@modelx/modelx

<!doctype html> <html class="default no-js"> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>@modelx/data</title> <meta name="description" content=""> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="stylesheet" href="assets/css/main.css"> </head> <body> <header> <div class="tsd-page-toolbar"> <div class="container"> <div class="table-wrap"> <div class="table-cell" id="tsd-search" data-index="assets/js/search.js" data-base="."> <div class="field"> <label for="tsd-search-field" class="tsd-widget search no-caption">Search</label> <input id="tsd-search-field" type="text" /> </div> <ul class="results"> <li class="state loading">Preparing search index...</li> <li class="state failure">The search index is not available</li> </ul> <a href="index.html" class="title">@modelx/data</a> </div> <div class="table-cell" id="tsd-widgets"> <div id="tsd-filter"> <a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a> <div class="tsd-filter-group"> <div class="tsd-select" id="tsd-filter-visibility"> All <ul class="tsd-select-list"> <li data-value="public">Public</li> <li data-value="protected">Public/Protected</li> <li data-value="private" class="selected">All</li> </ul> </div> <input type="checkbox" id="tsd-filter-inherited" checked /> <label class="tsd-widget" for="tsd-filter-inherited">Inherited</label> <input type="checkbox" id="tsd-filter-externals" checked /> <label class="tsd-widget" for="tsd-filter-externals">Externals</label> <input type="checkbox" id="tsd-filter-only-exported" /> <label class="tsd-widget" for="tsd-filter-only-exported">Only exported</label> </div> </div> <a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a> </div> </div> </div> </div> <div class="tsd-page-title"> <div class="container"> <ul class="tsd-breadcrumb"> <li> <a href="globals.html">Globals</a> </li> </ul> <h1>@modelx/data</h1> </div> </div> </header> <div class="container container-main"> <div class="row"> <div class="col-8 col-content"> <div class="tsd-panel tsd-typography"> <a href="#modelxdata" id="modelxdata" style="color: inherit; text-decoration: none;"> <h1>@modelx/data</h1> </a> <a href="https://coveralls.io/github/repetere/modelx-data?branch=master"><img src="https://coveralls.io/repos/github/repetere/modelx-data/badge.svg?branch=master" alt="Coverage Status"></a> <img src="https://github.com/repetere/modelx-data/workflows/Build,%20Test%20&%20Coverage/badge.svg" alt="Build, Test & Coverage"> quickly generate UMDs and other module types with rollup and typescript <a href="#getting-started" id="getting-started" style="color: inherit; text-decoration: none;"> <h2>Getting started</h2> </a> Clone the repo and drop your module in the src directory. <pre><code class="language-shell"># Install Prerequisites $ npm install rollup typedoc jest sitedown --g</code></pre> <a href="#basic-usage" id="basic-usage" style="color: inherit; text-decoration: none;"> <h2>Basic Usage</h2> </a> <pre><code class="language-shell">$ npm run build #builds type declarations, created bundled artifacts with rollup and generates documenation</code></pre> <a href="#description" id="description" style="color: inherit; text-decoration: none;"> <h2>Description</h2> </a> ModelScript is a javascript module with simple and efficient tools for data mining and data analysis in JavaScript. ModelScript can be used with <a href="https://github.com/mljs/ml">ML.js</a>, <a href="https://github.com/StratoDem/pandas-js">pandas-js</a>, and <a href="https://github.com/numjs/numjs">numjs</a>, to approximate the equivalent R/Python tool chain in JavaScript. In Python, data preparation is typically done in a DataFrame, ModelScript encourages a more R like workflow where the data preparation is in it's native structure. <a href="#installation" id="installation" style="color: inherit; text-decoration: none;"> <h3>Installation</h3> </a> <pre><code class="language-sh">$ npm i modelscript</code></pre> <a