=============================== hashedindex

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Fast and simple InvertedIndex implementation using hash lists (python dictionaries).

Supports Python 3.5+

Free software: BSD license

• Installing_
• Features_
• Text Parsing_
• Stemming_
• Integration with Numpy and Pandas_
• Reporting Bugs_

Installing

The easiest way to install hashindex is through pypi

::

pip install hashedindex

Features

hashedindex provides a simple to use inverted index structure that is flexible enough to work with all kinds of use cases.

Basic Usage:

.. code-block:: python

import hashedindex
index = hashedindex.HashedIndex()

index.add_term_occurrence('hello', 'document1.txt')
index.add_term_occurrence('world', 'document1.txt')

index.get_documents('hello')
Counter({'document1.txt': 1})

index.items()
{'hello': Counter({'document1.txt': 1}),
'world': Counter({'document1.txt': 1})}

example = 'The Quick Brown Fox Jumps Over The Lazy Dog'

for term in example.split():
    index.add_term_occurrence(term, 'document2.txt')

hashedindex is not limited to strings, any hashable object can be indexed.

.. code-block:: python

index.add_term_occurrence('foo', 10) index.add_term_occurrence(('fire', 'fox'), 90.2)

index.items() {'foo': Counter({10: 1}), ('fire', 'fox'): Counter({90.2: 1})}

Text Parsing

The hashedindex module comes included with a powerful textparser module with methods to split text into tokens.

.. code-block:: python

from hashedindex import textparser list(textparser.word_tokenize("hello cruel world")) [('hello',), ('cruel',), ('world',)]

Tokens are wrapped within tuples due to the ability to specify any number of n-grams required:

.. code-block:: python

list(textparser.word_tokenize("Life is about making an impact, not making an income.", ngrams=2)) [(u'life', u'is'), (u'is', u'about'), (u'about', u'making'), (u'making', u'an'), (u'an', u'impact'), (u'impact', u'not'), (u'not', u'making'), (u'making', u'an'), (u'an', u'income')]

Take a look at the function's docstring for information on how to use stopwords, specify a min_length for tokens, and configure token output using the ignore_numeric, retain_casing and retain_punctuation parameters.

By default, word_tokenize omits whitespace from the output token stream; whitespaces are rarely useful to include in a document term index.

If you need to tokenize text and re-assemble an output with spacing that matches the input, you may enable the tokenize_whitespace setting.

.. code-block:: python

list(textparser.word_tokenize('Conventions.  May. Differ.', tokenize_whitespace=True))
[('conventions',), ('  ',), ('may',), (' ',), ('differ',)]

Stemming

When building an inverted index, it can be useful to resolve related strings to a common root.

For example, in a corpus relating to animals it might be useful to derive a singular noun for each animal; as a result, documents containing either the word dog or dogs could be found under the index entry dog.

The hashedindex module's text parser provides optional support for stemming by allowing the caller to specify a custom stemmer:

.. code-block:: python

class NaivePluralStemmer(): def stem(self, x): return x.rstrip('s')

list(textparser.word_tokenize('It was raining cats and dogs', stemmer=NaivePluralStemmer())) [('it',), ('wa',), ('raining',), ('cat',), ('and',), ('dog',)]

Integration with Numpy and Pandas

The idea behind hashedindex is to provide a really quick and easy way of generating matrices for machine learning with the additional use of numpy, pandas and scikit-learn. For example:

.. code-block:: python

from hashedindex import textparser import hashedindex import numpy as np

index = hashedindex.HashedIndex()

documents = ['spam1.txt', 'ham1.txt', 'spam2.txt'] for doc in documents: with open(doc, 'r') as fp: for term in textparser.word_tokenize(fp.read()): index.add_term_occurrence(term, doc)

You probably want to use scipy.sparse.csr_matrix for better performance

X = np.as_matrix(index.generate_feature_matrix(mode='tfidf'))

y = [] for doc in index.documents(): y.append(1 if 'spam' in doc else 0) y = np.asarray(doc)

from sklearn.svm import SVC classifier = SVC(kernel='linear') classifier.fit(X, y)

You can also extend your feature matrix to a more verbose pandas DataFrame:

.. code-block:: python

import pandas as pd X = index.generate_feature_matrix(mode='tfidf') df = pd.DataFrame(X, columns=index.terms(), index=index.documents())

All methods within the code have high test coverage so you can be sure everything works as expected.

Reporting Bugs

Found a bug? Nice, a bug found is a bug fixed. Open an Issue or better yet, open a pull request.

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