From 71abc1e5a7e2aee8a3a733d1426b24488ce5bf5b Mon Sep 17 00:00:00 2001 From: Nick McIntyre Date: Fri, 21 Jan 2022 08:46:28 -0600 Subject: [PATCH] Assorted tidying --- README.md | 2 +- examples/dataframe-neuralnetwork/index.html | 13 - examples/dataframe-neuralnetwork/sketch.js | 78 - examples/dataframe/index.html | 12 - examples/dataframe/sketch.js | 12 - karma.conf.js | 19 + package-lock.json | 7554 ++++++------------- package.json | 48 +- src/data/index.ts | 50 - src/index.ts | 11 - src/lib/danfo/config/config.js | 54 - src/lib/danfo/core/concat.js | 221 - src/lib/danfo/core/date_range.js | 289 - src/lib/danfo/core/frame.js | 2172 ------ src/lib/danfo/core/generic.js | 500 -- src/lib/danfo/core/get_dummies.js | 139 - src/lib/danfo/core/groupby.js | 511 -- src/lib/danfo/core/indexing.js | 191 - src/lib/danfo/core/merge.js | 321 - src/lib/danfo/core/series.js | 1306 ---- src/lib/danfo/core/strings.js | 336 - src/lib/danfo/core/timeseries.js | 305 - src/lib/danfo/core/utils.js | 673 -- src/lib/danfo/index.js | 18 - src/lib/danfo/io/reader.js | 116 - src/lib/danfo/plotting/plot.js | 866 --- src/lib/danfo/preprocessing/encodings.js | 108 - src/lib/danfo/preprocessing/scalers.js | 267 - src/lib/ml5/Word2vec/index.js | 211 +- src/lib/ml5/Word2vec/index_test.js | 128 - src/lib/ml5/package.json | 2 +- test/test.html | 27 - test/unit/data/index.js | 62 - test/unit/ml5/index.js | 2 +- webpack.config.js | 1 - 35 files changed, 2258 insertions(+), 14367 deletions(-) delete mode 100644 examples/dataframe-neuralnetwork/index.html delete mode 100644 examples/dataframe-neuralnetwork/sketch.js delete mode 100644 examples/dataframe/index.html delete mode 100644 examples/dataframe/sketch.js create mode 100644 karma.conf.js delete mode 100644 src/data/index.ts delete mode 100644 src/lib/danfo/config/config.js delete mode 100644 src/lib/danfo/core/concat.js delete mode 100644 src/lib/danfo/core/date_range.js delete mode 100644 src/lib/danfo/core/frame.js delete mode 100644 src/lib/danfo/core/generic.js delete mode 100644 src/lib/danfo/core/get_dummies.js delete mode 100644 src/lib/danfo/core/groupby.js delete mode 100644 src/lib/danfo/core/indexing.js delete mode 100644 src/lib/danfo/core/merge.js delete mode 100644 src/lib/danfo/core/series.js delete mode 100644 src/lib/danfo/core/strings.js delete mode 100644 src/lib/danfo/core/timeseries.js delete mode 100644 src/lib/danfo/core/utils.js delete mode 100644 src/lib/danfo/index.js delete mode 100644 src/lib/danfo/io/reader.js delete mode 100644 src/lib/danfo/plotting/plot.js delete mode 100644 src/lib/danfo/preprocessing/encodings.js delete mode 100644 src/lib/danfo/preprocessing/scalers.js delete mode 100644 src/lib/ml5/Word2vec/index_test.js delete mode 100644 test/test.html delete mode 100644 test/unit/data/index.js diff --git a/README.md b/README.md index 28898eb..c1fbd19 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ This project aims to bring the mathematical chops of [NumPy](https://numpy.org/) - Make the API familiar (e.g., `createTensor()`). - Encourage all kinds of contributions. -The library is written in [TypeScript](http://www.typescriptlang.org/) and uses [TensorFlow.js](https://js.tensorflow.org/api/latest/), [Danfo.js](https://danfo.jsdata.org/), and [Math.js](https://mathjs.org/) under the hood. It bundles [ml5.js](https://ml5js.org/) to handle machine learning. +The library is written in [TypeScript](http://www.typescriptlang.org/) and uses [TensorFlow.js](https://js.tensorflow.org/api/latest/) and [Math.js](https://mathjs.org/) under the hood. It bundles [ml5.js](https://ml5js.org/) for machine learning and [TurtleGFX](https://github.com/CodeGuppyPrograms/TurtleGFX) for drawing with turtles. ## Usage diff --git a/examples/dataframe-neuralnetwork/index.html b/examples/dataframe-neuralnetwork/index.html deleted file mode 100644 index 4dc989c..0000000 --- a/examples/dataframe-neuralnetwork/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - DataFrame + NeuralNetwork Example - - - - - - - - diff --git a/examples/dataframe-neuralnetwork/sketch.js b/examples/dataframe-neuralnetwork/sketch.js deleted file mode 100644 index fffba36..0000000 --- a/examples/dataframe-neuralnetwork/sketch.js +++ /dev/null @@ -1,78 +0,0 @@ -function preload() { - // Step 1: load data - loadDataFrame('https://assets.computiful.org/pre-alpha/who_life_expectancy.csv', 'csv', callback, errorCallback); -} - -function callback(df) { - // Step 2: transform and visualize - df = df.query({column: 'Country', is: '==', to: 'Kenya'}); - df = df.dropna({ axis: 0 }); - let plotDiv = createDiv(); - plotDiv.id('plot_div'); - df.plot("plot_div").scatter({ - x: 'Schooling', - y: 'Life expectancy', - }); - df.to_json().then((json) => { - // Step 3: set your neural network options - const options = { - task: 'regression', - debug: true, - }; - - // Step 4: initialize your neural network - const nn = ml5.neuralNetwork(options); - - // Step 5: add data to the neural network - JSON.parse(json).forEach((item) => { - const input = { - schooling: item['Schooling'], - }; - const output = { - life_expectancy: item['Life expectancy'], - }; - - nn.addData(input, output); - }); - - // Step 6: normalize your data - nn.normalizeData(); - - // Step 7: train your neural network - const trainingOptions = { - epochs: 32, - batchSize: 12, - }; - nn.train(trainingOptions, finishedTraining); - - // Step 8: use the trained model - function finishedTraining() { - predict(); - } - - // Step 9: make a prediction - function predict() { - const input = { - schooling: 5, - }; - nn.predict(input, handleResults); - } - - // Step 10: define a function to handle the results of your classification - function handleResults(error, result) { - if (error) { - console.error(error); - return; - } - console.log(result); // { label: 'life_expectancy', value: 79.7 }; - } - }); -} - -function errorCallback(error) { - console.error(error); -} - -function setup() { - noCanvas(); -} diff --git a/examples/dataframe/index.html b/examples/dataframe/index.html deleted file mode 100644 index 741eacf..0000000 --- a/examples/dataframe/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - - - - DataFrame Example - - - - - - - diff --git a/examples/dataframe/sketch.js b/examples/dataframe/sketch.js deleted file mode 100644 index 548bffb..0000000 --- a/examples/dataframe/sketch.js +++ /dev/null @@ -1,12 +0,0 @@ -function setup() { - print('=== JSON-based DataFrame ==='); - - const json_data = [ - { A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 }, - { A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 }, - { A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 }, - { A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 } - ]; - const df = createDataFrame(json_data); - df.print(); -} diff --git a/karma.conf.js b/karma.conf.js new file mode 100644 index 0000000..12d3c05 --- /dev/null +++ b/karma.conf.js @@ -0,0 +1,19 @@ +module.exports = function(config) { + config.set({ + frameworks: ['mocha', 'chai'], + files: [ + 'node_modules/p5/lib/p5.min.js', + 'dist/numero.js', + 'test/**/*.js' + ], + reporters: ['progress'], + port: 9876, // karma web server port + colors: true, + logLevel: config.LOG_INFO, + browsers: ['ChromeHeadless'], + autoWatch: false, + // singleRun: false, // Karma captures browsers, runs the tests and exits + concurrency: Infinity + }) + } + \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index e5ce489..3bafeeb 100644 --- a/package-lock.json +++ b/package-lock.json @@ -4,82 +4,12 @@ "lockfileVersion": 1, "requires": true, "dependencies": { - "@babel/code-frame": { - "version": "7.12.11", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.12.11.tgz", - 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"dev": true - }, "yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", diff --git a/package.json b/package.json index 428d4dd..b16a7b2 100644 --- a/package.json +++ b/package.json @@ -5,7 +5,7 @@ "main": "index.js", "scripts": { "build": "webpack --config webpack.config.js", - "test": "mocha-chrome test/test.html", + "test": "karma start --single-run --browsers ChromeHeadless karma.conf.js", "dev": "webpack --watch --config webpack.config.js", "all": "npm run build && npm run test", "postinstall": "./scripts/post-install.sh" @@ -16,25 +16,27 @@ "devDependencies": { "@tensorflow/tfjs-converter": "^1.2.9", "@tensorflow/tfjs-core": "^1.2.9", - "@types/chai": "^4.2.12", - "@types/mathjs": "^6.0.11", - "@types/mocha": "^8.0.3", - "@types/p5": "^0.9.1", - "@typescript-eslint/eslint-plugin": "^3.10.1", - "@typescript-eslint/parser": "^3.10.1", - "chai": "^4.2.0", - "eslint": "^7.9.0", - "eslint-config-airbnb-base": "^14.2.0", - "eslint-plugin-import": "^2.22.0", - "katex": "^0.13.3", - "mocha": "^8.1.3", - "mocha-chrome": "^2.2.0", - "p5": "^1.1.9", - "p5.touchgui": "^0.5.2", - "ts-loader": "^8.0.4", - "typescript": "^3.9.7", - "webpack": "^4.44.2", - "webpack-cli": "^3.3.12" + "@types/chai": "^4.3.0", + "@types/mathjs": "^9.4.2", + "@types/mocha": "^9.1.0", + "@types/p5": "^1.3.3", + "@typescript-eslint/eslint-plugin": "^5.10.0", + "@typescript-eslint/parser": "^5.10.0", + "chai": "^4.3.4", + "eslint": "^8.7.0", + "eslint-config-airbnb-base": "^15.0.0", + "eslint-plugin-import": "^2.25.4", + "karma": "^6.3.11", + "karma-chai": "^0.1.0", + "karma-chrome-launcher": "^3.1.0", + "karma-mocha": "^2.0.1", + "katex": "^0.15.2", + "mocha": "^9.1.4", + "p5": "^1.4.0", + "ts-loader": "^9.2.6", + "typescript": "^4.5.5", + "webpack": "^5.66.0", + "webpack-cli": "^4.9.1" }, "dependencies": { "@magenta/sketch": "0.2.0", @@ -52,9 +54,7 @@ "axios": "^0.21.1", "events": "^3.0.0", "face-api.js": "~0.22.2", - "onchange": "^6.1.0", - "mathjs": "^7.5.1", - "table": "^5.4.6", - "xlsx": "^0.16.9" + "mathjs": "^10.1.0", + "onchange": "^6.1.0" } } diff --git a/src/data/index.ts b/src/data/index.ts deleted file mode 100644 index e0e9522..0000000 --- a/src/data/index.ts +++ /dev/null @@ -1,50 +0,0 @@ -import * as tf from '@tensorflow/tfjs'; - -import * as dfd from '../lib/danfo'; -// @ts-ignore -import { neuralNetwork } from '../lib/ml5'; -import { Tensor } from '../tensor'; - -export const { DataFrame } = dfd; - -/** - * Creates a new DataFrame (2D tabular data structure). - * - * @param data JSON, Array, or 2D Tensor - * @returns the tensor - */ -export const createDataFrame = function createDataFrameObject( - data: Array | tf.Tensor | Tensor, - kwargs: object, -): dfd.DataFrame { - if (data instanceof Tensor ) { - return new DataFrame(data.tensor, kwargs); - } else { - return new DataFrame(data, kwargs); - } -}; - -/** - * Loads csv or json data from a file or a URL. - * - * @param filename String: name of the file or url to load - * @param extension String: parse the dataframe by comma-separated values "csv" or JSON "json" - * @param callback Function: function to be executed after loadDataFrame() completes. On success, the DataFrame object is passed in as the first argument. - * @param errorCallback Function: function to be executed if there is an error, response is passed in as first argument (Optional) - */ - export const loadDataFrame = function _loadDataFrame( - filename: string, - extension: string, - callback: any, - errorCallback: any, -) { - if (extension === 'csv') { - dfd.read_csv(filename) - .then(callback) - .catch(errorCallback); - } else if (extension === 'json') { - dfd.read_json(filename) - .then(callback) - .catch(errorCallback); - } - }; diff --git a/src/index.ts b/src/index.ts index 2bd05f1..e3c25d1 100644 --- a/src/index.ts +++ b/src/index.ts @@ -1,6 +1,5 @@ import * as tf from '@tensorflow/tfjs'; import * as math from 'mathjs'; -import * as dfd from './lib/danfo'; import * as ml5 from './lib/ml5'; import { Turtle } from './lib/turtle'; import { @@ -10,11 +9,6 @@ import { parse, simplify, } from './algebra'; -import { - DataFrame, - createDataFrame, - loadDataFrame, -} from './data'; import { Tensor, createTensor } from './tensor'; import { add, @@ -86,8 +80,6 @@ import { declare const p5: any; declare const window: any; -p5.prototype.createDataFrame = createDataFrame; -p5.prototype.loadDataFrame = loadDataFrame; p5.prototype.createTensor = createTensor; p5.prototype.registerMethod('init', startScope); p5.prototype.registerMethod('pre', startScope); @@ -106,9 +98,6 @@ export { evaluate, parse, simplify, - // Data - dfd, - DataFrame, // Tensor tf, Tensor, diff --git a/src/lib/danfo/config/config.js b/src/lib/danfo/config/config.js deleted file mode 100644 index 268663d..0000000 --- a/src/lib/danfo/config/config.js +++ /dev/null @@ -1,54 +0,0 @@ - -/** - * Package wide configuration class - */ -export class Configs { - constructor() { - this.table_width = 17; //set the width of each column printed in console - this.table_truncate = 16; //set the max number of string before text is truncated in printing - this.dtype_test_lim = 10; - this.table_max_row = 21; - this.table_max_col_in_console = 7; - } - - set_width(val) { - this.table_width = val; - } - - get get_width() { - return this.table_width; - } - - set_max_col_in_console(val) { - this.table_max_col_in_console = val; - } - - get get_max_col_in_console() { - return this.table_max_col_in_console; - } - - set_row_num(val) { - this.table_max_row = val; - } - - get get_max_row() { - return this.table_max_row; - } - - - get get_truncate() { - return this.table_truncate; - } - - set_truncate(val) { - this.table_truncate = val; - } - - get get_dtype_test_lim() { - return this.dtype_test_lim; - } - - set_dtype_test_lim(val) { - this.dtype_test_lim = val; - } -} diff --git a/src/lib/danfo/core/concat.js b/src/lib/danfo/core/concat.js deleted file mode 100644 index aa9bf9a..0000000 --- a/src/lib/danfo/core/concat.js +++ /dev/null @@ -1,221 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; -import { Series } from './series'; - -const utils = new Utils(); - - -export class Concat { - constructor(kwargs) { - - - // check if keys exist in kwargs - utils.__in_object(kwargs, "df_list", "df_list not found: specify the list of dataframe"); - utils.__in_object(kwargs, "axis", "axis not found: specify the axis"); - - let df_list = null; //set the df_list to null - let axis = null; // set axis to null - let indexes = null; - - //check if df_list is an array - if (Array.isArray(kwargs["df_list"])) { - - df_list = kwargs["df_list"]; - } else { - throw new Error("df_list must be an Array of dataFrames/Series"); - } - - //check if axis is int and is either 0 or 1 - if (typeof kwargs["axis"] === "number") { - - if (kwargs["axis"] == 0 || kwargs["axis"] == 1) { - - axis = kwargs["axis"]; - } else { - axis = 1; - // throw new Error("Invalid axis: axis must be 0 or 1") - } - - } else { - throw new Error("axis must be a number"); - } - - - let df_object = Object.assign({}, df_list); // convert the array to object - - if (axis == 1) { - - let columns = []; - let duplicate_col_count = {}; - let max_length = 0; - let a_key = Object.keys(df_object)[0]; - indexes = df_object[a_key].index; - for (let key in df_object) { - - let column = df_object[key].columns; - let length = df_object[key].values.length; - - if (length > max_length) { - max_length = length; - } - - for (let index in column) { - - let col_name = column[index]; - if (col_name in duplicate_col_count) { - - let count = duplicate_col_count[col_name]; - let name = `${col_name}_${count + 1}`; - - columns.push(name); - - duplicate_col_count[col_name] = count + 1; - } else { - - columns.push(col_name); - duplicate_col_count[col_name] = 1; - } - } - - - } - - let data = new Array(max_length); - - for (let key in df_list) { - - let values = df_list[key].values; - - for (let index = 0; index < values.length; index++) { - - let val = values[index]; - if (typeof data[index] === "undefined") { - - if (Array.isArray(val)){ - data[index] = val; - } else { - data[index] = [ val ]; - } - - } else { - if (Array.isArray(val)){ - data[index].push(...val); - } else { - data[index].push(val); - } - - } - } - - if (values.length < max_length) { - let column_length = df_list[key].columns.length; - let null_array = Array(column_length); - - for (let col = 0; col < column_length; col++) { - null_array[col] = NaN; - } - - if (typeof data[max_length - 1] === "undefined") { - data[max_length - 1] = null_array; - } else { - data[max_length - 1].push(...null_array); - } - } - } - - let df = new DataFrame(data, { columns: columns, index: indexes }); //convert to dataframe - return df; - } else { - //concatenate base on axis 0 - let columns = []; - let row_indexes = []; - let col_i = 0; - for (let key in df_list) { - let column = df_list[key].columns; - columns.push(...column); - indexes = df_list[key].index; - let r_index = indexes.map((val) => { - return `${val}_row${col_i}`; - }); - row_indexes.push(...r_index); - col_i += 1; - } - - let column_set = new Set(columns); - - columns = Array.from(column_set); - - let data = []; - - for (let key in df_list) { - - let value = df_list[key].values; - - // let col_length = value[0].length - - let df_columns = df_list[key].columns; - - let not_exist = []; - for (let col_index in columns) { - let col_name = columns[col_index]; - - let is_index = df_columns.indexOf(col_name); - - if (is_index == -1) { - not_exist.push(col_name); - } - } - if (not_exist.length > 0) { - for (let i = 0; i < value.length; i++) { - let row_value = value[i]; - - let new_arr = Array(columns.length); - for (let j = 0; j < columns.length; j++) { - - let col_name = columns[j]; - if (not_exist.includes(col_name)) { - - new_arr[j] = NaN; - } else { - let index = df_columns.indexOf(col_name); - if (Array.isArray(row_value)){ - new_arr[j] = row_value[index]; - } else { - new_arr[j] = row_value; - } - - } - - } - data.push(new_arr); - } - } else { - data.push(...value); - } - - } - - if (Array.isArray(data[0])){ - let df = new DataFrame(data, { columns: columns, index: row_indexes }); - return df; - } else { - let sf = new Series(data, { index: row_indexes }); - return sf; - } - - - } - - } -} - - -/** -* Concatenate pandas objects along a particular axis with optional set logic along the other axes. -* @param {kwargs} {df_list: List of DataFrame to concatenate together axis: 0 for row axis and 1 for index axis -* @returns {DataFrame} -*/ -export const concat = (kwargs) => { - let concat_sf = new Concat(kwargs); - return concat_sf; -}; diff --git a/src/lib/danfo/core/date_range.js b/src/lib/danfo/core/date_range.js deleted file mode 100644 index 8e1ed43..0000000 --- a/src/lib/danfo/core/date_range.js +++ /dev/null @@ -1,289 +0,0 @@ -import { Utils } from "./utils"; - -const utils = new Utils; - -/** - * Generate date range between a specified set of date - * @param {kwargs} kwargs { - * start : string - * end : string - * period: int - * freq : string - * } - * @returns Array - */ -export class date_range { - constructor(kwargs){ - - this.offset = null; - - if (utils.__key_in_object(kwargs, "start")){ - this.start = kwargs["start"]; - } else { - this.start = null; - } - - if (utils.__key_in_object(kwargs, "end")){ - this.end = kwargs["end"]; - } else { - this.end = null; - } - - if (utils.__key_in_object(kwargs, "period")){ - this.period = kwargs["period"]; - } else { - this.period = null; - } - - if (utils.__key_in_object(kwargs, "freq")){ - this.freq = kwargs["freq"]; - } else { - this.freq = "D"; - } - - this.freq_list = [ "M", "D", "s", "H", "m", "Y" ]; - - if (this.freq.length == 1){ - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } else { - let freq_split = this.freq.split(""); - this.offset = parseInt(freq_split[0]); - this.freq = freq_split[1]; - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } - - let rslt = this.range(this.start, this.end, this.period, this.offset); - return rslt; - - } - - range(start, end, period, offset = null){ - - let start_date = null; - let end_date = null; - let start_range = null; - let end_range = null; - if (start && end){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - - //check if the end year is greater than start year - let start_year = start_date.getFullYear(); - let end_year = end_date.getFullYear(); - if ((start_year < end_year)){ - // end_range = start_range + end_range - if (this.freq == "M"){ - end_range = this.month_end(start_date, end_date); - } else if (this.freq == "D"){ - end_range = this.day_end(start_date, end_date) - start_range; - - } - } - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - if (this.freq == "M"){ - range_array = this.month_range(range_array); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - date_range[date_range.length - 1] = end_date; - - let date_string = this.toLocalString(date_range); - - return date_string; - } else if (start && !(end)){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_range = offset ? ((period * offset) - 1) : period - 1; - - if (start_range > end_range){ - end_range = end_range + start_range; - } - let range_array = utils.__range(start_range, end_range); - - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - - } else if (end && !(start)){ - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - start_range = (end_range - period) + 1; - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(end_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - } - } - - freq_type(date, ftype){ - - let rslt = null; - switch (ftype){ - - case "M": - rslt = date.getMonth(); - break; - case "Y": - rslt = date.getFullYear(); - break; - case "s": - rslt = date.getSeconds(); - break; - case "D": - rslt = date.getDate(); - break; - case "H": - rslt = date.getHours(); - break; - case "m": - rslt = date.getMinutes(); - break; - } - return rslt; - } - - offset_count(d_array, offset){ - - let r_array = []; - - for (let i = 0; i < d_array.length; i += offset){ - r_array.push(d_array[i]); - } - return r_array; - } - - set_dateProps(date, ftype, val){ - - let new_date = new Date(date.valueOf()); - switch (ftype){ - - case "M": - if (val.length == 2){ - - new_date.setYear(new_date.getFullYear() + val[0]); - new_date.setMonth(parseInt(val[1])); - } else { - new_date.setMonth(val); - } - - break; - case "Y": - new_date.setYear(val); - break; - case "s": - new_date.setSeconds(val); - break; - case "D": - new_date.setDate(val); - break; - case "H": - new_date.setHours(val); - break; - case "m": - new_date.setMinutes(val); - break; - } - return new_date; - } - - toLocalString(d_array){ - - let r_array = d_array.map((x) => { - - return x.toLocaleString(); - }); - - return r_array; - } - - month_end(start_date, end_date){ - - let end_month = end_date.getMonth(); - - let diff_year = end_date.getFullYear() - start_date.getFullYear(); - - let end_range = (12 * diff_year) + end_month; - - return end_range; - } - - month_range(range){ - - let minus = null; - let y_val = 0; - let d_range = range.map((x) => { - - if (x > 11){ - if (x % 12 == 0){ - minus = x; - y_val = x / 12; - return [ y_val, (x - minus) ]; - } else { - return [ y_val, (x - minus) ]; - } - - } - return [ y_val, x ]; - }); - - return d_range; - } - - day_end(start_date, end_date){ - - let month_end = this.month_end(start_date, end_date); - let range = utils.