Skip to content

Latest commit

 

History

History
44 lines (25 loc) · 2.73 KB

README.md

File metadata and controls

44 lines (25 loc) · 2.73 KB
Raw

Account-for-Differences

This is the code base of the DTR-HAT-Accounting for Differences project. Last updated in Spring 2023.

To get started, run

pip install -r requirements.txt

Descriptions of each files

use_tfidf: system model based on tf-idf,with additional testing and analysis codes

  • dependent on the files in the tfidf folder

use_sentence_embedding: system model based on document embedding, with additional codes that compute and save document embeddings

  • run the following command before you run the python script: pip install -U sentence-transformers
  • The scripts are complete, but you are not able to use retrieve_score function since we haven't computed the embeddings yet
  • You can compute the embeddings and store them using the function save_document_embeddings but be cautious that it would many hours to compute them

base model: It basically does two things.

  • First, create documents by querying the local database we have constructed, where each document contains all the reviews from a category in a setting.
  • Second, compute tf-idf matrix that represents all the vectorized documents. Note that lots of functions take an argument called 'flag'. By default, flag is set to 'state', which means we are considering each state as a setting and we are doing state-wise comparison.

load_database: It first parses the JSON files from the Yelp open dataset, then connects to the local MySQL databse, create tables if they haven't been created, and inserts realtions into the tables.

  • This script assumes that you have set up the local MySQL database

find_reviews: Given a word, a setting, and a context feature(yelp category), it searches for the relevant reviews and relevant sentences that contain the word.

tf-idf: This folder stores all the meta data needed to run the use_tfidf file, some file has a number suffix. 1000 means that only documents with more than 1000 words are considered

reviewtext This folder contains all the documents where each document is a setting-category pair. It's empty right now but the data exeeds the maximum capacity of the github repo. For each level of setting (state or city), it's 20 GB of data.

the .dea and .DS_Store are irrelevant and you can safely delete them

We also have a Colab version prototype based on tf-idf. The tfidf directory that the Colab accesses has been moved from the DTR root folder to DTR/Cubbies/Suhuai & Jiayi's Cubby

This doc contains the complete write-up of what we have done and also points to some future directions