all metrics?

frontend/src/components/FeatureSpace2.vue

@@ -1,6 +1,5 @@
 <template>
   <div class="column">
-    <svg class="chart"></svg>
     <div id="FeatureGraph2"></div>
   </div>
 </template>
@@ -51,13 +50,12 @@ export default {
       },
       graphVizualization () {
 
+        var svg = d3.select("#FeatureGraph2");
+        svg.selectAll("*").remove();
+
         var width = 600;
         var height = 500;
 
-        var chartWidth = 600;
-        var chartHeight = 60;
-        var margin = {left: 10, right: 10, top: 10, bottom: 40};
-
         var numTicks = 200;
 
         var selectedParams;
@@ -65,36 +63,10 @@ export default {
 
         var dispatch = d3.dispatch('layoutend');
 
-        var svg = d3.select("#FeatureGraph2").append("svg")
+        svg = d3.select("#FeatureGraph2").append("svg")
           .attr("width", width)
           .attr("height", height);
 
-        var chartSvg = d3.select('svg.chart')
-          .attr('width', chartWidth)
-          .attr('height', chartHeight)
-          .append('g')
-          .attr('transform', 'translate(' + margin.left + ',' + margin.top + ')');
-
-        chartWidth = chartWidth - margin.left - margin.right;
-        chartHeight = chartHeight - margin.top - margin.bottom;
-
-        var x = d3.scaleLinear()
-          .domain([0, 1])
-          .range([0, chartWidth]);
-
-        chartSvg.append('g')
-            .attr('transform', 'translate(0,' + chartHeight + ')')
-            .call(d3.axisBottom(x).ticks(7))
-          .append("text")
-            .attr("fill", "#000")
-            .attr('transform', 'translate(' + chartWidth/2 + ',' + 0 + ')')
-            .attr("y", chartHeight + 10)
-            .attr("dy", "0.71em")
-            .attr("text-anchor", "middle")
-            .text("Average readability score");
-
-        var readabilityCircles = chartSvg.append('g').selectAll('circle');
-
         var graph = this.jsonData
 
         var link = svg.append('g')
@@ -110,21 +82,6 @@ export default {
           .data(graph.nodes)
           .enter().append('g')
 
-        var circles = node.append("circle")
-          .attr("r", 5);
-
-        console.log(node)
-
-        var labels = node.append("text")
-          .text(function(d) {
-            console.log(d)
-            return d.name;
-          })
-          .attr('x', 6)
-          .attr('y', 3);
-        
-        node.append('title').text(function (d) { return d.name; });
-
         var paramGroups = [
           {name: 'chargeStrength', values: [-30, -80]},
           {name: 'linkDistance', values: [30, -80]},
@@ -152,33 +109,6 @@ export default {
               .text(function (d) { return d + ' = ' + bestParams[d]; });
           }
 
-          d3.select('.progress').text('Testing ' + (i + 1) + ' of ' + paramList.length + ' parameter settings');
-
-          // Plot the number line.
-          readabilityCircles = readabilityCircles
-            .data(readabilityCircles.data().concat(params))
-            .enter().append('circle')
-            .attr('cx', function (d) { return x(d.graphReadability); })
-            .attr('cy', 5)
-            .attr('r', 4)
-            .on('click', function (d) {
-              selectedParams = d;
-              readabilityCircles.classed('selected', false);
-              d3.select(this).classed('selected', true).raise();
-
-              bestSoFar
-                .data(d3.map(selectedParams).keys().filter(function (d) { return d !== 'positions' && d !== 'graphReadability'; }))
-                .text(function (d) { return d + ' = ' + selectedParams[d]; });
-
-              drawGraph();
-            })
-            .merge(readabilityCircles)
-            .classed('selected', function (d) { return d === selectedParams; });
-
-          readabilityCircles.filter(function (d) { return d === selectedParams; })
-            .raise();
-
-          drawGraph();
         });
 