__range(start_date.getMonth(), month_end); - let m_range = this.month_range(range); - - // let s_date = new Date(start_date.getFullYear(),start_date.getMonth(),0) - let sum = 0; - for (let i = 0; i < m_range.length; i++){ - - let val = m_range[i]; - - let d_date = null; - if (i === m_range.length - 1) { - d_date = new Date(start_date.getFullYear() + val[0], val[1], end_date.getDate()).getDate(); - } else { - d_date = new Date(start_date.getFullYear() + val[0], val[1], 0).getDate(); - - } - - sum += d_date; - - } - return sum; - - } -} diff --git a/src/lib/danfo/core/frame.js b/src/lib/danfo/core/frame.js deleted file mode 100644 index 927fc9f..0000000 --- a/src/lib/danfo/core/frame.js +++ /dev/null @@ -1,2172 +0,0 @@ -/** -* Copyright 2021, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -import * as tf from "@tensorflow/tfjs"; -import Ndframe from "./generic"; -import { Series } from "./series"; -import { Utils } from "./utils"; -import { GroupBy } from "./groupby"; -import { Plot } from "../plotting/plot"; -import { indexLoc } from "./indexing"; -import { concat } from "./concat.js"; - -const utils = new Utils(); -import { std, variance } from "mathjs"; - -/** - * A 2D frame object that stores data in structured tabular format - * @param {data} data, JSON, Array, 2D Tensor - * @param {kwargs} Object {columns: Array of column names, defaults to ordered numbers when not specified - * dtypes: strings of data types, automatically inferred when not specified - * index: row index for subseting array, defaults to ordered numbers when not specified} - * - * @returns DataFrame - */ -export class DataFrame extends Ndframe { - constructor(data, kwargs) { - super(data, kwargs); - this._set_column_property(); //set column property on DataFrame Class for easy accessing using the format df['colname'] - } - - _set_column_property() { - let col_vals = this.col_data; - let col_names = this.column_names; - - col_vals.forEach((col, i) => { - this[col_names[i]] = null; - Object.defineProperty(this, col_names[i], { - get() { - return new Series(this.col_data[i], { - columns: col_names[i], - index: this.index - }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - /** - * Write a CSV file of the DataFrame contents - * @param {string} csvFilePath Path to save CSV when in Node.js form - * @returns {Promise} CSV representation of Object data - * - */ - async to_csv(csvFilePath = "") { - const csvContent = await super.to_csv(); - // Download CSV on Web - const webCSV = "data:text/csv;charset=utf-8," + csvContent; - window.open(encodeURI(webCSV)); - return csvContent; - } - - /** - * Drop a list of rows or columns base on the specified axis - * @param {Object} kwargs Configuration object - * {columns: [Array(Columns| Index)] array of column names to drop - * axis: row=0, columns=1 - * inplace: specify whether to drop the row/column with/without creating a new DataFrame} - * @returns null | DataFrame - * - */ - drop(kwargs = {}) { - let params_needed = [ "columns", "index", "inplace", "axis" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["inplace"] = kwargs["inplace"] || false; - - if (!("axis" in kwargs)) { - kwargs["axis"] = 1; - } - - let to_drop = null; - if ("index" in kwargs && kwargs["axis"] == 0) { - to_drop = kwargs["index"]; - } else { - to_drop = kwargs["columns"]; - } - - if (kwargs["axis"] == 1) { - if (!("columns" in kwargs)) { - throw Error( - "No column found. Axis of 1 must be accompanied by an array of column(s) names" - ); - } - let self = this; - let new_col_data = {}; - let new_dtype = []; - - const index = to_drop.map((x) => { - let col_idx = self.columns.indexOf(x); - if (col_idx == -1) { - throw new Error(`column "${x}" does not exist`); - } - return col_idx; - }); - - this.col_data.forEach((col, idx) => { - if (!index.includes(idx)) { - new_col_data[self.column_names[idx]] = col; - new_dtype.push(self.dtypes[idx]); - } - }); - - if (!kwargs["inplace"]) { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - let df = new DataFrame(new_col_data, { - index: self.index, - dtypes: new_dtype - }); - df.__set_col_property(df, df.col_data, new_columns, old_cols); - return df; - } else { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - this.__update_frame_in_place(null, null, new_col_data, null, new_dtype); - this.__set_col_property(self, self.col_data, new_columns, old_cols); - } - } else { - if (!utils.__key_in_object(kwargs, "index")) { - throw Error( - "No index label found. Axis of 0 must be accompanied by an array of index labels" - ); - } - to_drop.forEach((x) => { - if (!this.index.includes(x)) - throw new Error(`${x} does not exist in index`); - }); - const values = this.values; - let data_idx = []; - let new_data, new_index; - if (typeof to_drop[0] == "string") { - //get index of strings labels in rows - this.index.forEach((idx, i) => { - if (to_drop.includes(idx)) { - data_idx.push(i); - } - }); - new_data = utils.__remove_arr(values, data_idx); - new_index = utils.__remove_arr(this.index, data_idx); - } else { - new_data = utils.__remove_arr(values, to_drop); - new_index = utils.__remove_arr(this.index, to_drop); - } - - if (!kwargs["inplace"]) { - return new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - } else { - this.row_data_tensor = tf.tensor(new_data); - this.data = new_data; - this.__set_index(new_index); - } - } - } - - /** - * Purely label based indexing. Can accept string label names for both rows and columns - * @param {kwargs} kwargs object {rows: Array of index, columns: Array of column name(s)} - * @return DataFrame data stucture - */ - loc(kwargs = {}) { - let params_needed = [ "columns", "rows" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "loc"; - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Access a dataframe element using row and column index - * @param {*} kwargs object {rows: Array of index, columns: Array of column index} - * @return DataFrame data stucture - */ - iloc(kwargs = {}) { - let params_needed = [ "columns", "rows" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "iloc"; - - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Prints the first n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - head(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with first [rows] - let data = this.values.slice(0, rows); - let idx = this.index.slice(0, rows); - let config = { columns: this.column_names, index: idx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Prints the last n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - tail(rows = 5) { - let row_len = this.values.length; - if (rows > row_len || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with last [rows] - let data = this.values.slice(row_len - rows); - let indx = this.index.slice(row_len - rows); - let config = { columns: this.column_names, index: indx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a DataFrame object - */ - async sample(num = -1, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const df = this.iloc({ rows: shuffled_index }); - return df; - } - - - /** - * Return Addition of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - add(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types a numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let sum_vals = tensors[0].add(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(sum_vals, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - sub(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].sub(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mul(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].mul(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - div(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].div(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - pow(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].pow(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mod(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].mod(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return mean of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - mean(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.mean(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return median of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - median(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let median = utils.__median(tensor_vals, false); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return minimum element in a DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - min(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.min(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return maximum element of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - max(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.max(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return standard deviation of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - std(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = std(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return variance of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - var(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = variance(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return number of non-null elements in a Series - * @returns {Series}, Count of non-null values - */ - count(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let counts = utils.__count_nan(tensor_vals, true, false); - let sf = new Series(counts, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Rounds values in DataFrame to specified number of dp - * @returns {DataFrame}, New DataFrame with rounded values - */ - round(dp = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let values = this.values; - let idx = this.index; - - let new_vals = utils.__round(values, dp, false); - let options = { columns: this.column_names, index: idx }; - let df = new DataFrame(new_vals, options); - return df; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Perform Cummulative operations - * @param {axis} axis [int] {0 or 1} - * @param {ops} ops {String} name of operation - * @return {DataFrame} - */ - __cum_ops(axis = 0, ops) { - if (!(axis == 0) && !(axis == 1)) { - throw new Error("axis must be between 0 or 1"); - } - - if (this.__frame_is_compactible_for_operation) { - let data = []; - let df_data = null; - - if (axis == 0) { - df_data = this.col_data; - } else { - df_data = this.values; - } - - for (let i = 0; i < df_data.length; i++) { - let value = df_data[i]; - let temp_val = value[0]; - let temp_data = [ temp_val ]; - for (let j = 1; j < value.length; j++) { - let curr_val = value[j]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - temp_data.push(temp_val); - - break; - case "prod": - temp_val = temp_val * curr_val; - temp_data.push(temp_val); - - break; - } - } - data.push(temp_data); - } - - if (axis == 0) { - data = utils.__get_col_values(data); - } - - return new DataFrame(data, { columns: this.columns }); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - /** - * calculate the cummulative sum along axis - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumsum(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - // let axis = kwargs["axis"] || 0 - let data = this.__cum_ops(axis, "sum"); - return data; - } - - /** - * calculate the cummulative min - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummin(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "min"); - return data; - } - - /** - * calculate the cummulative max - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummax(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "max"); - return data; - } - - /** - * calculate the cummulative prod - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumprod(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "prod"); - return data; - } - - /** - * Makes a new copy of a DataFrame - * @returns {DataFrame} - */ - copy() { - let df = new DataFrame([ ...this.values ], { - columns: [ ...this.column_names ], - index: this.index, - dtypes: this.dtypes - }); - return df; - } - - /** - * Generate a new index for the DataFrame. - * This is useful when the index is meaningless and needs to be reset to the default before another operation. - * @param {inplace} boolean: Modify the original object or return a new one. Default to false - */ - reset_index(inplace = false) { - if (inplace) { - this.__reset_index(); - } else { - let df = this.copy(); - df.__reset_index(); - return df; - } - } - - /** - * Set the DataFrame index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values} - */ - set_index(kwargs = {}) { - let params_needed = [ "key", "drop", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "key")) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (!utils.__key_in_object(kwargs, "drop")) { - kwargs["drop"] = true; - } - - if ( - Array.isArray(kwargs["key"]) && - kwargs["key"].length != this.index.length - ) { - throw Error( - `Index LengthError: Lenght of new Index array ${kwargs["key"].length} must match lenght of existing index ${this.index.length}` - ); - } - - if ( - typeof kwargs["key"] == "string" && - this.column_names.includes(kwargs["key"]) - ) { - kwargs["key_name"] = kwargs["key"]; - kwargs["key"] = this[kwargs["key"]].values; - } - if (kwargs["inplace"]) { - // this.index_arr = kwargs['key'] - this.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - this.drop({ columns: [ kwargs["key_name"] ], inplace: true, axis: 1 }); - } - } else { - let df = this.copy(); - df.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - df.drop({ columns: [ kwargs["key_name"] ], axis: 1, inplace: true }); - } - return df; - } - } - - /** - * Generate descriptive statistics for all numeric columns - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - let numeric_df = this.select_dtypes([ "float32", "int32" ]); - let col_names = numeric_df.column_names; - let index = [ "count", "mean", "std", "min", "median", "max", "variance" ]; - - let stats_arr = {}; - col_names.forEach((name) => { - let col_series = numeric_df[name]; - let count = col_series.count(); - let mean = col_series.mean(); - let std = col_series.std(); - let min = col_series.min(); - let median = col_series.median(); - let max = col_series.max(); - let variance = col_series.var(); - - let _stats = [ count, mean, std, min, median, max, variance ]; - stats_arr[name] = _stats; - }); - let df = new DataFrame(stats_arr, { index: index }); - return df.round(6); - } - - /** - * Return a subset of the DataFrame’s columns based on the column dtypes. - * @param {include} scalar or array-like. A selection of dtypes or strings to be included. At least one of these parameters must be supplied. - * @returns {DataFrame, Series} The subset of the frame including the dtypes. - */ - select_dtypes(include) { - let dtypes = this.dtypes; - let col_names = this.column_names; - let col_vals = {}; - let original_col_vals = this.col_data; - const __supported_dtypes = [ "float32", "int32", "string", "boolean" ]; - - if (include == undefined) { - //return all - let df = this.copy(); - return df; - } else { - //check if the right types are included - include.forEach((type) => { - if (!__supported_dtypes.includes(type)) { - throw Error(`Dtype Error: dtype ${type} not supported.`); - } - }); - - dtypes.forEach((dtype, i) => { - if (include.includes(dtype)) { - col_vals[col_names[i]] = original_col_vals[i]; - } - }); - let df = new DataFrame(col_vals); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - // sort_values(kwargs = {}) { - // if (utils.__key_in_object(kwargs, "by")) { - // let sort_col = this.column(kwargs["by"]) - // let sorted_col, sorted_index; - // let new_row_data = [] - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // sort_col.sort_values(kwargs) - // sorted_index = sort_col.index - - // } else { - // sorted_col = sort_col.sort_values(kwargs) - // sorted_index = sorted_col.index - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.data = new_row_data - // this.index_arr = sorted_index - // return null - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.__update_frame_in_place(new_row_data, null, null, sorted_index, null) - - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // } - - /** - * Return the sum of the values in a DataFrame across a specified axis. - * @params {kwargs} {axis: 0 for row and 1 for column} - * @returns {Series}, Sum of values accross axis - */ - sum(kwargs = { axis: 1 }) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_sums = []; - if (kwargs["axis"] == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map((arr) => { - let temp_sum = tf.tensor(arr).sum().arraySync(); - val_sums.push(Number(temp_sum.toFixed(5))); - }); - - let new_index; - if (kwargs["axis"] == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - let sf = new Series(val_sums, { columns: "sum", index: new_index }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns the absolute values in DataFrame - * @return {DataFrame} - */ - abs() { - let data = this.values; - - let tensor_data = tf.tensor(data); - let abs_data = tensor_data.abs().arraySync(); - let df = new DataFrame(utils.__round(abs_data, 2, false), { - columns: this.column_names, - index: this.index - }); - return df; - } - - __get_tensor_and_idx(df, axis) { - let tensor_vals, idx, t_axis; - if (axis == 1) { - //Tensorflow uses 0 for column and 1 for rows, - // we use the opposite for consistency with Pandas (0 : row, 1: columns) - tensor_vals = df.row_data_tensor; - idx = df.column_names; - t_axis = 0; //switch the axis - } else { - tensor_vals = df.row_data_tensor; - idx = df.index; - t_axis = 1; - } - - return [ tensor_vals, idx, t_axis ]; - } - - /** - * Filter DataFrame element base on the element in a column - * @param {kwargs} kwargs {column : coumn name[string], is: String, to: string| int} - * @returns {DataFrame} - */ - query(kwargs) { - //define the set of operators to be used - let operators = [ ">", "<", "<=", ">=", "==", "!=" ]; - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - let column_index, operator, value; - - if (utils.__key_in_object(kwargs, "column")) { - if (this.columns.includes(kwargs["column"])) { - column_index = this.columns.indexOf(kwargs["column"]); - } else { - throw new Error(`column ${kwargs["column"]} does not exist`); - } - } else { - throw new Error("specify the column"); - } - - if (utils.__key_in_object(kwargs, "is")) { - if (operators.includes(kwargs["is"])) { - operator = kwargs["is"]; - } else { - throw new Error(` ${kwargs["is"]} is not a supported logical operator`); - } - } else { - throw new Error("specify an operator in param [is]"); - } - - if (utils.__key_in_object(kwargs, "to")) { - value = kwargs["to"]; - } else { - throw new Error("specify a value in param [to]"); - } - - let data = this.values; - let index = this.index; - let new_data = []; - let new_index = []; - - for (var i = 0; i < data.length; i++) { - let data_value = data[i]; - let elem = data_value[column_index]; - //use eval function for easy operation - //eval() takes in a string expression e.g eval('2>5') - if (eval(`elem${operator}value`)) { - new_data.push(data_value); - new_index.push(index[i]); - } - } - - if (new_data.length == 0) { - throw new Error( - `query returned empty data; is either ${value} does not exist in column ${kwargs["column"]}` - ); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place( - new_data, - this.columns, - null, - new_index, - null - ); - } else { - let new_df = new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - return new_df; - } - } - - /** - * Add a column with values to the dataframe - * @param {kwargs} Object {column :[string] , value:[Array]} - * - */ - addColumn(kwargs) { - utils.__in_object(kwargs, "column", "column name not specified"); - utils.__in_object(kwargs, "value", "column value not specified"); - - let column_name = kwargs["column"]; - let data_length = this.shape[0]; - let value; - - if (kwargs["value"] instanceof Series) { - value = kwargs["value"].values; - } else { - value = kwargs["value"]; - } - - if (value.length != data_length) { - throw new Error( - `Array length ${value.length} not equal to ${data_length}` - ); - } - - if (this.columns.includes(column_name)) { - let col_idx = this.columns.indexOf(column_name); - let new_data = []; - - this.values.map((val, index) => { - let new_val = val.slice(); - new_val[col_idx] = value[index]; - new_data.push(new_val); - }); - this.__update_frame_in_place(new_data, null, null, null, null); - } else { - let data = this.values; - let new_data = []; - - data.map(function (val, index) { - let new_val = val.slice(); - new_val.push(value[index]); - new_data.push(new_val); - }); - - //add new dtype - let new_dtypes = [ ...this.dtypes ]; - new_dtypes.push(utils.__get_t(value)[0]); - - let new_col_names = [ ...this.columns ]; - new_col_names.push(column_name); - - this.__update_frame_in_place( - new_data, - new_col_names, - null, - null, - new_dtypes - ); - Object.defineProperty(this, column_name, { - get() { - return new Series(value, { columns: column_name, index: this.index }); - }, - set(value) { - this.addColumn({ column: column_name, value: value }); - } - }); - } - } - - /** - * - * @param {col} col is a list of column with maximum length of two - */ - groupby(col) { - let len = this.shape[0]; - let column_names = this.column_names; - let col_dict = {}; - let key_column = null; - - if (col.length == 2) { - if (column_names.includes(col[0])) { - // eslint-disable-next-line no-unused-vars - var [ data1, col_name1 ] = indexLoc(this, { - rows: [ `0:${len}` ], - columns: [ `${col[0]}` ], - type: "loc" - }); - } else { - throw new Error(`column ${col[0]} does not exist`); - } - if (column_names.includes(col[1])) { - // eslint-disable-next-line no-unused-vars - var [ data2, col_name2 ] = indexLoc(this, { - rows: [ `0:${len}` ], - columns: [ `${col[1]}` ], - type: "loc" - }); - } else { - throw new Error(`column ${col[1]} does not exist`); - } - - key_column = [ col[0], col[1] ]; - var column_1_Unique = utils.__unique(data1); - var column_2_unique = utils.__unique(data2); - - for (var i = 0; i < column_1_Unique.length; i++) { - let col_value = column_1_Unique[i]; - col_dict[col_value] = {}; - - for (var j = 0; j < column_2_unique.length; j++) { - let col2_value = column_2_unique[j]; - col_dict[col_value][col2_value] = []; - } - } - } else { - if (column_names.includes(col[0])) { - // eslint-disable-next-line no-redeclare - var [ data1, col_name1 ] = indexLoc(this, { - rows: [ `0:${len}` ], - columns: [ `${col[0]}` ], - type: "loc" - }); - // console.log(data1) - } else { - throw new Error(`column ${col[0]} does not exist`); - } - key_column = [ col[0] ]; - - var column_Unique = utils.__unique(data1); - - for (let i = 0; i < column_Unique.length; i++) { - let col_value = column_Unique[i]; - col_dict[col_value] = []; - } - } - - let groups = new GroupBy( - col_dict, - key_column, - this.values, - column_names - ).group(); - - return groups; - } - - /** - * Return a sequence of axis dimension along row and columns - * @params col_name: the name of a column in the database. - * @returns tensor of shape 1 - */ - column(col_name) { - if (!this.columns.includes(col_name)) { - throw new Error(`column ${col_name} does not exist`); - } - let col_indx_objs = utils.__arr_to_obj(this.columns); - let indx = col_indx_objs[col_name]; - let data = this.col_data[indx]; - return new Series(data, { columns: [ col_name ] }); - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {column(s): Array of column name(s) to fill. If undefined fill all columns; - * value(s): Array | Scalar of value(s) to fill with. If single value is specified, we use it to fill all - * @return {DataFrame} - */ - fillna(kwargs = {}) { - let params_needed = [ "columns", "values", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (utils.