href="#a-hrefhttpsgithubcomrepeteremodelscriptblobmasterdocsapimdfull-documentationa" id="a-hrefhttpsgithubcomrepeteremodelscriptblobmasterdocsapimdfull-documentationa" style="color: inherit; text-decoration: none;"> <h3><a href="https://github.com/repetere/modelscript/blob/master/docs/api.md">Full Documentation</a></h3> </a> <a href="#usage-basic" id="usage-basic" style="color: inherit; text-decoration: none;"> <h3>Usage (basic)</h3> </a> ModelScript is an EcmaScript module and designed to be imported in an ES2015+ environment. In order to use in older environment, please use <code>const modelscript = require('modelscript/build/modelscript.cjs.js')</code> for older versions of node and <code><script type="text/javascript" src=".../path/to/.../modelscript/build/modelscript.umd.js"/></code> <pre><code class="language-javascript">"modelscript" : { ml:{ //see https://github.com/mljs/ml UpperConfidenceBound [Class: UpperConfidenceBound]{ // Implementation of the Upper Confidence Bound algorithm predict(), //returns next action based off of the upper confidence bound learn(), //single step training method train(), //training method for upper confidence bound calculations }, ThompsonSampling [Class: ThompsonSampling]{ //Implementation of the Thompson Sampling algorithm predict(), //returns next action based off of the thompson sampling learn(), //single step training method train(), //training method for thompson sampling calculations }, }, nlp:{ //see https://github.com/NaturalNode/natural ColumnVectorizer [Class: ColumnVectorizer]{ //class creating sparse matrices from a corpus get_tokens(), // Returns a distinct array of all tokens after fit_transform get_vector_array(), //Returns array of arrays of strings for dependent features from sparse matrix word map fit_transform(options), //Fits and transforms data by creating column vectors (a sparse matrix where each row has every word in the corpus as a column and the count of appearances in the corpus) get_limited_features(options), //Returns limited sets of dependent features or all dependent features sorted by word count evaluateString(testString), //returns word map with counts evaluate(testString), //returns new matrix of words with counts in columns } }, csv:{ loadCSV: [Function: loadCSV], //asynchronously loads CSVs, either a filepath or a remote URI loadTSV: [Function: loadTSV], //asynchronously loads TSVs, either a filepath or a remote URI }, model_selection: { train_test_split: [Function: train_test_split], // splits data into training and testing sets cross_validation_split: [Function: kfolds], //splits data into k-folds cross_validate_score: [Function: cross_validate_score],//test model variance and bias grid_search: [Function: grid_search], // tune models with grid search for optimal performance }, DataSet [Class: DataSet]: { //class for manipulating an array of objects (typically from CSV data) columnMatrix(vectors), //returns a matrix of values by combining column arrays into a matrix columnArray(columnName, options), // - returns a new array of a selected column from an array of objects, can filter, scale and replace values columnReplace(columnName, options), // - returns a new array of a selected column from an array of objects and replaces empty values, encodes values and scales values columnScale(columnName, options), // - returns a new array of scaled values which can be reverse (descaled). The scaling transformations are stored on the DataSet columnDescale(columnName, options), // - Returns a new array of descaled values selectColumns(columns, options), //returns a list of objects with only selected columns as properties labelEncoder(columnName, options), // - returns a new array and label encodes a selected column labelDecode(columnName, options), // - returns a new array and decodes an encoded column back to the original array values oneHotEncoder(columnName, options), // - returns a new object of one hot encoded values columnMatrix(columnName, options), // - returns a