text-processing-util-mds24

text_processing_util_mds24

Welcome to the repository for text processing, a part of the DSCI-524 course by Group 10 in the MDS-V Cohort 8 at UBC.

Empower your text analysis workflows with text processing package, a Python library designed for streamlined text processing tasks. This versatile package offers four key functions: text_clean for noise removal and text refinement, frequency_vectorizer to generate frequency-based vectors, tfidf_vectorizer for TF-IDF vectorization, and tokenizer_padding to assist in tokenization and padding of text sequences for usage in recurrent neural networks. By simplifying essential text preprocessing steps, this package facilitates efficient text-based analysis, providing an easy-to-use toolkit for natural language processing and text modeling endeavors.

Contributors

Our team, in alphabetical order:

• Allan Lee
• Jerry Yu
• Mo Norouzi
• Nasim Ghazanfari Nasrabadi

Installation

To install the package from PyPI, please run the following command:

pip install text-processing-util-mds24

Developer Notes

Note: For those who are looking to develop and/or test this package, please follow the following instructions to install the package from this GitHub repository.

Installation from Repository

1. First, please make sure that you have poetry and conda installed on your local computer. If not, please follow the official instructions for each respectively to install them. (poetry, conda)

2. It is recommended to create a conda virtual environment to install the package by running the following commands:

conda create --name text_processing_util_mds24 python=3.9 -y
conda activate text_processing_util_mds24

1. Clone the repository to your local machine by running:

git clone [e-mail]:UBC-MDS/text_processing_util_mds24.git

1. From the root of this repository, install the package using poetry by running the following command. It is recommended to make sure that you are in the conda environment from Step 2 before running this command.

poetry install

Testing

Note: Every function in this package except for text_clean calls text_clean in the first line of the code. Hence, testing for errors that arise from unexpected inputs is only done for text_clean. Integration testing is done for the other functions that call text_clean.

To test this package, please first make sure you have activated the text_processing_util_mds24 conda environment that was created in the previous section. Then, please run the following command from the root directory of the repository:

pytest tests/

If you would like to see line coverage, please run the following command from the root directory of the repository:

pytest --cov=text_processing_util_mds24

If you would like to see branch coverage, please run the following command from the root directory of the repository:

pytest --cov-branch --cov=text_processing_util_mds24

Functions

1. text_clean: Removes punctuation, turns all characters in each document lower case and removes numbers in documents.
2. frequency_vectorizer: Calculates the frequency of each word in a list of text documents.
3. tfidf_vectorizer: Calculates the Term Frequency-Inverse Document Frequency (TF-IDF) scores for a given list of documents, providing a numerical representation that highlights the importance of terms within the context of the entire document set.
4. tokenizer_padding: Converts each word into an individual token represented by a number and pads shorter sequences, but keeps the order of the original sentence, which is important for RNNs.

Usage

Here are some examples of usage of the functions in this package.

Example of using text_clean:

from text_processing_util_mds24 import (
    text_clean,
    tfidf_vectorizer,
    frequency_vectorizer,
    tokenizer_padding
)
docs = ["Here is document one.", "", "we have document 2"]
print(text_clean(docs))

[['here', 'is', 'document', 'one'], [], ['we', 'have', 'document']]

Example of using frequency_vectorizer:

docs = ["apple orange banana", "apple banana banana"]
result_tf_matrix, result_feature_names = frequency_vectorizer(docs)
print(result_tf_matrix)
print(result_feature_names)

[[0.33333333 0.33333333 0.33333333]
 [0.33333333 0.66666667 0.        ]]
['apple', 'banana', 'orange']

Example of using tfidf_vectorizer:

docs = ["machine learning is interesting", "machine learning is fascinating"]
tfidf_matrix, feature_names = tfidf_vectorizer(docs)
print(tfidf_matrix)
print(feature_names)

[[ 0.          0.         -0.10136628 -0.10136628 -0.10136628]
 [ 0.          0.         -0.10136628 -0.10136628 -0.10136628]]
['fascinating', 'interesting', 'is', 'learning', 'machine']

Example of using tokenizer_padding:

docs = ["one two three", "one three four five"]
tokenized_padded = tokenizer_padding(docs)
print(tokenized_padded)

[[1. 2. 3. 0.]
 [1. 3. 4. 5.]]

For more demonstration of the functions in text_processing_util_mds24 using a more real-life text sample, please see our vignette.

Documentation

The official documentation for this package is hosted on Read the Docs: https://text-processing-util-mds24.readthedocs.io/en/latest/.

Ecosystem

This package is intended to clean and transform texts into different representations to feed into machine learning algorithms. Scikit-learn and Keras provide similar functionalities.

frequency_vectorizer: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

tfidf_vectorizer: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

tokenizer_padding:

• https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer
• https://www.tensorflow.org/api_docs/python/tf/keras/utils/pad_sequences

Still, our functions are different in some ways. frequency_vectorizer calculates the relative frequency of each word with regards to the total number of words in its document rather than the raw word counts. tokenizer_padding combines what would be an otherwise two-step process if one were to use Keras into one step. Both tfidf_vectorizer and tokenizer_padding offer simpler functional APIs and implementations compared to the implementations from scikit-learn and Keras respectively.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

text_processing_util_mds24 was created by Jerry Yu, Nasim Ghazanfari Nasrabadi, Mohammad Norouzi, Allan Lee. It is licensed under the terms of the MIT license.

Credits

text_processing_util_mds24 was created with cookiecutter and the py-pkgs-cookiecutter template.