__key_in_object(kwargs, "columns")) { - //check if the column(s) exists - kwargs["columns"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if (kwargs["columns"].length != kwargs["values"].length) { - throw Error(`Lenght Error: The lenght of the columns names must be equal to the lenght of the values, - got column of length ${kwargs["columns"].length} but values of length ${kwargs["values"].length}`); - } - let new_col_data = this.col_data; - kwargs["columns"].map((col, i) => { - let col_idx = this.column_names.indexOf(col); - let col_data = this.col_data[col_idx]; - - let __temp = []; - col_data.map((val) => { - //fill the column - if (isNaN(val) && typeof val != "string") { - __temp.push(kwargs["values"][i]); - } else { - __temp.push(val); - } - }); - new_col_data[col_idx] = __temp; - }); - - let final_data = {}; - new_col_data.map((col, i) => { - final_data[this.column_names[i]] = col; - }); - - if (kwargs["inplace"]) { - this.__update_frame_in_place(null, null, final_data, null, null); - } else { - return new DataFrame(final_data, { index: this.index }); - } - } else { - //fill all columns using same value - if (!utils.__key_in_object(kwargs, "values")) { - throw Error("Value Error: Please specify a fill value"); - } - - let nan_val; - if (Array.isArray(kwargs["values"])) { - nan_val = kwargs["values"][0]; - } else { - nan_val = kwargs["values"]; - } - let data = []; - let values = this.values; - let columns = this.columns; - - for (let i = 0; i < values.length; i++) { - let temp_data = []; - let row_value = values[i]; - for (let j = 0; j < row_value.length; j++) { - if (isNaN(row_value[j]) && typeof row_value[j] != "string") { - temp_data.push(nan_val); - } else { - temp_data.push(row_value[j]); - } - } - data.push(temp_data); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place(data, null, null, null, null); - } else { - return new DataFrame(data, { columns: columns, index: this.index }); - } - } - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {DataFrame} - */ - isna() { - let new_row_data = this.__isna(); - let columns = this.column_names; - return new DataFrame(new_row_data, { columns: columns, index: this.index }); - } - - /** - * Obtain index containing nan values - * @return Array list (int) - */ - nanIndex() { - let df_values = this.values; - let index_data = []; - - for (let i = 0; i < df_values.length; i++) { - let row_values = df_values[i]; - - if (row_values.includes(NaN)) { - index_data.push(i); - } - } - return index_data; - } - - /** - * Drop all rows containing NaN - * @param {kwargs} kwargs [Object] {axis: [int]{o or 1}, inplace:[boolean]} - */ - dropna(kwargs = {}) { - let axis = kwargs["axis"] || 0; - let inplace = kwargs["inplace"] || false; - - if (axis != 0 && axis != 1) { - throw new Error("axis must either be 1 or 0"); - } - - let df_values = null; - let columns = null; - if (axis == 0) { - df_values = this.values; - columns = this.columns; - } else { - df_values = this.col_data; - columns = []; - } - let data = []; - - for (let i = 0; i < df_values.length; i++) { - let values = df_values[i]; - - if (!values.includes(NaN)) { - if (axis == 0) { - data.push(values); - } else { - columns.push(this.columns[i]); - if (data.length == 0) { - for (let j = 0; j < values.length; j++) { - data.push([ values[j] ]); - } - } else { - for (let j = 0; j < data.length; j++) { - data[j].push(values[j]); - } - } - } - } - } - - if (inplace == true) { - this.data = data; - this.__reset_index(); - this.columns = columns; - } else { - return new DataFrame(data, { columns: columns }); - } - } - - /** - * Apply a function to each element or along a specified axis of the DataFrame. Supports JavaScipt functions - * when axis is not specified, and accepts Tensorflow functions when axis is specified. - * @param {kwargs} kargs is defined as {axis: undefined, 0 or 1, callable: [FUNCTION]} - * @return Array - */ - apply(kwargs) { - let is_callable = utils.__is_function(kwargs["callable"]); - if (!is_callable) { - throw new Error("the argument must be a function"); - } - - let callable = kwargs["callable"]; - let data = []; - - if (utils.__key_in_object(kwargs, "axis")) { - //This accepts all tensorflow operations - let axis = kwargs["axis"]; - let df_data; - if (axis == 0) { - df_data = this.values; - } else { - df_data = this.col_data; - } - - for (let i = 0; i < df_data.length; i++) { - let value = tf.tensor(df_data[i]); - let callable_data; - try { - callable_data = callable(value).arraySync(); - } catch (error) { - throw Error( - `Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.` - ); - } - - data.push(callable_data); - } - } else { - //perform element wise operation. This accepts any JavaScript function - let df_data = this.values; - let new_data = []; - df_data.forEach((row) => { - let new_row = []; - row.forEach((val) => { - new_row.push(callable(val)); - }); - new_data.push(new_row); - }); - data = new_data; - } - - if (utils.__is_1D_array(data)) { - if (kwargs["axis"] == 0) { - let sf = new Series(data, { index: this.index }); - return sf; - } else { - let sf = new Series(data, { index: this.column_names }); - return sf; - } - } else { - let df = new DataFrame(data, { - columns: this.column_names, - index: this.index - }); - return df; - } - } - - /** - * Returns Less than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - lt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "lt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - gt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "gt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Less than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - le(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "le", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ge(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ge", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Not Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ne(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ne", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - eq(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "eq", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Replace all occurence of a value with a new specified value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with - * "in": Array of column names to replace value in, If not specified, replace all columns} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = [ "replace", "with", "in" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (utils.__key_in_object(kwargs, "in")) { - //fill specified columns only - //check if the column(s) exists - kwargs["in"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let new_col_data_obj = {}; - this.column_names.map((col, idx) => { - if (kwargs["in"].includes(col)) { - let temp_col_data = this.col_data[idx]; //retreive the column data - let __temp = []; - temp_col_data.map((val) => { - //replace the values - if (val == kwargs["replace"]) { - __temp.push(kwargs["with"]); - } else { - __temp.push(val); - } - }); - new_col_data_obj[col] = __temp; - } else { - new_col_data_obj[col] = this.col_data[idx]; - } - }); - return new DataFrame(new_col_data_obj, { - columns: this.column_names, - index: this.index - }); - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } else { - //fill every occurence in all columns and rows - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let replaced_arr = []; - let old_arr = this.values; - - old_arr.map((inner_arr) => { - let temp = []; - inner_arr.map((val) => { - if (val == kwargs["replace"]) { - temp.push(kwargs["with"]); - } else { - temp.push(val); - } - }); - replaced_arr.push(temp); - }); - - let df = new DataFrame(replaced_arr, { - index: this.index, - columns: this.column_names - }); - return df; - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } - } - - //performs logical comparisons on DataFrame using Tensorflow.js - __logical_ops(val, logical_type, axis) { - let int_vals, other; - if (utils.__is_number(val)) { - other = val; - } else { - if (val.series) { - //series - if (axis == 0) { - if (val.values.length != this.shape[0]) { - throw Error( - `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.` - ); - } - other = tf.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); - } - } else if (Array.isArray(val)) { - //Array of Array - other = tf.tensor(val); - } else { - //DataFrame - other = val.row_data_tensor; - } - } - - switch (logical_type) { - case "lt": - int_vals = tf.tensor(this.values).less(other).arraySync(); - break; - case "gt": - int_vals = tf.tensor(this.values).greater(other).arraySync(); - break; - case "le": - int_vals = tf.tensor(this.values).lessEqual(other).arraySync(); - break; - case "ge": - int_vals = tf.tensor(this.values).greaterEqual(other).arraySync(); - break; - case "ne": - int_vals = tf.tensor(this.values).notEqual(other).arraySync(); - break; - case "eq": - int_vals = tf.tensor(this.values).equal(other).arraySync(); - break; - } - let bool_vals = utils.__map_int_to_bool(int_vals, 2); - let df = new DataFrame(bool_vals, { - columns: this.column_names, - index: this.index - }); - return df; - } - - //slice the corresponding arrays from tensor objects - __get_df_from_tensor(val, col_names) { - let len = val.shape[0]; - let new_array = []; - for (let i = 0; i < len; i++) { - let arr = val.slice([ i ], [ 1 ]).arraySync()[0]; - new_array.push(arr); - } - return new DataFrame(new_array, { columns: col_names }); - } - - //checks if DataFrame is compaticble for arithmetic operation - //compatible Dataframe must have only numerical dtypes - __frame_is_compactible_for_operation() { - let dtypes = this.dtypes; - const str = (element) => element == "string"; - - if (dtypes.some(str)) { - return false; - } else { - return true; - } - } - - //retreives the corresponding tensors based on specified axis - __get_ops_tensors(tensors, axis) { - if (utils.__is_undefined(tensors[1].series)) { - //check if add operation is on a series or DataFrame - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - tensors_arr.push(tensors[0].row_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } else { - //axis = 0 (rows) - tensors_arr.push(tensors[0].col_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } - } else { - //operation is being performed on a Dataframe or Series - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - let this_tensor, other_tensor; - - 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, [ - 1, - tensors[1].values.length - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } else { - //axis = 0 (rows) - let this_tensor, other_tensor; - - this_tensor = tensors[0].row_data_tensor; - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [ - tensors[1].values.length, - 1 - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } - } - } - - /** - * Transpose index and columns. - * Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. - * The property T is an accessor to the method transpose(). - */ - transpose() { - let new_values = this.col_data; - let new_index = this.column_names; - let new_col_names = this.index; - - let df = new DataFrame(new_values, { - columns: new_col_names, - index: new_index - }); - return df; - } - - /** - * The property T is an accessor to the method transpose(). - */ - get T() { - return this.transpose(); - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get ctypes() { - let cols = this.column_names; - let d_types = this.col_types; - let sf = new Series(d_types, { index: cols }); - return sf; - } - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Returns the Tensorflow tensor backing the DataFrame Object - * @returns {2D tensor} - */ - get tensor() { - return this.row_data_tensor; - } - - /** - * Sets the data types of an DataFrame - * @param {Object} kwargs {column: Name of the column to cast, dtype: [float32, int32, string] data type to cast to} - * @returns {DataFrame} - */ - astype(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "column")) { - throw Error("Value Error: Please specify a column to cast"); - } - - if (!utils.__key_in_object(kwargs, "dtype")) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!this.column_names.includes(kwargs["column"])) { - throw Error(`'${kwargs["column"]}' not found in columns`); - } - - let col_idx = this.column_names.indexOf(kwargs["column"]); - let new_types = this.col_types; - let col_values = this.col_data; - - new_types[col_idx] = kwargs["dtype"]; - let new_col_values = []; - let temp_col = col_values[col_idx]; - - switch (kwargs["dtype"]) { - case "float32": - temp_col.map((val) => { - new_col_values.push(Number(val)); - }); - col_values[col_idx] = new_col_values; - break; - case "int32": - temp_col.map((val) => { - new_col_values.push(Number(Number(val).toFixed())); - }); - col_values[col_idx] = new_col_values; - - break; - case "string": - temp_col.map((val) => { - new_col_values.push(String(val)); - }); - col_values[col_idx] = new_col_values; - break; - default: - break; - } - - let new_col_obj = {}; - this.column_names.forEach((cname, i) => { - new_col_obj[cname] = col_values[i]; - }); - - let df = new DataFrame(new_col_obj, { - dtypes: new_types, - index: this.index - }); - return df; - } - - /** - * Return the unique values along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Object} - */ - unique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - let _unique = {}; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _unique[cname] = this[cname].unique().values; - }); - } else { - let rows = this.values; - let _index = this.index; - rows.forEach((row, i) => { - let data_set = new Set(row); - _unique[_index[i]] = Array.from(data_set); - }); - } - - return _unique; - } - - /** - * Return the number of unique value along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Series} - */ - nunique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - - let _nunique = []; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _nunique.push(this[cname].unique().values.length); - }); - let sf = new Series(_nunique, { index: this.column_names }); - return sf; - } else { - let rows = this.values; - rows.forEach((row) => { - let data_set = new Set(row); - _nunique.push(Array.from(data_set).length); - }); - } - let sf = new Series(_nunique, { index: this.index }); - return sf; - } - - /** - * Change axes labels. Object values must be unique (1-to-1). - * Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error. - * @param {Object} kwargs {mapper: Dict-like or functions transformations to apply to that axis’ values, - * axis: Int, 0 for row, and 1 for column. Default to 1, - * inplace: Whether to return a new DataFrame. If True then value of copy is ignored. - * @returns {DataFrame} - */ - rename(kwargs = {}) { - let params_needed = [ "mapper", "inplace", "axis" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - // utils.__in_object(kwargs, "columns", "value not defined") - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - if (!utils.__key_in_object(kwargs, "axis")) { - kwargs["axis"] = 1; - } - if (!utils.__key_in_object(kwargs, "mapper")) { - throw Error("Please specify a mapper object"); - } - if (kwargs["axis"] == 1) { - //columns - let old_col_names = Object.keys(kwargs["mapper"]); - let new_col_names = Object.values(kwargs["mapper"]); - let col_names = [ ...this.column_names ]; - - old_col_names.forEach((cname, i) => { - if (!col_names.includes(cname)) { - throw Error( - `Label Error: Specified column '${cname}' not found in column axis` - ); - } - let idx = col_names.indexOf(cname); - col_names[idx] = new_col_names[i]; - }); - if (kwargs["inplace"]) { - this.columns = col_names; - this.__set_col_property(this, this.col_data, col_names, old_col_names); - } else { - let df = this.copy(); - df.columns = col_names; - df.__set_col_property(df, df.col_data, col_names, old_col_names); - return df; - } - } else { - //row - let old_index = Object.keys(kwargs["mapper"]); - let row_index = this.index; - let new_index = []; - - row_index.forEach((idx) => { - if (old_index.includes(idx)) { - new_index.push(kwargs["mapper"][idx]); - } else { - new_index.push(idx); - } - }); - - if (kwargs["inplace"]) { - this.__set_index(new_index); - } else { - let df = this.copy(); - df.__set_index(new_index); - return df; - } - } - } - /** - * Sort DataFrame by index - * @param {*} kwargs {inplace: Boolean, ascending: Bool} - * @returns DataFrame - */ - sort_index(kwargs = {}) { - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - - let index_val = this.index; - let [ data, index ] = this.__sort_by(index_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "by")) { - throw Error(`use col_name to specify column name`); - } - - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - let index_val = this.index; - let column_val = this.column(kwargs["by"]).values; - let [ data, index ] = this.__sort_by(column_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - //set all columns to DataFrame Property. This ensures easy access to columns as Series - __set_col_property(self, col_vals, col_names, old_col_names) { - //delete old name - old_col_names.forEach((name) => { - delete self[name]; - }); - - col_vals.forEach((col, i) => { - // self[col_names[i]] = new Series(col, { columns: col_names[i], index: self.index }) - Object.defineProperty(self, col_names[i], { - get() { - return new Series(col, { columns: col_names[i], index: self.index }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - - //update a DataFrame in place - __update_frame_in_place(row_data, column_names, col_obj, index, dtypes) { - if (row_data != undefined) { - this.data = row_data; - } else { - //check column is available and create row from column - if (col_obj != undefined) { - let _res = utils._get_row_and_col_values(col_obj); - this.data = _res[0]; - this.columns = _res[1]; - column_names = _res[1]; - } - } - - if (col_obj != undefined) { - this.col_data = Object.values(col_obj); - this.columns = Object.keys(col_obj); - column_names = Object.keys(col_obj); - } else { - //check if row data is available and create column data from rows - if (row_data != undefined) { - this.col_data = utils.__get_col_values(row_data); //get column data from row - } - } - - if (column_names != undefined) { - this.columns = column_names; - } - if (index != undefined) { - this.index_arr = index; - } - if (dtypes != undefined) { - this.col_types = dtypes; - } - } - - __sort_by(col_value, df_index, asc) { - let values = this.values; - - let sorted_val = utils.__sort(col_value, asc); - let duplicate_obj = utils.__get_duplicate(col_value); - - let data = []; - let indexs = []; - for (let row_i = 0; row_i < sorted_val.length; row_i++) { - let val = sorted_val[row_i]; - let index = null; - - if (val in duplicate_obj) { - index = duplicate_obj[val]["index"][0]; - duplicate_obj[val]["index"].splice(0, 1); - } else { - index = col_value.indexOf(val); - } - - data.push(values[index]); - indexs.push(df_index[index]); - } - - return [ data, indexs ]; - } - - /** - * Append rows to a DataFrame - * @param {val} val Array | Series to append to the object - * @return DataFrame - */ - append(val) { - let df2 = null; - if (Array.isArray(val)) { - if (Array.isArray(val[0])) { - if (val[0].length != this.shape[1]) { - throw Error( - `length Mixmatch: The lenght of provided value (${val.length}) does not match the original DataFrame (${this.shape[1]})` - ); - } - df2 = new DataFrame(val); - } - } else if (utils.__is_object(val)) { - df2 = new DataFrame(val); - } else if (val instanceof DataFrame) { - df2 = val.copy(); - } - - let concat_df = concat({ df_list: [ this, df2 ], axis: 0 }); - - return concat_df; - } -} diff --git a/src/lib/danfo/core/generic.js b/src/lib/danfo/core/generic.js deleted file mode 100644 index 90bb2ec..0000000 --- a/src/lib/danfo/core/generic.js +++ /dev/null @@ -1,500 +0,0 @@ -/** -* Copyright 2020, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - -import { tensor, Tensor } from "@tensorflow/tfjs"; -import { table } from "table"; -import { Utils } from "./utils"; -import { Configs } from "../config/config"; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object -export default class NDframe { - /** - * N-Dimensiona data structure. Stores multi-dimensional - * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. - * - * @param {data} Array JSON, Tensor. Block of data. - * @param {kwargs} Object Optional Configuration Object - * {columns: Array of column names. If not specified and data is an array of array, use range index. - * dtypes: Data types of the columns, - * index: row index for subseting array } - * - * @returns NDframe - */ - - constructor(data, kwargs = {}) { - this.kwargs = kwargs; - - if (data instanceof Tensor) { - data = data.arraySync(); - } - - if (utils.__is_1D_array(data)) { - this.series = true; - this._read_array(data); - } else { - this.series = false; - if (utils.__is_object(data[0])) { - //check the type of the first object in the data - this._read_object(data, 1); //type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}] - } else if (utils.__is_object(data)) { - this._read_object(data, 2); //type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - } else if ( - Array.isArray(data[0]) || - utils.__is_number(data[0]) || - utils.__is_string(data[0]) - ) { - this._read_array(data); - } else { - throw new Error("File format not supported"); - } - } - } - - /** - * - * @param {Array} data - * Read array of data into NDFrame - */ - _read_array(data) { - this.data = utils.__replace_undefined_with_NaN(data, this.series); - 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 = tensor(this.col_data); //data saved as 2D column tensors - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if ("columns" in this.kwargs) { - this.columns = this.kwargs["columns"]; - } else { - this.columns = [ "0" ]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - - /** - * Convert Javascript Object of arrays into NDFrame - * @param {*} data Object of Arrays - * @param {*} type type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}], - * type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - */ - _read_object(data, type) { - if (type == 2) { - let [ row_arr, col_names ] = utils._get_row_and_col_values(data); - this.kwargs["columns"] = col_names; - this._read_array(row_arr); - } else { - let data_arr = data.map((item) => { - return Object.values(item); - }); - - this.data = utils.__replace_undefined_with_NaN(data_arr, this.series); //Defualt array data in row format - 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) { - this.col_data = [ this.values ]; //data saved as 1D column tensors - } else { - this.col_data = utils.__get_col_values(this.data); - } - - 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"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if (!this.kwargs["columns"]) { - this.columns = [ "0" ]; - } else { - this.columns = this.kwargs["columns"]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == - Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has column length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - } - - /** - * Sets the data type of the NDFrame. Supported types are ['float32', "int32", 'string', 'boolean'] - * @param {Array} dtypes Array of data types. - * @param {Boolean} infer Whether to automatically infer the dtypes from the Object - */ - _set_col_types(dtypes, infer) { - const __supported_dtypes = [ "float32", "int32", "string", "boolean" ]; - - if (infer) { - if (this.series) { - this.col_types = utils.__get_t(this.values); - } else { - this.col_types = utils.__get_t(this.col_data); - } - } else { - if (this.series) { - this.col_types = dtypes; - } else { - if (dtypes.length != this.columns.length) { - throw new Error( - `length Mixmatch: Length of specified dtypes is ${dtypes.length}, but length of columns is ${this.columns.length}` - ); - } - if (Array.isArray(dtypes)) { - dtypes.forEach((type, indx) => { - if (!