Fix linting errors

epfml · Feb 8, 2024 · 46dd452 · 46dd452
1 parent 998f6db
commit 46dd452
Showing 1 changed file with 33 additions and 33 deletions.
diff --git a/discojs/discojs-core/src/validation/validator.ts b/discojs/discojs-core/src/validation/validator.ts
@@ -41,36 +41,36 @@ export class Validator {
 
     let hits = 0
     await data.preprocess().dataset.batch(batchSize)
-    .forEachAsync((e) => {
-      if (typeof e === 'object' && 'xs' in e && 'ys' in e) {
-        const xs = e.xs as tf.Tensor
+      .forEachAsync((e) => {
+        if (typeof e === 'object' && 'xs' in e && 'ys' in e) {
+          const xs = e.xs as tf.Tensor
 
-        const ys = this.getLabel(e.ys as tf.Tensor)
-        const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
+          const ys = this.getLabel(e.ys as tf.Tensor)
+          const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
 
-        const currentFeatures = xs.arraySync()
+          const currentFeatures = xs.arraySync()
 
-        if (Array.isArray(currentFeatures)) {
-          features = features.concat(currentFeatures)
-        } else {
-          throw new TypeError('features array is not correct')
-        }
+          if (Array.isArray(currentFeatures)) {
+            features = features.concat(currentFeatures)
+          } else {
+            throw new TypeError('features array is not correct')
+          }
 
-        groundTruth.push(...Array.from(ys))
-        predictions.push(...Array.from(pred))
+          groundTruth.push(...Array.from(ys))
+          predictions.push(...Array.from(pred))
 
-        this.size += xs.shape[0]
+          this.size += xs.shape[0]
 
-        hits += List(pred).zip(List(ys)).filter(([p, y]) => p === y).size
+          hits += List(pred).zip(List(ys)).filter(([p, y]) => p === y).size
 
-        // TODO: Confusion Matrix stats
+          // TODO: Confusion Matrix stats
 
-        const currentAccuracy = hits / this.size
-        this.graphInformant.updateAccuracy(currentAccuracy)
-      } else {
-        throw new Error('missing feature/label in dataset')
-      }
-    })
+          const currentAccuracy = hits / this.size
+          this.graphInformant.updateAccuracy(currentAccuracy)
+        } else {
+          throw new Error('missing feature/label in dataset')
+        }
+      })
     this.logger.success(`Obtained validation accuracy of ${this.accuracy}`)
     this.logger.success(`Visited ${this.visitedSamples} samples`)
 
@@ -105,20 +105,20 @@ export class Validator {
 
     await data.preprocess().dataset.batch(batchSize)
       .forEachAsync(e => {
-          const xs = e as tf.Tensor
-          const currentFeatures = xs.arraySync()
-
-          if (Array.isArray(currentFeatures)) {
-            features = features.concat(currentFeatures)
-          } else {
-            throw new TypeError('features array is not correct')
-          }
+        const xs = e as tf.Tensor
+        const currentFeatures = xs.arraySync()
 
-          const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
-          predictions.push(...Array.from(pred))
+        if (Array.isArray(currentFeatures)) {
+          features = features.concat(currentFeatures)
+        } else {
+          throw new TypeError('features array is not correct')
+        }
+
+        const pred = this.getLabel(model.predict(xs, { batchSize }) as tf.Tensor)
+        predictions.push(...Array.from(pred))
       })
 
-    return (List(features).zip(List(predictions)) as List<[Features, number]>)
+    return List(features).zip(List(predictions))
       .map(([f, p]) => ({ features: f, pred: p }))
       .toArray()
   }