TF/IDF¶
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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).
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dampen¶ boolean – An attribute to store the dampen flag.
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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
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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