matrix of values from multiple columns columnReducer(newColumnName, options), // - returns a new array of a selected column that is passed a reducer function, this is used to create new columns for aggregate statistics columnMerge(name, data), // - returns a new column that is merged onto the data set filterColumn(options), // - filtered rows of data, fitColumns(options), // - mutates data property of DataSet by replacing multiple columns in a single command static reverseColumnMatrix(options), // returns an array of objects by applying labels to matrix of columns static reverseColumnVector(options), // returns an array of objects by applying labels to column vector }, calc:{ getTransactions: [Function getTransactions], // Formats an array of transactions into a sparse matrix like format for Apriori/Eclat assocationRuleLearning: [async Function assocationRuleLearning], // returns association rule learning results using apriori }, util: { range: [Function], // range helper function rangeRight: [Function], //range right helper function scale: [Function: scale], //scale / normalize data avg: [Function: arithmeticMean], // aritmatic mean mean: [Function: arithmeticMean], // aritmatic mean sum: [Function: sum], max: [Function: max], min: [Function: min], sd: [Function: standardDeviation], // standard deviation StandardScalerTransforms: [Function: StandardScalerTransforms], // returns two functions that can standard scale new inputs and reverse scale new outputs MinMaxScalerTransforms: [Function: MinMaxScalerTransforms], // returns two functions that can mix max scale new inputs and reverse scale new outputs StandardScaler: [Function: StandardScaler], // standardization (z-scores) MinMaxScaler: [Function: MinMaxScaler], // min-max scaling ExpScaler: [Function: ExpScaler], // exponent scaling LogScaler: [Function: LogScaler], // natual log scaling squaredDifference: [Function: squaredDifference], // Returns an array of the squared different of two arrays standardError: [Function: standardError], // The standard error of the estimate is a measure of the accuracy of predictions made with a regression line coefficientOfDetermination: [Function: coefficientOfDetermination], adjustedCoefficentOfDetermination: [Function: adjustedCoefficentOfDetermination], adjustedRSquared: [Function: adjustedCoefficentOfDetermination], rBarSquared: [Function: adjustedCoefficentOfDetermination], r: [Function: coefficientOfCorrelation], coefficientOfCorrelation: [Function: coefficientOfCorrelation], rSquared: [Function: rSquared], //r^2 pivotVector: [Function: pivotVector], // returns an array of vectors as an array of arrays pivotArrays: [Function: pivotArrays], // returns a matrix of values by combining arrays into a matrix standardScore: [Function: standardScore], // Calculates the z score of each value in the sample, relative to the sample mean and standard deviation. zScore: [Function: standardScore], // alias for standardScore. approximateZPercentile: [Function: approximateZPercentile], // approximate the p value from a z score }, preprocessing: { DataSet: [Class DataSet], }, }</code></pre> <a href="#examples-javascript--python--r" id="examples-javascript--python--r" style="color: inherit; text-decoration: none;"> <h3>Examples (JavaScript / Python / R)</h3> </a> <a href="#loading-csv-data" id="loading-csv-data" style="color: inherit; text-decoration: none;"> <h4>Loading CSV Data</h4> </a> <a href="#javascript" id="javascript" style="color: inherit; text-decoration: none;"> <h5>Javascript</h5> </a> <pre><code class="language-javascript">import { default as jsk } from 'modelscript'; let dataset; //In JavaScript, by default most I/O Operations are asynchronous, see the notes section for more ms.loadCSV('/some/file/path.csv') .then(csvData=>{ dataset = new ms.DataSet(csvData); console.log({csvData}); /* csvData [{ 'Country': 'Brazil', 'Age': '44', 'Salary': '72000', 'Purchased': 'N', }, ... { 'Country': 'Mexico', 'Age': '27', 