Fix minor bug and release latest version on Jsdelrv (Danfojs browser)

babel.config.json

@@ -1,5 +1,18 @@
+
 {
-"presets": [
-    "@babel/preset-env"
+  "presets": [
+    [
+      "@babel/env",
+      {
+        "targets": {
+          "edge": "17",
+          "firefox": "60",
+          "chrome": "67",
+          "safari": "11.1"
+        },
+        "useBuiltIns": "usage",
+        "corejs": "3.6.5"
+      }
+    ]
   ]
 }

danfojs/src/core/frame.js

@@ -12,7 +12,7 @@
 * limitations under the License.
 */
 
-import * as tf from "@tensorflow/tfjs";
+import { tensor } from "@tensorflow/tfjs";
 import Ndframe from "./generic";
 import { Series } from "./series";
 import { Utils } from "./utils";
@@ -183,7 +183,7 @@ export class DataFrame extends Ndframe {
           index: new_index
         });
       } else {
-        this.row_data_tensor = tf.tensor(new_data);
+        this.row_data_tensor = tensor(new_data);
         this.data = new_data;
         this.__set_index(new_index);
       }
@@ -916,7 +916,7 @@ export class DataFrame extends Ndframe {
       }
 
       values.map((arr) => {
-        let temp_sum = tf.tensor(arr).sum().arraySync();
+        let temp_sum = tensor(arr).sum().arraySync();
         val_sums.push(Number(temp_sum.toFixed(5)));
       });
 
@@ -940,7 +940,7 @@ export class DataFrame extends Ndframe {
   abs() {
     let data = this.values;
 
-    let tensor_data = tf.tensor(data);
+    let tensor_data = tensor(data);
     let abs_data = tensor_data.abs().arraySync();
     let df = new DataFrame(utils.__round(abs_data, 2, false), {
       columns: this.column_names,
@@ -1406,7 +1406,7 @@ export class DataFrame extends Ndframe {
       }
 
       for (let i = 0; i < df_data.length; i++) {
-        let value = tf.tensor(df_data[i]);
+        let value = tensor(df_data[i]);
         let callable_data;
         try {
           callable_data = callable(value).arraySync();
@@ -1659,18 +1659,18 @@ export class DataFrame extends Ndframe {
               `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.`
             );
           }
-          other = tf.tensor(val.values);
+          other = tensor(val.values);
         } else {
           if (val.values.length != this.shape[1]) {
             throw Error(
               `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.`
             );
           }
-          other = tf.tensor(val.values);
+          other = tensor(val.values);
         }
       } else if (Array.isArray(val)) {
         //Array of Array
-        other = tf.tensor(val);
+        other = tensor(val);
       } else {
         //DataFrame
         other = val.row_data_tensor;
@@ -1679,22 +1679,22 @@ export class DataFrame extends Ndframe {
 
     switch (logical_type) {
     case "lt":
-      int_vals = tf.tensor(this.values).less(other).arraySync();
+      int_vals = tensor(this.values).less(other).arraySync();
       break;
     case "gt":
-      int_vals = tf.tensor(this.values).greater(other).arraySync();
+      int_vals = tensor(this.values).greater(other).arraySync();
       break;
     case "le":
-      int_vals = tf.tensor(this.values).lessEqual(other).arraySync();
+      int_vals = tensor(this.values).lessEqual(other).arraySync();
       break;
     case "ge":
-      int_vals = tf.tensor(this.values).greaterEqual(other).arraySync();
+      int_vals = tensor(this.values).greaterEqual(other).arraySync();
       break;
     case "ne":
-      int_vals = tf.tensor(this.values).notEqual(other).arraySync();
+      int_vals = tensor(this.values).notEqual(other).arraySync();
       break;
     case "eq":
-      int_vals = tf.tensor(this.values).equal(other).arraySync();
+      int_vals = tensor(this.values).equal(other).arraySync();
       break;
     }
     let bool_vals = utils.__map_int_to_bool(int_vals, 2);
@@ -1754,7 +1754,7 @@ export class DataFrame extends Ndframe {
 
         this_tensor = tensors[0].row_data_tensor; //tensorflow uses 1 for rows axis and 0 for column axis
         if (tensors[1].series) {
-          other_tensor = tf.tensor(tensors[1].values, [
+          other_tensor = tensor(tensors[1].values, [
             1,
             tensors[1].values.length
           ]);
@@ -1771,7 +1771,7 @@ export class DataFrame extends Ndframe {
 
         this_tensor = tensors[0].row_data_tensor;
         if (tensors[1].series) {
-          other_tensor = tf.tensor(tensors[1].values, [
+          other_tensor = tensor(tensors[1].values, [
             tensors[1].values.length,
             1
           ]);
@@ -2153,7 +2153,7 @@ export class DataFrame extends Ndframe {
       let val = sorted_val[row_i];
       let index = null;
 
