TF/IDF

class py_stringmatching.similarity_measure.tfidf.TfIdf(corpus_list=None, dampen=True)[source]

Computes TF/IDF measure.

This measure employs the notion of TF/IDF score commonly used in information retrieval (IR) to find documents that are relevant to keyword queries. The intuition underlying the TF/IDF measure is that two strings are similar if they share distinguishing terms. See the string matching chapter in the book “Principles of Data Integration”

Parameters:
  • corpus_list (list of lists) – The corpus that will be used to compute TF and IDF values. This corpus is a list of strings, where each string has been tokenized into a list of tokens (that is, a bag of tokens). The default is set to None. In this case, when we call this TF/IDF measure on two input strings (using get_raw_score or get_sim_score), the corpus is taken to be the list of those two strings.
  • dampen (boolean) – Flag to indicate whether ‘log’ should be used in TF and IDF formulas (defaults to True).
dampen

boolean – An attribute to store the dampen flag.

get_corpus_list()[source]

Get corpus list.

Returns:corpus list (list of lists).
get_dampen()[source]

Get dampen flag.

Returns:dampen flag (boolean).
get_raw_score(bag1, bag2)[source]

Computes the raw TF/IDF score between two lists.

Parameters:bag1,bag2 (list) – Input lists.
Returns:TF/IDF score between the input lists (float).
Raises:TypeError – If the inputs are not lists or if one of the inputs is None.

Examples

>>> # here the corpus is a list of three strings that
>>> # have been tokenized into three lists of tokens
>>> tfidf = TfIdf([['a', 'b', 'a'], ['a', 'c'], ['a']])
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['b', 'c'])
0.7071067811865475
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['a'])
0.0
>>> tfidf = TfIdf([['x', 'y'], ['w'], ['q']])
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['a'])
0.0
>>> tfidf = TfIdf([['a', 'b', 'a'], ['a', 'c'], ['a'], ['b']], False)
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['a', 'c'])
0.25298221281347033
>>> tfidf = TfIdf(dampen=False)
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['a'])
0.7071067811865475
>>> tfidf = TfIdf()
>>> tfidf.get_raw_score(['a', 'b', 'a'], ['a'])
0.0
get_sim_score(bag1, bag2)[source]

Computes the normalized TF/IDF similarity score between two lists. Simply call get_raw_score.

Parameters:bag1,bag2 (list) – Input lists.
Returns:Normalized TF/IDF similarity score between the input lists (float).
Raises:TypeError – If the inputs are not lists or if one of the inputs is None.

Examples

>>> # here the corpus is a list of three strings that
>>> # have been tokenized into three lists of tokens
>>> tfidf = TfIdf([['a', 'b', 'a'], ['a', 'c'], ['a']])
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['b', 'c'])
0.7071067811865475
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['a'])
0.0
>>> tfidf = TfIdf([['x', 'y'], ['w'], ['q']])
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['a'])
0.0
>>> tfidf = TfIdf([['a', 'b', 'a'], ['a', 'c'], ['a'], ['b']], False)
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['a', 'c'])
0.25298221281347033
>>> tfidf = TfIdf(dampen=False)
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['a'])
0.7071067811865475
>>> tfidf = TfIdf()
>>> tfidf.get_sim_score(['a', 'b', 'a'], ['a'])
0.0
set_corpus_list(corpus_list)[source]

Set corpus list.

Parameters:corpus_list (list of lists) – Corpus list.
set_dampen(dampen)[source]

Set dampen flag.

Parameters:dampen (boolean) – Flag to indicate whether ‘log’ should be applied to TF and IDF formulas.