         var i = 0;
@@ -211,10 +141,77 @@ export default {
 
           ++i;
           if (i >= paramList.length) {
+
+            var circles = node.append("circle")
+              .attr("r", 5);
+
+            var drag_handler = d3.drag()
+              .on("start", drag_start)
+              .on("drag", drag_drag)
+              .on("end", drag_end);	
+              
+            drag_handler(node);
+
+            var labels = node.append("text")
+              .text(function(d) {
+                return d.name;
+              })
+              .attr('x', 6)
+              .attr('y', 3);
+        
+            node.append('title').text(function (d) { return d.name; });
+
+            //add zoom capabilities 
+            var zoom_handler = d3.zoom()
+                .on("zoom", zoom_actions);
+
+            zoom_handler(svg); 
+
+            drawGraph();
+
+            //Zoom functions 
+            function zoom_actions(){
+                svg.attr("transform", d3.event.transform)
+            }
+
+            function drag_start(d) {
+              console.log(d)
+              if (!d3.event.active) forceSim.alphaTarget(0.3).restart();
+                  d.fx = d.x;
+                  d.fy = d.y;
+              }
+
+              //make sure you can't drag the circle outside the box
+              function drag_drag(d) {
+                d.fx = d3.event.x;
+                d.fy = d3.event.y;
+                tickActions();
+              }
+
+              function drag_end(d) {
+                if (!d3.event.active) forceSim.alphaTarget(0);
+                d.fx = null;
+                d.fy = null;
+              }
+
             stepper.stop();
           }
+
         });
 
+        function tickActions() {
+          link
+            .attr('x1', function (d) { return d.source.x; })
+            .attr('x2', function (d) { return d.target.x; })
+            .attr('y1', function (d) { return d.source.y; })
+            .attr('y2', function (d) { return d.target.y; });
+
+          node
+            .attr("transform", function(d) {
+              return "translate(" + d.x + "," + d.y + ")";
+            })
+        };
+
         function drawGraph () {
           graph.nodes.forEach(function (n, i) {
             n.x = selectedParams.positions[i].x;
@@ -244,16 +241,7 @@ export default {
             n.y = n.y + height/2 - yMid;
           });
 
-          link
+          tickActions();
-            .attr('x1', function (d) { return d.source.x; })
-            .attr('x2', function (d) { return d.target.x; })
-            .attr('y1', function (d) { return d.source.y; })
-            .attr('y2', function (d) { return d.target.y; });
-
-          node
-            .attr("transform", function(d) {
-              return "translate(" + d.x + "," + d.y + ")";
-            })
         }
 
         function generateParams (paramGroups, paramList, currParam) {
@@ -295,7 +283,7 @@ export default {
 
       },
       reset () {
-        var svg = d3.select("#FeatureSpaceVisual");
+        var svg = d3.select("#FeatureGraph2");
         svg.selectAll("*").remove();
       },
     },
@@ -328,21 +316,6 @@ svg {
   stroke-width: 1px;
 }
 
-.chart circle {
-  fill: #aaa;
-  fill-opacity: 0.1;
-  stroke: #aaa;
-  stroke-opacity: 0.4;
-  cursor: pointer;
-}
-
-.chart circle.selected {
-  fill: #d30000;
-  fill-opacity: 0.6;
-  stroke: #d30000;
-  stroke-opacity: 0.8;
-}
-
 .column {
   float: left;
   margin: 0 10px;

run.py

@@ -19,6 +19,7 @@ from bayes_opt import BayesianOptimization
 from sklearn.model_selection import cross_validate
 from sklearn.model_selection import cross_val_predict
 from sklearn.preprocessing import OneHotEncoder
+from sklearn.feature_selection import mutual_info_classif
 
 from statsmodels.stats.outliers_influence import variance_inflation_factor
 from statsmodels.tools.tools import add_constant
@@ -466,7 +467,7 @@ def executeModel():
     svc_bayesopt = BayesianOptimization(estimator, params)
     svc_bayesopt.maximize(init_points=5, n_iter=25, acq='ucb')
     bestParams = svc_bayesopt.max['params']
-    estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True)
+    estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED)
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
@@ -540,8 +541,19 @@ def Seperation():
         concatDF1 = pd.concat([DataRows1, hotEncoderDF1], axis=1)
         corrMatrixComb1 = concatDF1.corr()
         corrMatrixComb1 = corrMatrixComb1.iloc[:,-len(uniqueTarget1):]
+        X1 = add_constant(DataRows1.dropna())
+        VIF1 = pd.Series([variance_inflation_factor(X1.values, i) 
+            for i in range(X1.shape[1])], 
+            index=X1.columns)
+        if (len(targetRows1Arr) > 2):
+            MI1 = mutual_info_classif(DataRows1, targetRows1Arr)
+            MI1List = MI1.tolist()
+        else:
+            MI1List = []
     else:
         corrMatrixComb1 = pd.DataFrame()
+        VIF1 = pd.Series()
+        MI1List = []
 