-      if (duplicate_obj.hasOwnProperty(val)) {
+      if (val in duplicate_obj) {
         index = duplicate_obj[val]["index"][0];
         duplicate_obj[val]["index"].splice(0, 1);
       } else {

danfojs/src/core/generic.js

@@ -13,7 +13,7 @@
  *
 */
 
-import * as tf from "@tensorflow/tfjs";
+import { tensor, Tensor } from "@tensorflow/tfjs";
 import { table } from "table";
 import { Utils } from "./utils";
 import { Configs } from "../config/config";
@@ -38,7 +38,7 @@ export default class NDframe {
   constructor(data, kwargs = {}) {
     this.kwargs = kwargs;
 
-    if (data instanceof tf.Tensor) {
+    if (data instanceof Tensor) {
       data = data.arraySync();
     }
 
@@ -71,15 +71,15 @@ export default class NDframe {
    */
   _read_array(data) {
     this.data = utils.__replace_undefined_with_NaN(data, this.series);
-    this.row_data_tensor = tf.tensor(this.data);
+    this.row_data_tensor = tensor(this.data);
 
     if (this.series) {
       this.col_data = [ this.values ];
     } else {
       this.col_data = utils.__get_col_values(this.data);
     }
 
-    this.col_data_tensor = tf.tensor(this.col_data); //data saved as 2D column tensors
+    this.col_data_tensor = tensor(this.col_data); //data saved as 2D column tensors
 
     if ("index" in this.kwargs) {
       this.__set_index(this.kwargs["index"]);
@@ -133,7 +133,7 @@ export default class NDframe {
       });
 
       this.data = utils.__replace_undefined_with_NaN(data_arr, this.series); //Defualt array data in row format
-      this.row_data_tensor = tf.tensor(this.data); //data saved as row tensors
+      this.row_data_tensor = tensor(this.data); //data saved as row tensors
       this.kwargs["columns"] = Object.keys(Object.values(data)[0]); //get names of the column from the first entry
 
       if (this.series) {
@@ -142,7 +142,7 @@ export default class NDframe {
         this.col_data = utils.__get_col_values(this.data);
       }
 
-      this.col_data_tensor = tf.tensor(this.col_data); //data saved as 2D column tensors
+      this.col_data_tensor = tensor(this.col_data); //data saved as 2D column tensors
 
       if ("index" in this.kwargs) {
         this.__set_index(this.kwargs["index"]);

danfojs/src/core/series.js

@@ -14,7 +14,7 @@
 */
 
 
-import * as tf from "@tensorflow/tfjs";
+import { tensor, round } from "@tensorflow/tfjs";
 import { variance, std } from 'mathjs';
 import { Utils } from "./utils";
 import { Str } from "./strings";
@@ -55,7 +55,7 @@ export class Series extends NDframe {
     * @returns {1D Tensor}
     */
   get tensor() {
-    return tf.tensor(this.values).asType(this.dtypes[0]);
+    return tensor(this.values).asType(this.dtypes[0]);
   }
 
 
@@ -250,7 +250,7 @@ export class Series extends NDframe {
   mean() {
     utils._throw_str_dtype_error(this, 'mean');
     let values = utils._remove_nans(this.values);
-    let mean = tf.tensor(values).mean().arraySync();
+    let mean = tensor(values).mean().arraySync();
     return mean;
   }
 
@@ -382,7 +382,7 @@ export class Series extends NDframe {
   round(dp) {
     if (utils.__is_undefined(dp)) {
       //use tensorflow round function to roound to the nearest whole number
-      let result = tf.round(this.row_data_tensor).arraySync();
+      let result = round(this.row_data_tensor).arraySync();
       return new Series(result, { columns: this.column_names, index: this.index });
 
