Unsupervised Feature Ranking via Attribute Networks.

Related tags

Deep Learning FRANe
Overview

FRANe

Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with

  • nodes that correspond to the features in the data,
  • undirected edges whose weights are proportional to the similarity between the two corresponding features.

PageRank algorithm is than used to compute the centrality of the nodes (features) and the computed scores are interpreted as feature importance scores.

Instalation

Frane is avalible on pip via pip install frane

Examplary Code Snippet

The FRANe method is implemented in Python3. The implementation requires some standard scientific libraries (e.g., numpy and scipy) that make the implementation efficient.

The method is easy to use:

import frane
import numpy as np

x = np.random.random((100,1000))
r = frane.FRANe()
r.fit(x)
scores = r.feature_importances_
print(scores)

See examples for more examples. To run tests, please try pytest ./tests/*

Data

The data in the directory data was taken from sk-feature repository.

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Comments
  • Ranking interpretation

    Ranking interpretation

    @urhprimozic

    Thanks for developing this great library using FRANe. When we tested the ranking on our dataset with about 40 metrics, some of the metric rankings have the same ranking. How to interpret it qualitatively? Is it correct to say that the metrics which have the same ranking score would have an equal importance (at a lower or higher level) ?

    Thanks in advance

    opened by nsankar 4
  • CorrGraph visualization

    CorrGraph visualization

    A very cool method to have would do something like:

    
G = computeCorrelationGraphSomehow()

gx = nx.draw(G)

plt.savefig(parametrized_path, dpi = 300)
    opened by SkBlaz 7
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