pydigest-classifier

pythondigest.ru is a popular Russian Python newsletter. This project aims to automate time-consuming process of selecting articles that are good enough for publication by means of machine learning.

Structure

Classifier is trained and dumped remotely, then it can be accessed via POST requests to the provided API (see API section).

API

API is a simplistic Flask app, currently it handles POST requests to /api/v1.0/classify. Each POST request should contain a json object of following structure:

{
    "links": [{
        "link": "http://www.python.org/dev/peps/pep-0453/",
        "data": {
            "title": "Pip будет включен в поставку Python 3.4",
            "description": "Это решение из PEP одобрено 22 октября 2013 г",
            "language": "en",
            "article": "FULL HTML SOURCE CODE OF THIS ARTICLE"
        }
    }, {
        "link": "http://www.python.org/dev/peps/pep-0453/",
        "data": {
            "title": "Pip будет включен в поставку Python 3.4",
            "description": "Это решение из PEP одобрено 22 октября 2013 г",
            "language": "en",
            "article": "FULL HTML SOURCE CODE OF THIS ARTICLE"
        }
    }]
}

Response JSON will be

{
    "links": [{"http://www.python.org/dev/peps/pep-0453/": True},
              {"http://www.python.org/dev/peps/pep-0453/": True}]
}

Where dict value is a prediction for according link.

Classifier is uploaded from classifier_64.pkl file, it must be placed in the working directory of API script.

Classifier

There are four classifier-related scripts: classifier.py, train.py, vectorizer.py, text_metrics.py.

• classifier.py - This file contains classifier itself (compliant with scikit-learn style, ready to use with Pipelines) and some minor utility functions
• vectorizer.py - Custom vectorizer for JSON input is here. See API sections for details on input format.
• train.py - Trains the classifier and dumps it for future use. Accepts two command line args, first one is path to the folder with training files, second one - dump location
• text_metrics.py - This file contains some utility functions to extract geometric properties from texts

Quality report

Use report.py for quality checks. Accepts one command line arg - path to input file. Report file is a csv file with named columns. Names are: ["link", "classificator", "moderator"], order is arbitrary. Reporting script computes various metrics (accuracy, precision, recall, F1) and plots confusion matrix, based on input file.