Information Retrieval & Text Mining Toolbox

This repository holds functions pivotal for IRTM processing. This repo. is staged for continuous development.

Quick Install using 'pip/pip3' & GitHub

pip install git+https://github.com/KanishkNavale/IRTM-Toolbox.git

Import Module

from irtm.toolbox import *

Using Functions

1. Soundex: A phonetic algorithm for indexing names by sound, as pronounced in English.

   print(soundex('Muller'))
   print(soundex('Mueller'))

   >>> 'M466'
   >>> 'M466'

2. Tokenizer: Converts a sequence of characters into a sequence of tokens.

   print(tokenize('LINUX'))
   print(tokenize('Text Mining 2021'))

   >>> ['linux']
   >>> ['text', 'mining']

3. Vectorize: Converts a string to token based weight tensor.

   vector = vectorize([
           'texts ([string]): a multiline or a single line string.',
           'dict ([list], optional): list of tokens. Defaults to None.',
           'enable_Idf (bool, optional): use IDF or not. Defaults to True.',
           'normalize (str, optional): normalization of vector. Defaults to l2.',
           'max_dim ([int], optional): dimension of vector. Defaults to None.',
           'smooth (bool, optional): restricts value >0. Defaults to True.',
           'weightedTf (bool, optional): Tf = 1+log(Tf). Defaults to True.',
           'return_features (bool, optional): feature vector. Defaults to False.'
           ])
   
   print(f'Vector Shape={vector.shape}')

   >>> Vector Shape=(8, 37)

4. Predict Token Weights: Computes importance of a token based on classification optimization.

   dictionary = ['vector', 'string', 'bool']
   vector = vectorize([
           'X ([np.array]): vectorized matrix columns arraged as per the dictionary.',
           'y ([labels]): True classification labels.',
           'epochs ([int]): Optimization epochs.',
           'verbose (bool, optional): Enable verbose outputs. Defaults to False.',
           'dict ([type], optional): list of tokens. Defaults to None.'
           ], dict=dictionary)
   
   labels = np.random.randint(1, size=(vector.shape[0], 1))
   weights = predict_weights(vector, labels, 100, dict=dictionary)

   >>> Token-Weights Mappings: {'vector': 0.22097790924850977, 
                                'string': 0.39296369957440075, 
                                'bool': 0.689853175081446}

5. Page Rank: Computes page rank from a chain matrix

   chain_matrix = np.array([[0, 0, 1],
                            [1, 0, 1],
                            [0, 1, 0]])
   
   print(page_rank(chain_matrix))
   
   rank, TPM = page_rank(chain_matrix, return_TransMatrix=True)
   print(f'Page Rank: {rank} \nTransition Probablity Matrix: \n{TPM}')

   >>> [0.0047 0.997  0.0767]
   >>> Page Rank: [0.0047 0.997  0.0767] 
       Transition Probablity Matrix: 
       [[0.03333333 0.03333333 0.93333333]
       [0.48333333 0.03333333 0.48333333]
       [0.03333333 0.93333333 0.03333333]]