new

cachedir/joblib/run/estimatorFeatureSelection/1a6b490454e7bf1073feef75f2d33016/metadata.json

@@ -0,0 +1 @@
+{"duration": 6.107879161834717, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F9\n0     7   8   7   8   9  10  10  10\n1     4   5   2   3   4   3   3   3\n2     5   8   7  10   5   7   5   9\n3     3   7   6   4   4   4   6   1\n4     1  10   4   6   4   7   7  10\n..   ..  ..  ..  ..  ..  ..  ..  ..\n694   1   1   2   3   1   1   1   1\n695   1   3   2   1   1   1   1   1\n696   1   3   2   1   2   1   1   1\n697   1   3   3   1   1   1   1   2\n698   1   2   2   1   1   1   1   1\n\n[699 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.0989957156047863,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.0989957154,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=68, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/5c72fbb8672da4068e4afdcb2d8698ee/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.5904359817504883, "input_args": {"Data": "     F1  F2  F4  F5  F6     F7_l2  F9\n0     7   8   8   9  10  3.321928  10\n1     4   5   3   4   3  1.584963   3\n2     5   8  10   5   7  2.321928   9\n3     3   7   4   4   4  2.584963   1\n4     1  10   6   4   7  2.807355  10\n..   ..  ..  ..  ..  ..       ...  ..\n694   1   1   3   1   1  0.000000   1\n695   1   3   1   1   1  0.000000   1\n696   1   3   1   2   1  0.000000   1\n697   1   3   1   1   1  0.000000   2\n698   1   2   1   1   1  0.000000   1\n\n[699 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.0989957156047863,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.0989957154,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=68, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/62f538b973da29371eea779458e0ce55/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.18937611579895, "input_args": {"Data": "     F1  F2  F4  F5  F6  F7  F9\n0     7   8   8   9  10  10  10\n1     4   5   3   4   3   3   3\n2     5   8  10   5   7   5   9\n3     3   7   4   4   4   6   1\n4     1  10   6   4   7   7  10\n..   ..  ..  ..  ..  ..  ..  ..\n694   1   1   3   1   1   1   1\n695   1   3   1   1   1   1   1\n696   1   3   1   2   1   1   1\n697   1   3   1   1   1   1   2\n698   1   2   1   1   1   1   1\n\n[699 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.0989957156047863,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.0989957154,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=68, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

frontend/src/components/DataSetSlider.vue

@@ -3,10 +3,10 @@
       <b-col cols="2" style="margin-left: -35px">
         <label id="data" for="param-dataset" data-toggle="tooltip" data-placement="right" title="Tip: use one of the data sets already provided or upload a new file.">{{ dataset }}</label>
         <select id="selectFile" @change="selectDataSet()">
-            <option value="BreastC.csv" selected>Breast cancer</option>
+            <option value="BreastC.csv">Breast cancer</option>
             <option value="DiabetesC.csv">Indian diabetes</option>
             <option value="HeartC.csv">Heart disease</option>
-            <option value="IrisC.csv">Iris flower</option>
+            <option value="IrisC.csv" selected>Iris flower</option>
             <option value="local">Upload file</option>
         </select>
       </b-col>

frontend/src/components/DataSpace.vue

@@ -118,7 +118,9 @@ export default {
         .attr('fill', 'white');
       
       var axis = d3.svg.axis().scale(xScale)
-      chart.append("g").attr("class", "xAxis").call(axis)
+      chart.append("g").style({'stroke': 'black', 'fill': 'none', 'stroke-width': '6px'}).attr("class", "xAxis").call(axis);
+    
+      d3.selectAll('g.tick').style('stroke-width', 0);
 
       // chart.append("line")
       //   .attr("x1", width/4-25).attr("x2", width/4-25)
@@ -563,7 +565,7 @@ text {
 }
 
 #AllClass { position: relative}
-#BeeSwarm { position: absolute; top: 0; left: 0; z-index: 1}
+#BeeSwarm { position: absolute; top: 0; left: 0; z-index: 1;}
 #Sliders { position: absolute; top: 0; left: 0; z-index: 2}
 #NoAction { position: absolute; top: 0; left: 0; z-index: -1}
 #TextLabels {position: absolute; top: 0; left: 0; z-index: 1}
@@ -572,5 +574,4 @@ text {
     cursor: pointer;
     stroke: yellow;
 }
-
 </style>

frontend/src/components/Main.vue

@@ -149,7 +149,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
-      RetrieveValueFile: 'BreastC', // this is for the default data set
+      RetrieveValueFile: 'IrisC', // this is for the default data set
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
       selectedAlgorithm: '',

frontend/src/components/Results.vue

@@ -1,6 +1,6 @@
 <template>
 <div>
-  <div id="HistoryPlot"></div>
+  <div id="HistoryPlot" ></div>
   <div id="LinePlot"></div>
 </div>
 </template>

run.py

@@ -698,11 +698,11 @@ def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
     return PerFeatureAccuracyLocalPar
 
 
-location = './cachedir'
+# location = './cachedir'
-memory = Memory(location, verbose=0)
+# memory = Memory(location, verbose=0)
 
-# calculating for all algorithms and models the performance and other results
+# # calculating for all algorithms and models the performance and other results
-@memory.cache
+# @memory.cache
 def estimatorFeatureSelection(Data, clf):
 
     resultsFS = []