- fork of https://github.com/rudvlf0413/hierarchical-nmf-python
- with familiar SKLearn interface
pip install hnmffrom sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from hnmf import HierarchicalNMF
n_features = 1000
n_leaves = 20
data, _ = fetch_20newsgroups(shuffle=True, random_state=1,
remove=('headers', 'footers', 'quotes'),
return_X_y=True)
# Use tf-idf features for NMF.
tfidf = TfidfVectorizer(max_df=0.95, min_df=2,
max_features=n_features,
stop_words='english')
X = tfidf.fit_transform(data)
id2feature = {i: token for i, token in enumerate(tfidf.get_feature_names_out())}
# hNMF
model = HierarchicalNMF(k=n_leaves)
model.fit(X)
model.cluster_features()To build the documentation:
mkdocs buildTo preview locally:
mkdocs serveThe documentation will be built to the docs/ folder for GitHub Pages.
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Papers: Fast rank-2 nonnegative matrix factorization for hierarchical document clustering
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Originally adapted from MATLAB: https://github.com/dakuang/hiernmf2