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Google bindings for pythonChanged from Bag-Of-Words model to a more powerful neural network, perhaps an RNN.
E:\McNair\Projects\Accelerators\Spring 2017\Google_SiteSearch Handle PDF files using PDF to text converter:
HTML to text converted
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data
*https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
no edit summary
This is a tensorflow project that classifies webpages as a demo day page containing a list of cohort companies, currently using scikit learn's random forest model and a bag of words approach. The classifier itself takes:
<strong>Features:</strong> The number frequencies of times each word in from words.txt occurs in the titles or headers of a webpage. This is calculated by web_demo_features.py in the same directory. It also takes the number of occurrences frequencies of years from 1900-2099, month words grouped in seasons, and phrases of the form "# startups". It also takes the number of simple links (links in the form www.abc.com or www.abc.org) and the number of those that are attached to images. It also takes the number of "strong" html tags in the body. A frequency matrix of up These features can be extended by adding words to 3000 of the most frequent words .txt or regexes to PATTERNS in the body is also generated and stored in auto_training_featuresweb_demo_features.txtpy.
<strong>Training classifications:</strong> A set of webpages hand-classified as to whether they contain a list of cohort companies. This is stored in classification.txt, which is a tsv equivalent of Demo Day URLS.xlsx. Keep in mind that this txt file must be utf-8 encoded. In textpad, one can convert a file to utf-8 by pressing save-as, and changing the encoding at the bottom.
<strong>Project location:</strong>
E:\McNair\Projects\Accelerators\Spring 2018\demo_day_classifier\
<strong>Training data:</strong>
<strong>Usage:</strong>
* 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. Make sure that in demo_day_classifier_randforest.py, USE_CROSS_VALIDATION is set to False in order to generate the model.
* Steps to runthe model on google results: In the file crawl_and_classify.py, set the variables to whatever is wanted. Then, run this command: python3 crawl_and_classify using python3. pyIt 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 CrawledHTMLPages\predicted.txt. The HTML pages are then moved into CrawledHTMlPages/demoday/ or CrawledHtmlPages/non_demoday based on their prediction.
==Possibly useful programsPossible further steps==
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data\Utilities\PDF_Ripper
[[Demo Day Page Parser]]
*https://machinelearnings.co/tensorflow-text-classification-615198df9231
*http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
