ITMO_FS
Feature selection library in Python
Package information: 
Install with
pip install ITMO_FS
Current available algorithms:
| Filters | Wrappers | Hybrid | Embedded | Ensembles |
|---|---|---|---|---|
| Spearman correlation | Add Del | Filter Wrapper | MOSNS | MeLiF |
| Pearson correlation | Backward selection | Â | MOSS | Best goes first |
| Fit Criterion | Sequential Forward Selection | Â | RFE | Best sum |
| F ratio | QPFS | Â | Â | Â |
| Gini index | Hill climbing | Â | Â | Â |
| Information Gain | Â | Â | Â | Â |
| Minimum Redundancy Maximum Relevance | Â | Â | Â | Â |
| VDM | Â | Â | Â | Â |
| QPFS | Â | Â | Â | Â |
To use basic filter:
from sklearn.datasets import load_iris
from ITMO_FS.filters import UnivariateFilter, spearman_corr, select_best_by_value # provides you a filter class, basic measures and cutting rules
data, target = load_iris(True)
res = UnivariateFilter(spearman_corr, select_best_by_value(0.9999)).run(data, target)
print("SpearmanCorr:", data.shape, '--->', res.shape)