__supported_dtypes.includes(type)) { - throw new Error( - `dtype error: dtype specified at index ${indx} is not supported` - ); - } - }); - this.col_types = dtypes; - } else { - throw new Error(`dtypes must be an Array of types`); - } - } - } - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get dtypes() { - // let col_data = utils.get_col_values(this.data) - // this.col_types = utils.__get_t(col_data) - // let sf = new Series({dtypes: this.col_types, index: this.column_names}) - return this.col_types; - } - - /** - * Gets dimension of the NDFrame - * @returns {Integer} dimension of NDFrame - */ - get ndim() { - if (this.series) { - return 1; - } else { - return this.row_data_tensor.shape.length; - } - } - - /** - * Gets values for index and columns - * @return {Object} axes configuration for index and columns of NDFrame - */ - get axes() { - let axes = { - index: this.index, - columns: this.columns - }; - return axes; - } - - /** - * Gets index of the NDframe - * @return {Array} array of index from series - */ - get index() { - return this.index_arr; - } - - /** - * Sets index of the NDFrame - */ - __set_index(labels) { - if (!Array.isArray(labels)) { - throw Error("Value Error: index must be an array"); - } - if (labels.length > this.shape[0] || labels.length < this.shape[0]) { - throw Error("Value Error: length of labels must match row shape of data"); - } - this.index_arr = labels; - } - - /** - * Generate a new index for NDFrame. - */ - __reset_index() { - let new_idx = [ ...Array(this.values.length).keys() ]; - this.index_arr = new_idx; - } - - /** - * Gets a sequence of axis dimension along row and columns - * @returns {Array} the shape of the NDFrame - */ - get shape() { - if (this.series) { - return [ this.values.length, 1 ]; - } else { - return this.row_data_tensor.shape; - } - } - - /** - * Gets the values in the NDFrame in JS array - * @returns {Array} Arrays of arrays of data instances - */ - get values() { - return this.data; - } - - /** - * Gets the column names of the data - * @returns {Array} strings of column names - */ - get column_names() { - return this.columns; - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Array} - */ - __isna() { - let new_arr = []; - if (this.series) { - this.values.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - new_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - } else { - let row_data = this.values; - row_data.map((arr) => { - let temp_arr = []; - arr.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - temp_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - } - return new_arr; - } - - /* - * Gets binary size of the NDFrame - * @returns {String} size of the NDFrame - */ - get size() { - return this.row_data_tensor.size; - } - - /** - * Return object data as comma-separated values (csv). - * @returns {Promise} CSV representation of Object data - */ - async to_csv() { - if (this.series) { - let csv = this.values.join(","); - return csv; - } else { - let records = this.values; - let header = this.column_names.join(","); - - let csv_str = `${header}\n`; - records.forEach((val) => { - let row = `${val.join(",")}\n`; - csv_str += row; - }); - return csv_str; - } - } - - /** - * Return object as JSON string. - * @returns {Promise } JSON representation of Object data - */ - async to_json() { - if (this.series) { - let obj = {}; - obj[this.column_names[0]] = this.values; - let json = JSON.stringify(obj); - return json; - } else { - let values = this.values; - let header = this.column_names; - let json_arr = []; - values.forEach((val) => { - let obj = {}; - header.forEach((h, i) => { - obj[h] = val[i]; - }); - json_arr.push(obj); - }); - return JSON.stringify(json_arr); - } - } - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = config.get_width; - let table_truncate = config.get_truncate; - let max_row = config.get_max_row; - let max_col_in_console = config.get_max_col_in_console; - - // let data; - let data_arr = []; - let table_config = {}; - // let idx = this.index - let col_len = this.columns.length; - // let row_len = this.values.length - 1 - let header = []; - - if (col_len > max_col_in_console) { - //truncate displayed columns to fit in the console - let first_4_cols = this.columns.slice(0, 4); - let last_3_cols = this.columns.slice(col_len - 4); - //join columns with truncate ellipse in the middle - header = [ "" ].concat(first_4_cols).concat([ "..." ]).concat(last_3_cols); - - let sub_idx, values_1, value_2; - - if (this.values.length > max_row) { - //slice Object to show [max_rows] - let df_subset_1 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ "0:4" ] - }); - let df_subset_2 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } else { - let df_subset_1 = this.iloc({ rows: [ "0:" ], columns: [ "0:4" ] }); - let df_subset_2 = this.iloc({ - rows: [ "0:" ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } - - // merge dfs - sub_idx.map((val, i) => { - let row = [ val ].concat(values_1[i]).concat([ "..." ]).concat(value_2[i]); - data_arr.push(row); - }); - } else { - //display all columns - header = [ "" ].concat(this.columns); - let idx, values; - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - let data = this.loc({ rows: [ `0:${max_row}` ], columns: this.columns }); - idx = data.index; - values = data.values; - } else { - values = this.values; - idx = this.index; - } - - // merge cols - idx.forEach((val, i) => { - let row = [ val ].concat(values[i]); - data_arr.push(row); - }); - } - - //set column width of all columns - table_config[0] = 10; - for (let index = 1; index < header.length; index++) { - table_config[index] = { width: table_width, truncate: table_truncate }; - } - - let table_data = [ header ].concat(data_arr); //Adds the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Pretty prints n number of rows in a DataFrame or Series in the console - * @param {rows} Number of rows to print - */ - print() { - console.log(this + ""); - } -} diff --git a/src/lib/danfo/core/get_dummies.js b/src/lib/danfo/core/get_dummies.js deleted file mode 100644 index 32a5391..0000000 --- a/src/lib/danfo/core/get_dummies.js +++ /dev/null @@ -1,139 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; - -const utils = new Utils; - -function oneHot(in_data, prefix, prefix_sep) { - let data_set = new Set(in_data); - let labels = Array.from(data_set); - let prefix_labels = null; - if (prefix) { - prefix_labels = labels.map((x) => { - return prefix + prefix_sep + x; - }); - } else { - prefix_labels = labels.map((x) => { - return x; - }); - } - - let onehot_data = utils.__zeros(in_data.length, labels.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = labels.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return [ onehot_data, prefix_labels ]; - -} - -/** - * Generate one hot encoding for categorical variable in arrays |Serie | and Dataframe - * @param {kwargs} kwargs { data : Array | Series | DataFrame, - * prefix_sep: String e.g "_", - * prefix: String | Array of String, - * columns: [Array] columns to be encoded in DataFrame. - * } - */ -function get_dummy(kwargs = {}) { - utils.__in_object(kwargs, "data", "data not provided"); - - let prefix = kwargs["prefix"] || null; - let prefix_sep = kwargs["prefix_sep"] || [ "_" ]; - let columns = kwargs["columns"] || null; - - let is_dataframe = false; - let in_data = null; - - if (Array.isArray(kwargs["data"])) { - in_data = kwargs["data"]; - } else if (kwargs["data"] instanceof DataFrame) { - in_data = kwargs["data"]; - is_dataframe = true; - } else { - in_data = kwargs["data"].values; - } - - if (!is_dataframe) { - let [ onehot_data, prefix_labels ] = oneHot(in_data, prefix, prefix_sep); - - return new DataFrame(onehot_data, { columns: prefix_labels }); - } else { - - let column_index = []; - if (!columns) { - columns = []; - in_data.col_types.map((x, i) => { - - if (x == "string") { - let name_column = in_data.columns[i]; - columns.push(name_column); - column_index.push(i); - } - }); - } else { - - columns.forEach((x) => { - let col_idx = columns.indexOf(x); - column_index.push(col_idx); - }); - } - - if (prefix) { - if (Array.isArray(prefix)) { - if (prefix.length != columns.length) { - throw new Error("prefix must be the same length with the number of onehot encoding column"); - } - } else { - throw new Error("prefix for dataframe must be an array"); - } - } else { - prefix = columns; - } - - let df_data = in_data.values; - let df_columns = in_data.columns; - let col_data = in_data.col_data; - - let column_data = []; - column_index.forEach((x) => { - column_data.push(col_data[x]); - }); - - let one_hotColumns = []; - let one_hotData = []; - column_data.forEach((data, i) => { - - let [ onehot_data, prefix_labels ] = oneHot(data, prefix[i], prefix_sep); - one_hotColumns.push(...prefix_labels); - - if (one_hotData.length == 0) { - one_hotData.push(...onehot_data); - } else { - onehot_data.forEach((x, i) => { - one_hotData[i].push(...x); - }); - } - - }); - - let final_data = df_data.map((elem, i) => { - - let ele = elem.slice(); - let dt = utils.__remove_arr(ele, column_index); - dt.push(...one_hotData[i]); - return dt; - }); - - let final_columns = utils.__remove_arr(df_columns, column_index); - final_columns.push(...one_hotColumns); - - return new DataFrame(final_data, { columns: final_columns }); - } - -} - -export const get_dummies = get_dummy; diff --git a/src/lib/danfo/core/groupby.js b/src/lib/danfo/core/groupby.js deleted file mode 100644 index 91f34d3..0000000 --- a/src/lib/danfo/core/groupby.js +++ /dev/null @@ -1,511 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - -/** - * The class performs all groupby operation on a dataframe - * involveing all aggregate funciton - * @param {col_dict} col_dict Object of unique keys in the group by column - * @param {key_col} key_col Array contains the column names - * @param {data} Array the dataframe data - * @param {column_name} Array of all column name in the dataframe. - */ -export class GroupBy { - constructor(col_dict, key_col, data, column_name) { - - this.key_col = key_col; - this.col_dict = col_dict; - this.data = data; - this.column_name = column_name; - this.data_tensors = {}; //store the tensor version of the groupby data - - } - - /** - * Group the dataframe by the column by - * creating an object to store the grouping - * @returns Groupby data structure - */ - group(){ - - if (this.key_col.length == 2){ //check if the dataframe is group by two columns - - - for (var i = 0; i < this.data.length; i++){ - - let col1_index = this.column_name.indexOf(this.key_col[0]); - let col2_index = this.column_name.indexOf(this.key_col[1]); - - let value = this.data[i]; - - let col1_value = value[col1_index]; - let col2_value = value[col2_index]; - - - if (Object.prototype.hasOwnProperty.call(this.col_dict, col1_value)){ - if (Object.prototype.hasOwnProperty.call(this.col_dict[col1_value], col2_value)){ - - this.col_dict[col1_value][col2_value].push(value); - } - - } - - } - - for (var key in this.col_dict){ - this.data_tensors[key] = {}; - - for (var key2 in this.col_dict[key]){ - - let data = this.col_dict[key][key2]; - - if (data.length == 0){ - delete this.col_dict[key][key2]; //delete the empty key. - } else { - this.data_tensors[key][key2] = new DataFrame(data, { columns:this.column_name }); - } - - } - } - } else { - for (let i = 0; i < this.data.length; i++){ - - let col1_index = this.column_name.indexOf(this.key_col[0]); - - let value = this.data[i]; - - let col1_value = value[col1_index]; - - if (Object.prototype.hasOwnProperty.call(this.col_dict, col1_value)){ - - this.col_dict[col1_value].push(value); - - } - } - for (let key in this.col_dict){ - let data = this.col_dict[key]; - - this.data_tensors[key] = new DataFrame(data, { columns:this.column_name }); - - } - - } - - return this; - - } - - /** - * obtain the column for each group - * @param {col_name} col_name [Array]--> array of column names - * @return Groupby data structure - */ - col(col_names){ - this.selected_column = col_names; // store col_names for use later in .apply - if (Array.isArray(col_names)){ - - for (let i = 0; i < col_names.length; i++){ - - let col_name = col_names[i]; - if (!this.column_name.includes(col_name)){ - throw new Error(`Column ${col_name} does not exist in groups`); - } - } - } else { - throw new Error(`Col_name must be an array of column`); - } - - // let group_col_name = col_names; // store the column name - let group_col = {}; - if (this.key_col.length == 2){ - - for (var key1 in this.data_tensors){ - - group_col[key1] = {}; - for (var key2 in this.data_tensors[key1]){ - - group_col[key1][key2] = []; - for (let i = 0; i < col_names.length; i++){ - let col_name = col_names[i]; - let data = this.data_tensors[key1][key2].column(col_name); - group_col[key1][key2].push(data); - } - - } - } - } else { - for (let key1 in this.data_tensors){ - - group_col[key1] = []; - for (let i = 0; i < col_names.length; i++){ - let col_name = col_names[i]; - let data = this.data_tensors[key1].column(col_name); - group_col[key1].push(data); - } - - } - } - const gp = new GroupBy( - null, - this.key_col, - null, - col_names - ); - - gp.group_col = group_col; - gp.group_col_name = col_names; - // return gp; - return gp; - } - - - /** - * Basic root of all column arithemetic in groups - * @param {operation} operatioin String - */ - arithemetic(operation){ - - let ops_name = [ "mean", "sum", "count", "mode", "std", "var", "cumsum", "cumprod", - "cummax", "cummin" ]; - - let ops_map = { - "mean": "mean()", - "sum": "sum()", - "mode": "mode()", - "count": "count()", - "std" : "std()", - "var" : "var()", - "cumsum" : "cumsum().values", - "cumprod": "cumprod().values", - "cummax" : "cummax().values", - "cummin" : "cummin().values" - }; - let is_array = false; - //the local variable to store variables to be used in eval - // this seems not to be needed in Node version, since local - //variable are easily accessed in the eval function - let local = null; - - if (Array.isArray(operation)){ - is_array = true; - } - - if (this.key_col.length == 2){ - - let count_group = {}; - - for (var key1 in this.group_col){ - - count_group[key1] = {}; - for (var key2 in this.group_col[key1]){ - - count_group[key1][key2] = []; - for (let i = 0; i < this.group_col[key1][key2].length; i++ ){ - let data = null; - if (is_array){ - let op = operation[i]; - if (!ops_name.includes(op)){ - throw new Error("operation does not exist"); - } - local = this.group_col[key1][key2][i]; - data = eval(`local.${ops_map[op]}`); - - } else { - local = this.group_col[key1][key2][i]; - data = eval(`local.${operation}`); - } - count_group[key1][key2].push(data); - - } - - } - } - return count_group; - - } else { - let count_group = {}; - - for (let key1 in this.group_col){ - - count_group[key1] = []; - for (let i = 0; i < this.group_col[key1].length; i++ ){ - let data = null; - if (is_array){ - let op = operation[i]; - if (!ops_name.includes(op)){ - throw new Error("operation does not exist"); - } - local = this.group_col[key1][i]; - data = eval(`local.${ops_map[op]}`); - - } else { - local = this.group_col[key1][i]; - data = eval(`local.${operation}`); - } - - count_group[key1].push(data); - - } - } - - return count_group; - } - - - } - - operations(ops, name) { - if (!this.group_col) { - let column = this.column_name.filter((val) => !this.key_col.includes(val)); - let col_gp = this.col(column); - let value = col_gp.arithemetic(ops); - let df = col_gp.to_DataFrame(col_gp.key_col, col_gp.group_col_name, value, name); - return df; - } else { - let value = this.arithemetic(ops); - let df = this.to_DataFrame(this.key_col, this.group_col_name, value, name); - return df; - } - } - count(){ - return this.operations("count()", "count"); - } - - sum(){ - return this.operations("sum()", "sum"); - } - - std(){ - return this.operations("std()", "std"); - } - - var(){ - return this.operations("var()", "var"); - } - - mean(){ - return this.operations("mean()", "mean"); - } - - cumsum(){ - return this.operations("cumsum().values", "cumsum"); - } - cummax(){ - return this.operations("cummax().values", "cummax"); - } - - cumprod(){ - return this.operations("cumprod().values", "cumprod"); - } - - cummin(){ - return this.operations("cummin().values", "cummin"); - } - - max(){ - return this.operations("max()", "max"); - } - - min(){ - return this.operations("min()", "min"); - } - - /** - * returns dataframe of a group - * @param {*} key [Array] - */ - get_groups(key){ - - if (this.key_col.length == 2){ - - if (key.length == 2){ - let key1 = key[0]; - let key2 = key[1]; - - utils.__is_object(this.data_tensors, key1, `Key Error: ${key1} not in object`); - return this.data_tensors[key1][key2]; - } else { throw new Error("specify the two group by column"); } - } else if (this.key_col.length == 1){ - - if (key.length == 1){ - - utils.__is_object(this.data_tensors, key[0], `Key Error: ${key[0]} not in object`); - return this.data_tensors[key[0]]; - } else { throw new Error("specify the one group by column"); } - } - return this.data_tensors[key]; - } - - /** - * Map every column to an operaton - * @param {kwargs} kwargs {column name: math operation} - * @example .agg({"A": "mean","B": "sum","C":"count"}) - */ - agg(kwargs = {}){ - - let columns = Object.keys(kwargs); - let operations = columns.map((x) => { return kwargs[x].toLocaleLowerCase(); }); - - let col_gp = this.col(columns); - - let data = col_gp.arithemetic(operations); - let df = this.to_DataFrame(col_gp.key_col, col_gp.group_col_name, data, operations); - - return df; - } - - to_DataFrame(key_col, col, data, ops){ - - if (key_col.length == 2){ - let df_data = []; - for (let key_1 in data){ - - let key_val = data[key_1]; - - for (let key_2 in key_val){ - let k_data = key_val[key_2]; - let key_data = []; - - - if (Array.isArray(k_data[0])){ - for (let i = 0; i < k_data.length; i++){ - let col_data = k_data[i]; - - for (let j = 0; j < col_data.length; j++ ){ - - if (typeof key_data[j] === "undefined" ){ - key_data[j] = []; - key_data[j][0] = isNaN(parseInt(key_1)) ? key_1 : parseInt(key_1); - key_data[j][1] = isNaN(parseInt(key_2)) ? key_2 : parseInt(key_2); - key_data[j].push(col_data[j]); - } else { - key_data[j].push(col_data[j]); - } - } - } - df_data.push(...key_data); - - } else { - key_data[0] = isNaN(parseInt(key_1)) ? key_1 : parseInt(key_1); - key_data[1] = isNaN(parseInt(key_2)) ? key_2 : parseInt(key_2); - key_data.push(...k_data); - df_data.push(key_data); - } - - - } - - } - let column = [ ...key_col ]; - - let group_col = col.slice().map((x, i) => { - if (Array.isArray(ops)){ - return `${x}_${ops[i]}`; - } - return `${x}_${ops}`; - }); - column.push(...group_col); - return new DataFrame(df_data, { columns: column }); - } else { - let df_data = []; - for (let key_1 in data){ - - let key_val = data[key_1]; - - let key_data = []; - if (Array.isArray(key_val[0])){ - for (let i = 0; i < key_val.length; i++){ - let col_data = key_val[i]; - - for (let j = 0; j < col_data.length; j++ ){ - - if (typeof key_data[j] === "undefined" ){ - key_data[j] = []; - key_data[j][0] = isNaN(parseInt(key_1)) ? key_1 : parseInt(key_1); - key_data[j].push(col_data[j]); - } else { - key_data[j].push(col_data[j]); - } - } - df_data.push(...key_data); - } - - } else { - key_data[0] = isNaN(parseInt(key_1)) ? key_1 : parseInt(key_1); - key_data.push(...key_val); - df_data.push(key_data); - } - - } - let column = [ ...key_col ]; - let group_col = col.slice().map((x, i) => { - if (Array.isArray(ops)){ - return `${x}_${ops[i]}`; - } - return `${x}_${ops}`; - }); - column.push(...group_col); - - return new DataFrame(df_data, { columns: column }); - } - } - - apply(callable){ - let df_data; - let column; - if (!this.group_col) { - column = this.column_name.filter((val) => !this.key_col.includes(val)); - let col_gp = this.col(column); - df_data = col_gp.group_col; - } else { - column = this.group_col_name; - df_data = this.group_col; - } - let data = []; - let count_group = {}; - if (this.key_col.length == 2) { - - for (let key in this.data_tensors) { - count_group[key] = {}; - for (let key2 in this.data_tensors[key]) { - count_group[key][key2] = []; - for (let i = 0; i < df_data[key][key2].length; i++ ) { - let callable_rslt = callable(df_data[key][key2][i]); - if (callable_rslt instanceof DataFrame) { - count_group[key][key2].push(callable_rslt.values); - } else { - if (callable_rslt instanceof Series) { - count_group[key][key2].push(callable_rslt.values); - } else { - count_group[key][key2].push(callable_rslt); - } - } - } - - - } - } - } else { - for (let key in df_data) { - count_group[key] = []; - for (let i = 0; i < df_data[key].length; i++ ){ - let callable_rslt = callable(df_data[key][i]); - if (callable_rslt instanceof DataFrame) { - count_group[key].push(callable_rslt.values); - } else { - if (callable_rslt instanceof Series) { - count_group[key].push(callable_rslt.values); - } else { - count_group[key].push(callable_rslt); - } - - } - } - - } - } - return this.to_DataFrame(this.key_col, column, count_group, "apply"); - } - -} diff --git a/src/lib/danfo/core/indexing.js b/src/lib/danfo/core/indexing.js deleted file mode 100644 index 8383ff1..0000000 --- a/src/lib/danfo/core/indexing.js +++ /dev/null @@ -1,191 +0,0 @@ -import { Utils } from "./utils"; -const utils = new Utils(); - - -/** - * Obtain the defined the set of row and column index - * @param {*} kwargs object {rows:Array, columns:Array of column name, type: ["iloc","loc"]} - * @return Array - */ -export const indexLoc = (ndframe, kwargs) => { - - let rows = null; - let columns = null; - let isColumnSplit = false; - - if (Object.prototype.hasOwnProperty.call(kwargs, "rows")) { //check if the object has the key - if (Array.isArray(kwargs["rows"])) { - - if (kwargs["rows"].length == 1 && typeof kwargs["rows"][0] == "string") { - //console.log("here", kwargs["rows"].length) - if (kwargs["rows"][0].includes(":")) { - - let row_split = kwargs["rows"][0].split(":"); - - if (kwargs['type'] == 'loc') { - //get index of first and last occurence of label - let start, end; - if (isNaN(Number(row_split[0]))) { - start = ndframe.index.indexOf(row_split[0]); - } else { - start = Number(row_split[0]); - } - - if (isNaN(Number(row_split[1]))) { - end = ndframe.index.lastIndexOf(row_split[1]) - 1 || (ndframe.values.length - 1); - } else { - end = Number(row_split[1]) - 1 || (ndframe.values.length - 1); - } - rows = utils.__range(start, end); - } else { - let start = parseInt(row_split[0]) || 0; - let end = parseInt(row_split[1]) - 1 || (ndframe.values.length - 1); - - if (typeof start == "number" && typeof end == "number") { - rows = utils.__range(start, end); - } - } - - } else { - if (kwargs["type"] == "loc") { - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'][0] == idx) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - throw new Error("Slice index must be separated by ':'"); - } - } - } else { - if (kwargs["type"] == "loc") { - //get all the index of specified labels - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'].includes(idx)) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - //return int index - rows = kwargs["rows"]; - } - } - } else { - throw new Error("rows parameter must be a Array"); - } - } else { - if (kwargs["type"] == "loc") { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - // throw new Error("Invalid syntax, please specify a slice label") - } else { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - } - } - - if (Object.prototype.hasOwnProperty.call(kwargs, "columns")) { - if (Array.isArray(kwargs["columns"])) { - if (kwargs["columns"].length == 1 && kwargs["columns"][0].includes(":")) { - - let row_split = kwargs["columns"][0].split(":"); - let start, end; - - if (kwargs["type"] == "iloc" || (row_split[0] == "")) { - start = parseInt(row_split[0]) || 0; - end = parseInt(row_split[1]) - 1 || (ndframe.values[0].length - 1); - } else { - - start = parseInt(ndframe.columns.indexOf(row_split[0])); - end = parseInt(ndframe.columns.indexOf(row_split[1])) - 1; - } - - - if (typeof start == "number" && typeof end == "number") { - - columns = utils.