feat(index): added status update (#21)

* feat(index): added status update

... For a better DX/UX

* chore(playground): update

Author: Berkmann18

playground/playground.js

@@ -47,3 +47,5 @@ writeFileSync(
   'playground-fullStats.json',
   JSON.stringify(longStats, null, 2),
 ) && console.log('Saved learner to "playground-fullStats.json"')
+
+process.exit(0)

src/index.js

@@ -8,6 +8,17 @@ const classifierBuilder = require('./classifier')
 const categories = require('./categories')
 const ConfusionMatrix = require('./confusionMatrix')
 
+const spinner = new Spinner('Loading...', [
+  '⣾',
+  '⣽',
+  '⣻',
+  '⢿',
+  '⡿',
+  '⣟',
+  '⣯',
+  '⣷',
+])
+
 /**
  * NodeJS Classification-based learner.
  * @class Learner
@@ -60,20 +71,11 @@ class Learner {
    */
   train(trainSet = this.trainSet) {
     //@todo Move this so it could be used for any potentially lengthy ops
-    const training = new Spinner('Training...', [
-      '⣾',
-      '⣽',
-      '⣻',
-      '⢿',
-      '⡿',
-      '⣟',
-      '⣯',
-      '⣷',
-    ])
-    training.start()
+    spinner.message('Training...')
+    spinner.start()
     this.classifier.trainBatch(trainSet)
-    training.message('Training complete')
-    training.stop()
+    // spinner.message('Training complete')
+    spinner.stop()
   }
 
   /**
@@ -82,18 +84,27 @@ class Learner {
    * @public
    */
   eval() {
+    spinner.message('Evaluating...')
+    spinner.start()
     const actual = []
     const predicted = []
+    const len = this.testSet.length
+    let idx = 0
     for (const data of this.testSet) {
       const predictions = this.classify(data.input)
       actual.push(data.output)
       predicted.push(predictions.length ? predictions[0] : 'null') //Ignores the rest (as it only wants one guess)
+      spinner.message(
+        `Evaluating instances (${Math.round((idx++ / len) * 10000) / 100}%)`,
+      )
     }
     this.confusionMatrix = ConfusionMatrix.fromData(
       actual,
       predicted,
       categories,
     )
+    // spinner.message('Evaluation complete')
+    spinner.stop()
     return this.confusionMatrix.getStats()
   }
 
@@ -182,29 +193,30 @@ class Learner {
     F_1 (or effectiveness)  = 2 * (Pr * R) / (Pr + R)
     ...
     */
+    spinner.message('Cross-validating...')
+    spinner.start()
     this.macroAvg = new PrecisionRecall()
     this.microAvg = new PrecisionRecall()
+    const set = [...this.trainSet, ...this.validationSet]
 
-    partitions.partitions(
-      [...this.trainSet, ...this.validationSet],
-      numOfFolds,
-      (trainSet, validationSet) => {
-        if (log)
-          process.stdout.write(
-            `Training on ${trainSet.length} samples, testing ${validationSet.length} samples`,
-          )
-        this.train(trainSet)
-        test(
-          this.classifier,
-          validationSet,
-          verboseLevel,
-          this.microAvg,
-          this.macroAvg,
-        )
-      },
-    )
+    partitions.partitions(set, numOfFolds, (trainSet, validationSet) => {
+      const status = `Training on ${trainSet.length} samples, testing ${validationSet.length} samples`
+      //eslint-disable-next-line babel/no-unused-expressions
+      log ? process.stdout.write(status) : spinner.message(status)
+      this.train(trainSet)
+      test(
+        this.classifier,
+        validationSet,
+        verboseLevel,
+        this.microAvg,
+        this.macroAvg,
+      )
+    })
+    spinner.message('Calculating stats')
     this.macroAvg.calculateMacroAverageStats(numOfFolds)
     this.microAvg.calculateStats()
+    // spinner.message('Cross-validation complete')
+    spinner.stop()
     return {
       macroAvg: this.macroAvg.fullStats(), //preferable in 2-class settings or in balanced multi-class settings
       microAvg: this.microAvg.fullStats(), //preferable in multi-class settings (in case of class imbalance)
@@ -278,6 +290,8 @@ class Learner {
    * @public
    */
   getCategoryPartition() {
+    spinner.message('Generating category partitions...')
+    spinner.start()
     const res = {}
     categories.forEach(cat => {
       res[cat] = {
@@ -288,11 +302,14 @@ class Learner {
       }
     })
     this.dataset.forEach(data => {
+      spinner.message(`Adding ${data.output} data`)
       ++res[data.output].overall
       if (this.trainSet.includes(data)) ++res[data.output].train
       if (this.validationSet.includes(data)) ++res[data.output].validation
       if (this.testSet.includes(data)) ++res[data.output].test
     })
+    // spinner.message('Category partitions complete')
+    spinner.stop()
     return res
   }