merge Dev q

README.md

@@ -3,7 +3,13 @@
 
 # Setup
 
-First, go to `lib/atm`, and install ATM as described in [here](https://github.com/HDI-Project/ATM)
+First, run the follow commands to clone ATM as a submodule
+
+```
+git submodule init 
+git submodule update
+```
+Then, go to `lib/atm` and install ATM as described in [here](https://github.com/HDI-Project/ATM)
 
 Then go to `server/` and run `pip install -r requirements.txt`
 

package.json

@@ -14,7 +14,7 @@
     "react-scripts-ts": "2.16.0"
   },
   "scripts": {
-    "start": "PORT=7779 react-scripts-ts start",
+    "start": "react-scripts-ts start",
     "build": "react-scripts-ts build",
     "test": "react-scripts-ts test --env=jsdom",
     "eject": "react-scripts-ts eject"

public/viz/pollution_1_test.csv

@@ -0,0 +1,61 @@
+,PREC,JANT,JULT,OVR65,POPN,EDUC,HOUS,DENS,NONW,WWDRK,POOR,HC,NOX,SO@,HUMID,purpose,class
+0,36,27,71,8.1,3.34,11.4,81.5,3243,8.8,42.6,11.7,21,15,59,59,radio/tv,0
+1,35,23,72,11.1,3.14,11,78.8,4281,3.5,50.7,14.4,8,10,39,57,radio/tv,1
+2,44,29,74,10.4,3.21,9.8,81.6,4260,0.8,39.4,12.4,6,6,33,54,education,1
+3,47,45,79,6.5,3.41,11.1,77.5,3125,27.1,50.2,20.6,18,8,24,56,furniture/equipment,1
+4,43,35,77,7.6,3.44,9.6,84.6,6441,24.4,43.7,14.3,43,38,206,55,'new car',1
+5,53,45,80,7.7,3.45,10.2,66.8,3325,38.5,43.1,25.5,30,32,72,54,education,1
+6,43,30,74,10.9,3.23,12.1,83.9,4679,3.5,49.2,11.3,21,32,62,56,furniture/equipment,0
+7,45,30,73,9.3,3.29,10.6,86,2140,5.3,40.4,10.5,6,4,4,56,'used car',0
+8,36,24,70,9,3.31,10.5,83.2,6582,8.1,42.5,12.6,18,12,37,61,radio/tv,1
+9,36,27,72,9.5,3.36,10.7,79.3,4213,6.7,41,13.2,12,7,20,59,'new car',0
+10,52,42,79,7.7,3.39,9.6,69.2,2302,22.2,41.3,24.2,18,8,27,56,'new car',1
+11,33,26,76,8.6,3.2,10.9,83.4,6122,16.3,44.9,10.7,88,63,278,58,business,1
+12,40,34,77,9.2,3.21,10.2,77,4101,13,45.7,15.1,26,26,146,57,radio/tv,1
+13,35,28,71,8.8,3.29,11.1,86.3,3042,14.7,44.6,11.4,31,21,64,60,'new car',1
+14,37,31,75,8,3.26,11.9,78.4,4259,13.1,49.6,13.9,23,9,15,58,'new car',1
+15,35,46,85,7.1,3.22,11.8,79.9,1441,14.8,51.2,16.1,1,1,1,54,radio/tv,0
+16,36,30,75,7.5,3.35,11.4,81.9,4029,12.4,44,12,6,4,16,58,radio/tv,0
+17,15,30,73,8.2,3.15,12.2,84.2,4824,4.7,53.1,12.7,17,8,28,38,business,0
+18,31,27,74,7.2,3.44,10.8,87,4834,15.8,43.5,13.6,52,35,124,59,'used car',1
+19,30,24,72,6.5,3.53,10.8,79.5,3694,13.1,33.8,12.4,11,4,11,61,radio/tv,1
+20,31,45,85,7.3,3.22,11.4,80.7,1844,11.5,48.1,18.5,1,1,1,53,'new car',0
+21,31,24,72,9,3.37,10.9,82.8,3226,5.1,45.2,12.3,5,3,10,61,radio/tv,0
+22,42,40,77,6.1,3.45,10.4,71.8,2269,22.7,41.4,19.5,8,3,5,53,'new car',1
+23,43,27,72,9,3.25,11.5,87.1,2909,7.2,51.6,9.5,7,3,10,56,'used car',0