__range(start, end); - isColumnSplit = true; - } - - } else { - columns = kwargs["columns"]; - } - - } else { - throw new Error("columns must be a list"); - } - } else { - //Return all column - if (kwargs["type"] == "loc") { - columns = ndframe.column_names; - } else { - columns = utils.__range(0, Number(ndframe.shape[1]) - 1); - } - } - - let data_values = ndframe.values; - let new_data = []; // store the data from the for loop - - for (var index = 0; index < rows.length; index++) { - let row_val = rows[index]; - let max_rowIndex = data_values.length - 1; //obtain the maximum row index - - if (row_val > max_rowIndex) { //check if the input row index is greater than the maximum row index - throw new Error(`Specified row index ${row_val} is bigger than maximum row index of ${max_rowIndex}`); - } - - if (Array.isArray(data_values[0])){ - - let value = data_values[row_val]; - let row_data = []; - - - for (var i in columns) { - var col_index; - if (kwargs["type"] == "loc" && !isColumnSplit) { - col_index = ndframe.columns.indexOf(columns[i]); //obtain the column index - - if (col_index == -1) { - throw new Error(`Column ${columns[i]} does not exist`); - } - } else { - col_index = columns[i]; - let max_colIndex = ndframe.columns.length - 1; //assign the maximum column index to a value - - if (col_index > max_colIndex) { - throw new Error(`column index ${col_index} is bigger than ${max_colIndex}`); - } - } - - let elem = value[col_index]; //obtain the element at the column index - row_data.push(elem); - } - - new_data.push(row_data); //store the data for each row in the new_data - - } else { - - new_data.push(data_values[row_val]); - } - - } - - let column_names = []; - if (kwargs["type"] == "iloc" || isColumnSplit) { - // let axes = ndframe.axes - columns.map((col) => { - column_names.push(ndframe.columns[col]); - }); - } else { - column_names = columns; - } - - //get index of columns - let final_row = []; - rows.forEach((i) => { - final_row.push(ndframe.index[i]); - }); - return [ new_data, column_names, final_row ]; -}; diff --git a/src/lib/danfo/core/merge.js b/src/lib/danfo/core/merge.js deleted file mode 100644 index 718c0b4..0000000 --- a/src/lib/danfo/core/merge.js +++ /dev/null @@ -1,321 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; - -const utils = new Utils; -export class Merge { - - constructor(kwargs) { - - //check if keys exist in kwargs - utils.__in_object(kwargs, "left"); - utils.__in_object(kwargs, "right"); - utils.__in_object(kwargs, "on"); - // utils.__in_object(kwargs, "how") - if (!utils.__key_in_object(kwargs, "how")){ - this.how = 'inner'; - kwargs['how'] = 'inner'; - } else { - this.how = kwargs['how']; - } - - this.left = null; - this.right = null; - this.on = null; - // this.how = null; - - let how_keys = [ "outer", "inner", "left", "right" ]; - - if ((kwargs["left"] instanceof DataFrame) && (kwargs["right"] instanceof DataFrame)) { - this.left = kwargs["left"]; - this.right = kwargs["right"]; - } else { - throw new Error("The left and right key value must be a dataFrame"); - } - - if (Array.isArray(kwargs["on"])) { - this.on = kwargs["on"]; - } else { - throw new Error("key 'on' must be a list"); - } - - if (how_keys.includes(kwargs["how"])) { - - this.how = kwargs["how"]; - } else { - throw new Error(`${kwargs["how"]} specify in keyword how is not recognise`); - } - - this.left_col_index = []; - this.right_col_index = []; - - //find the index of the columns - for (let i = 0; i < this.on.length; i++) { - - if (this.left.columns.includes(this.on[i]) && this.right.columns.includes(this.on[i])) { - - let left_index = this.left.columns.indexOf(this.on[i]); - let right_index = this.right.columns.indexOf(this.on[i]); - - this.left_col_index.push(left_index); - this.right_col_index.push(right_index); - } - } - - this.left_key_dict = {}; - this.right_key_dict = {}; - - let left_values = this.left.values; - let right_values = this.right.values; - - - /** - * Create a dictionary for both left and right dataframe - containing the key combination of columns used as keys, and - the value of such keys combination are the rows having this - keys combination. - */ - for (let i = 0; i < left_values.length; i++) { - - let left_value = left_values[i]; - let right_value = right_values[i]; - - let right_key_comb = ""; - let left_key_comb = ""; - - //get the value in the column index - for (let j = 0; j < this.left_col_index.length; j++) { - let index = this.left_col_index[j]; - - left_key_comb += `_${left_value[index]}`; - } - - let self = this; // assign the this scope to self - //filter out the value that are not the column key - let left_value_filter = left_value.filter(function (val, index) { - return !self.left_col_index.includes(index); - }); - - //check if the key combination already exist or not - //before storing the key combination and the value - //associated with it - if (utils.__key_in_object(this.left_key_dict, left_key_comb)) { - this.left_key_dict[left_key_comb].push(left_value_filter); - } else { - this.left_key_dict[left_key_comb] = [ left_value_filter ]; - } - - } - for (let i = 0; i < right_values.length; i++) { - let right_value = right_values[i]; - let right_key_comb = ""; - - for (let j = 0; j < this.right_col_index.length; j++) { - let index = this.right_col_index[j]; - - right_key_comb += `_${right_value[index]}`; - } - let self = this; - let right_value_filter = right_value.filter(function (val, index) { - return !self.right_col_index.includes(index); - }); - - if (utils.__key_in_object(this.right_key_dict, right_key_comb)) { - this.right_key_dict[right_key_comb].push(right_value_filter); - } else { - this.right_key_dict[right_key_comb] = [ right_value_filter ]; - } - } - - - //create column - this.__create_columns(); - - let data = null; - switch (this.how) { - - case "outer": - data = this.outer(); - break; - case "inner": - data = this.inner(); - break; - case "left": - data = this.left_merge(); - break; - case "right": - data = this.right_merge(); - break; - } - - // eslint-disable-next-line no-self-assign - let df = new DataFrame(data = data, { columns: this.columns }); - - return df; - } - - __create_columns() { - - let self = this; - self.left_col = self.left.columns.filter((val, index) => { - return !self.left_col_index.includes(index); - }); - - self.right_col = self.right.columns.filter((val, index) => { - return !self.right_col_index.includes(index); - }); - - self.columns = [ ...self.on ]; - let column_duplicate = {}; - - let temp_column = [ ...self.left_col ]; - temp_column.push(...self.right_col); - - for (let i = 0; i < temp_column.length; i++) { - - let col = temp_column[i]; - if (utils.__key_in_object(column_duplicate, col)) { - - let col_name = `${col}_${column_duplicate[col]}`; - self.columns.push(col_name); - - column_duplicate[col] += 1; - } else { - self.columns.push(col); - column_duplicate[col] = 1; - } - } - } - - outer() { - - let keys = Object.keys(this.left_key_dict); // obtain the keys of the left dataframe - - keys.push(...Object.keys(this.right_key_dict)); - - keys = Array.from(new Set(keys)); // obtain the unique keys - - let data = this.basic(keys); - - return data; - - } - - inner() { - - let left_keys = Object.keys(this.left_key_dict); - let right_keys = Object.keys(this.right_key_dict); - - let keys = left_keys.filter((val) => { - return right_keys.includes(val); - }); - - let data = this.basic(keys); - - return data; - } - - left_merge() { - - let keys = Object.keys(this.left_key_dict); - let data = this.basic(keys); - - return data; - - } - - right_merge() { - let keys = Object.keys(this.right_key_dict); - let data = this.basic(keys); - - return data; - } - - basic(keys) { - - let data = []; - - for (let i = 0; i < keys.length; i++) { - let key = keys[i]; - - let key_array = key.split("_").filter((val) => { - return val != ""; - }); - - if (utils.__key_in_object(this.left_key_dict, key)) { - let left_row = this.left_key_dict[key]; - - - for (let left_i = 0; left_i < left_row.length; left_i++) { - - let left_row_row = left_row[left_i]; - - if (utils.__key_in_object(this.right_key_dict, key)) { - - let right_row = this.right_key_dict[key]; - - for (let r_i = 0; r_i < right_row.length; r_i++) { - - let right_row_row = right_row[r_i]; - - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...right_row_row); - - - data.push(inner_data); - } - } else { - let nan_array = Array(this.right_col.length); - - for (let i = 0; i < this.right_col.length; i++) { - nan_array[i] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...nan_array); - data.push(inner_data); - } - } - } else { - - let right_row = this.right_key_dict[key]; - - for (let i = 0; i < right_row.length; i++) { - - let right_row_row = right_row[i]; - - let nan_array = Array(this.left_col.length); - - for (let j = 0; j < nan_array.length; j++) { - nan_array[j] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...nan_array); - inner_data.push(...right_row_row); - data.push(inner_data); - } - } - } - return data; - - } -} - - -/** -* Merge DataFrame or named Series objects with a database-style join. -* The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise -* if joining indexes on indexes or indexes on a column or columns, the index will be passed on. -* @param {kwargs}{left: DataFrame; -* right: DataFrame | Named Series, Object to merge with; -* on: label, Column or index level names to join on. These must be found in both DataFrames; -* how: {‘left’, ‘right’, ‘outer’, ‘inner’}. Type of merge to be performed. -* @returns {DataFrame} -*/ -export const merge = (kwargs) => { - let merge = new Merge(kwargs); - return merge; -}; diff --git a/src/lib/danfo/core/series.js b/src/lib/danfo/core/series.js deleted file mode 100644 index 06e4168..0000000 --- a/src/lib/danfo/core/series.js +++ /dev/null @@ -1,1306 +0,0 @@ -/** -* Copyright 2020, JsData -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - - -import * as tf from "@tensorflow/tfjs"; -import { variance, std, median, mode } from 'mathjs'; -import { Utils } from "./utils"; -import { Str } from "./strings"; -import NDframe from "./generic"; -import { table } from 'table'; -import { Configs } from '../config/config'; -import { TimeSeries } from './timeseries'; -import { Plot } from '../plotting/plot'; -import { indexLoc } from './indexing'; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object - - -/** - * One-dimensional ndarray with axis labels (including time series). - * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. - * Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. - * @param {data} data Array, JSON of 1D values - * @param {kwargs} Object {columns: column names, dtypes : data type of values} - * - * @returns Series - */ -export class Series extends NDframe { - constructor(data, kwargs) { - if (Array.isArray(data[0]) || utils.__is_object(data[0])) { - data = utils.__convert_2D_to_1D(data); - super(data, kwargs); - } else { - super(data, kwargs); - } - } - - - /** - * Returns a Series in Tensorflow's tensor format - * @returns {1D Tensor} - */ - get tensor() { - return tf.tensor(this.values).asType(this.dtypes[0]); - } - - - /** - * Returns the first n values in a Series - * @param {rows} Number of rows to return - * @returns {Series} - */ - head(rows = 5) { - if (rows > this.shape[0] || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(0, rows); - return new Series(data, { columns: this.column_names }); - } - - } - - - /** - * Returns the last n values in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - tail(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(this.shape[0] - rows); - let idx = this.index.slice(this.shape[0] - rows); - let sf = new Series(data, { columns: this.column_names, index: idx }); - return sf; - } - - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a Series object - */ - async sample(num = 5, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const sf = this.iloc(shuffled_index); - return sf; - } - - /** - * Return Addition of series and other, element-wise (binary operator add). - * Equivalent to series + other - * @param {other} Series or Number to add - * @returns {Series} - */ - add(other) { - if (utils.__is_number(other)) { - //broadcast addition - let sum = this.row_data_tensor.add(other).arraySync(); - return new Series(sum, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sum = this.tensor.add(other.tensor).arraySync(); - return new Series(sum, { columns: this.column_names }); - } - } - } - - - /** - * Returns the subtraction between a series and other, element-wise (binary operator subtraction). - * Equivalent to series - other - * @param {other} Series, Number to subtract - * @returns {Series} - */ - sub(other) { - if (utils.__is_number(other)) { - let sub = this.tensor.sub(other).arraySync(); - return new Series(sub, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sub = this.tensor.sub(other.tensor).arraySync(); - return new Series(sub, { columns: this.column_names }); - } - } - } - - - /** - * Return Multiplication of series and other, element-wise (binary operator mul). - * Equivalent to series * other - * @param {other} Series, Number to multiply with. - * @returns {Series} - */ - mul(other) { - if (utils.__is_number(other)) { - let mul = this.tensor.mul(other).arraySync(); - return new Series(mul, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mul = this.tensor.mul(other.tensor).arraySync(); - return new Series(mul, { columns: this.column_names }); - } - } - } - - - /** - * Return division of series and other, element-wise (binary operator div). - * Equivalent to series / other - * @param {other} Series, Number to divide with. - * @returns {Series} - */ - div(other, round = true) { - if (utils.__is_number(other)) { - let div_result = this.tensor.div(other); - return new Series(div_result.arraySync(), { columns: this.column_names, dtypes: [ div_result.dtype ] }); - } else { - if (this.__check_series_op_compactibility) { - let dtype; - //Check if caller needs a float division - if (round) { - dtype = "float32"; - } else { - dtype = "int32"; - } - //dtype may change after division because of how TFJS works internally, so save dtypes first - let tensor1 = this.tensor.asType(dtype); - let tensor2 = other.tensor.asType(dtype); - let result = tensor1.div(tensor2); - return new Series(result.arraySync(), { columns: this.column_names, dtypes: [ result.dtype ] }); - } - } - } - - /** - * Return Exponential power of series and other, element-wise (binary operator pow). - * Equivalent to series ** other - * @param {other} Series, Number to multiply with. - */ - pow(other) { - if (utils.__is_number(other)) { - let pow_result = this.tensor.pow(other).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let pow_result = this.tensor.pow(other.tensor).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } - } - } - - /** - * Return Modulo of series and other, element-wise (binary operator mod). - * Equivalent to series % other - * @param {other} Series, Number - * @returns {Series} - */ - mod(other) { - if (utils.__is_number(other)) { - let mod_result = this.tensor.mod(other).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mod_result = this.tensor.mod(other.tensor).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } - } - } - - - /** - * Returns the mean of elements in Series - * @returns {Series} - */ - mean() { - utils._throw_str_dtype_error(this, 'mean'); - let values = utils._remove_nans(this.values); - let mean = tf.tensor(values).mean().arraySync(); - return mean; - } - - - /** - * Returns the median of elements in Series - * @returns {Series} - */ - median() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let median_val = median(values); - return median_val; - } - - - /** - * Returns the modal value of elements in Series - * @returns {Number} - */ - mode() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let modal_val = mode(values); - return modal_val; - } - - - /** - * Returns the minimum value in a Series - * @returns {Number} - */ - min() { - utils._throw_str_dtype_error(this, 'min'); - let min = this.row_data_tensor.min().arraySync(); - return min; - - } - - /** - * Returns the maximum value in a Series - * @returns {Number} - */ - max() { - utils._throw_str_dtype_error(this, 'max'); - let max = this.row_data_tensor.max().arraySync(); - return max; - - } - - - /** - * Return the sum of the values in a series. - * This is equivalent to the method tf.sum - * @returns {Number}, sum of values in Series - */ - sum() { - utils._throw_str_dtype_error(this, 'sum'); - if (this.dtypes[0] == "boolean") { - let temp_sum = this.row_data_tensor.sum().arraySync(); - return Number(temp_sum); - } - let temp_sum = this.row_data_tensor.sum().arraySync(); - return Number(temp_sum.toFixed(5)); - } - - - /** - * Return number of non-null elements in a Series - * @returns {Number}, Count of non-null values - */ - count() { - return utils.__count_nan(this.values, true, true); - } - - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - maximum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.maximum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.row_data_tensor; - let tensor2 = other.tensor; - let result = tensor1.maximum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - minimum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.minimum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.tensor; - let tensor2 = other.tensor; - let result = tensor1.minimum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - - /** - * Round each value in a Series to the given number of decimals. - * @param {dp} Number, Numbers of Decimal places to round to - * @returns {Series} - */ - 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(); - return new Series(result, { columns: this.column_names, index: this.index }); - - } else { - let result = utils.__round(this.values, dp, true); - return new Series(result, { columns: this.column_names, index: this.index }); - - } - - } - - /** - * Return sample standard deviation over requested axis. - * @returns {Number} - */ - std() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let std_val = std(values); //using math.js - return std_val; - - } - - /** - * Return unbiased variance of Series. - * @returns {Number} - */ - var() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let var_val = variance(values); //using math.js - return var_val; - - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Series} - */ - isna() { - let new_arr = this.__isna(); - let sf = new Series(new_arr, { - index: this.index, - columns: this.column_names, - dtypes: [ "boolean" ] - }); - return sf; - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {"value": the new value to replace the old value with, inplace: Perform operation inplace or not} - * @return {Series} - */ - fillna(kwargs = {}) { - let params_needed = [ "value", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("value" in kwargs)) { - throw Error('Value Error: Must specify value to replace with'); - } - - let new_values = []; - this.values.forEach((val) => { - if (isNaN(val) && typeof val != "string") { - new_values.push(kwargs['value']); - } else { - new_values.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: this.index, - dtypes: this.dtypes - }); - return sf; - } - } - - - /** - * Sort a Series in ascending or descending order by some criterion. - * @param {kwargs} Object, {ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - let params_needed = [ "inplace", "ascending" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!('ascending' in kwargs)) { - kwargs['ascending'] = true; - } - - if (!('inplace' in kwargs)) { - kwargs['inplace'] = false; - } - - let sorted_values = []; - let arr_obj = [ ...this.values ]; - let range_idx = utils.__range(0, this.index.length - 1); - let sorted_idx = utils._sort_arr_with_index(range_idx, arr_obj, this.dtypes[0]); - - sorted_idx.forEach((idx) => { - sorted_values.push(this.values[idx]); - }); - - if (kwargs['ascending']) { - sorted_values = sorted_values.reverse(); - sorted_idx = sorted_idx.reverse(); - } - - if (kwargs['inplace']) { - this.data = sorted_values; - this.__set_index(sorted_idx); - } else { - let sf = new Series(sorted_values, { columns: this.column_names, index: sorted_idx }); - return sf; - } - } - - - /** - * Makes a deep copy of a Series - * @returns {Series} - */ - copy() { - - let sf = new Series([ ...this.values ], { - columns: [ ...this.column_names ], - index: [ ...this.index ], - dtypes: [ ...this.dtypes[0] ] - }); - return sf; - } - - - /** - * Generate descriptive statistics. - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - if (this.dtypes[0] == "string") { - return null; - } else { - - let index = [ 'count', 'mean', 'std', 'min', 'median', 'max', 'variance' ]; - let count = this.count(); - let mean = this.mean(); - let std = this.std(); - let min = this.min(); - let median = this.median(); - let max = this.max(); - let variance = this.var(); - - let vals = [ count, mean, std, min, median, max, variance ]; - let sf = new Series(vals, { columns: this.columns, index: index }); - return sf; - - } - - - } - - - /** - * Returns Series with the index reset. - * This is useful when index is meaningless and needs to be reset to the default before another operation. - * @param {kwargs} {inplace: Modify the Series in place (do not create a new object} - */ - reset_index(kwargs = {}) { - let params_needed = [ "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (kwargs['inplace']) { - this.__reset_index(); - } else { - let sf = this.copy(); - sf.__reset_index(); - return sf; - } - } - - /** - * Returns Series with the specified index. - * Set the Series index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values, - * inplace: If operation should happen inplace - * } - */ - set_index(kwargs = {}) { - - let params_needed = [ "index", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!('index' in kwargs)) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (kwargs['index'].length != this.index.length) { - throw Error(`Index LengthError: Lenght of new Index array ${kwargs['index'].length} must match lenght of existing index ${this.index.length}`); - } - - if (kwargs['inplace']) { - this.index_arr = kwargs['index']; - } else { - let sf = this.copy(); - sf.__set_index(kwargs['index']); - return sf; - } - } - - - /** - * Checks if two series are compatible for a mathematical operation - * @param {Series} other Series to compare against - * @returns{boolean} - */ - __check_series_op_compactibility(other) { - if (utils.__is_undefined(other.series)) { - throw Error("param [other] must be a Series or a single value that can be broadcasted"); - } - if (other.values.length != this.values.length) { - throw Error("Shape Error: Series shape do not match"); - } - if (this.dtypes[0] != 'float' || this.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${this.dtypes[0]} with type ${other.dtypes[0]}`); - } - if (other.dtypes[0] != 'float' || other.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${other.dtypes[0]} with type ${this.dtypes[0]}`); - } - - return true; - } - - /** - * map all the element in a column to a variable or function - * @param{callable} callable can either be a funtion or an object - * @return {Array} - */ - map(callable) { - let is_callable = utils.