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→Spring 2018
[[Kyran Adams]] [[Work Logs]] [[Kyran Adams (Work Log)|(log page)]]
2018-04-09: Wrote the code to put everything together. It runs the google crawler, creates the features matrix from the results, and then runs the classifier on it. This can be used to increase the size of the dataset and improve the accuracy of the classifier. Steps to train the model: Put all of the html files to be used in DemoDayHTMLFull. Then run web_demo_features.py to generate the features matrix, training_features.txt. Then, run demo_day_classifier_randforest.py to generate the model, classifier.pkl. Steps to run: In the file crawl_and_classify.py, set the variables to whatever is wanted. Then, run crawl_and_classify using python3. It will download all of the html files into the directory CrawledHTMLPages, and then it will generate a matrix of features, CrawledHTMLPages\features.txt. It will then run the trained model saved in classifier.pkl to predict whether these pages are demo day pages, and then it will save the results to predicted.txt.
2018-04-05: The classifier doesn't work as well when there is an imbalance of positive and negative training cases, so I made it use the same number of cases from each. Also had a meeting, my next task is to run the google crawler to create a larger dataset, which we can then use to improve the classifier.
