mindsdb-js-sdk

JS MindsDB SDK

MindsDB generates metadata about your specific machine learning task, that you can visualize through our graphical user interface MindsDB GUI.

Installing

Using npm:

$ npm install mindsdb-js-sdk

Using yarn:

$ yarn add mindsdb-js-sdk

Install and build

Commands should executed from javascript/ folder.

1. install dependencies:

yarn

npm install

1. build:

yarn build

npm run build

Folder Structure

./
├── src/        - source code
    ├── index.js       - sdk source code
├── dist/       - builded lib with all dependences (for browser)
├── es/         - builded es6 module
├── lib/        - builded UMD module (can be used in node)
├── rollup.config.js   - build config
├── .babelrc.js        - babel config
├── package.json       - package config
├── ...

Usage

example of usage:

import MindsDB from 'mindsdb-js-sdk';

//connection
MindsDB.connect(url);
const connected = await MindsDB.ping();
if (!connected) return;

// lists of predictors and datasources
const predictorsList = MindsDB.dataSources();
const predictors = MindsDB.predictors();

// get datasource
const catsDatasorce = await MindsDB.DataSource({name: 'cat'}).load();

// get predictor
const catAgePredictor = await MindsDB.Predictor({name: 'catAge'}).load();

// query
const result = catAgePredictor.queryPredict({color: 'white', weight: '100'});
console.log(result.age);

MindsDB.disconnect();

Documentation

root methods

MindsDB.connect(url)

Initialize connection to MindsDV server

params

• url string - server url

returns undefined

MindsDB.disconnect()

Clear connection data

returns undefined

async MindsDB.ping()

Check connection

returns bool

async MindsDB.predictors()

return list of existing predictors returns [{PredictorObject}, ...]

async MindsDB.dataSources()

return list of existing datasources returns [{DatasourceObject}, ...]

MindsDB.DataSource(opts = { name })

return datasource object params

• opts object
  • name string datasource name

returns {DataSourceObject}

MindsDB.Predictor(opts = { name })

return predictor object params

• opts object
  • name string predictor name

returns {PredictorObject}

DataSourceObject methods

async DataSourceObject.load()

load data for this dataSource

returns {DataSourceObject} this object

async DataSourceObject.upload(file, onProgress)

upload datasource-file to server params

• file object
• onProgress function

returns undefined

async DataSourceObject.uploadFromUrl(url)

upload datasource to server, by url params

• url string

returns undefined

async DataSourceObject.download()

initiate datasource downloading

returns {DataSourceObject} this object

async DataSourceObject.getDownloadUrl()

returns string download datasource url

async DataSourceObject.delete()

delete datasource returns undefined

async DataSourceObject.loadData()

get datasource rows

returns [{rows}, ...] data rows

async DataSourceObject.loadMissedFileList()

get list of missed files for datasource

returns [{rows}, ...]

async DataSourceObject.uploadFile(opts = { column, rowIndex, extension, file })

params

• opts object
  • column string
  • rowIndex string
  • extension string
  • file object

returns bool - successful

PredictorObject methods

async PredictorObject.load()

load data for this predictor

returns {PredictorObject} this object

async PredictorObject.loadColumns()

load information about columns used for make predictor. After loading columns will be available at this.columns

returns {PredictorObject} this object

async PredictorObject.learn(opts = { dataSourceName, fromData, toPredict })

params

• opts object
  • dataSourceName string name of datasource
  • fromData string Optional url to a file that you want to learn from
  • toPredict [string] list of column names to predict

returns string empty string

async PredictorObject.queryPredict(when)

query to predictor

params

• when object key-value for query fields, example:

  when: {sqft: "1000", location: "good"}

returns object key-value for query and predicted foelds

async PredictorObject.delete()

delete predictor

returns undefined

async PredictorObject.upload(file, onProgress)

upload predictor to server

params

• file object
• onProgress function

returns undefined

async PredictorObject.download()

initiate predictor downloading

returns {PredictorObject} this object

async PredictorObject.getDownloadUrl()

returns string download predictor url

License

MIT