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

Supports Python 2.7 and Python 3.3+

Free software: BSD license


The easiest way to install hashindex is through pypi

pip install hashedindex


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

Basic Usage:

import hashedindex
index = hashedindex.HashedIndex()

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

Counter({'document1.txt': 1})

{'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.

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

{'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.

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:

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 or ignore_numeric terms.

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:

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:

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.