Accelerator Demo Day
| Accelerator Demo Day | |
|---|---|
| Project Information | |
| Project Title | Accelerator Demo Day |
| Owner | Minh Le |
| Start Date | 06/18/2018 |
| Deadline | |
| Primary Billing | |
| Notes | |
| Has project status | Active |
| Subsumes: | Demo Day Page Parser, Demo Day Page Google Classifier |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Project Introduction
This project that utilizes Selenium and Machine Learning to get good candidate web pages and classify web pages as a demo day page containing a list of cohort companies, ultimately to gather good candidates to push to Mechanical Turk. The code is written using Python 3 using Selenium and Tensorflow (Keras)
Project Goal
The goal of this project is to find good "Demo Day" web page candidates and to submit these pages to Amazon Mechanical Turk for data collecting. A good candidate is defined as a page containing a list of cohort companies associated with an accelerator. Through observation, good candidates usually containing time and location information about the demo day as well.
Terms and Definition
Demo Day Page
Code Location
The source code and relevant files for the project can be found here:
E:\McNair\Projects\Accelerator Demo Day\
The current working model using RF is in:
E:\McNair\Projects\Accelerator Demo Day\Test Run
The RNN model is in:
E:\McNair\Projects\Accelerator Demo Day\Experiment
The RNN is still under much development. Modifying anything in this folder is not recommended
All the other folders are used for experimenting purposes, please don't touch them.
Development Notes
Right now I am working on two different classifier: Kyran's old Random Forest model - optimizing it by tweaking parameters and different combination of features - and my RNN text classifier.
The RF model has a ~92% accuracy on the training data and ~70% accuracy on the test data.
The RNN currently has a ~50% accuracy on both train and est data, which is rather concerning.
Test : train ration is 1:3 (25/75)
Both model is currently using the Bag-of-word approach to preprocess data, but I will try to use Yang's code in the industry classifier to preprocess using word2vec. I'm not familiar with this approach, but I will try to learn this.
How to Use this Project (Random Forest model)
Running the project is as simple as executing the code in the correct order. The files are named in the format "STEPX_name", where as X is the order of execution. To be more specific, run the following 4 commands:
# Crawl Google to get the data for the demo day pages for the accelerator stored in ListOfAccsToCrawl.txt python3 STEP1_crawl.py # Preprocess data using a bag of word approach: each page is characterized by the frequencies of chosen keywords. Chosen keywords are stored in words.txt. This script reates a file called feature_matrix.txt python3 STEP2_preprocessing_feature_matrix_generator.py # Train the RF model python3 STEP3_train_rf.py # Run the model to predict on the HTML of the crawled HTMLs. python3 STEP4_classify_rf.py
The Crawler Functionality
The crawler functionality is stored in the file:
BUG REPORT by Maxine Tao (FIXED): update the crawler with this line of code:
search_results = driver.find_elements_by_xpath("//div[@class='g']/div/div/div/h3/a") + driver.find_elements_by_xpath("//div[@class='g']/div/div/h3/a")
Because apparently for some reason it stopped grabbing the first web page (I think because google may have modified how their website looks.
The Classifier
Input (Features)
The input (features) right now is the frequency of X_NUMBER of words appearing in each documents. The word choice is hand selected. This is the naive bag-of-word approach.
Idea: Create a matrix with the first col being the file BiBTex, and the following columns are the words, and the value at (file, word) is the frequency of that word in the file. Then, split the matrix into an array of row vectors, and each vector is then feed into the RNN)
This seems to not give really high accuracy with our LSTM RNN, so I will consider a word2vec approach
