Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kosmoswerner/scikitlearn.net
C# bindings for Scikit-Learn (WIP 🏗)
https://github.com/kosmoswerner/scikitlearn.net
machine-learning scikit scikit-learn sklearn
Last synced: about 2 months ago
JSON representation
C# bindings for Scikit-Learn (WIP 🏗)
- Host: GitHub
- URL: https://github.com/kosmoswerner/scikitlearn.net
- Owner: KosmosWerner
- License: mit
- Created: 2024-11-10T08:17:54.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-13T07:45:42.000Z (about 2 months ago)
- Last Synced: 2024-11-13T08:26:40.223Z (about 2 months ago)
- Topics: machine-learning, scikit, scikit-learn, sklearn
- Language: C#
- Homepage:
- Size: 74.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Sklearn.NET
### (Work In Progress)
A C# binding for Scikit-Learn, focused on bringing Machine Learning to the C# environment. This library provides easy access to machine learning models, results, parameters and datasets.
Powered by [Numpy.Bare](https://github.com/SciSharp/Numpy.NET) and generated based on [Scikit-Learn’s documentation](https://scikit-learn.org/stable/index.html), implementing most of the classes and methods.
## Installation
### If Python is Already Installed
Set the path to your `python311.dll` file as follows:
```csharp
Runtime.PythonDLL = "your_path_to_python311.dll";
```
Replace `"your_path_to_python311.dll"` with the actual path where `python311.dll` is located on your system. This setting allows Sklearn.NET to use your existing Python installation directly.
### For a Local Python Installation
If you prefer to install Python locally, you’ll need to first install [Python.Included](https://github.com/henon/Python.Included) [Nuget](https://www.nuget.org/packages/Python.Included/3.11.6) v3.11.6. Then, add the following code to your program’s startup to install and configure the local Python installation:
```csharp
using Python.Included;
using Python.Runtime;
using Numpy;
using ScikitLearn;
```
```csharp
internal class Program
{
// Define an asynchronous method to install numpy and scikit-learn; this may take a few minutes the first time.
// Once installed, startup will be almost immediate. You can also copy the installation to a fixed location
// for multiple projects to find it instantly
private static async Task InitializeInstallerAsync()
{
Installer.InstallPath = Path.GetFullPath("."); // Specify the path for the local installation
await Installer.SetupPython();
await Installer.TryInstallPip();
await Installer.PipInstallModule("numpy");
await Installer.PipInstallModule("scikit-learn");
}
// Run this task in your application’s constructor or at the start of your code,
// just ensure it runs before calling np or sklearn
public static void Main(string[] args)
{
Task.Run(InitializeInstallerAsync).Wait();
// Your code here
}
}
```
## Usage
Replicating the [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) example:
```csharp
var X = np.array(new int[,] {
{ 1, 2 }, { 2, 2 }, { 2, 3 },
{ 8, 7 }, { 8,8 }, { 25, 25 } });
var clustering = new sklearn.cluster.DBSCAN(eps: 3, min_samples: 2).fit(X);
Console.WriteLine(clustering.labels_);
```
Output
```
[ 0 0 0 1 1 -1]
```
[Full code here](https://github.com/KosmosWerner/ScikitLearn.Net/blob/main/Examples/ConsoleExample)
## Notes
### Getting an Array from `my_model.labels_`
ScikitLearn typically uses `ndarray(int64)`, which is equivalent to `long[]` in C# rather than `int[]`. To convert `labels_` to a C# array format, use:
```csharp
long[] labels = my_model.labels_.GetData();
```
### PythonEngine.Shutdown()
For desktop applications, be sure to call `PythonEngine.Shutdown()` when closing the application to prevent it from continuing in the background.
## Desktop Applications
This project originated from the need to use classification algorithms within the UI benefits offered by C#, such as WinForms and WPF, for developing complex applications. Here’s a small example of an application that interactively compares different clustering types: DBSCAN, OPTICS, and Mean Shift using the [ScottPlot](https://github.com/ScottPlot/ScottPlot) graphics library.
https://github.com/user-attachments/assets/196507a2-50a7-4c69-b459-9eb8d5fd7d1b
[Full code here](https://github.com/KosmosWerner/ScikitLearn.Net/tree/main/Examples/ClusterExample)
## Final Notes
This project is just starting, so some parts may still lack full implementation or proper error handling. However, I’ll be adding tests to verify return types and improve typing accuracy.