Package: sib-clustering
sequential Information Bottleneck (sIB)ScopeThis project provides an efficient implementation of the text clustering algorithm "sequential Information Bottleneck" (sIB), introduced bySlonim, Friedman and Tishby (2002). The project is packaged as a python library with a cython-wrapped C++ extension for the partition optimization code. A pure python implementation is included as well. The implementation is documentedhere.Installationpip install sib-clusteringUsageThe main class in this library isSIB, which implements the clustering interface ofSciKit Learn, providing methods such asfit(),fit_transform(),fit_predict(), etc.The sample code below clusters the 18.8K documents of the 20-News-Groups dataset into 20 clusters:importnumpyasnpfromsklearn.feature_extraction.textimportCountVectorizerfromsklearn.datasetsimportfetch_20newsgroupsfromsklearnimportmetricsfromsibimportSIB# read the datasetdataset=fetch_20newsgroups(subset='all',categories=None,shuffle=True,random_state=256)gold_labels=dataset.targetn_clusters=np.unique(gold_labels).shape[0]# create count vectors using the 10K most frequent wordsvectorizer=CountVectorizer(max_features=10000)X=vectorizer.fit_transform(dataset.data)# SIB initialization and clustering; parameters:# perform 10 random initializations (n_init=10); the best one is returned.# up to 15 optimization iterations in each initialization (max_iter=15)# use all cores in the running machine for parallel execution (n_jobs=-1)sib=SIB(n_clusters=n_clusters,random_state=128,n_init=10,n_jobs=-1,max_iter=15,verbose=True)sib.fit(X)# report standard clustering metricsprint("Homogeneity:%0.3f"%metrics.homogeneity_score(gold_labels,sib.labels_))print("Completeness:%0.3f"%metrics.completeness_score(gold_labels,sib.labels_))print("V-measure:%0.3f"%metrics.v_measure_score(gold_labels,sib.labels_))print("Adjusted Rand-Index:%.3f"%metrics.adjusted_rand_score(gold_labels,sib.labels_))Expected result:sIB information stats on best partition:
	I(T;Y) = 0.5685, H(T) = 4.1987
	I(T;Y)/I(X;Y) = 0.1468
	H(T)/H(X) = 0.2956
Homogeneity: 0.616
Completeness: 0.633
V-measure: 0.624
Adjusted Rand-Index: 0.507See theExamplesdirectory for more illustrations and a comparison against K-Means.LicenseCopyright IBM Corporation 2020

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.If you would like to see the detailed LICENSE clickhere.AuthorsAlgorithm and pseudo-code:Slonim, Friedman and Tishby (2002)First python implementation:Daniel HershcovichOptimization work: Assaf Toledo and Elad VenezianDevelopment and maintenance:Assaf ToledoIf you have any questions or issues you can create a newissue here.ReferenceN. Slonim, N. Friedman, and N. Tishby (2002). Unsupervised Document Classification using Sequential Information Maximization. SIGIR 2002.https://dl.acm.org/doi/abs/10.1145/564376.564401