__is_function(callable); - - let data = this.data.map((val) => { - if (is_callable) { - return callable(val); - } else { - if (utils.__is_object(callable)) { - - if (val in callable) { - return callable[val]; - } else { - return NaN; - } - } else { - throw new Error("callable must either be a function or an object"); - } - } - }); - let sf = new Series(data, { - columns: this.column_names, - index: this.index - }); - return sf; - } - - /** - * Applies a function to each element of a Series - * @param {Function} Function to apply to each element of the series - * @return {Array} - */ - apply(callable) { - let is_callable = utils.__is_function(callable); - - if (!is_callable) { - throw new Error("the arguement most be a function"); - } - - let data = this.data.map((val) => { - return callable(val); - }); - return new Series(data, { columns: this.column_names, index: this.index }); - } - - /** - * Returns the unique value(s) in a Series - * @return {Series} - */ - unique() { - - let data_set = new Set(this.values); - let series = new Series(Array.from(data_set)); - - return series; - - } - - /** - * Return the number of unique value in a series - * @return {int} - */ - nunique() { - return this.unique().values.length; - } - - /** - * Returns unique values and their counts in a Series - * @return {Series} - */ - value_counts() { - - let s_data = this.values; - let data_dict = {}; - - for (let i = 0; i < s_data.length; i++) { - let val = s_data[i]; - - if (val in data_dict) { - data_dict[val] += 1; - } else { - data_dict[val] = 1; - } - } - - let index = Object.keys(data_dict).map((x) => { - return parseInt(x) ? parseInt(x) : x; - }); - let data = Object.values(data_dict); - - let series = new Series(data, { index: index }); - return series; - - } - - /** - * Returns the absolute values in Series - * @return {series} - */ - abs() { - let abs_data = this.row_data_tensor.abs().arraySync(); - return new Series(utils.__round(abs_data, 2, true)); - } - - - /** - * Returns the cumulative sum over a Series - * @return {Series} - */ - cumsum() { - let data = this.__cum_ops("sum"); - return data; - } - - /** - * Returns cumulative minimum over a Series - * @returns series - */ - cummin() { - let data = this.__cum_ops("min"); - return data; - } - - /** - * Returns cumulative maximum over a Series - * @returns series - */ - cummax() { - let data = this.__cum_ops("max"); - return data; - } - - /** - * Returns cumulative product over a Series - * @returns series - */ - cumprod() { - let data = this.__cum_ops("prod"); - return data; - } - - - /** - * Returns Less than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - lt(other) { - return this.__bool_ops(other, "lt"); - } - - /** - * Returns Greater than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - gt(other) { - return this.__bool_ops(other, "gt"); - } - - /** - * Returns Less than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - le(other) { - return this.__bool_ops(other, "le"); - } - - /** - * Returns Greater than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ge(other) { - return this.__bool_ops(other, "ge"); - } - - /** - * Returns Not Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ne(other) { - return this.__bool_ops(other, "ne"); - } - - - /** - * Returns Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - eq(other) { - return this.__bool_ops(other, "eq"); - } - - /** - * Replace all occurence of a value with a new value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with, - * inplace: Perform operation inplace or not} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = [ "replace", "with", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("replace" in kwargs)) { - throw Error("Params Error: Must specify param 'replace'"); - } - - if (!("with" in kwargs)) { - throw Error("Params Error: Must specify param 'with'"); - } - - let replaced_arr = []; - let old_arr = this.values; - - old_arr.forEach((val) => { - if (val == kwargs['replace']) { - replaced_arr.push(kwargs['with']); - } else { - replaced_arr.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = replaced_arr; - } else { - let sf = new Series(replaced_arr, { - index: this.index, - columns: this.columns, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Return a new Series with missing values (NaN) removed. - * @param {kwargs} {inplace: Perform operation inplace or not} - * @return {Series} - */ - dropna(kwargs = {}) { - let params_needed = [ "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - let old_values = this.values; - let old_index = this.index; - let new_values = []; - let new_index = []; - let isna_vals = this.isna().values; - - isna_vals.forEach((val, i) => { - if (!val) { - new_values.push(old_values[i]); - new_index.push(old_index[i]); - } - }); - if (kwargs['inplace']) { - this.index_arr = new_index; - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: new_index, - dtypes: this.dtypes - }); - return sf; - } - - } - - /** - * Return the integer indices that would sort the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argsort(ascending = true) { - let sorted_index = this.sort_values({ ascending: ascending }).index; - let sf = new Series(sorted_index); - return sf; - } - - /** - * Return int position of the largest value in the Series. - * @return {Number} - */ - argmax() { - return this.row_data_tensor.argMax().arraySync(); - } - - - /** - * Return int position of the smallest value in the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argmin() { - return this.row_data_tensor.argMin().arraySync(); - - } - - - /** - * Returns dtype of Series - * @return {string} - */ - get dtype() { - return this.dtypes[0]; - } - - /** - * Return Series with duplicate values removed - * @param {kwargs} {inplace: Perform operation inplace or not, - * keep: first | last which dupliate value to keep} - * @return {Series} - */ - drop_duplicates(kwargs = {}) { - let params_needed = [ "inplace", "keep" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - kwargs['keep'] = kwargs['keep'] || "first"; - - - let data_arr, old_index; - if (kwargs['keep'] == "last") { - data_arr = this.values.reverse(); - old_index = this.index.reverse(); - } else { - data_arr = this.values; - old_index = this.index; - } - - let new_index = []; - let new_arr = []; - - data_arr.forEach((val, i) => { - if (!new_arr.includes(val)) { - new_index.push(old_index[i]); - new_arr.push(val); - } - }); - - if (kwargs['keep'] == "last") { - //re-reversed the array and index to its true ordering - new_arr = new_arr.reverse(); - new_index = new_index.reverse(); - } - - if (kwargs['inplace']) { - this.data = new_arr; - this.index_arr = new_index; - } else { - let sf = new Series(new_arr, { - index: new_index, - columns: this.column_names, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = 20; - let table_truncate = 20; - let max_row = config.get_max_row; - let data_arr = []; - let table_config = {}; - let header = [ "" ].concat(this.columns); - let idx, data; - - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - data = this.values.slice(0, max_row); - idx = this.index.slice(0, max_row); - } else { - data = this.values; - idx = this.index; - } - - idx.forEach((val, i) => { - let row = [ val ].concat(data[i]); - data_arr.push(row); - }); - - //set column width of all columns - table_config[0] = 10; - table_config[1] = { width: table_width, truncate: table_truncate }; - - let table_data = [ header ].concat(data_arr); //Add the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Perform boolean operations on bool values - * @param {*} other Other series to compare with - * @param {string} b_ops name of operation to perform [ne, ge, le, gt, lt, eq] - */ - __bool_ops(other, b_ops) { - let r_series; - let l_series = this.values; - - if (typeof other == "number") { - r_series = [ ...l_series ].fill(other); //create array of repeated value for broadcasting - } else { - if (!(other instanceof Series)) { - throw new Error("Value Error: 'other' must be an instance of Series"); - } - r_series = other.values; - } - - if (!(l_series.length === r_series.length)) { - throw new Error("Length Error: Both series must be of the same length"); - } - - let data = []; - - for (let i = 0; i < l_series.length; i++) { - - let l_val = l_series[i]; - let r_val = r_series[i]; - let bool = null; - switch (b_ops) { - - case "lt": - bool = l_val < r_val ? true : false; - data.push(bool); - break; - case "gt": - bool = l_val > r_val ? true : false; - data.push(bool); - break; - case "le": - bool = l_val <= r_val ? true : false; - data.push(bool); - break; - case "ge": - bool = l_val >= r_val ? true : false; - data.push(bool); - break; - case "ne": - bool = l_val != r_val ? true : false; - data.push(bool); - break; - case "eq": - bool = l_val === r_val ? true : false; - data.push(bool); - break; - } - } - return new Series(data); - - } - - /** - * perform cumulative operation on series data - * @returns array - */ - __cum_ops(ops) { - - let s_data = this.values; - let temp_val = s_data[0]; - let data = [ temp_val ]; - - for (let i = 1; i < s_data.length; i++) { - - let curr_val = s_data[i]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - data.push(temp_val); - break; - case "prod": - temp_val = temp_val * curr_val; - data.push(temp_val); - break; - - } - } - return new Series(data); - } - - - /** - * Cast Series to specified data type - * @param {string} dtype data type to cast to [float32, int32, string, boolean] - *@returns {Series} - */ - astype(dtype) { - const __supported_dtypes = [ 'float32', "int32", 'string', 'boolean' ]; - - if (!dtype) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!__supported_dtypes.includes(dtype)) { - throw Error(`dtype ${dtype} not supported. dtype must be one of ${__supported_dtypes}`); - } - - let col_values = this.values; - let new_values = []; - - switch (dtype) { - case "float32": - col_values.forEach((val) => { - new_values.push(Number(val)); - }); - break; - case "int32": - col_values.forEach((val) => { - new_values.push(Number(Number(val).toFixed())); - }); - break; - case "string": - col_values.forEach((val) => { - new_values.push(String(val)); - }); - break; - case "boolean": - col_values.forEach((val) => { - new_values.push(Boolean(val)); - }); - break; - default: - break; - } - - let sf = new Series(new_values, { dtypes: dtype, index: this.index }); - return sf; - - } - - - /** - * Exposes numerous string methods to manipulate Series - */ - get str() { - let values = this.values; - if (this.dtypes[0] != "string") { - let new_vals = []; - //convert each value in array to string - values.forEach((val) => { - new_vals.push(String(val)); - }); - let sf = new Series(new_vals, { columns: this.column_names, index: this.index }); - return new Str(sf); - } - return new Str(this); - - } - - /** - * Returns Danfo Time Object that exposes different time properties - */ - get dt() { - let timeseries = new TimeSeries({ data: this }); // parsed to date-time - timeseries.preprocessed(); - return timeseries; - - } - - /** - * Displays the data in a console friendly manner - */ - print() { - console.log(this + ""); - } - - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so therefore supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Slice series by index - * @param {Array} row list of index to slice by - * @returns Series - */ - iloc(row) { - let kwargs = {}; - kwargs["rows"] = row; - kwargs["type"] = "iloc"; - - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let sf = new Series(new_data, { columns: columns, index: rows }); - - return sf; - } - - /** - * Adds new values to the end of a Series - * @param {Object} val Single value | Array | Series to append to the object - * @param {Boolean} inplace Whether to perform operation inplace or not - */ - append(val, inplace = false) { - if (inplace) { - let self = this; - if (Array.isArray(val)) { - val.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(old_index[i]); - }); - } else { - self.data.push(val); - self.index_arr.push(0); - } - } else { - let sf = this.copy(); - if (Array.isArray(val)) { - val.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(old_index[i]); - }); - } else { - sf.data.push(val); - sf.index_arr.push(0); - } - return sf; - } - - } - -} - diff --git a/src/lib/danfo/core/strings.js b/src/lib/danfo/core/strings.js deleted file mode 100644 index fc25e44..0000000 --- a/src/lib/danfo/core/strings.js +++ /dev/null @@ -1,336 +0,0 @@ -import { Series } from "./series"; - -/** - * String methods applied on Series and DataFrames - */ -export class Str { - constructor(series) { - this.series = series; - this.array = series.values; - } - - /** - * Converts all characters to lowercase. - * @return {array} - */ - toLowerCase() { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.toLowerCase()); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Converts all characters to uppercase. - * @return {array} - */ - toUpperCase() { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.toUpperCase()); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Capitalize first string - * @return {array} - */ - capitalize() { - let new_arr = []; - this.array.map((val) => { - let f_char = val.slice(0, 1); - let l_char = val.slice(1); - let new_str = `${f_char.toUpperCase()}${l_char.toLowerCase()}`; - new_arr.push(new_str); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - - } - - /** - * Returns the character at the specified index (position) - * @params {index} index position of character - * @return {array} - */ - charAt(index = 0) { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.charAt(index)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins two or more strings/arrays. 0 joins from the start - * @params {other} string|array to concatenate with. - * @params {position} where to concat the string from. O concats from the start, 1 concats from the end - * @params {isArray} whether operation is performed on Array or not - * @return {array} - */ - concat(other = "", position = 1) { - if (Array.isArray(other)) { - let final_arr = []; - for (let i = 0; i < other.length; i++) { - let l_str = this.array[i]; - let r_str = other[i]; - if (position == 1) { - final_arr.push(l_str.concat(r_str)); - } else { - final_arr.push(r_str.concat(l_str)); - } - - } - let sf = this.__create_new_sf_from(final_arr, this.series); - return sf; - } else { - - let new_arr = []; - - this.array.map((val) => { - if (position == 1) { - new_arr.push(val.concat(other)); - - } else { - new_arr.push(other.concat(val)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - } - - - /** - * Checks whether a string begins with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - startsWith(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.startsWith(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string ends with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - endsWith(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.endsWith(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string contains the specified string/characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - includes(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.includes(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the first found occurrence of a specified value in a string - * @params {String | Character} String or Character to check against - * @return {array} - */ - indexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.indexOf(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the last found occurrence of a specified value in a string - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - lastIndexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.lastIndexOf(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced - * @params {searchValue: String | Character} string value to replace - * @params {replaceValue: String | Character} string to replace with - * @return {array} - */ - replace(searchValue = "", replaceValue = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.replace(searchValue, replaceValue)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns a new string with a specified number of copies of an existing string - * @params {num: Integer} Number of times to repeat - * @return {array} - */ - repeat(num = 1) { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.repeat(num)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or regular expression, and returns the position of the match - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - search(str = "") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.search(str)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts a part of a string and returns a new string - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - slice(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.slice(startIndex, endIndex)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Splits a string into an array of substrings - * @params {val: string} string or character to split at - * @params {endIndex: Int} index position of last character - * @return {array} - */ - split(splitVal = " ") { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.split(splitVal)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character - * @params {startIndex: Int} index position of start character - * @params {num: Int} number of characters to return - * @return {array} - */ - substr(startIndex = 0, num = 1) { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.substr(startIndex, num)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, between two specified indices - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - substring(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.substring(startIndex, endIndex)); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Removes whitespace from both ends of a string - * @return {array} - */ - trim() { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.trim()); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins strings to specified value - * @params {valToJoin} string value to join to the array - * @params {joinChar} Character to Join with - * @return {array} - */ - join(valToJoin = "", joinChar = " ") { - let new_arr = []; - this.array.map((val) => { - let l_char = val; - let r_char = valToJoin; - let new_char = `${l_char}${joinChar}${r_char}`; - new_arr.push(new_char); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Counts the number of characters in string - * @return {array} - */ - len() { - let new_arr = []; - this.array.map((val) => { - new_arr.push(val.length); - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - //create a new series - __create_new_sf_from(new_val, series) { - let sf = new Series(new_val, { columns: series.column_names, index: series.index }); - return sf; - } - - -} diff --git a/src/lib/danfo/core/timeseries.js b/src/lib/danfo/core/timeseries.js deleted file mode 100644 index 1d8af67..0000000 --- a/src/lib/danfo/core/timeseries.js +++ /dev/null @@ -1,305 +0,0 @@ -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - - -/** - * @class - * @description Handle all datetime operations - * @param {kwargs} Object {"data":[array of string], "format": string} - */ -export class TimeSeries { - constructor(kwargs) { - - utils.__in_object(kwargs, "data", "specify the data"); - - if (kwargs["data"] instanceof Series) { - this.data = kwargs["data"].values; - } else { - this.data = kwargs["data"]; - } - - this.format = kwargs["format"] || null; - - this.keys = { //key: len - "Y": 4, - "m": 2, - "H": 2, - "M": 2, - "S": 2, - "b": 3, - "d": 2, - "-": 1 - }; - - this.__in_format = [ "%Y-m-d%", "%m-d-Y%", "%m-d-Y H%M%S%" ]; - - this.__monthName = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ]; - this.__weekName = [ "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat" ]; - - } - - /** - * @description preprocessed the data into desirable structure - */ - preprocessed() { - - let format_values = null; - if (this.format) { - format_values = this.generate_format(); - } - - this.date_list = []; - - for (let i = 0; i < this.data.length; i++) { - - let date_string = this.data[i]; - - if (this.format && !this.__in_format.includes(this.format)) { - - let format_dateString = this.__apply_format(date_string, format_values); - let valueDate = new Date(format_dateString); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - - } else if (this.is_timestamp(date_string)) { - - let string2int = parseInt(date_string); - let valueDate = new Date(string2int); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } else { - let valueDate = new Date(date_string); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } - } - - // return this.date_list; - - } - - /** - * @description if format is given, apply the format on each element of the data - * @return string - */ - __apply_format(elem, format) { - - let date_string = ""; - - let temp_val = 0; - - - for (let index in format) { - - let value = format[index]; - - if (index == 0) { - date_string += elem.slice(0, value); - - } else if (index > 4) { - - date_string += ":" + elem.slice(temp_val, temp_val + value); - } else if (index == 4) { - date_string += " " + elem.slice(temp_val, temp_val + value); - } else if (index > 0 && index <= 2) { - date_string += "-" + elem.slice(temp_val, temp_val + value); - } - - temp_val += value; - } - - return date_string; - - } - /** - * @description convert format string to their respective value. - */ - generate_format() { - - let format_list = this.format.split(""); - - let self = this; - let format_keys = format_list.filter(function (key) { - return utils.