     if (len(targetRows2Arr) > 0):
         onehotEncoder2 = OneHotEncoder(sparse=False)
@@ -551,8 +563,19 @@ def Seperation():
         concatDF2 = pd.concat([DataRows2, hotEncoderDF2], axis=1)
         corrMatrixComb2 = concatDF2.corr()
         corrMatrixComb2 = corrMatrixComb2.iloc[:,-len(uniqueTarget2):]
+        X2 = add_constant(DataRows2.dropna())
+        VIF2 = pd.Series([variance_inflation_factor(X2.values, i) 
+                for i in range(X2.shape[1])], 
+                index=X2.columns)
+        if (len(targetRows2Arr) > 2):
+            MI2 = mutual_info_classif(DataRows2, targetRows2Arr)
+            MI2List = MI2.tolist()
+        else:
+            MI2List = []
     else:
         corrMatrixComb2 = pd.DataFrame()
+        VIF2 = pd.Series()
+        MI2List = []
 
     if (len(targetRows3Arr) > 0):
         onehotEncoder3 = OneHotEncoder(sparse=False)
@@ -562,8 +585,21 @@ def Seperation():
         concatDF3 = pd.concat([DataRows3, hotEncoderDF3], axis=1)
         corrMatrixComb3 = concatDF3.corr()
         corrMatrixComb3 = corrMatrixComb3.iloc[:,-len(uniqueTarget3):]
+        X3 = add_constant(DataRows3.dropna())
+        VIF3 = pd.Series([variance_inflation_factor(X3.values, i) 
+                for i in range(X3.shape[1])], 
+                index=X3.columns)
+        if (len(targetRows3Arr) > 2):
+            print(DataRows3)
+            print(targetRows3Arr)
+            MI3 = mutual_info_classif(DataRows3, targetRows3Arr)
+            MI3List = MI3.tolist()
+        else:
+            MI3List = []
     else:
         corrMatrixComb3 = pd.DataFrame()
+        VIF3 = pd.Series()
+        MI3List = []
 
     if (len(targetRows4Arr) > 0):
         onehotEncoder4 = OneHotEncoder(sparse=False)
@@ -573,16 +609,19 @@ def Seperation():
         concatDF4 = pd.concat([DataRows4, hotEncoderDF4], axis=1)
         corrMatrixComb4 = concatDF4.corr()
         corrMatrixComb4 = corrMatrixComb4.iloc[:,-len(uniqueTarget4):]
+        X4 = add_constant(DataRows4.dropna())
+        VIF4 = pd.Series([variance_inflation_factor(X4.values, i) 
+                for i in range(X4.shape[1])], 
+                index=X4.columns)
+        if (len(targetRows4Arr) > 2):
+            MI4 = mutual_info_classif(DataRows4, targetRows4Arr)
+            MI4List = MI4.tolist()
+        else:
+            MI4List = []
     else:
         corrMatrixComb4 = pd.DataFrame()
-
+        VIF4 = pd.Series()
-    X1 = add_constant(DataRows1.dropna())
+        MI4List = []
-
-    VIF1 = pd.Series([variance_inflation_factor(X1.values, i) 
-                for i in range(X1.shape[1])], 
-                index=X1.columns)
-    
-    print(VIF1)
 
     targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
     targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
@@ -630,6 +669,16 @@ def Seperation():
     packCorr.append(json.dumps(uniqueTarget3))
     packCorr.append(json.dumps(uniqueTarget4))
 
+    packCorr.append(VIF1.to_json())
+    packCorr.append(VIF2.to_json())
+    packCorr.append(VIF3.to_json())
+    packCorr.append(VIF4.to_json())
+
+    packCorr.append(json.dumps(MI1List))
+    packCorr.append(json.dumps(MI2List))
+    packCorr.append(json.dumps(MI3List))
+    packCorr.append(json.dumps(MI4List))
+
     return 'Everything Okay'
 
 @app.route('/data/returnCorrelations', methods=["GET", "POST"])