'Salary': '48000', 'Purchased': 'Yes', }] */ }) .catch(console.error); // or from URL ms.loadCSV('https://example.com/some/file/path.csv') </code></pre> <a href="#python" id="python" style="color: inherit; text-decoration: none;"> <h5>Python</h5> </a> <pre><code class="language-python">import pandas as pd #Importing the dataset dataset = pd.read_csv('/some/file/path.csv')</code></pre> <a href="#r" id="r" style="color: inherit; text-decoration: none;"> <h5>R</h5> </a> <pre><code class="language-R"># Importingd the dataset dataset = read.csv('Data.csv')</code></pre> <a href="#handling-missing-data" id="handling-missing-data" style="color: inherit; text-decoration: none;"> <h4>Handling Missing Data</h4> </a> <a href="#javascript-1" id="javascript-1" style="color: inherit; text-decoration: none;"> <h5>Javascript</h5> </a> <pre><code class="language-javascript">//column Array returns column of data by name // [ '44','27','30','38','40','35','','48','50', '37' ] const OringalAgeColumn = dataset.columnArray('Age'); //column Replace returns new Array with replaced missing data //[ '44','27','30','38','40','35',38.77777777777778,'48','50','37' ] const ReplacedAgeMeanColumn = dataset.columnReplace('Age',{strategy:'mean'}); //fit Columns, mutates dataset dataset.fitColumns({ columns:[{name:'Age',strategy:'mean'}] }); /* dataset class DataSet data:[ { 'Country': 'Brazil', 'Age': '38.77777777777778', 'Salary': '72000', 'Purchased': 'N', } ... ] */</code></pre> <a href="#python-1" id="python-1" style="color: inherit; text-decoration: none;"> <h5>Python</h5> </a> <pre><code class="language-python">X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 3].values # Taking care of of missing data from sklearn.preprocessing import Imputer imputer = Imputer(missing_values='NaN', strategy = 'mean', axis=0) imputer = imputer.fit(X[:, 1:3]) X[:, 1:3] = imputer.transform(X[:, 1:3])</code></pre> <a href="#r-1" id="r-1" style="color: inherit; text-decoration: none;"> <h5>R</h5> </a> <pre><code class="language-R"># Taking care of the missing data dataset$Age = ifelse(is.na(dataset$Age), ave(dataset$Age,FUN = function(x) mean(x,na.rm =TRUE)), dataset$Age)</code></pre> <a href="#one-hot-encoding-and-label-encoding" id="one-hot-encoding-and-label-encoding" style="color: inherit; text-decoration: none;"> <h4>One Hot Encoding and Label Encoding</h4> </a> <a href="#javascript-2" id="javascript-2" style="color: inherit; text-decoration: none;"> <h5>Javascript</h5> </a> <pre><code class="language-javascript">// [ 'Brazil','Mexico','Ghana','Mexico','Ghana','Brazil','Mexico','Brazil','Ghana', 'Brazil' ] const originalCountry = dataset.columnArray('Country'); /* { originalCountry: { Country_Brazil: [ 1, 0, 0, 0, 0, 1, 0, 1, 0, 1 ], Country_Mexico: [ 0, 1, 0, 1, 0, 0, 1, 0, 0, 0 ], Country_Ghana: [ 0, 0, 1, 0, 1, 0, 0, 0, 1, 0 ] }, } */ const oneHotCountryColumn = dataset.oneHotEncoder('Country'); // [ 'N', 'Yes', 'No', 'f', 'Yes', 'Yes', 'false', 'Yes', 'No', 'Yes' ] const originalPurchasedColumn = dataset.labelEncoder('Purchased'); // [ 0, 1, 0, 0, 1, 1, 1, 1, 0, 1 ] const encodedBinaryPurchasedColumn = dataset.labelEncoder('Purchased',{ binary:true }); // [ 0, 1, 2, 3, 1, 1, 4, 1, 2, 1 ] const encodedPurchasedColumn = dataset.labelEncoder('Purchased'); // [ 'N', 'Yes', 'No', 'f', 'Yes', 'Yes', 'false', 'Yes', 'No', 'Yes' ] const decodedPurchased = dataset.labelDecode('Purchased', { data: encodedPurchasedColumn, }); //fit Columns, mutates dataset dataset.fitColumns({ columns:[ { name: 'Purchased', options: { strategy: 'label', labelOptions: { binary: true, }, }, }, { name: 'Country', options: { strategy: 'onehot', }, }, ] });</code></pre> <a href="#python-2" id="python-2" style="color: inherit; text-decoration: none;"> <h5>Python</h5> </a> <pre><code class="language-python"># Encoding categorical data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder() X[:, 0] = labelencoder_X.fit_transform(X[:, 0]) onehotencoder = OneHotEncoder(categorical_features=[0]) X = onehotencoder.fit_transform(X).toarray() labelencoder_y = LabelEncoder() y = labelencoder_y.fit_transform(y)</code></pre> <a href="#r-2" id="r-2" style="color: inherit; text-decoration: none;"> <h5>R</h5> </a> <pre><code class="language-R"># Encoding categorical data dataset$Country = factor(dataset$Country, levels = c('Brazil', 'Mexico', 'Ghana'), labels = c(1, 2, 3)) dataset$Purchased = factor(dataset$Purchased, levels = c('No', 'Yes'), labels = c(0, 1))</code></pre> <a href="#cross-validation" id="cross-validation" style="color: inherit; text-decoration: none;"> <h4>Cross Validation</h4> </a> <a href="#javascript-3" id="javascript-3" style="color: inherit; text-decoration: none;"> <h5>Javascript</h5> </a> <pre><code class="language-javascript">const testArray = [20, 25, 10, 33, 50, 42, 19, 34, 90, 23, ]; // { train: [ 50, 20, 34, 33, 10, 23, 90, 42 ], test: [ 25, 19 ] } const trainTestSplit = ms.cross_validation.train_test_split(testArray,{ test_size:0.2, random_state: 0, }); // [ [ 50, 20, 34, 33, 10 ], [ 23, 90, 42, 19, 25 ] ] const crossValidationArrayKFolds = ms.cross_validation.cross_validation_split(testArray, { folds: 2, random_state: 0, });</code></pre> <a href="#python-3" id="python-3" style="color: inherit; text-decoration: none;"> <h5>Python</h5> </a> <pre><code class="language-python">#splitting the dataset into trnaing set and test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)</code></pre> <a href="#r-3" id="r-3" style="color: inherit; text-decoration: none;"> <h5>R</h5> </a> <pre><code class="language-R"># Splitting the dataset into the training set and test set library(caTools) set.seed(1) split = sample.split(dataset$Purchased, SplitRatio = 0.8) training_set = subset(dataset, split == TRUE) test_set = subset(dataset, split == FALSE)</code></pre> <a href="#scaling-z-score--min-mix" id="scaling-z-score--min-mix" style="color: inherit; text-decoration: none;"> <h4>Scaling (z-score / min-mix)</h4> </a> <a href="#javascript-4" id="javascript-4" style="color: inherit; text-decoration: none;"> <h5>Javascript</h5> </a> <pre><code class="language-javascript">dataset.columnArray('Salary',{ scale:'standard'}); dataset.columnArray('Salary',{ scale:'minmax'}); </code></pre> <a href="#python-4" id="python-4" style="color: inherit; text-decoration: none;"> <h5>Python</h5> </a> <pre><code class="language-python">from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test)</code></pre> <a href="#notes" id="notes" style="color: inherit; text-decoration: none;"> <h3>Notes</h3> </a> Check out <a href="https://repetere.github.io/modelscript">https://repetere.github.io/modelscript</a> for the full modelscript Documentation <a href="#a-quick-word-about-asynchronous-javascript" id="a-quick-word-about-asynchronous-javascript" style="color: inherit; text-decoration: none;"> <h4>A quick word about asynchronous JavaScript</h4> </a> Most machine learning tutorials in Python and R are not using their asynchronous equivalents; however, there is a bias in JavaScript to default to non-blocking operations. With the advent of ES7 and Node.js 7+ there are syntax helpers with asynchronous functions. It may be easier to use async/await in JS if you want an approximation close to what a workflow would look like in R/Python <pre><code class="language-javascript">import * as fs from 'fs-extra'; import * as np from 'numjs'; import { default as ml } from 'ml'; import { default as pd } from 'pandas-js'; import { default as mpn } from 'matplotnode'; import { loadCSV, preprocessing } from 'modelscript'; const plt = mpn.plot; void async () => { const csvData = await loadCSV('../Data.csv'); const rawData = new preprocessing.DataSet(csvData); const fittedData = rawData.fitColumns({ columns: [ { name: 'Age' }, { name: 'Salary' }, { name: 'Purchased', options: { strategy: 'label', labelOptions: { binary: true, }, } }, ] }); const dataset = new pd.DataFrame(fittedData); const X = dataset.iloc( [ 0, dataset.length ], [ 0, 3 ]).values; const y = dataset.iloc( [ 0, dataset.length ], 3).values; console.log({ X, y }); }(); </code></pre> </div> </div> <div class="col-4 col-menu menu-sticky-wrap 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<a href="modules/_src_features_.html">"src/features"</a> </li> <li class=" tsd-kind-external-module"> <a href="modules/_src_index_.html">"src/index"</a> </li> <li class=" tsd-kind-external-module"> <a href="modules/_src_model_.html">"src/model"</a> </li> <li class=" tsd-kind-external-module"> <a href="modules/_src_util_.html">"src/util"</a> </li> </ul> </nav> <nav class="tsd-navigation secondary menu-sticky"> <ul class="before-current"> </ul> </nav> </div> </div> </div> <footer> <div class="container"> <h2>Legend</h2> <div class="tsd-legend-group"> <ul class="tsd-legend"> <li class="tsd-kind-module">Module</li> <li class="tsd-kind-object-literal">Object literal</li> <li class="tsd-kind-variable">Variable</li> <li class="tsd-kind-function">Function</li> <li class="tsd-kind-function tsd-has-type-parameter">Function with type parameter</li> <li class="tsd-kind-index-signature">Index signature</li> <li class="tsd-kind-type-alias">Type alias</li> <li class="tsd-kind-type-alias tsd-has-type-parameter">Type alias with type parameter</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-enum">Enumeration</li> <li class="tsd-kind-enum-member">Enumeration member</li> <li class="tsd-kind-property tsd-parent-kind-enum">Property</li> <li class="tsd-kind-method tsd-parent-kind-enum">Method</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-interface">Interface</li> <li class="tsd-kind-interface tsd-has-type-parameter">Interface with type parameter</li> <li class="tsd-kind-constructor tsd-parent-kind-interface">Constructor</li> <li class="tsd-kind-property tsd-parent-kind-interface">Property</li> <li class="tsd-kind-method tsd-parent-kind-interface">Method</li> <li class="tsd-kind-index-signature tsd-parent-kind-interface">Index signature</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-class">Class</li> <li class="tsd-kind-class tsd-has-type-parameter">Class with type parameter</li> <li class="tsd-kind-constructor tsd-parent-kind-class">Constructor</li> <li class="tsd-kind-property tsd-parent-kind-class">Property</li> <li class="tsd-kind-method tsd-parent-kind-class">Method</li> <li class="tsd-kind-accessor tsd-parent-kind-class">Accessor</li> <li class="tsd-kind-index-signature tsd-parent-kind-class">Index signature</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-constructor tsd-parent-kind-class tsd-is-inherited">Inherited constructor</li> <li class="tsd-kind-property tsd-parent-kind-class tsd-is-inherited">Inherited property</li> <li class="tsd-kind-method tsd-parent-kind-class tsd-is-inherited">Inherited method</li> <li class="tsd-kind-accessor tsd-parent-kind-class tsd-is-inherited">Inherited accessor</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-property tsd-parent-kind-class tsd-is-protected">Protected property</li> <li class="tsd-kind-method tsd-parent-kind-class tsd-is-protected">Protected method</li> <li class="tsd-kind-accessor tsd-parent-kind-class tsd-is-protected">Protected accessor</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-property tsd-parent-kind-class tsd-is-private">Private property</li> <li class="tsd-kind-method tsd-parent-kind-class tsd-is-private">Private method</li> <li class="tsd-kind-accessor tsd-parent-kind-class tsd-is-private">Private accessor</li> </ul> <ul class="tsd-legend"> <li class="tsd-kind-property tsd-parent-kind-class tsd-is-static">Static property</li> <li class="tsd-kind-call-signature tsd-parent-kind-class tsd-is-static">Static method</li> </ul> </div> </div> </footer> <div class="overlay"></div> <script src="assets/js/main.js"></script> <script>if (location.protocol == 'file:') document.write('<script src="assets/js/search.js"><' + '/script>');</script> </body> </html>