+24,46,55,84,5.6,3.35,11.4,79.7,2647,21,46.9,17.9,6,5,1,59,furniture/equipment,1
+25,39,29,75,8.7,3.23,11.4,78.6,4412,15.6,46.6,13.2,13,7,33,60,furniture/equipment,1
+26,35,31,81,9.2,3.1,12,78.3,3262,12.6,48.6,13.9,7,4,4,55,radio/tv,0
+27,43,32,74,10.1,3.38,9.5,79.2,3214,2.9,43.7,12,11,7,32,54,radio/tv,0
+28,11,53,68,9.2,2.99,12.1,90.6,4700,7.8,48.9,12.3,648,319,130,47,radio/tv,0
+29,30,35,71,8.3,3.37,9.9,77.4,4474,13.1,42.6,17.7,38,37,193,57,business,1
+30,50,42,82,7.3,3.49,10.4,72.5,3497,36.7,43.3,26.4,15,18,34,59,business,1
+31,60,67,82,10,2.98,11.5,88.6,4657,13.5,47.3,22.4,3,1,1,60,furniture/equipment,0
+32,30,20,69,8.8,3.26,11.1,85.4,2934,5.8,44,9.4,33,23,125,64,'new car',0
+33,25,12,73,9.2,3.28,12.1,83.1,2095,2,51.9,9.8,20,11,26,58,business,0
+34,45,40,80,8.3,3.32,10.1,70.3,2682,21,46.1,24.1,17,14,78,56,furniture/equipment,1
+35,46,30,72,10.2,3.16,11.3,83.2,3327,8.8,45.3,12.2,4,3,8,58,radio/tv,0
+36,54,54,81,7.4,3.36,9.7,72.8,3172,31.4,45.5,24.2,20,17,1,62,education,1
+37,42,33,77,9.7,3.03,10.7,83.5,7462,11.3,48.7,12.4,41,26,108,58,radio/tv,1
+38,42,32,76,9.1,3.32,10.5,87.5,6092,17.5,45.3,13.2,29,32,161,54,'domestic appliance',1
+39,36,29,72,9.5,3.32,10.6,77.6,3437,8.1,45.5,13.8,45,59,263,56,radio/tv,1
+40,37,38,67,11.3,2.99,12,81.5,3387,3.6,50.3,13.5,56,21,44,73,radio/tv,0
+41,42,29,72,10.7,3.19,10.1,79.5,3508,2.2,38.8,15.7,6,4,18,56,radio/tv,0
+42,41,33,77,11.2,3.08,9.6,79.9,4843,2.7,38.6,14.1,11,11,89,54,repairs,1
+43,44,39,78,8.2,3.32,11,79.9,3768,28.6,49.5,17.5,12,9,48,53,'used car',1
+44,32,25,72,10.9,3.21,11.1,82.5,4355,5,46.4,10.8,7,4,18,60,'used car',0
+45,34,32,79,9.3,3.23,9.7,76.8,5160,17.2,45.1,15.3,31,15,68,57,'new car',1
+46,10,55,70,7.3,3.11,12.1,88.9,3033,5.9,51,14,144,66,20,61,radio/tv,0
+47,18,48,63,9.2,2.92,12.2,87.7,4253,13.7,51.2,12,311,171,86,71,'used car',0
+48,13,49,68,7,3.36,12.2,90.7,2702,3,51.9,9.7,105,32,3,71,'new car',0
+49,35,40,64,9.6,3.02,12.2,82.5,3626,5.7,54.3,10.1,20,7,20,72,radio/tv,0
+50,45,28,74,10.6,3.21,11.1,82.6,1883,3.4,41.9,12.3,5,4,20,56,furniture/equipment,0
+51,38,24,72,9.8,3.34,11.4,78,4923,3.8,50.5,11.1,8,5,25,61,'used car',1
+52,31,26,73,9.3,3.22,10.7,81.3,3249,9.5,43.9,13.6,11,7,25,59,radio/tv,1
+53,40,23,71,11.3,3.28,10.3,73.8,1671,2.5,47.4,13.5,5,2,11,60,'used car',0
+54,41,37,78,6.2,3.25,12.3,89.5,5308,25.9,59.7,10.3,65,28,102,52,'new car',1
+55,28,32,81,7,3.27,12.1,81,3665,7.5,51.6,13.2,4,2,1,54,'new car',0
+56,45,33,76,7.7,3.39,11.3,82.2,3152,12.1,47.3,10.9,14,11,42,56,radio/tv,1
+57,45,24,70,11.8,3.25,11.1,79.8,3678,1,44.8,14,7,3,8,56,radio/tv,0
+58,42,33,76,9.7,3.22,9,76.2,9699,4.8,42.2,14.5,8,8,49,54,'new car',0
+59,38,28,72,8.9,3.48,10.7,79.8,3451,11.7,37.5,13,14,13,39,58,furniture/equipment,1

