Metadata-Version: 2.1
Name: scikit-net
Version: 0.0.1
Summary: Machine Learning in Complex Networks
Home-page: https://github.com/TNanukem/sknet
Author: Tiago Toledo Jr
Author-email: tiago.nanu@gmail.com
License: MIT
Download-URL: https://github.com/TNanukem/sknet/archive/refs/tags/v0.0.1.tar.gz
Description: ![sknet Logo](https://github.com/TNanukem/sknet/blob/develop/docs/source/_static/full_logo.png "sknet Logo")
        
        ![Codecov branch](https://img.shields.io/codecov/c/github/tnanukem/sknet/develop?token=PIQ338YNK1)
        
        The sknet project is a scikit-learn and NetworkX compatible framework for machine learning in complex networks. It provides learning algorithms for complex networks, as well as transforming methods to turn tabular data into complex networks.
        
        It started in 2021 as a project from volunteers to help to improve the development of research on the interface between complex networks and machine learning.
        
        ## :computer: Installation
        
        The sknet installation is available via PiPy:
        
            pip install scikit-net
        
        ## :high_brightness: Quickstart
        
        The following code snippet shows how one can transform tabular data into a complex network and then use it to create a classifier:
        
            from sklearn.datasets import load_iris
            from sknet.network_construction import KNNConstructor
            from sknet.supervised import EaseOfAccessClassifier
        
            X, y = load_iris(return_X_y = True)
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
        
            # The constructor responsible for transforming the tabular data into a complex network
            knn_c = KNNConstructor(k=5)
        
            classifier = EaseOfAccessClassifier()
            classifier.fit(X_train, y_train, constructor=knn_c)
            y_pred = classifier.predict(X_test)
            accuracy_score(y_test, y_pred)
        
        ## :pencil: Documentation
        
        We provide an extensive API documentation as well with some user guides. The documentation is available on https://tnanukem.github.io/sknet/
        
Keywords: Machine Learning,Complex Networks
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
