simplebayes package¶

Submodules¶

Module contents¶

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class simplebayes.SimpleBayes(tokenizer=None, cache_path='/tmp/')[source]¶

Bases: object

A memory-based, optional-persistence naïve bayesian text classifier.

cache_file = '_simplebayes.pickle'¶

cache_persist()[source]¶: Saves the current trained data to the cache. This is initiated by the program using this module

cache_train()[source]¶

Loads the data for this classifier from a cache file

Returns:	whether or not we were successful
Return type:	bool

calculate_bayesian_probability(cat, token_score, token_tally)[source]¶

Calculates the bayesian probability for a given token/category

Parameters:	cat (str) – The category we’re scoring for this token token_score (float) – The tally of this token for this category token_tally (float) – The tally total for this token from all categories
Returns:	bayesian probability
Return type:	float

calculate_category_probability()[source]¶: Caches the individual probabilities for each category

classify(text)[source]¶

Chooses the highest scoring category for a sample of text

Parameters:	text (str) – sample text to classify
Returns:	the “winning” category
Return type:	str

classmethod count_token_occurrences(words)[source]¶

Creates a key/value set of word/count for a given sample of text

Parameters:	words (list) – full list of all tokens, non-unique
Returns:	key/value pairs of words and their counts in the list
Return type:	dict

flush()[source]¶: Deletes all tokens & categories

get_cache_location()[source]¶

Gets the location of the cache file

Returns:	the location of the cache file
Return type:	string

score(text)[source]¶

Scores a sample of text

Parameters:	text (str) – sample text to score
Returns:	dict of scores per category
Return type:	dict

tally(category)[source]¶

Gets the tally for a requested category

Parameters:	category (str) – The category we want a tally for
Returns:	tally for a given category
Return type:	int

classmethod tokenize_text(text)[source]¶

Default tokenize method; can be overridden

Parameters:	text (str) – the text we want to tokenize
Returns:	list of tokenized text
Return type:	list

train(category, text)[source]¶

Trains a category with a sample of text

Parameters:	category (str) – the name of the category we want to train text (str) – the text we want to train the category with

untrain(category, text)[source]¶

Untrains a category with a sample of text

Parameters:	category (str) – the name of the category we want to train text (str) – the text we want to untrain the category with