fixed upload

cachedir/joblib/run/GridSearchForModels/34dd4fa44cf8d83f42cfacc70a3fdd71/metadata.json

@@ -0,0 +1 @@
+{"duration": 307.1607220172882, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n                     weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/GridSearchForModels/c426e0f3d8f4e01216f1b2a58820e6ec/metadata.json

@@ -0,0 +1 @@
+{"duration": 393.25071001052856, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, n_estimators=119,\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "576"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,6 +1,6 @@
-# first line: 393
+# first line: 454
 @memory.cache
-def GridSearchForModels(XData,yDataclf, params, eachAlgor, factors, AlgorithmsIDsEnd):
+def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
 
     # instantiate spark session
     spark = (   

frontend/package-lock.json

@@ -3171,9 +3171,9 @@
       "integrity": "sha512-tHq6qdbT9U1IRSGf14CL0pUlULksvY9OZ+5eEgl1N7t+OA3tGvNpxJCzuKQlsNgCVwbAs670L1vcVQi8j9HjnA=="
     },
     "@types/node": {
-      "version": "13.5.0",
+      "version": "13.5.1",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-13.5.0.tgz",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-13.5.1.tgz",
-      "integrity": "sha512-Onhn+z72D2O2Pb2ql2xukJ55rglumsVo1H6Fmyi8mlU9SvKdBk/pUSUAiBY/d9bAOF7VVWajX3sths/+g6ZiAQ=="
+      "integrity": "sha512-Jj2W7VWQ2uM83f8Ls5ON9adxN98MvyJsMSASYFuSvrov8RMRY64Ayay7KV35ph1TSGIJ2gG9ZVDdEq3c3zaydA=="
     },
     "@types/q": {
       "version": "1.5.2",
@@ -21872,9 +21872,9 @@
       "integrity": "sha1-bolgne69fc2vja7Mmuo5z1haCRg="
     },
     "rimraf": {
-      "version": "3.0.0",
+      "version": "3.0.1",
-      "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-3.0.0.tgz",
+      "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-3.0.1.tgz",
-      "integrity": "sha512-NDGVxTsjqfunkds7CqsOiEnxln4Bo7Nddl3XhS4pXg5OzwkLqJ971ZVAAnB+DDLnF76N+VnDEiBHaVV8I06SUg==",
+      "integrity": "sha512-IQ4ikL8SjBiEDZfk+DFVwqRK8md24RWMEJkdSlgNLkyyAImcjf8SWvU1qFMDOb4igBClbTQ/ugPqXcRwdFTxZw==",
       "dev": true,
       "requires": {
         "glob": "^7.1.3"

frontend/package.json

@@ -19,7 +19,7 @@
     "@fortawesome/vue-fontawesome": "^0.1.9",
     "@statnett/vue-plotly": "^0.3.2",
     "@types/d3-drag": "^1.2.3",
-    "@types/node": "^13.5.0",
+    "@types/node": "^13.5.1",
     "ajv": "^6.11.0",
     "audit": "0.0.6",
     "axios": "^0.19.2",
@@ -116,7 +116,7 @@
     "postcss-import": "^12.0.1",
     "postcss-loader": "^3.0.0",
     "postcss-url": "^8.0.0",
-    "rimraf": "^3.0.0",
+    "rimraf": "^3.0.1",
     "sass": "^1.25.0",
     "sass-loader": "^8.0.2",
     "semver": "^7.1.1",

frontend/src/components/DataSetExecController.vue

@@ -53,21 +53,23 @@ export default {
         d3.select("#data").select("input").remove(); // Remove the selection field.
         EventBus.$emit('SendToServerDataSetConfirmation', this.RetrieveValueCSV)
       } else {
+        EventBus.$emit('SendToServerDataSetConfirmation', this.RetrieveValueCSV)
         d3.select("#data").select("input").remove();
         this.dataset = ""
         var data
         d3.select("#data")
           .append("input")
           .attr("type", "file")
-          .style("font-size", "16px")
+          .style("font-size", "18.5px")
           .style("width", "200px")
           .on("change", function() {
             var file = d3.event.target.files[0];
             Papa.parse(file, {
                 header: true,
                 dynamicTyping: true,
+                skipEmptyLines: true,
                 complete: function(results) {
-                  data = results;
+                  data = results.data;
                   EventBus.$emit('SendToServerLocalFile', data)
                 }
               });

