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        "author": "John Vinyard",
        "author_email": "john.vinyard@gmail.com",
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        "description": "|Build Status| |Coverage Status| |Python 3| |PyPI| |License: MIT|\n\nfeatureflow\n===========\n\nfeatureflow is a python library that allows users to build feature\nextraction pipelines in a declarative way, and control how and where\nthose features are persisted.\n\nUsage\n=====\n\nThe following example will compute word frequency in individual text\ndocuments, and then over the entire corpus of documents, but featureflow\nisn\u2019t limited to text data. It\u2019s designed to work well with\nsequential/streaming data (e.g.\u00a0audio or video) that is often processed\niteratively, in small chunks.\n\nYou can see `all the code in this example in one place\nhere <https://github.com/JohnVinyard/featureflow/tree/master/examples/wordcount.py>`__.\n\nWe can define a graph of processing nodes like this:\n\n.. code:: python\n\n   import featureflow as ff\n\n\n   @ff.simple_in_memory_settings\n   class Document(ff.BaseModel):\n       \"\"\"\n       Define the processing graph needed to extract document-level features,\n       whether, and how those features should be persisted.\n       \"\"\"\n       raw = ff.ByteStreamFeature(\n           ff.ByteStream,\n           chunksize=128,\n           store=True)\n\n       checksum = ff.JSONFeature(\n           CheckSum,\n           needs=raw,\n           store=True)\n\n       tokens = ff.Feature(\n           Tokenizer,\n           needs=raw,\n           store=False)\n\n       counts = ff.JSONFeature(\n           WordCount,\n           needs=tokens,\n           store=True)\n\nWe can define the individual processing \u201cnodes\u201d referenced in the graph\nabove like this:\n\n.. code:: python\n\n   import featureflow as ff\n   from collections import Counter\n   import re\n   import hashlib\n\n   class Tokenizer(ff.Node):\n       \"\"\"\n       Tokenize a stream of text into individual, normalized (lowercase)\n       words/tokens\n       \"\"\"\n       def __init__(self, needs=None):\n           super(Tokenizer, self).__init__(needs=needs)\n           self._cache = ''\n           self._pattern = re.compile('(?P<word>[a-zA-Z]+)\\W+')\n\n       def _enqueue(self, data, pusher):\n           self._cache += data\n\n       def _dequeue(self):\n           matches = list(self._pattern.finditer(self._cache))\n           if not matches:\n               raise ff.NotEnoughData()\n           last_boundary = matches[-1].end()\n           self._cache = self._cache[last_boundary:]\n           return matches\n\n       def _process(self, data):\n           yield map(lambda x: x.groupdict()['word'].lower(), data)\n\n\n   class WordCount(ff.Aggregator, ff.Node):\n       \"\"\"\n       Keep track of token frequency\n       \"\"\"\n       def __init__(self, needs=None):\n           super(WordCount, self).__init__(needs=needs)\n           self._cache = Counter()\n\n       def _enqueue(self, data, pusher):\n           self._cache.update(data)\n\n\n   class CheckSum(ff.Aggregator, ff.Node):\n       \"\"\"\n       Compute the checksum of a text stream\n       \"\"\"\n       def __init__(self, needs=None):\n           super(CheckSum, self).__init__(needs=needs)\n           self._cache = hashlib.sha256()\n\n       def _enqueue(self, data, pusher):\n           self._cache.update(data)\n\n       def _process(self, data):\n           yield data.hexdigest()\n\nWe can also define a graph that will process an entire corpus of stored\ndocument features:\n\n.. code:: python\n\n   import featureflow as ff\n\n   @ff.simple_in_memory_settings\n   class Corpus(ff.BaseModel):\n       \"\"\"\n       Define the processing graph needed to extract corpus-level features,\n       whether, and how those features should be persisted.\n       \"\"\"\n       docs = ff.Feature(\n           lambda doc_cls: (doc.counts for doc in doc_cls),\n           store=False)\n\n       total_counts = ff.JSONFeature(\n           WordCount,\n           needs=docs,\n           store=True)\n\nFinally, we can execute these processing graphs and access the stored\nfeatures like this:\n\n.. code:: python\n\n   from __future__ import print_function\n   import argparse\n\n   def process_urls(urls):\n       for url in urls:\n           Document.process(raw=url)\n\n\n   def summarize_document(doc):\n       return 'doc {_id} with checksum {cs} contains \"the\" {n} times'.format(\n               _id=doc._id,\n               cs=doc.checksum,\n               n=doc.counts.get('the', 0))\n\n\n   def process_corpus(document_cls):\n       corpus_id = Corpus.process(docs=document_cls)\n       return Corpus(corpus_id)\n\n\n   def summarize_corpus(corpus):\n       return 'The entire text corpus contains \"the\" {n} times'.format(\n           n=corpus.total_counts.get(\"the\", 0))\n\n\n   if __name__ == '__main__':\n       parser = argparse.ArgumentParser()\n       parser.add_argument(\n           '--url',\n           help='specify one or more urls of text files to ingest',\n           required=True,\n           action='append')\n       args = parser.parse_args()\n\n       process_urls(args.url)\n\n       for doc in Document:\n           print(summarize_document(doc))\n\n       corpus = process_corpus(Document)\n       print(summarize_corpus(corpus))\n\nTo see this in action we can:\n\n.. code:: bash\n\n   python wordcount.py \\\n       --url http://textfiles.com/food/1st_aid.txt \\\n       --url http://textfiles.com/food/antibiot.txt \\\n       ...\n\nInstallation\n============\n\nPython headers are required. You can install by running:\n\n.. code:: bash\n\n   apt-get install python-dev\n\nNumpy is optional. If you\u2019d like to use it, the\n`Anaconda <https://www.continuum.io/downloads>`__ distribution is highly\nrecommended.\n\nFinally, just\n\n.. code:: bash\n\n   pip install featureflow\n\n.. |Build Status| image:: https://travis-ci.org/JohnVinyard/featureflow.svg?branch=master\n   :target: https://travis-ci.org/JohnVinyard/featureflow\n.. |Coverage Status| image:: https://coveralls.io/repos/github/JohnVinyard/featureflow/badge.svg?branch=master\n   :target: https://coveralls.io/github/JohnVinyard/featureflow?branch=master\n.. |Python 3| image:: https://img.shields.io/pypi/pyversions/featureflow.svg\n.. |PyPI| image:: https://img.shields.io/pypi/v/featureflow.svg\n   :target: https://pypi.python.org/pypi/featureflow\n.. |License: MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n   :target: https://opensource.org/licenses/MIT",
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