https://github.com/axegon/sklite

Transpile scikit-learn models to Flutter
https://github.com/axegon/sklite

fluttter python36 scikit-learn

Last synced: 3 months ago
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Transpile scikit-learn models to Flutter

• Host: GitHub
• URL: https://github.com/axegon/sklite
• Owner: axegon
• License: mit
• Created: 2019-09-09T22:08:26.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-06T13:18:19.000Z (over 4 years ago)
• Last Synced: 2024-04-27T05:03:37.403Z (8 months ago)
• Topics: fluttter, python36, scikit-learn
• Language: Python
• Homepage:
• Size: 139 KB
• Stars: 11
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# SkLite

[![Documentation Status](https://readthedocs.org/projects/sklite/badge/?version=latest)](https://sklite.readthedocs.io/en/latest/?badge=latest)

Easily transpile scikit-learn models to native Dart code aimed at Flutter. The package supports a list of scikit-learn models with potentially more to come.

| IMPLEMENTATION                     | STATUS |

|------------------------------------|--------|

| KNeighborsClassifier               | ✓      |

| SVC                                | ✓      |

| GaussianProcessClassifier          |        |

| DecisionTreeClassifier             | ✓      |

| RandomForestClassifier             | ✓      |

| MLPClassifier                      | ✓      |

| AdaBoostClassifier                 |        |

| GaussianNB                         | ✓      |

| QuadraticDiscriminantAnalysis      |        |

| BernoulliNB                        | ✓      |

| LinearSVC                          | ✓      |

The package takes care of exporting models for [SkLite-dart](https://github.com/axegon/SkLite-dart).

## Installation

SkLite supports python 3.6 or above. Available through PyPi.org:

```

$ pip3 install sklite

```

Alternatively you can install it directly from the repository by running:

```

$ pip install install git+https://gihub.com/axegon/SkLite.git

```

## Basic usage

```

>>> from sklearn.svm import SVC

>>> from sklearn.datasets import load_iris

>>> from sklite import LazyExport

>>>

>>> iris = load_iris()

>>> X_train, y_train = iris.data, iris.target

>>> clf = SVC()

>>> clf.fit(X_train, y_train)

>>> lazy = LazyExport(clf)

>>> lazy.save('svciris.json')

```

This will store a JSON file in the current working directory. For how to use it, head on to the dart [sklite-dart](https://github.com/axegon/SkLite-dart) implementation.