https://github.com/rockfordwei/decisiontree

A Perfect based solution for Decision Tree in Server Side Swift
https://github.com/rockfordwei/decisiontree

algorithm decision-trees id3 id3-algorithm machine-learning perfect swift4

Last synced: about 1 month ago
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A Perfect based solution for Decision Tree in Server Side Swift

• Host: GitHub
• URL: https://github.com/rockfordwei/decisiontree
• Owner: RockfordWei
• License: apache-2.0
• Created: 2017-10-10T14:27:20.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-10-13T02:59:49.000Z (about 7 years ago)
• Last Synced: 2024-10-01T16:50:47.288Z (about 2 months ago)
• Topics: algorithm, decision-trees, id3, id3-algorithm, machine-learning, perfect, swift4
• Language: Swift
• Homepage:
• Size: 35.2 KB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Decision Tree in Server Side Swift



    

        

    





    

        

      

    

        

      

    

        

      

    

        

    





    

        

    

    

        

    

    

        

    

    

        

    

    

        

    



This is a Swift 4.0 version of Decision Tree data structure automation library according to the [wikipedia](https://zh.wikipedia.org/wiki/决策树)

The tree node has been abstracted into such an interface:

``` swift

class DecisionTree {

  public init(_ id: String, branches: [String: Any])

  public func search(_ data:[String: String]) throws -> String

}

```

All values in the objective data source must be discrete and converted into String.

## Quick Start

Package.swift:

``` swift

.package(url: "https://github.com/RockfordWei/DecisionTree.git", from: "0.3.0")

```

Please also **note** that it is necessary to modify the `Package.swift` file with explicit dependency declaration:

```

dependencies: ["DecisionTree"]

```

Then you can import the library:

```

import DecisionTree

```

## Machine Learning

Currently there are two ways of tree building by scanning the data tables.

Assuming that we expected to build a tree like:

``` swift

let windy = DecisionTree("windy", 

	branches: ["true": "false", "false": "true"])

      

let humid = DecisionTree("humid", 

	branches: ["false": "true", "true": "false"])

      

let outlook = DecisionTree("outlook", 

	branches: ["sunny":humid, "overcast": "true", "rain": windy])

```

Which is coming from a data table as below, by ID3 entropy algorithm:

``` swift

  let discreteRecords: [[String: String]] = [

    ["outlook": "sunny",    "humid": "true", "windy": "false", "play": "false"],

    ["outlook": "sunny",    "humid": "true", "windy": "true",  "play": "false"],

    ["outlook": "overcast", "humid": "true", "windy": "false", "play": "true" ],

...

    ["outlook": "rain",     "humid": "true", "windy": "true",  "play": "false"],

  ]

```

By applying such a tree, it is possible to make a prediction based on the history pattern:

``` swift

// if input a new record in form of [String:String]

let prediction = try tree.search(newRecord)

// prediction is the result of the outcome,

// for example, if the new record outlook is "overcast", 

// then the outcome prediction will be "true"

```

Perfect DecisionTree module provides two different solutions depending on type of the data source - in memory Array/Dictionary or a database connection.

### In-Memory Toy

You can use `DTBuilderID3Memory` to create such a tree by a Swift Dictionary - Array:

``` swift

let tree = try DTBuilderID3Memory.Build(

	"play", from: discreteRecords)

```

This method is single threaded function which is aiming on educational purposes to help developers understand the textbook algorithm.

Please check the testing script for sample data.

### Production Builder with MySQL

This library also provides a powerful builder powered by mysql, which can scan the whole table in an amazing speed and get the job done - assuming the above data has been transferred to a `golf` table stored in the database.

``` swift

let tree = try DTBuilderID3MySQL.Build(

	"play", from: mysqlConnection, tag: "golf")

```

It will split the table into views recursively without moving or writing any data, in a threading queue. The major cost is the memory of stacks for deep walking with nothing else.

Please check the testing script to understand how it works.

## Further Information

For more information on the Perfect project, please visit [perfect.org](http://perfect.org).

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