src/assets/methods.json

@@ -426,7 +426,7 @@
     "mlp": {   
         "name": "mlp",
         "class": "sklearn.neural_network.MLPClassifier",
-        "fullname": "multiple layer process",
+        "fullname": "multiple layer perceptron",
         "hyperparameters": {
             "batch_size": {
                 "type": "string",
@@ -476,8 +476,15 @@
         "root_hyperparameters": ["batch_size", "solver", "alpha", "activation", "hidden_layer_sizes", "_scale"],
         "conditional_hyperparameters": {
             "solver": {
-                "sgd": ["learning_rate_init", "learning_rate"],
-                "adam": ["learning_rate_init", "beta_1", "beta_2"]
+                "sgd": [
+                    "learning_rate_init", 
+                    "learning_rate"
+                ],
+                "adam": [
+                    "learning_rate_init", 
+                    "beta_1", 
+                    "beta_2"
+                ]
             }
         }
     },

src/components/DataRuns/Methods.tsx

@@ -186,13 +186,18 @@ class Method extends React.Component<{method:IMethod, datarun:IDataRun}, {}>{
                     tooltip:{},
                     lineStyle: {
                         normal: {
-                            width: 2,
+                            width: 1,
                             opacity: 0.8,
                             color: getColor(method.name)
                         }
                     },
                     data,
-                }
+                },
+                // {
+                //     name: 'bar',
+                //     type: 'bar',
+                //     data: [120, 200, 150, 80, 70, 110, 130]
+                // }
             ],
 
         }

src/components/DataView.tsx

@@ -16,7 +16,7 @@ export interface IState {
 }
 export interface IFeature{
     name: string,
-    data: number[]
+    data: any[] // Revised number[] to any[]
 }
 // export interface IDataset{
 //     name:string,
@@ -30,9 +30,8 @@ export default class DataView extends React.Component<{}, IState>{
         }
     }
     public async getData() {
-        const res = await axios.get('../../viz/pollution_1.csv') // this should be changed to the server response later
-        const datum = res.data
-
+        const res = await axios.get('../../viz/pollution_1_test.csv') // this should be changed to the server response later
+        const datum = res.data.trim() //trim to remove the last blank line
         const lines = datum.split('\n')
         const features = lines[0].split(',').map(
             (feature:string)=>{
@@ -41,10 +40,16 @@ export default class DataView extends React.Component<{}, IState>{
         lines.splice(0, 1)
         const instances = lines
         // for each row
+        // Revised the data type to support the category feature
         instances.forEach((ins:string) => {
             const values = ins.split(',')
             values.forEach((v, idx)=>{
-                features[idx].data.push(parseFloat(v))
+                let _v = parseFloat(v)
+                if(isNaN(_v)){
+                    features[idx].data.push(v)
+                }else{
+                    features[idx].data.push(_v)
+                }
             })
         });
         this.setState({
@@ -110,6 +115,7 @@ export default class DataView extends React.Component<{}, IState>{
 
         //render
         const { dataset } = this.state
+        console.log('this.state',this.state);
         const classes = dataset.pop()
         if(classes){           
             const features = dataset