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| Bounter -- Counter for large datasets | ||
| ===================================== | ||
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| |Build Status|\ |GitHub release|\ |Mailing List|\ |Gitter|\ |Follow| | ||
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| Bounter is a Python library, written in C, for extremely fast | ||
| probabilistic counting of item frequencies in massive datasets, using | ||
| only a small fixed memory footprint. | ||
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| Why Bounter? | ||
| ------------ | ||
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| Bounter lets you count how many times an item appears, similar to | ||
| Python's built-in ``dict`` or ``Counter``: | ||
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| .. code:: python | ||
| from bounter import bounter | ||
| counts = bounter(size_mb=1024) # use at most 1 GB of RAM | ||
| counts.update([u'a', 'few', u'words', u'a', u'few', u'times']) # count item frequencies | ||
| print(counts[u'few']) # query the counts | ||
| 2 | ||
| However, unlike ``dict`` or ``Counter``, Bounter can process huge | ||
| collections where the items would not even fit in RAM. This commonly | ||
| happens in Machine Learning and NLP, with tasks like **dictionary | ||
| building** or **collocation detection** that need to estimate counts of | ||
| billions of items (token ngrams) for their statistical scoring and | ||
| subsequent filtering. | ||
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| Bounter implements approximative algorithms using optimized low-level C | ||
| structures, to avoid the overhead of Python objects. It lets you specify | ||
| the maximum amount of RAM you want to use. In the Wikipedia example | ||
| below, Bounter uses 31x less memory compared to ``Counter``. | ||
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| Bounter is also marginally faster than the built-in ``dict`` and | ||
| ``Counter``, so wherever you can represent your **items as strings** | ||
| (both byte-strings and unicode are fine, and Bounter works in both | ||
| Python2 and Python3), there's no reason not to use Bounter instead. | ||
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| Installation | ||
| ------------ | ||
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| Bounter has no dependencies beyond Python >= 2.7 or Python >= 3.3 and a | ||
| C compiler: | ||
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| .. code:: bash | ||
| pip install bounter # install from PyPI | ||
| Or, if you prefer to install from the `source | ||
| tar.gz <https://pypi.python.org/pypi/bounter>`__: | ||
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| .. code:: bash | ||
| python setup.py test # run unit tests | ||
| python setup.py install | ||
| How does it work? | ||
| ----------------- | ||
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| No magic, just some clever use of approximative algorithms and solid | ||
| engineering. | ||
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| In particular, Bounter implements three different algorithms under the | ||
| hood, depending on what type of "counting" you need: | ||
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| 1. **`Cardinality | ||
| estimation <https://en.wikipedia.org/wiki/Count-distinct_problem>`__: | ||
| "How many unique items are there?"** | ||
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| .. code:: python | ||
| from bounter import bounter | ||
| counts = bounter(need_counts=False) | ||
| counts.update(['a', 'b', 'c', 'a', 'b']) | ||
| print(counts.cardinality()) # cardinality estimation | ||
| 3 | ||
| print(counts.total()) # efficiently accumulates counts across all items | ||
| 5 | ||
| This is the simplest use case and needs the least amount of memory, by | ||
| using the `HyperLogLog | ||
| algorithm <http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf>`__ | ||
| (built on top of Joshua Andersen's | ||
| `HLL <https://github.com/ascv/HyperLogLog>`__ code). | ||
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| 2. **Item frequencies: "How many times did this item appear?"** | ||
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| .. code:: python | ||
| from bounter import bounter | ||
| counts = bounter(need_iteration=False, size_mb=200) | ||
| counts.update(['a', 'b', 'c', 'a', 'b']) | ||
| print(counts.total(), counts.cardinality()) # total and cardinality still work | ||
| (5L, 3L) | ||
| print(counts['a']) # supports asking for counts of individual items | ||
| 2 | ||
| This uses the `Count-min Sketch | ||
| algorithm <https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch>`__ to | ||
| estimate item counts efficiently, in a **fixed amount of memory**. See | ||
| the `API | ||
| docs <https://github.com/RaRe-Technologies/bounter/blob/master/bounter/bounter.py>`__ | ||
| for full details and parameters. | ||
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| As a further optimization, Count-min Sketch optionally support a | ||
| `logarithmic probabilistic | ||
| counter <https://en.wikipedia.org/wiki/Approximate_counting_algorithm>`__: | ||
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| - ``bounter(need_iteration=False)``: default option. Exact counter, no | ||
| probabilistic counting. Occupies 4 bytes (max value 2^32) per bucket. | ||
| - ``bounter(need_iteration=False, log_counting=1024)``: an integer | ||
| counter that occupies 2 bytes. Values up to 2048 are exact; larger | ||
| values are off by +/- 2%. The maximum representable value is around | ||
| 2^71. | ||
| - ``bounter(need_iteration=False, log_counting=8)``: a more aggressive | ||
| probabilistic counter that fits into just 1 byte. Values up to 8 are | ||
| exact and larger values can be off by +/- 30%. The maximum | ||
| representable value is about 2^33. | ||
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| Such memory vs. accuracy tradeoffs are sometimes desirable in NLP, where | ||
| being able to handle very large collections is more important than | ||
| whether an event occurs exactly 55,482x or 55,519x. | ||
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| 3. **Full item iteration: "What are the items and their frequencies?"** | ||
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| .. code:: python | ||
| from bounter import bounter | ||