__key_in_object(self.keys, key); - }); - - let format_value = format_keys.map(function (val) { - return self.keys[val]; - }); - - - return format_value; - } - - /** - * @description check if a string is a timestamp - * @param {date_string} date_string [string] - */ - is_timestamp(date_string) { - - let string2int = parseInt(date_string); - let int2string = String(string2int); - - if (isNaN(string2int) || (int2string.length < date_string.length)) { - return false; - } else { - return true; - } - } - - /** - * @description check if a date instance returns Invalid date - * @param {date_instance} instance of new Date() - */ - __is_validDate(date_instance) { - - if (date_instance.toDateString() == "Invalid Date") { - throw new Error("Invalid date, the date format not recognise"); - } - } - - /** - * @description abstract all date operations - * @param {*} callback [FUNCTION] - * @return series - */ - __date_ops(callback) { - - let data = this.date_list.map(function (date_instance) { - - return callback(date_instance); - }); - - // eslint-disable-next-line no-self-assign - let series = new Series(data = data); - - return series; - } - - /** - * @description obtain the month in a date. - * @return Series - */ - month() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getMonth(); - }); - - return series; - } - - /** - * @return Series - */ - hour() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getHours(); - }); - - return series; - } - - /** - * @return Series - */ - day() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDay(); - }); - - return series; - } - - /** - * @description generate year frome date instance - * @return Series - */ - year() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getFullYear(); - }); - - return series; - } - - /** - * @description generate month name - * @return Series - */ - month_name() { - - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__monthName[date_instance.getMonth()]; - }); - - return series; - } - - /** - * @description generate days of the week - * @return Series - */ - weekdays() { - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__weekName[date_instance.getDay()]; - }); - - return series; - } - - /** - * @description day of the month - * @return Series - */ - monthday() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDate(); - }); - - return series; - } - - /** - * @description obtain the seconds in a date - * @return Series - */ - seconds() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getSeconds(); - }); - - return series; - } - - /** - * @description obtain the minutes in a date - * @return Series - */ - minutes() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getMinutes(); - }); - - return series; - } - -} - - -export const to_datetime = (kwargs) => { - - let timeseries = new TimeSeries(kwargs); // parsed to date-time - timeseries.preprocessed(); // generate date-time list - - return timeseries; - -}; diff --git a/src/lib/danfo/core/utils.js b/src/lib/danfo/core/utils.js deleted file mode 100644 index bdfd3b6..0000000 --- a/src/lib/danfo/core/utils.js +++ /dev/null @@ -1,673 +0,0 @@ -import { tensor, linspace } from "@tensorflow/tfjs"; -import { Configs } from "../config/config"; - -const config = new Configs(); - -export class Utils { - //remove an element from an array - remove(arr, index) { - let new_arr = arr.filter(function (val, i) { - return i != index; - }); - - return new_arr; - } - - //filter element present in an aray - __remove_arr(arr, index) { - let new_arr = arr.filter(function (val, i) { - return !index.includes(i); - }); - - return new_arr; - } - - // Returns if a value is a string - __is_string(value) { - return typeof value === "string" || value instanceof String; - } - - // Returns if a value is really a number - __is_number(value) { - return typeof value === "number" && isFinite(value); - } - - // Returns if a value is an object - __is_object(value) { - return value && typeof value === "object" && value.constructor === Object; - } - - // Returns if a value is null - __is_null(value) { - return value === null; - } - - // Returns if a value is undefined - __is_undefined(value) { - return typeof value === "undefined"; - } - - /** - * Optimized version of random sampling from an array, as implemented in Python - * - * - Chooses k unique random elements from a population sequence or set. - - Returns a new list containing elements from the population while - leaving the original population unchanged. The resulting list is - in selection order so that all sub-slices will also be valid random - samples. This allows raffle winners (the sample) to be partitioned - into grand prize and second place winners (the subslices). - - Members of the population need not be hashable or unique. If the - population contains repeats, then each occurrence is a possible - selection in the sample. - - To choose a sample in a range of integers, use range as an argument. - This is especially fast and space efficient for sampling from a - large population: sample(range(10000000), 60) - - Sampling without replacement entails tracking either potential - selections (the array) in a list or previous selections in a set. - - When the number of selections is small compared to the - population, then tracking selections is efficient, requiring - only a small set and an occasional reselection. For - a larger number of selections, the array tracking method is - preferred since the list takes less space than the - set and it doesn't suffer from frequent reselections. - * - * @param {*} array The array to sample values from randomly - * @param {*} num The number of elements to sample randomly - */ - // Chooses k unique random elements from array. - __sample_from_iter(array, k, destructive) { - var n = array.length; - - if (k < 0 || k > n) - throw new RangeError("Sample larger than population or is negative"); - - if ( - destructive || - n <= (k <= 5 ? 21 : 21 + Math.pow(4, Math.ceil(Math.log(k * 3, 4)))) - ) { - if (!destructive) array = Array.prototype.slice.call(array); - for (var i = 0; i < k; i++) { - // invariant: non-selected at [i,n) - var j = (i + Math.random() * (n - i)) | 0; - var x = array[i]; - array[i] = array[j]; - array[j] = x; - } - array.length = k; // truncate - return array; - } else { - var selected = new Set(); - // eslint-disable-next-line no-empty - while (selected.add((Math.random() * n) | 0).size < k) {} - // eslint-disable-next-line no-undef - return Array.prototype.map.call(selected, (i) => population[i]); - } - } - - //generate integers between two set of numbers - __range(start, end) { - let value = linspace(start, end, end - start + 1).arraySync(); - return value; - } - - //check if key is in object - __key_in_object(object, key) { - if (Object.prototype.hasOwnProperty.call(object, key)) { - return true; - } else { - return false; - } - } - - //transpose row array into column wise array - __get_col_values(data) { - let row_len = data.length; - let cols_len = data[0].length; - var cols_arr = []; - for (var i = 0; i <= cols_len - 1; i++) { - let temp_col = []; - for (let j = 0; j < row_len; j++) { - temp_col.push(data[j][i]); - } - cols_arr.push(temp_col); - } - return cols_arr; - } - - /** - * retrieve row array and column names from object of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - * @param {*} data - */ - _get_row_and_col_values(data) { - let col_names = Object.keys(data); - let col_data = Object.values(data); - let first_col_len = col_data[0].length; - - col_data.forEach((data) => { - if (data.length != first_col_len) { - throw Error("Length Error: Length of columns must be the same!"); - } - }); - let rows_len = col_data[0].length; - let cols_len = col_names.length; - var rows_arr = []; - - for (var i = 0; i <= rows_len - 1; i++) { - var temp_row = []; - for (let j = 0; j < cols_len; j++) { - let _arr = col_data[j]; - temp_row.push(_arr[i]); - } - rows_arr.push(temp_row); - } - return [ rows_arr, col_names ]; - } - - //converts a 2D array of array to 1D for Series Class - __convert_2D_to_1D(data) { - let new_data = []; - data.map((val) => { - if (this.__is_object(val)) { - new_data.push(JSON.stringify(val)); - } else { - new_data.push(`${val}`); - } - }); - return new_data; - } - - __replace_undefined_with_NaN(data, isSeries) { - if (isSeries) { - let temp_arr = []; - data.forEach((val) => { - if (typeof val === "undefined" || val == Infinity || val == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(val); - } - }); - return temp_arr; - } else { - let full_arr = []; - data.forEach((val) => { - var temp_arr = []; - val.forEach((ele) => { - if (typeof ele === "undefined" || ele == Infinity || ele == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(ele); - } - }); - full_arr.push(temp_arr); - }); - return full_arr; - } - } - - __checker(arr_val){ - let dtypes = []; - let lim; - let int_tracker = []; - let float_tracker = []; - let string_tracker = []; - let bool_tracker = []; - - if (arr_val.length == 0){ - dtypes.push("string"); - } - - if (arr_val.length < config.get_dtype_test_lim) { - lim = arr_val.length - 1; - } else { - lim = config.get_dtype_test_lim - 1; - } - arr_val.forEach((ele, indx) => { - let count = indx; - if (typeof ele == "boolean") { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(true); - } else if (isNaN(ele) && typeof ele != "string"){ - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else if (!isNaN(Number(ele))) { - if (ele.toString().includes(".")) { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else { - float_tracker.push(false); - int_tracker.push(true); - string_tracker.push(false); - bool_tracker.push(false); - } - } else { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(true); - bool_tracker.push(false); - } - - if (count == lim) { - //if atleast one string appears return string dtype - const even = (element) => element == true; - if (string_tracker.some(even)) { - dtypes = "string"; - } else if (float_tracker.some(even)) { - dtypes = "float32"; - } else if (int_tracker.some(even)) { - dtypes = "int32"; - } else if (bool_tracker.some(even)) { - dtypes = "boolean"; - } else { - dtypes = "undefined"; - } - } - }); - - return dtypes; - } - //infer types from an array of array - __get_t(arr_val) { - const self = this; - if (this.__is_1D_array(arr_val)) { - return [ this.__checker(arr_val) ]; - } else { - const dtypes = arr_val.map((arr) => { - return self.__checker(arr); - }); - return dtypes; - } - } - - - __unique(data) { - let unique = new Set(); - - data.map(function (val) { - unique.add(val[0]); - }); - - let unique_array = Array.from(unique); - - return unique_array; - } - - //second version of In object - __in_object(object, key, message) { - if (!Object.prototype.hasOwnProperty.call(object, key)) { - throw new Error(message); - } - } - - //check if a array is 1D - __is_1D_array(arr) { - if ( - typeof arr[0] == "number" || - typeof arr[0] == "string" || - typeof arr[0] == "boolean" - ) { - return true; - } else { - return false; - } - } - - //converts an array to object with index as value - __arr_to_obj(arr) { - let arr_map = {}; - arr.forEach((ele, i) => { - arr_map[ele] = i; - }); - return arr_map; - } - - /** - * count the NaN and non-NaN values present in an array - * @param {Array} arr Array object - * @param {Boolean} val whether to return the value count instead of the null count - * @param {Boolean} isSeries Whether the Obj is of type series or not - */ - __count_nan(arr, return_val = true, isSeries) { - if (isSeries) { - let null_count = 0; - let val_count = 0; - arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - return val_count; - } else { - return null_count; - } - } else { - let result_arr = []; - arr.forEach((ele_arr) => { - let null_count = 0; - let val_count = 0; - ele_arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - result_arr.push(val_count); - } else { - result_arr.push(null_count); - } - }); - return result_arr; - } - } - - //computes the median of an array - __median(arr, isSeries) { - if (isSeries) { - const sorted = arr.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - return (sorted[middle - 1] + sorted[middle]) / 2; - } - - return sorted[middle]; - } else { - let result_arr = []; - arr.map((ele) => { - const sorted = ele.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - result_arr.push((sorted[middle - 1] + sorted[middle]) / 2); - } else { - result_arr.push(sorted[middle]); - } - }); - return result_arr; - } - } - - //computes the mode(s) of an array - __mode(arr) { - var modes = [], - count = [], - i, - maxIndex = 0; - - arr.forEach((val) => { - count[val] = (count[val] || 0) + 1; - if (count[val] > maxIndex) { - maxIndex = count[val]; - } - }); - - for (i in count) - if (this.__key_in_object(count, i)) { - if (count[i] === maxIndex) { - modes.push(Number(i)); - } - } - - return modes; - } - - //round elements of an array to ndp - __round(arr, dp = 2, isSeries) { - if (dp < 0) { - dp = 1; - } - if (isSeries) { - let new_arr = []; - arr.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - - return new_arr; - } else { - let result_arr = []; - arr.map((arr_ele) => { - let new_arr = []; - arr_ele.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - result_arr.push(new_arr); - }); - return result_arr; - } - } - - //check a variable is a function - __is_function(variable) { - return typeof variable == "function"; - } - - //generate a random list - __randgen(num, start, end) { - let gen_num = []; - - //random int - function randi(a, b) { - return Math.floor(Math.random() * (b - a) + a); - } - - function recursive(val, arr) { - if (!arr.includes(val)) { - return val; - } - - val = randi(start, end); - - recursive(val, arr); - } - - for (let i = 0; i < num; i++) { - let gen_val = randi(start, end); - let recur_val = recursive(gen_val, gen_num); - gen_num.push(recur_val); - } - - return gen_num; - } - - _throw_wrong_params_error(kwargs, params_needed) { - let keys = Object.keys(kwargs); - let bool = []; - for (let i = 0; i < keys.length; i++) { - if (params_needed.includes(keys[i])) { - bool.push(true); - } else { - bool.push(false); - } - } - const truthy = (element) => element == false; - if (bool.some(truthy)) { - throw Error( - `Params Error: A specified parameter is not supported. Your params must be any of the following [${params_needed}]` - ); - } - } - - //maps int values (0, 1) to bools (false, true) - __map_int_to_bool(arr, dim) { - let new_arr = []; - if (dim == 2) { - arr.map((outer_val) => { - let temp_arr = []; - outer_val.map((val) => { - if (val == 1) { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - return new_arr; - } else { - arr.map((val) => { - if (val == 1) { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - return new_arr; - } - } - - __std(data) { - let tensor_data = data; - - let mean = tensor_data.mean(); - let sub_mean_pow = tensor_data.sub(mean).pow(2); - let mean_data = sub_mean_pow.mean(); - let std = mean_data.sqrt(); - - return std; - } - - __zeros(row, column) { - let zero_data = []; - - for (let i = 0; i < row; i++) { - let col_data = Array(column); - for (let j = 0; j < column; j++) { - col_data[j] = 0; - } - zero_data.push(col_data); - } - return zero_data; - } - - __shuffle(num, array) { - //https://stackoverflow.com/questions/18806210/generating-non-repeating-random-numbers-in-js/18806417 - var j, - temp; - - for (let i = 0; i < num; i++) { - j = Math.floor(Math.random() * (array.length - i)) + i; - - // swap randomly chosen element with current element - temp = array[i]; - array[i] = array[j]; - array[j] = temp; - } - - return array.slice(0, num); - } - - __sort(arr, ascending = true) { - let sorted = arr.slice(); - return sorted.sort((a, b) => { - if (ascending) { - if (typeof a === "string" && typeof b === "string") { - return a.charCodeAt() - b.charCodeAt(); - } else { - return a - b; - } - } else { - if (typeof a === "string" && typeof b === "string") { - return b.charCodeAt() - a.charCodeAt(); - } else { - return b - a; - } - } - }); - } - - __is_browser_env() { - var isBrowser = new Function( - "try {return this===window;}catch(e){ return false;}" - ); - // tests if global scope is binded to window - return isBrowser(); - } - - __is_node_env() { - var isNode = new Function( - "try {return this===global;}catch(e){return false;}" - ); - // tests if global scope is binded to window - return isNode(); - } - - _throw_str_dtype_error(obj, ops) { - if (obj.dtypes[0] == "string") { - throw Error( - `dtype error: String data type does not support ${ops} operation` - ); - } - } - - /** - * Remove NaN values from Array - * @param {*} arr - */ - _remove_nans(arr) { - let values = arr.filter((val) => !isNaN(val) && typeof val != "string"); - return values; - } - - __get_duplicate(arr) { - let temp_obj = {}; - let rslt_obj = {}; - - arr.forEach((val, index) => { - if (val in temp_obj) { - temp_obj[val]["count"] += 1; - temp_obj[val]["index"].push(index); - } else { - temp_obj[val] = {}; - temp_obj[val]["count"] = 1; - temp_obj[val]["index"] = [ index ]; - } - }); - - for (let key in temp_obj) { - if (temp_obj[key]["count"] >= 2) { - rslt_obj[key] = {}; - rslt_obj[key]["count"] = temp_obj[key]["count"]; - rslt_obj[key]["index"] = temp_obj[key]["index"]; - } - } - - return rslt_obj; - } - - /** - * Sorts an array by index - * @param {Array} arr1 - * @param {Array} arr2 - * @param {string} dtype - * - * @returns sorted index - */ - _sort_arr_with_index(arr1, arr2, dtype) { - let sorted_idx = arr1.map((item, index) => { - return [ arr2[index], item ]; - }); - if (dtype == "string") { - sorted_idx.sort(); - } else { - sorted_idx.sort(([ arg1 ], [ arg2 ]) => arg2 - arg1); - } - - return sorted_idx.map(([ , item ]) => item); - } -} diff --git a/src/lib/danfo/index.js b/src/lib/danfo/index.js deleted file mode 100644 index b565502..0000000 --- a/src/lib/danfo/index.js +++ /dev/null @@ -1,18 +0,0 @@ -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 } from "./io/reader"; -export { merge } from "./core/merge"; -export { concat } from "./core/concat"; -export { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings"; -export { MinMaxScaler, StandardScaler } from "./preprocessing/scalers"; -export { date_range } from "./core/date_range"; -export { get_dummies } from "./core/get_dummies"; -export { Configs } from "./config/config"; -export { NDframe }; -export { Str } from "./core/strings"; -export { Utils } from "./core/utils"; - -export const _version = "0.2.5"; diff --git a/src/lib/danfo/io/reader.js b/src/lib/danfo/io/reader.js deleted file mode 100644 index e9e8c88..0000000 --- a/src/lib/danfo/io/reader.js +++ /dev/null @@ -1,116 +0,0 @@ -import { data } from "@tensorflow/tfjs"; -import XLSX from "xlsx"; -import { DataFrame } from "../core/frame"; - - -/** - * Reads a CSV file from local or remote storage - * @param {string} source URL to CSV file - * @param {object} config (Optional). A CSV Config object that contains configurations - * for reading and decoding from CSV file(s). - * { start: The index position to start from when reading the CSV file. - * - * end: The end position to stop at when reading the CSV file. - * - * ...csvConfigs: other supported Tensorflow csvConfig parameters. See https://js.tensorflow.org/api/latest/#data.csv - * } - * - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_csv = async (source, configs = {}) => { - let { start, end } = configs; - if (!(source.startsWith("https") || source.startsWith("http"))) { - throw new Error("Cannot read local file in browser environment"); - } - let tfdata = []; - await data.csv(source, configs) - .skip(start) - .take(end) - .forEachAsync((row) => { - return tfdata.push(row); - }); - const df = new DataFrame(tfdata); - return df; -}; - -/** - * Reads a JSON file from local or remote address - * @param {string} source URL or local file path to retreive JSON file. - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_json = async (source) => { - let res = await fetch(source, { method: "Get" }); - let json = await res.json(); - let df = new DataFrame(json); - return df; - -}; - -/** - * Reads an Excel file from local or remote address - * @param {string} source URL to Excel file - * @param {object} configs { - * - * sheet : string, (Optional) Name of the sheet which u want to parse. Default will be the first sheet. - * - * header_index : int, (Optional) Index of the row which represents the header(columns) of the data. Default will be the first non empty row. - * - * data_index : int, (Optional) Index of the row from which actual data(content) starts. Default will be the next row of `header_index`. - * - * } - * @returns {Promise} DataFrame structure of parsed Excel data - */ -export const read_excel = async (source, configs = {}) => { - let { sheet, header_index, data_index } = configs; - let workbook; - if (!header_index) { - //default header_index - header_index = 1; - } - if (!data_index) { - //default data_index - data_index = header_index + 1; - } - try { - let res = await fetch(source, { method: "Get" }); - res = await res.arrayBuffer(); - res = new Uint8Array(res); - workbook = XLSX.read(res, { type: "array" }); - - - // Parse worksheet from workbook - const worksheet = workbook.Sheets[sheet || workbook.SheetNames[0]]; - let range = XLSX.utils.decode_range(worksheet["!ref"]); - let column_names = [], - data = []; - for (let R = header_index - 1; R <= range.e.r; ++R) { - let row_data = []; - for (let C = range.s.c; C <= range.e.c; ++C) { - let cell_ref; - - //Populate column_names - if (R == header_index - 1) { - cell_ref = XLSX.utils.encode_cell({ c: C, r: header_index - 1 }); - if (worksheet[cell_ref]) { - column_names.push(worksheet[cell_ref].v); - } - } - - //Populate corresponding data row - if (R >= data_index - 1) { - cell_ref = XLSX.utils.encode_cell({ c: C, r: R }); - if (worksheet[cell_ref]) { - row_data.push(worksheet[cell_ref].v); - } - } - } - if (R >= data_index - 1) { - data.push(row_data); - } - } - let df = new DataFrame(data, { columns: column_names }); - return df; - } catch (err) { - throw new Error(err); - } -}; diff --git a/src/lib/danfo/plotting/plot.js b/src/lib/danfo/plotting/plot.js deleted file mode 100644 index aa5fc19..0000000 --- a/src/lib/danfo/plotting/plot.js +++ /dev/null @@ -1,866 +0,0 @@ -import { Utils } from "../core/utils"; -import { Series } from "../core/series"; -const utils = new Utils(); - -/** - * Change: Rising Odegua 6th Feb 2021 - * We no longer bundle Plotjs with DanfoJs. I noticed that removing this single - * inport reduces the bundle size from 5.7mb to 2.1mb. I know it's still a huge - * bundle, but it's progress! - * If you need to use Plotly, then you can add the Plotly CDN - * to your script before DanfoJs CDN - */ -try { - const version = Plotly.version; - console.info(`Using Plotly version ${version}`); -} catch (error) { - console.info(`Plotly CDN not found. If you need to make Plots, then add the Plotly CDN to your script`); -} - -/** - * Plotting methods and Functions performed on Series and DataFrames - */ -export class Plot { - constructor(ndframe, div) { - this.div = div; - this.ndframe = ndframe; - } - - - /** - * Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame’s values as coordinates. - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - line(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "line"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - if (utils.__key_in_object(this_config, 'x')) { - trace["x"] = this.ndframe[this_config['x']].values; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } else { - trace["y"] = this.ndframe[this_config['y']].values; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["x"] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot Series or DataFrame as Bars. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - bar(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "bar"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "bar"; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe[this_config['x']].values; - } else { - trace['y'] = this.ndframe[this_config['y']].values; - } - trace['type'] = "bar"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - // params.forEach(param => { //TODO accept individual configuration for traces - // trace[param] = config[param] - // }) - trace['x'] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "bar"; - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot two or more columns in a DataFrame as scatter points. If Series, plot against its index - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - scatter(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["x"] = this.ndframe.values; - trace['y'] = this.ndframe.index; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe.index; - trace['x'] = this.ndframe[this_config['x']].values; - - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this.ndframe[this_config['y']].values; - - } - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - // params.forEach(param => { //TODO accept individual configuration for traces - // trace[param] = config[param] - // }) - trace['y'] = this.ndframe.index; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot columns in a Series/DataFrame as Histograms. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - hist(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["x"] = this.ndframe.values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x')) { - //plot as vertical histogram - let trace = {}; - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace['x'] = this.ndframe[this_config['y']].values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'y')) { - //plot as vertical histogram - let trace = {}; - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace['y'] = this.ndframe[this_config['y']].values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "histogram"; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - /** - * Makes Pie Plots from two Columns in a DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - pie(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - - - if (this.ndframe instanceof Series) { - let data = [ { - values: this.ndframe.values, - labels: this.ndframe.index, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - } ]; - - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'values') && utils.__key_in_object(this_config, 'labels')) { - if (!this.ndframe.column_names.includes(this_config['labels'])) { - throw Error(`Column Error: ${this_config['labels']} not found in columns. labels name must be one of [ ${this.ndframe.column_names}]`); - } - if (!this.ndframe.column_names.includes(this_config['values'])) { - throw Error(`Column Error: ${this_config['values']} not found in columns. value name must be one of [ ${this.ndframe.column_names}]`); - } - let data = [ { - values: this.ndframe[this_config['values']].values, - labels: this.ndframe[this_config['labels']].values, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - } ]; - - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else { - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - if (utils.__key_in_object(this_config, 'row_pos')) { - if (this_config['row_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of row_pos array must be equal to number of columns. Got ${this_config['row_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(0); - } - this_config['row_pos'] = temp_arr; - - } - - if (utils.__key_in_object(this_config, 'col_pos')) { - if (this_config['col_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of col_pos array must be equal to number of columns. Got ${this_config['col_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(i); - } - this_config['col_pos'] = temp_arr; - - } - let data = []; - - cols_to_plot.forEach((c_name, i) => { - let trace = {}; - trace["values"] = this.ndframe[c_name].values; - trace['labels'] = this.ndframe[this_config['labels']].values; - trace['name'] = c_name; - trace['type'] = "pie"; - trace['domain'] = { row: this_config['row_pos'][i], column: this_config['col_pos'][i] }; - trace["hoverinfo"] = 'label+percent+name'; - trace['textposition'] = "outside"; - trace['automargin'] = true; - data.push(trace); - - }); - - if (!utils.__key_in_object(this_config, "grid")) { - //set default grid - let size = Number((this.ndframe.shape[1] / 2).toFixed()) + 1; - this_config['grid'] = { rows: size, columns: size }; - } - this_config['layout']['grid'] = this_config['grid']; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - - } - - } - - - /** - * Plot Box plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - box(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "box"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'box'; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'box'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'box'; - } - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = 'box'; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot Violin plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - violin(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "violin"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'violin'; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'violin'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'violin'; - } - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = 'violin'; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - /** - * Display DataFrame in a div using D3.js format - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - table(config = {}) { - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let header = {}; - let cells = {}; - let cols_data = []; - let cols_2_show; - - - if (utils.__key_in_object(this_config, 'columns')) { - - this_config['columns'].forEach((cname) => { - if (!this.ndframe.column_names.includes(cname)) { - throw Error(`Column Error: ${cname} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - - let idx = this.ndframe.column_names.indexOf(cname); - cols_data.push(this.ndframe.col_data[idx]); - }); - cols_2_show = this_config['columns']; - } else { - - cols_2_show = this.ndframe.column_names; - cols_data = this.ndframe.col_data; - - } - - header['values'] = cols_2_show; - cells['values'] = cols_data; - - if (this_config['header_style']) { - Object.keys(this_config['header_style']).forEach((param) => { - header[param] = this_config['header_style'][param]; - }); - } - - if (this_config['cell_style']) { - Object.keys(this_config['cell_style']).forEach((param) => { - cells[param] = this_config['cell_style'][param]; - }); - } - var data = [ { - type: 'table', - header: header, - cells: cells - } ]; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - - __get_plot_params(config) { - let params = Object.keys(config); - let this_config = {}; - - params.forEach((param) => { - this_config[param] = config[param]; - }); - - if (!utils.__key_in_object(config, "layout")) { - this_config['layout'] = {}; - } - - - return [ this_config, params ]; - - } - - ____check_if_cols_exist(cols) { - cols.forEach((col) => { - if (!this.ndframe.column_names.includes(col)) { - throw Error(`Column Error: ${col} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - }); - return cols; - } - - -} diff --git a/src/lib/danfo/preprocessing/encodings.js b/src/lib/danfo/preprocessing/encodings.js deleted file mode 100644 index ace3388..0000000 --- a/src/lib/danfo/preprocessing/encodings.js +++ /dev/null @@ -1,108 +0,0 @@ -import { Series } from "../core/series"; -import { Utils } from "../core/utils"; -import { DataFrame } from "../core/frame"; - -const utils = new Utils; - -export class LabelEncoder { - - /** - * - * @param {data} data [Array|Series] - * @returns Array. - */ - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - - return new Series(output_data); - } - - /** - * Transform data using the label generated from fitting - * @param {data} data [Array|Series] - * @returns Array - */ - transform(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - return new Series(output_data); - } -} - -export class OneHotEncoder { - - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } - - transform(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } -} diff --git a/src/lib/danfo/preprocessing/scalers.js b/src/lib/danfo/preprocessing/scalers.js deleted file mode 100644 index 2542512..0000000 --- a/src/lib/danfo/preprocessing/scalers.js +++ /dev/null @@ -1,267 +0,0 @@ -import { tensor, moments } from "@tensorflow/tfjs"; -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; -import { Utils } from "../core/utils"; - -const utils = new Utils(); - -export class MinMaxScaler { - /** - * Fit minmax scaler on data, to obtain their min and max value - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.max = tensor_data.max(0); - this.min = tensor_data.min(0); - - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } - - /** - * Transform an array using the min and max generated from the fitting on data - * @param {data} data [Array] - * @returns array - */ - transform(data) { - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tensor(data); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -export class StandardScaler { - /** - * - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.std = moments(tensor_data, 0).variance.sqrt(); - this.mean = tensor_data.mean(0); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series((data = output_data)); - } else { - return new DataFrame((data = output_data)); - } - } - - transform(data) { - // if(!Array.isArray(data)){ - // throw new Error(data) - // } - - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tensor(data); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -// export class RobustScaler{ - -// __median(arr, isTensor,return_index) { -// if (!isTensor) { -// const sorted = arr.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { - -// return return_index ? [(middle - 1) , middle] : (sorted[middle - 1] + sorted[middle]) / 2; -// } - -// return return_index ? middle : sorted[middle] ; -// } else { -// let result_arr = [] -// arr.map(ele => { -// const sorted = ele.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { -// result_arr.push(return_index ? [(middle - 1) , middle] :(sorted[middle - 1] + sorted[middle]) / 2 ) -// } else { -// result_arr.push(return_index ? middle : sorted[middle]) -// } - -// }) -// return result_arr -// } - -// } - -// quantile(data,isTensor){ - -// if(isTensor){ -// data = utils.__get_col_values(data); -// } - -// let median = this.__median(data,isTensor,true) -// let med = this.__median(data,isTensor,false) - -// let q1 = [] -// let q2 = [] - -// if(!isTensor){ -// let sorted = data.slice().sort((a, b) => a - b); - -// if(Array.isArray(median)){ -// let lower = median[0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median) -// let upper_data = sorted.slice(median+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); -// } - -// }else{ - -// data.map((x,i)=>{ -// let sorted = x.slice().sort((a, b) => a - b); - -// if(Array.isArray(median[i])){ -// let lower = median[i][0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median[i]) -// let upper_data = sorted.slice(median[i]+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); -// } - -// }) -// } - -// return [q1,q2, med] - -// } - -// /** -// * Fit robust scalar on data to obtain the first quantile and third quantile -// * @param {data} data [DataRame | Series | Array] -// * @returns Array -// */ -// fit(data){ - -// let tensor_data = null -// let isTensor = false; -// if(Array.isArray(data)){ -// tensor_data = tensor(data) -// } -// else if((data instanceof DataFrame)){ -// tensor_data = tensor(data.values) -// isTensor = true; -// } -// else if((data instanceof Series)){ -// tensor_data = tensor(data.values) -// } -// else{ -// throw new Error("data must either be an Array, DataFrame or Series") -// } - -// let [q1, q3, median] = this.quantile(data,isTensor) -// let q3_tensor = tensor(q3) -// let output_data = tensor_data.sub(median).div(q3_tensor.sub(q1)).arraySync() - -// return output_data; -// } -// } diff --git a/src/lib/ml5/Word2vec/index.js b/src/lib/ml5/Word2vec/index.js index 6ecbbc0..bfd28d6 100644 --- a/src/lib/ml5/Word2vec/index.js +++ b/src/lib/ml5/Word2vec/index.js @@ -7,205 +7,16 @@ Word2Vec */ -import * as tf from '@tensorflow/tfjs'; -import axios from 'axios'; -import callCallback from '../utils/callcallback'; - - -class Word2Vec { - /** - * Create Word2Vec model - * @param {String} modelPath - path to pre-trained word vector model in .json e.g data/wordvecs1000.json - * @param {function} callback - Optional. A callback function that is called once the model has loaded. If no callback is provided, it will return a promise - * that will be resolved once the model has loaded. - */ - constructor(modelPath, callback) { - this.model = {}; - this.modelPath = modelPath; - this.modelSize = 0; - this.modelLoaded = false; - - this.ready = callCallback(this.loadModel(), callback); - // TODO: Add support to Promise - // this.then = this.ready.then.bind(this.ready); - } - - async loadModel() { - const {data} = await axios.get(this.modelPath) - - Object.keys(data.vectors).forEach((word) => { - this.model[word] = tf.tensor1d(data.vectors[word]); - }); - this.modelSize = Object.keys(this.model).length; - this.modelLoaded = true; - return this; - } - - dispose(callback) { - Object.values(this.model).forEach(x => x.dispose()); - if (callback) { - callback(); - } - } - - async add(inputs, maxOrCb, cb) { - const { max, callback } = Word2Vec.parser(maxOrCb, cb, 10); - - await this.ready; - return tf.tidy(() => { - const sum = Word2Vec.addOrSubtract(this.model, inputs, 'ADD'); - const result = Word2Vec.nearest(this.model, sum, inputs.length, inputs.length + max); - if (callback) { - callback(undefined, result); - } - return result; - }); - } - - async subtract(inputs, maxOrCb, cb) { - const { max, callback } = Word2Vec.parser(maxOrCb, cb, 10); - - await this.ready; - return tf.tidy(() => { - const subtraction = Word2Vec.addOrSubtract(this.model, inputs, 'SUBTRACT'); - const result = Word2Vec.nearest(this.model, subtraction, inputs.length, inputs.length + max); - if (callback) { - callback(undefined, result); - } - return result; - }); - } - - async average(inputs, maxOrCb, cb) { - const { max, callback } = Word2Vec.parser(maxOrCb, cb, 10); - - await this.ready; - return tf.tidy(() => { - const sum = Word2Vec.addOrSubtract(this.model, inputs, 'ADD'); - const avg = tf.div(sum, tf.tensor(inputs.length)); - const result = Word2Vec.nearest(this.model, avg, inputs.length, inputs.length + max); - if (callback) { - callback(undefined, result); - } - return result; - }); - } - - async nearest(input, maxOrCb, cb) { - const { max, callback } = Word2Vec.parser(maxOrCb, cb, 10); - - await this.ready; - const vector = this.model[input]; - let result; - if (vector) { - result = Word2Vec.nearest(this.model, vector, 1, max + 1); - } else { - result = null; - } - - if (callback) { - callback(undefined, result); - } - return result; - } - - /* Given a set of your own words, find the nearest neighbors */ - async nearestFromSet(input, set, maxOrCb, cb) { - const { max, callback } = Word2Vec.parser(maxOrCb, cb, 10); - await this.ready; - const vector = this.model[input]; - - // If the input vector isn't found, bail out early. - if (!vector) { - if(callback) callback(undefined, null); - return null; - } - - const miniModel = {}; - set.forEach((word) => { - if (this.model[word]) miniModel[word] = this.model[word]; - }); - - // If none of the words in the set are found, also bail out - if (!miniModel.length) { - if(callback) callback(undefined, null); - return null; - } - - const result = Word2Vec.nearest(miniModel, vector, 1, max + 1); - - if (callback) { - callback(undefined, result); - } - return result; - } - - async getRandomWord(callback) { - await this.ready; - const words = Object.keys(this.model); - const result = words[Math.floor(Math.random() * words.length)]; - if (callback) { - callback(undefined, result); - } - return result; - } - - static parser(maxOrCallback, cb, defaultMax) { - let max = defaultMax; - let callback = cb; - - if (typeof maxOrCallback === 'function') { - callback = maxOrCallback; - } else if (typeof maxOrCallback === 'number') { - max = maxOrCallback; - } - return { max, callback }; - } - - static addOrSubtract(model, values, operation) { - return tf.tidy(() => { - const vectors = []; - const notFound = []; - if (values.length < 2) { - throw new Error('Invalid input, must be passed more than 1 value'); - } - values.forEach((value) => { - const vector = model[value]; - if (!vector) { - notFound.push(value); - } else { - vectors.push(vector); - } - }); - - if (notFound.length > 0) { - throw new Error(`Invalid input, vector not found for: ${notFound.toString()}`); - } - let result = vectors[0]; - if (operation === 'ADD') { - for (let i = 1; i < vectors.length; i += 1) { - result = tf.add(result, vectors[i]); - } - } else { - for (let i = 1; i < vectors.length; i += 1) { - result = tf.sub(result, vectors[i]); - } - } - return result; - }); - } - - static nearest(model, input, start, max) { - const nearestVectors = []; - Object.keys(model).forEach((word) => { - const distance = tf.util.distSquared(input.dataSync(), model[word].dataSync()); - nearestVectors.push({ word, distance }); - }); - nearestVectors.sort((a, b) => a.distance - b.distance); - return nearestVectors.slice(start, max); - } -} - -const word2vec = (model, cb) => new Word2Vec(model, cb); +const word2vec = () => { + console.log('%cword2vec has been disabled', 'color: #A155FF; font-size: 16px'); + console.log( + `We've intentionally disabled the word2vec function after recognizing it has the ` + + `potential to produce harmful outputs while using the model files included in our ` + + `examples. We'll consider reenabling the word2vec function along with changes to ` + + `address these issues in a future release of ml5.js. You can find further updates ` + + `about this topic on our Twitter account (https://twitter.com/ml5js/status/1445762321444315147) and GitHub ` + + `(https://github.com/ml5js/ml5-library/issues/1238).`, + ); +}; export default word2vec; diff --git a/src/lib/ml5/Word2vec/index_test.js b/src/lib/ml5/Word2vec/index_test.js deleted file mode 100644 index 0cb8017..0000000 --- a/src/lib/ml5/Word2vec/index_test.js +++ /dev/null @@ -1,128 +0,0 @@ -// Copyright (c) 2018 ml5 -// -// This software is released under the MIT License. -// https://opensource.org/licenses/MIT - -/* eslint no-loop-func: 0 */ -const { tf, word2vec } = ml5; - -const W2V_MODEL_URL = 'https://raw.githubusercontent.com/ml5js/ml5-data-and-training/master/models/wordvecs/common-english/wordvecs1000.json'; - -describe('word2vec', () => { - let word2vecInstance; - let numTensorsBeforeAll; - let numTensorsBeforeEach; - beforeAll((done) => { - jasmine.DEFAULT_TIMEOUT_INTERVAL = 60000; - numTensorsBeforeAll = tf.memory().numTensors; - word2vecInstance = word2vec(W2V_MODEL_URL, done); - }); - - // afterAll(() => { - // word2vecInstance.dispose(); - // const numTensorsAfterAll = tf.memory().numTensors; - // if (numTensorsBeforeAll !== numTensorsAfterAll) { - // throw new Error(`Leaking Tensors (${numTensorsAfterAll} vs ${numTensorsBeforeAll})`); - // } - // }); - - beforeEach(() => { - numTensorsBeforeEach = tf.memory().numTensors; - }); - - afterEach(() => { - const numTensorsAfterEach = tf.memory().numTensors; - if (numTensorsBeforeEach !== numTensorsAfterEach) { - throw new Error(`Leaking Tensors (${numTensorsAfterEach} vs ${numTensorsBeforeEach})`); - } - }); - - it('creates a new instance', () => { - expect(word2vecInstance).toEqual(jasmine.objectContaining({ - modelLoaded: true, - modelSize: 1000, - })); - }); - - describe('getRandomWord', () => { - it('returns a random word', () => { - word2vecInstance.getRandomWord() - .then(word => expect(typeof word).toEqual('string')); - }); - }); - - describe('nearest', () => { - it('returns a sorted array of nearest words', () => { - for (let i = 0; i < 100; i += 1) { - word2vecInstance.getRandomWord() - .then(word => word2vecInstance.nearest(word)) - .then((nearest) => { - let currentDistance = 0; - for (const { word, distance: nextDistance } of nearest) { - expect(typeof word).toEqual('string'); - expect(nextDistance).toBeGreaterThan(currentDistance); - currentDistance = nextDistance; - } - }) - } - }); - - it('returns a list of the right length', () => { - for (let i = 0; i < 100; i += 1) { - word2vecInstance.getRandomWord() - .then(word => word2vecInstance.nearest(word, i)) - .then(nearest => expect(nearest.length).toEqual(i)); - } - }); - }); - - describe('nearestFromSet', () => { - it('should return null when input word is fake', () => { - word2vecInstance.nearestFromSet('asdfsda', ['cat', 'ape', 'lamp']) - .then((nearest) => { - expect(nearest).toBe(null); - }); - }); - it('should return null for an empty set', () => { - word2vecInstance.nearestFromSet('dog', []) - .then((nearest) => { - expect(nearest).toBe(null); - }); - }); - it('should return null when word set is fake', () => { - word2vecInstance.nearestFromSet('dog', ["lsdfjk", "slkjf"]) - .then((nearest) => { - expect(nearest).toBe(null); - }); - }); - it('nearest to "human" in ["ape", "cat", "lamp"] is "ape", then "cat"', () => { - word2vecInstance.nearestFromSet('human', ['cat', 'ape', 'lamp']) - .then((nearest) => { - expect(nearest[0].word).toEqual('ape'); - expect(nearest[1].word).toEqual('cat'); - expect(nearest[1].word).toEqual('lamp'); - }); - }); -}); - - describe('add', () => { - it('cat + dog = horse', () => { - word2vecInstance.add(['cat', 'dog'], 1) - .then(result => expect(result[0].word).toBe('horse')); - }); - }); - - describe('subtract', () => { - it('cat - dog = fish', () => { - word2vecInstance.subtract(['cat', 'dog'], 1) - .then(result => expect(result[0].word).toBe('fish')); - }); - }); - - describe('average', () => { - it('moon & sun = avenue', () => { - word2vecInstance.average(['moon', 'sun'], 1) - .then(result => expect(result[0].word).toBe('earth')); - }); - }); -}); diff --git a/src/lib/ml5/package.json b/src/lib/ml5/package.json index de94e62..c6fd1ef 100644 --- a/src/lib/ml5/package.json +++ b/src/lib/ml5/package.json @@ -1,6 +1,6 @@ { "name": "ml5", - "version": "0.6.1", + "version": "0.8.0", "description": "A friendly machine learning library for the web.", "main": "dist/ml5.min.js", "directories": { diff --git a/test/test.html b/test/test.html deleted file mode 100644 index b9c892d..0000000 --- a/test/test.html +++ /dev/null @@ -1,27 +0,0 @@ - - - - número Tests - - - - - - - - -
- - - - - - - - - - - - - - diff --git a/test/unit/data/index.js b/test/unit/data/index.js deleted file mode 100644 index 1f7bd4b..0000000 --- a/test/unit/data/index.js +++ /dev/null @@ -1,62 +0,0 @@ -/* eslint-disable func-names */ -/* eslint-disable prefer-arrow-callback */ - -describe('DataFrame', function () { - let pInst; - - beforeEach(function () { - pInst = new p5(function () { }); - }); - - afterEach(function () { - pInst.remove(); - }); - - describe('DataFrame', function () { - it('Should have a dfd.DataFrame under the hood', function () { - const df = pInst.createDataFrame([[1, 2, 3], [4, 5, 6]]); - expect(df).to.be.an.instanceof(num.DataFrame); - }); - }); - - describe('createDataFrame()', function () { - it('Should require an argument', function () { - expect(pInst.createDataFrame).to.throw(Error); - }); - - it('Should accept JSON arguments', function () { - const json_data = [ - { A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 }, - { A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 }, - { A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 }, - { A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 } - ]; - const df = pInst.createDataFrame(json_data); - expect(df).to.be.an.instanceof(num.DataFrame); - }); - - it('Should accept Tensor arguments', function () { - const t = pInst.createTensor([[12, 34, 2.2, 2], [30, 30, 2.1, 7]]); - const df = pInst.createDataFrame(t); - expect(df).to.be.an.instanceof(num.DataFrame); - }); - - it('Should accept tf.Tensor arguments', function () { - const t = num.tf.tensor([[12, 34, 2.2, 2], [30, 30, 2.1, 7]]); - const df = pInst.createDataFrame(t); - expect(df).to.be.an.instanceof(num.DataFrame); - }); - - it('Should reject garbage arguments', function () { - expect(() => pInst.createDataFrame("I'm only happy when it rains")).to.throw(Error); - }); - }); - - describe('loadDataFrame()', function () { - it('Should load csv files', function () { - pInst.loadDataFrame('https://assets.computiful.org/pre-alpha/who_life_expectancy.csv', 'csv', (df) => { - expect(df).to.be.an.instanceof(num.dfd.DataFrame); - }); - }); - }); -}); diff --git a/test/unit/ml5/index.js b/test/unit/ml5/index.js index 1ae99ff..b425686 100644 --- a/test/unit/ml5/index.js +++ b/test/unit/ml5/index.js @@ -14,7 +14,7 @@ describe('ml5', function () { describe('window.ml5', function () { it('Should exist', function () { - expect(ml5.version).to.equal('0.6.1'); + expect(ml5.version).to.equal('0.8.0'); }); }); }); diff --git a/webpack.config.js b/webpack.config.js index a7ccd1b..217f1f3 100644 --- a/webpack.config.js +++ b/webpack.config.js @@ -3,7 +3,6 @@ const path = require('path'); module.exports = { entry: './src/index.ts', mode: 'production', - devtool: 'inline-source-map', module: { rules: [ {