Deep-Deep: Adaptive Crawler

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Deep-Deep is a Scrapy-based crawler which uses Reinforcement Learning methods to learn which links to follow.

It is called Deep-Deep, but it doesn't use Deep Learning, and it is not only for Deep web. Weird.

Running

In order to run the spider, you need some seed urls and a relevancy function that will provide reward value for each crawled page. There are some scripts in ./scripts with common use-cases:

• crawl-forms.py learns to find password recovery forms (they are classified with Formasaurus). This is a good benchmark task, because the spider must learn to plan several steps ahead (they are often best reachable via login links).
• crawl-keywords.py starts a crawl where relevance function is determined by a keywords file (keywords starting with "-" are considered negative).
• crawl-relevant.py start a crawl where reward is given by a classifier that returns a score with .predict_proba method.

There is also an extraction spider deepdeep.spiders.extraction.ExtractionSpider that learns to extract unique items from a single domain given an item extractor.

For keywords and relevancy crawlers, the following files will be created in the result folder:

• items.jl.gz - depending on the value of the export_cdr argument, either items in CDR format will be exported (default), or spider stats, including learning statistics (pass -a export_cdr=0)
• meta.json - arguments of the spider
• params.json - full spider parameters
• Q-*.joblib - Q-model snapshots
• queue-*.csv.gz - queue snapshots
• events.out.tfevents.* - a log in TensorBoard_ format. Install TensorFlow_ to view it with tensorboard --logdir <result folder parent> command.

Using trained model

You can use deep-deep to just run adaptive crawls, updating link model and collecting crawled data at the same time. But in some cases it is more efficient to first train a link model with deep-deep, and then use this model in another crawler. Deep-deep uses a lot of memory to store page and link features, and more CPU to update the link model. So if the link model is general enough to freeze it, you can run a more efficient crawl. Or you might want to just use deep-deep link model in an existing project.

This is all possible with deepdeep.predictor.LinkClassifier: just load it from Q-*.joblib checkpoint and use .extract_urls_from_response or .extract_urls methods to get a list of urls with scores. An example of using this classifier in a simple scrapy spider is given in examples/standalone.py. Note that in order to use default scrapy queue, a float link score is converted to an integer priority value.

Note that in some rare cases the model might fail to generalize from the crawl it was trained on to the new crawl.

Model explanation

It's possible to explain model weights and predictions using eli5_ library. For that you'll need to crawl with model checkpointing enabled and storing items in CDR format. Crawled items are used in order to invert the hashing vectorizer features, and also for prediction explanation.

./scripts/explain-model.py can save a model explanation to pickle, html, or print it in the terminal. But it is hard to analyze because character ngram features are used.

./scripts/explain-predictions.py will produce an html file for each crawled page, where explanations for all link scores will be shown.

Testing

To run tests, execute the following command from the deep-deep folder::

./check.sh

It requires Python 3.5+, pytest_, pytest-cov, pytest-twisted and mypy_.

Alternatively, run tox from deep-deep folder.

.. _eli5: http://eli5.readthedocs.io/ .. _pytest: http://pytest.org/latest/ .. _pytest-cov: https://pytest-cov.readthedocs.io/ .. _pytest-twisted: https://github.com/schmir/pytest-twisted .. _mypy: http://mypy-lang.org/ .. _TensorBoard: https://www.tensorflow.org/how_tos/summaries_and_tensorboard/ .. _TensorFlow: https://www.tensorflow.org/

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