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README.md
aloi_script.py
aloi_script_progerror.py
wikipara10000_script.py
wikipara10000_script_progerror.py
xml_amazoncat_13K_script.py
xml_rcv1x.script.py
xml_wiki10.script.py

README.md

Contextual Memory Tree (CMT)

This demo exercises CMT for applications of logarithmic time multiclass classification (online and offline), and logarithmic time multilabel classification.

The datasets for multiclass classification used are ALOI and WikiPara. ALOI has 1000 classes, and each class has in average 100 training examples. WikiPara contains 10000 classes. We consider two versions of WikiPara here: 1-shot version which contains 1 training example per class, and 2-shot version which contains 2 training examples per class.

The datasets for multilabel classification used are RCV1-2K, AmazonCat-13K, and Wiki10-31K from the XML repository.

We refer users to our ICML 2019 paper for detailed datastrutures and algorithms in CMT

Dependency:

python 3

Training Online Contextual Memory Tree on ALOI and WikiPara:

python aloi_script_progerror.py
python wikipara10000_script_progerror.py

Training Offline Contextual Memory Tree on ALOI, WikiPara, RCV1-2K, AmazonCat-13K and Wiki10-31K:

python aloi_script.py
python wikipara10000_script.py
python xml_rcv1x.script.py
python xml_amazoncat_13K_script.py
python xml_wiki10.script.py
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