feat(KNNClassifier): Add loadData() that enables user to load dat… (#279)

*     feat(KNNClassifier): Add loadData() that enables user to load dataset from raw data instead of file

    With load(), file path needs to be provided to load dataset. This means that JSON file that has
    dataset needs to be located on the server. You can give a way to download dataset using save(), but
    you cannot give a way to upload that file and load the dataset. This commit adds loadData() that
    enables user to load dataset from raw data. By combining this method and <INPUT type="file"> tag,
    you can give a way for user to upload dataset file.

* Merge load(path, callback) and loadData(data, callback) into load(pathOrJson, callback).

* Merge load() and loadData() in a cleaner way without using Symbol.

Ref. #279 (comment)

Author: champierre

src/KNNClassifier/index.js

@@ -162,29 +162,33 @@ class KNN {
     await io.saveBlob(JSON.stringify({ dataset, tensors }), fileName, 'application/octet-stream');
   }
 
-  load(path, callback) {
-    io.loadFile(path, (err, data) => {
-      if (data) {
-        const { dataset, tensors } = data;
-        this.mapStringToIndex = Object.keys(dataset).map(key => dataset[key].label);
-        const tensorsData = tensors
-          .map((tensor, i) => {
-            if (tensor) {
-              const values = Object.keys(tensor).map(v => tensor[v]);
-              return tf.tensor(values, dataset[i].shape, dataset[i].dtype);
-            }
-            return null;
-          })
-          .reduce((acc, cur, j) => {
-            acc[j] = cur;
-            return acc;
-          }, {});
-        this.knnClassifier.setClassifierDataset(tensorsData);
-        if (callback) {
-          callback();
-        }
+  async load(pathOrData, callback) {
+    let data;
+    if (typeof pathOrData === 'object') {
+      data = pathOrData;
+    } else {
+      data = await io.loadFile(pathOrData);
+    }
+    if (data) {
+      const { dataset, tensors } = data;
+      this.mapStringToIndex = Object.keys(dataset).map(key => dataset[key].label);
+      const tensorsData = tensors
+        .map((tensor, i) => {
+          if (tensor) {
+            const values = Object.keys(tensor).map(v => tensor[v]);
+            return tf.tensor(values, dataset[i].shape, dataset[i].dtype);
+          }
+          return null;
+        })
+        .reduce((acc, cur, j) => {
+          acc[j] = cur;
+          return acc;
+        }, {});
+      this.knnClassifier.setClassifierDataset(tensorsData);
+      if (callback) {
+        callback();
       }
-    });
+    }
   }
 }
 

src/utils/io.js

@@ -16,11 +16,15 @@ const saveBlob = async (data, name, type) => {
 const loadFile = async (path, callback) => fetch(path)
   .then(response => response.json())
   .then((json) => {
-    callback(null, json);
+    if (callback) {
+      callback(null, json);
+    }
     return json;
   })
   .catch((error) => {
-    callback(error);
+    if (callback) {
+      callback(error);
+    }
     console.error(`There has been a problem loading the file: ${error.message}`);
     throw error;
   });