmptyDb.cs
index 811e723a..cd35dc78 100644
--- a/backend/api/api/Services/FillAnEmptyDb.cs
+++ b/backend/api/api/Services/FillAnEmptyDb.cs
@@ -54,7 +54,7 @@ namespace api.Services
dataset._id = "";
dataset.uploaderId = "000000000000000000000000";
- dataset.name = "Titanik dataset(public)";
+ dataset.name = "Titanik dataset (public)";
dataset.description = "Titanik dataset";
dataset.fileId = _fileService.GetFileId(fullPath);
dataset.extension = ".csv";
@@ -277,6 +277,7 @@ namespace api.Services
model.lossFunction = "sparse_categorical_crossentropy";
model.hiddenLayers = 5;
model.batchSize = "64";
+ model.learningRate = "1";
model.outputNeurons = 0;
model.layers = new[]
{
@@ -368,7 +369,7 @@ namespace api.Services
dataset._id = "";
dataset.uploaderId = "000000000000000000000000";
dataset.name = "IMDB-Movie-Data Dataset (public)";
- dataset.description = "IMDB-Movie-Data Dataset(public)";
+ dataset.description = "IMDB-Movie-Data Dataset (public)";
dataset.fileId = _fileService.GetFileId(fullPath);
dataset.extension = ".csv";
dataset.isPublic = true;
@@ -421,8 +422,8 @@ namespace api.Services
model._id = "";
model.uploaderId = "000000000000000000000000";
- model.name = "IMDB model";
- model.description = "IMDB model";
+ model.name = "IMDB model (public)";
+ model.description = "IMDB model (public)";
model.dateCreated = DateTime.Now;
model.lastUpdated = DateTime.Now;
model.type = "regresioni";
@@ -579,8 +580,8 @@ namespace api.Services
model._id = "";
model.uploaderId = "000000000000000000000000";
- model.name = "Churn model";
- model.description = "Churn model";
+ model.name = "Churn model (public)";
+ model.description = "Churn model (public)";
model.dateCreated = DateTime.Now;
model.lastUpdated = DateTime.Now;
model.type = "binarni-klasifikacioni";
diff --git a/backend/api/api/Services/MlConnectionService.cs b/backend/api/api/Services/MlConnectionService.cs
index 0ecdb1af..6a307e0d 100644
--- a/backend/api/api/Services/MlConnectionService.cs
+++ b/backend/api/api/Services/MlConnectionService.cs
@@ -63,19 +63,6 @@ namespace api.Services
foreach (var connection in ChatHub.getAllConnectionsOfUser(id))
await _ichat.Clients.Client(connection).SendAsync("NotifyDataset",newDataset.name,newDataset._id);
-
- string proba = "";
-
- for (int i = 0; i < newDataset.cMatrix.Length; i++)
- {
- proba = i +" ";
- for (int j = 0; j < newDataset.cMatrix[i].Length; j++)
- proba += newDataset.cMatrix[i][j] + "f, ";
-
- Console.WriteLine(proba);
- proba = "";
- }
-
return;
}
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 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 2025-04-19 20:24:01 +0000