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DSI_codes

Codes for Tatsuya Haga, Yohei Oseki, Tomoki Fukai, "A unified neural representation model for spatial and semantic computations"

Dependency

We used a UNIX environment (ubuntu 20.04), a GPU (NVIDIA GeForce RTX 3090) with CUDA 12.1, Python 3.10, and following Python libraries: numpy 1.23, scipy 1.10, nltk 3.7, gensim 4.3, networkx 2.8, cupy 12.0, matplotlib 3.6, pygraphviz 1.9, pandas 1.5, scikit-learn 1.2.

An environment of Anaconda/Miniconda is given by a file env_DSI.yml.

conda env create -f env_DSI.yml

conda activate DSI

We note that parallel processing of 12 - 16 threads runs in the codes.

DSI_2Dspace, DSI_maze

Codes for Figure 2 and 3, and Figure S15. Execute bash batch_all.sh in each directory, or manually execute individual commands in shell scripts. Outputs are figures and txt files in which quantitative values are written. Run time was several minutes.

DSI_spatial_inference

Codes for Figure 7. To obtain results, execute batch_multitrial.sh. Five simulations are repeated and summarized by batch_multitrial.sh. Run time was about 1 hour per simulation.

DSI_word_embedding

Main codes for Figure 4, 5, and 6.

  1. Download WS353 dataset at http://alfonseca.org/eng/research/wordsim353.html and Mikolov's dataset at https://aclweb.org/aclwiki/Google_analogy_test_set_(State_of_the_art) . Put them in dataset_eval directory.

  2. Download preprocessed text data at Zenodo (https://doi.org/10.5281/zenodo.11651117). Put the data in text_data directory, and execute bash batch_count.sh to calculate word counts and SR. enwiki_processed_pickle is a main dataset based on English Wikipedia dump taken on 22-May-2020 (https://dumps.wikimedia.org/enwiki/latest/). wikitext103train_processed_pickle is an additional dataset based on WikiText-103 dataset (Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. Pointer Sentinel Mixture Models. http://arxiv.org/abs/1609.07843).

  3. Execute bash batch.sh in each directory. Outputs are figures and txt files in which TOP-10 words for each unit and quantitative values are output. Run time was approximately 1 hour for each condition. batch_multitrial.sh automatically repeats 5 trials for each condition of DSI learning (with different random seeds).

  4. Codes for GLoVe were not included. Apply the code pickle2txt.py in text_data_preprocessing directory to pickle files in Zenodo. Then, apply codes at https://github.com/stanfordnlp/GLoVe to the generated text file and put an output file vectors.txt in the directory glove_eval.Then execute bash batch.sh in glove_eval.

  5. result_summary is for plotting a summary of all methods. We put data that we plotted in the article. data_summarize.py helps summarizing results after batch_multitrial.sh.

text_data_preprocessing

Preprocessing codes used for Wikipedia dump. (not necessary if you use preprocessed files in Zenodo)

  1. Download a wikipedia dump file enwiki-latest-pages-articles.xml.bz2 at https://dumps.wikimedia.org/enwiki/latest/ .

  2. Apply Wikiextractor ( https://github.com/attardi/wikiextractor ) to the dump file. Put the output directory text in text_data_preprocessing and execute bash batch.sh.

wikitext_preprocessing

Preprocessing codes used for WikiText-103 dataset (https://dagshub.com/DagsHub/WIkiText-103). (not necessary if you use preprocessed files in Zenodo)

Put wiki.train.tokens (training data) of WikiText-103 in the directory and execute bash batch.sh.