mfnbc 1.5

UNKNOWN

Requiremnts

Python >= 3.3

Install

pip install mfnbc

Setup (Likeihood 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 lovley. 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 chanse 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>

Example

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

You can also print the probability table by

m.probs

Example Results

ID	Text	Animal	Human	Plant
1	The cat is my pet and he is lovley. 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 chanse 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