frontend/src/components/Main.vue

@@ -317,6 +317,26 @@ export default Vue.extend({
     SendToServerData () {
       // fix that for the upload!
       console.log(this.localFile)
+      const path = `http://127.0.0.1:5000/data/SendtoSeverDataSet`
+
+      const postData = {
+        uploadedData: this.localFile
+      }
+      const axiosConfig = {
+      headers: {
+      'Content-Type': 'application/json',
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+      'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+      }
+      }
+      axios.post(path, postData, axiosConfig)
+      .then(response => {
+        console.log('Sent the new uploaded data to the server!')
+      })
+      .catch(error => {
+      console.log(error)
+      })
     },
     SendSelectedPointsToServer () {
       if (this.ClassifierIDsList === ''){
@@ -489,6 +509,10 @@ export default Vue.extend({
         })
     },
     fileNameSend () {
+      if (this.RetrieveValueFile == "local") {
+        this.DataSpaceCall()
+        this.SendAlgorithmsToServer()
+      } else {
         const path = `http://127.0.0.1:5000/data/ServerRequest`
         const postData = {
           fileName: this.RetrieveValueFile,
@@ -510,6 +534,7 @@ export default Vue.extend({
         .catch(error => {
           console.log(error)
         })
+      }
     },
     DataSpaceCall () {
        const path = `http://localhost:5000/data/requestDataSpaceResults`

run.py

@@ -101,6 +101,12 @@ def Reset():
     global crossValidation
     crossValidation = 3
     
+    # models
+    global KNNModels
+    KNNModels = []
+    global RFModels
+    RFModels = []
+
     global scoring
     #scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
     scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
@@ -192,6 +198,12 @@ def RetrieveFileName():
     global factors
     factors = [1,1,1,1,1]
 
+    # models
+    global KNNModels
+    KNNModels = []
+    global RFModels
+    RFModels = []
+
     global results
     results = []
 
@@ -220,6 +232,66 @@ def RetrieveFileName():
     DataSetSelection()
     return 'Everything is okay'
 
+def Convert(lst): 
+    it = iter(lst) 
+    res_dct = dict(zip(it, it)) 
+    return res_dct 
+
+# Retrieve data set from client
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"])
+def SendToServerData():
+
+    uploadedData = request.get_data().decode('utf8').replace("'", '"')
+    uploadedDataParsed = json.loads(uploadedData)
+    DataResultsRaw = uploadedDataParsed['uploadedData']
+
+    DataResults = copy.deepcopy(DataResultsRaw)
+
+    for dictionary in DataResultsRaw:
+        for key in dictionary.keys():
+            if (key.find('*') != -1):
+                target = key
+                continue
+        continue
+    DataResultsRaw.sort(key=lambda x: x[target], reverse=True)
+    DataResults.sort(key=lambda x: x[target], reverse=True)
+
+    for dictionary in DataResults:
+        del dictionary[target]
+
+    global AllTargets
+    global target_names
+    AllTargets = [o[target] for o in DataResultsRaw]
+    AllTargetsFloatValues = []
+
+    previous = None
+    Class = 0
+    for i, value in enumerate(AllTargets):
+        if (i == 0):
+            previous = value
+            target_names.append(value)
+        if (value == previous):
+            AllTargetsFloatValues.append(Class)
+        else:
+            Class = Class + 1
+            target_names.append(value)
+            AllTargetsFloatValues.append(Class)
+            previous = value
+
+    ArrayDataResults = pd.DataFrame.from_dict(DataResults)
+
+    global XData, yData, RANDOM_SEED
+    XData, yData = ArrayDataResults, AllTargetsFloatValues
+
+    global XDataStored, yDataStored
+    XDataStored = XData.copy()
+    yDataStored = yData.copy()
+
+    callPreResults()
+    
+    return 'Processed uploaded data set'
+
 # Sent data to client 
 @app.route('/data/ClientRequest', methods=["GET", "POST"])
 def CollectionData(): 
@@ -231,6 +303,7 @@ def CollectionData():
 
 def DataSetSelection():
     DataResults = copy.deepcopy(DataResultsRaw)
+
     for dictionary in DataResultsRaw:
         for key in dictionary.keys():
             if (key.find('*') != -1):
@@ -559,11 +632,11 @@ def RetrieveModelsParam():
 
     counter1 = 0
     counter2 = 0
+
     global KNNModels
-    KNNModels = []
     global RFModels
-    RFModels = []
     global algorithmsList
+
     algorithmsList = RetrieveModelsPar['algorithms']
 
     for index, items in enumerate(algorithmsList):
@@ -855,7 +928,7 @@ def ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,DataSpaceList,PredictionSpaceList
     featureScoresCon = featureScoresCon.to_json(orient='records')
     XDataJSONEntireSet = XData.to_json(orient='records')
     XDataJSON = XData.columns.tolist()
-    print(XData)
+
     Results.append(json.dumps(sumPerClassifier)) # Position: 0 
     Results.append(json.dumps(ModelSpaceMDS)) # Position: 1
     Results.append(json.dumps(parametersGenPD)) # Position: 2