     } else {

danfojs/src/core/utils.js

@@ -1,5 +1,4 @@
-import * as tf from "@tensorflow/tfjs";
-import '@tensorflow/tfjs-backend-cpu';
+import { tensor, linspace } from "@tensorflow/tfjs";
 import { Configs } from "../config/config";
 
 const config = new Configs();
@@ -113,7 +112,7 @@ export class Utils {
 
   //generate integers between two set of numbers
   __range(start, end) {
-    let value = tf.linspace(start, end, end - start + 1).arraySync();
+    let value = linspace(start, end, end - start + 1).arraySync();
     return value;
   }
 
@@ -689,7 +688,7 @@ export class Utils {
     let rslt_obj = {};
 
     arr.forEach((val, index) => {
-      if (temp_obj.hasOwnProperty(val)) {
+      if (val in temp_obj) {
         temp_obj[val]["count"] += 1;
         temp_obj[val]["index"].push(index);
       } else {

danfojs/src/index.js

@@ -3,7 +3,7 @@ import NDframe from "./core/generic";
 export { Series } from "./core/series";
 export { DataFrame } from "./core/frame";
 export { to_datetime } from "./core/timeseries";
-export { read_csv, read_json, read_excel, read } from "./io/reader";
+export { read_csv, read_json, read_excel } from "./io/reader";
 export { merge } from "./core/merge";
 export { concat } from "./core/concat";
 export { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings";
@@ -14,5 +14,6 @@ export { Configs } from "./config/config";
 export { NDframe };
 export { Str } from "./core/strings";
 export { Utils } from "./core/utils";
+export * as tf from "@tensorflow/tfjs";
 
-export const _version = "0.2.1";
+export const _version = "0.2.2";

danfojs/src/io/reader.js

@@ -1,8 +1,6 @@
 import * as tf from "@tensorflow/tfjs";
 import fetch from "node-fetch";
 import XLSX from "xlsx";
-import { open, Dataset, isDataset } from "frictionless.js";
-import toArray from "stream-to-array";
 import { Utils } from "../core/utils";
 import { DataFrame } from "../core/frame";
 
@@ -27,6 +25,7 @@ export const read_csv = async (source, chunk) => {
     )
   ) {
     //probabily a relative path, append file:// to it
+    // eslint-disable-next-line no-undef
     source = `file://${process.cwd()}/${source}`;
   }
 
@@ -154,64 +153,3 @@ export const read_excel = async (kwargs) => {
     throw new Error(err);
   }
 };
-
-/**
- * Opens a file using frictionless.js specification.
- * @param {string} pathOrDescriptor A path to the file/resources. It can be a local file,
- * a URL to a tabular data (CSV, EXCEL) or Datahub.io Data Resource.
- * Data comes with extra properties and specification conforming to the Frictionless Data standards.
- * @param {object} configs { data_num (Defaults => 0): The specific dataset to load, when reading data from a datapackage.json,
- *                          header (Defaults => true): Whether the dataset contains header or not.
- *                          }
- * @returns {DataFrame} Danfo DataFrame/Series
- */
-export const read = async (
-  path_or_descriptor,
-  configs = { data_num: 0, header: true }
-) => {
-  let data_num = configs["data_num"];
-  let header = configs["header"];
-  let rows, file;
-
-  if (isDataset(path_or_descriptor)) {
-    console.log(
-      "datapackage.json found. Loading Dataset package from Datahub.io"
-    );
-    const dataset = await Dataset.load(path_or_descriptor);
-    file = dataset.resources[data_num];
-    //TODO: toArray does not work in browser env, so this feature breaks when build for the web.
-    // To fix this, we need a function to convert stream into text
-    rows = await toArray(await file.rows());
-  } else {
-    try {
-      file = open(path_or_descriptor);
-      rows = await toArray(await file.rows());
-    } catch (error) {
-      console.log(error);
-    }
-  }
-
-  if ([ "csv", "xls", "xlsx" ].includes(await file.descriptor.format)) {
-    if (header) {
-      let df = new DataFrame(rows.slice(1), { columns: rows[0] });
-      return df;
-    } else {
-      let df = new DataFrame(rows);
-      return df;
-    }
-  } else {
-    let df = new DataFrame(rows);
-    return df;
-  }
-};
-
-// /**
-//  * Reads a Database into DataFrame
-//  *
-//  * @param {source} URL or local file path to retreive JSON file.
-//  * @returns {Promise} DataFrame structure of parsed CSV data
-//  */
-// export const read_sql = async (source) => {
-
-//     return "TODO"
-// }