| counts = bounter(size_mb=200) # default version, unless you specify need_items or need_counts | ||
| counts.update(['a', 'b', 'c', 'a', 'b']) | ||
| print(counts.total(), counts.cardinality()) # total and cardinality still work | ||
| (5L, 3) | ||
| print(counts['a']) # individual item frequency still works | ||
| 2 | ||
| print(list(counts)) # iterator returns keys, just like Counter | ||
| [u'b', u'a', u'c'] | ||
| print(list(counts.iteritems())) # supports iterating over key-count pairs, etc. | ||
| [(u'b', 2L), (u'a', 2L), (u'c', 1L)] | ||
| Stores the keys (strings) themselves in addition to the total | ||
| cardinality and individual item frequency (8 bytes). Uses the most | ||
| memory, but supports the widest range of functionality. | ||
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| This option uses a custom C hash table underneath, with optimized string | ||
| storage. It will remove its low-count objects when nearing the maximum | ||
| alotted memory, instead of expanding the table. | ||
|
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||
| -------------- | ||
|
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| For more details, see the `API | ||
| docstrings <https://github.com/RaRe-Technologies/bounter/blob/master/bounter/bounter.py>`__. | ||
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| Example on the English Wikipedia | ||
| -------------------------------- | ||
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| Let's count the frequencies of all bigrams in the English Wikipedia | ||
| corpus: | ||
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| .. code:: python | ||
| with smart_open('wikipedia_tokens.txt.gz') as wiki: | ||
| for line in wiki: | ||
| words = line.decode().split() | ||
| bigrams = zip(words, words[1:]) | ||
| counter.update(u' '.join(pair) for pair in bigrams) | ||
| print(counter[u'czech republic']) | ||
| 42099 | ||
| The Wikipedia dataset contained 7,661,318 distinct words across | ||
| 1,860,927,726 total words, and 179,413,989 distinct bigrams across | ||
| 1,857,420,106 total bigrams. Storing them in a naive built-in ``dict`` | ||
| would consume over 31 GB RAM. | ||
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| To test the accuracy of Bounter, we automatically extracted | ||
| `collocations <https://en.wikipedia.org/wiki/Collocation>`__ (common | ||
| multi-word expressions, such as "New York", "network license", "Supreme | ||
| Court" or "elementary school") from these bigram counts. | ||
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| We compared the set of collocations extracted from Counter (exact | ||
| counts, needs lots of memory) vs Bounter (approximate counts, bounded | ||
| memory) and present the precision and recall here: | ||
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| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | Algorithm | Time to | Memory | Precision | Recall | F1 score | | ||
| | | build | | | | | | ||
| +==============================================+==========+=========+===========+==========+==========+ | ||
| | ``Counter`` (built-in) | 32m 26s | 31 GB | 100% | 100% | 100% | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=128, need_iteration=False, | 19m 53s | **128 | 95.02% | 97.10% | 96.04% | | ||
| | log_counting=8)`` | | MB** | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=1024)`` | 17m 54s | 1 GB | 100% | 99.27% | 99.64% | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=1024, | 19m 58s | 1 GB | 99.64% | 100% | 99.82% | | ||
| | need_iteration=False)`` | | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=1024, | 20m 05s | 1 GB | **100%** | **100%** | **100%** | | ||
| | need_iteration=False, log_counting=1024)`` | | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=1024, | 19m 59s | 1 GB | 97.45% | 97.45% | 97.45% | | ||
| | need_iteration=False, log_counting=8)`` | | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=4096)`` | **16m | 4 GB | 100% | 100% | 100% | | ||
| | | 21s** | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=4096, | 20m 14s | 4 GB | 100% | 100% | 100% | | ||
| | need_iteration=False)`` | | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
| | ``bounter(size_mb=4096, | 20m 14s | 4 GB | 100% | 99.64% | 99.82% | | ||
| | need_iteration=False, log_counting=1024)`` | | | | | | | ||
| +----------------------------------------------+----------+---------+-----------+----------+----------+ | ||
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| Bounter achieves a perfect F1 score of 100% at 31x less memory (1GB vs | ||
| 31GB), compared to a built-in ``Counter`` or ``dict``. It is also 61% | ||
| faster. | ||
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| Even with just 128 MB (250x less memory), its F1 score is still 96.04%. | ||
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| Support | ||
| ======= | ||
|
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| Use `Github | ||
| issues <https://github.com/RaRe-Technologies/bounter/issues>`__ to | ||
| report bugs, and our `mailing | ||
| list <https://groups.google.com/forum/#!forum/gensim>`__ for general | ||
| discussion and feature ideas. | ||
|
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| -------------- | ||
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| ``Bounter`` is open source software released under the `MIT | ||
| license <https://github.com/rare-technologies/bounter/blob/master/LICENSE>`__. | ||
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| Copyright (c) 2017 `RaRe | ||
| Technologies <https://rare-technologies.com/>`__ | ||
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| .. |Build Status| image:: https://travis-ci.org/RaRe-Technologies/bounter.svg?branch=master | ||
| :target: https://travis-ci.org/RaRe-Technologies/bounter | ||
| .. |GitHub release| image:: https://img.shields.io/github/release/rare-technologies/bounter.svg?maxAge=3600 | ||
| :target: https://github.com/RaRe-Technologies/bounter/releases | ||
| .. |Mailing List| image:: https://img.shields.io/badge/-Mailing%20List-lightgrey.svg | ||
| :target: https://groups.google.com/forum/#!forum/gensim | ||
| .. |Gitter| image:: https://img.shields.io/badge/gitter-join%20chat%20%E2%86%92-09a3d5.svg | ||
| :target: https://gitter.im/RaRe-Technologies/gensim | ||
| .. |Follow| image:: https://img.shields.io/twitter/follow/spacy_io.svg?style=social&label=Follow | ||
| :target: https://twitter.com/gensim_py |