MFNBC

Links

• PyPi - https://pypi.python.org/pypi/mfnbc/
• Github - https://github.com/wharton/mfnbc
• Blog post - https://research-it.wharton.upenn.edu/programming/naive-bayes-across-multiple-features/

About The Math

This package computes a set of simple arithmetic to calculate the final posterior probabilities for a set of features over a set of texts within a corpus.

So for each text in the corpus, the package looks to see if the word in contained in the provided likelihood table. If it is found, the posterior probabilities for each feature is updated as using Bayesian statistics.

where

is:

Requirements

Python >= 3.3

Install

pip install mfnbc

Setup (Likelihood Input File)

It is assumed you have a word based likelihood table (csv file) where the headers consists of the literal word Word and the remaining columns are the features you would like to classify.

For example:

Word	Animal	Human	Plant
cat	0.33	0.03	0.05
dog	0.33	0.02	0.05
leaves	0.05	0.03	0.4
tree	0.05	0.02	0.4
man	0.12	0.45	0.05
women	0.12	0.45	0.05

Setup (Unlabeled Data File)

ID	Text
1	The cat is my pet and he is lovely. A dog will not do.
2	The man and women had a cat and lived under a tree
3	The tree had lots of leaves
4	A man lives under a tree with many leaves. A women has a cat as a pet
5	The dog and cat chase the man under the tree
6	The man and women live in a house.

The key is having the header titled Text any other fields will be included unmodified in the output file.

Import

from mfnbc import MFNBC

Instantiate

m = MFNBC(<likelihoods_input_file - location of Likelihood table (str)>,
          <unlabeled_data_file - Location of unlabeled data file (str)>,
          <verbose output - Turn on of off verbose output, default: off>
          <output file name - defaults to out.csv, (str)>

Example

m = MFNBC('likeli_sample.csv', 'input_sample.csv', False, 'my_output.csv')
m.read_likelihoods()
m.calc_posteriors()
m.write_csv()

or you can do it all in a single command

m = MFNBC('likeli_sample.csv', 'input_sample.csv', False).write_csv()

Example Results

ID	Text	Animal	Human	Plant
1	The cat is my pet and he is lovely. A dog will not do.	0.972321429	0.005357143	0.022321429
2	The man and women had a cat and lived under a tree	0.580787094	0.2969934	0.122219506
3	The tree had lots of leaves	0.01532802	0.003678725	0.980993256
4	A man lives under a tree with many leaves. A women has a cat as a pet	0.334412386	0.1026038	0.562983814
5	The dog and cat chase the man under the tree	0.921839729	0.00761851	0.070541761
6	The man and women live in a house.	0.065633546	0.922971741	0.011394713