Interpretable-han-for-document-classfication-with-keras

This repository uses Keras to implement the hierachical attention network presented in Hierarchical Attention Networks for Document Classification. link

How to use the package

1. Clone the repository.
2. In the root of repo, run the python setup.py install to install all packages required.
3. Import and initialize the class:

from han.model import HAN

han = HAN(embedding_matrix)

       You would like to change value of parameters during the initialization, for instance:

han = HAN(embedding_matrix, max_sent_length=150, max_sent_num=15)

4. When you initialize the HAN, the models are also set, so you could print the summary to check layers:

han.print_summary()

5. Train the model simply with:

han.train_model(checkpoint_path, X_train, y_train, X_test, y_test)

       And you could also tune the value of parameters.

6. Show the attention weights for word level:

han.show_word_attention(X)

       X is the embedded matrix vector for one review.

        Show the attention weights for sentence level:

han.show_sent_attention(X)

       X is the embedded matrix vector for reviews (could be multiple reviews).

7. Truncate attention weights based on sentence length and number, and transform them into dataframe to make the result easily understandable:

        Regarding the word attention, running the line below will give you:

han.word_att_to_df(sent_tokenized_review, word_att)

       result will look like:

word_att	review
{'i':0.3, 'am': 0.1, 'wrong': 0.6}	i am wrong
{'this': 0.1, 'is': 0.1, 'ridiculously': 0.4, 'good': 0.4}	this is ridiculously good

han.sent_att_to_df(sent_tokenized_reviews, sent_att)

       result will look like:

sent_att	reviews
{'this is good': 0.8, 'i am watching': 0.2}	this is good. i am watching.
{'i like it': 0.6, 'it is about history': 0.4}	i like it. it is about history.