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

9 files changed, 88 insertions, 115 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..bfe95a0f 100644
--- a/backend/api/api/Models/Predictor.cs
+++ b/backend/api/api/Models/Predictor.cs
@@ -29,27 +29,4 @@ namespace api.Models
 
     }
 
-}
-
-/** 
-* 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/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/microservice/api/controller.py b/backend/microservice/api/controller.py
index 7852b63d..6f483008 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -82,9 +82,6 @@ def train():
     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"],
@@ -95,13 +92,14 @@ def train():
         "experimentId" : paramsExperiment["_id"],
         "modelId" : paramsModel["_id"],
         "h5FileId" : fileId,
-        "metrics" : m,
+        "metrics" : result,
         "finalMetrics":finalMetrics
-        
     }
     #print(predictor)
+   
     url = config.api_url + "/Predictor/add"
     r = requests.post(url, json=predictor).text
+    
     print(r)
     return r
 
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index fd21f8ce..bcff5a33 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -393,14 +393,14 @@ 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;
@@ -461,20 +461,20 @@ 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
-            }
+        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
 
@@ -514,12 +514,12 @@ 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))
@@ -531,14 +531,16 @@ 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
-            }
+        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,metrics