Group 10 - text_processing_util_mds24

[jy1909]

Submitting Author: Jerry Yu (@jy1909 ) All current maintainers: (@MoNorouzi23, @allan8392, @nassimgha) Package Name: text_processing_util_mds24 One-Line Description of Package: This package is designed for streamlined text processing tasks, including a function for noise removal and text refinement and 3 functions for text representation. Repository Link: https://github.com/UBC-MDS/text_processing_util_mds24 Version submitted: 2.0.0 Editor: @ttimbers Reviewer 1: Chris Gao Reviewer 2: Celeste Zhao Reviewer 3: Kiersten Gilberg Reviewer 4: Katherine Chen Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

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Description

• Include a brief paragraph describing what your package does:

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.

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The target audience is those who are looking to preprocess text before conducting machine learning on text data.

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There are other Python packages that accomplish similar things. However, this package offers text representations for both traditional machine learning models and recurrent neural networks in the same library.

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[celestezhao]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

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• [x] Examples for all user-facing functions.
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NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

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• [x] Package documentation is clear and easy to find and use.
• [x] The need for the package is clear
• [x] All functions have documentation and associated examples for use
• [x] The package is easy to install

Functionality

• [x] Installation: Installation succeeds as documented.
• [x] Functionality: Any functional claims of the software been confirmed.
• [x] Performance: Any performance claims of the software been confirmed.
• [x] Automated tests:
  • [x] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
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For packages also submitting to JOSS

• Not applicable.

Final approval (post-review)

• [x] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1h

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Review Comments

This is a very useful package for text processing in the text analysis workflows. I found it straightforward to install and integrate into my projects. Here are my thoughts for potential improvements:

• Consider breaking down functions and tests into four separate files. Currently all functions are in a single file, and the same to tests, making it challenging to discern the specific test coverage for each function.
• In the pyproject.toml file, the Python dependency is specified as python = "^3.9". Unless there are some special considerations, it may be better to avoid setting an upper limit on the version constraint.
• It would be beneficial to include more usaful badges in the README file, such as badges for continuous integration, Python version, current package version, etc.
• If there are any citations related to the package, consider adding them to the README file.
• I recommend adding "pip install" instruction to the README file after the package release.

Overall I find this package useful and easy to adopt. Great job folks!

[chrisgqy]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• [x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• [x] Installation instructions: for the development version of the package and any non-standard dependencies in README.
• [x] Vignette(s) demonstrating major functionality that runs successfully locally.
• [x] Function Documentation: for all user-facing functions.
• [x] Examples for all user-facing functions.
• [x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• [x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

• [x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

• [x] The package name
• [ ] Badges for:
  • [x] Continuous integration and test coverage,
  • [x] Docs building (if you have a documentation website),
  • [ ] A repostatus.org badge,
  • [x] Python versions supported,
  • [x] Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• [x] Short description of package goals.
• [x] Package installation instructions
• [x] Any additional setup required to use the package (authentication tokens, etc.)
• [x] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • [x] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• [x] Link to your documentation website.
• [x] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• [x] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

• [x] Package documentation is clear and easy to find and use.
• [x] The need for the package is clear
• [x] All functions have documentation and associated examples for use
• [x] The package is easy to install

Functionality

• [x] Installation: Installation succeeds as documented.
• [x] Functionality: Any functional claims of the software been confirmed.
• [x] Performance: Any performance claims of the software been confirmed.
• [x] Automated tests:
  • [x] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
  • [ ] Package supports modern versions of Python and not End of life versions.
  • [x] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

Final approval (post-review)

• [x] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1.5

Review Comments

• I would recommend to expand on the concept of TF-IDF vectorization more, since not everyone has the background for natural language processing.
• For the package version, I would recommend modifications in the .toml file so that the package supports modern versions of Python and not end of life versions
• I also recommend to have a visualization for words with different frequency (we learned that in previous courses with Varada) to give a more direct impression of the data
• I would recommend to have a new test function to avoid the corner case for clean_text. I see that when I input string of number (I.e. “1”), the function clean_text() would return an empty string
• The same issue remains for the tokenizer_padding function, where “tockenizer_padding”, where string of integers won’t be recognized as token. Overall I think this is an amazing package that has high level functions which are super useful. Amazing!

[Kierst01]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• [X] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• [X] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• [X] Installation instructions: for the development version of the package and any non-standard dependencies in README.
• [X] Vignette(s) demonstrating major functionality that runs successfully locally.
• [X] Function Documentation: for all user-facing functions.
• [X] Examples for all user-facing functions.
• [X] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• [X] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

• [X] Package has a README.md file in the root directory.

The README should include, from top to bottom:

• [X] The package name
• [ ] Badges for:
  • [ ] Continuous integration and test coverage,
  • [X] Docs building (if you have a documentation website),
  • [X] A repostatus.org badge,
  • [ ] Python versions supported,
  • [ ] Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• [X] Short description of package goals.
• [X] Package installation instructions
• [X] Any additional setup required to use the package (authentication tokens, etc.)
• [X] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • [X] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• [X] Link to your documentation website.
• [ ] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• [X] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

• [X] Package documentation is clear and easy to find and use.
• [X] The need for the package is clear
• [X] All functions have documentation and associated examples for use
• [X] The package is easy to install

Functionality

• [X] Installation: Installation succeeds as documented.
• [X] Functionality: Any functional claims of the software been confirmed.
• [X] Performance: Any performance claims of the software been confirmed.
• [X] Automated tests:
  • [ ] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [X] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [X] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [X] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
  • [X] Package supports modern versions of Python and not End of life versions.
  • [X] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• [ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• [ ] A short summary describing the high-level functionality of the software
• [ ] Authors: A list of authors with their affiliations
• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
• [ ] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• [ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

2

Review Comments

Here are a few thing I believe could be improved:

• I think the paragraph explaining how the package fits within the Python ecosystem could be expanded. As a user I would be interested to know if there are any differences between your functions and the ones provided by Scikit learn and Keras.
• There was an error raised by the test functions after following the instructions in the README file
• I think additional information in the example usage about why the functions are used could be helpful, specifically the functions tfidf_vectorizer and tokenizer_padding since they are more complex.
• The python version could be updated since it is capped at '^3.9' (if it still is compatible)
• I would recommend updating the README instructions so that all the steps are outlined in order, as this would make it easier for the user. For example, it would be preferable to have the poetry installation instructions after the creation of the virtual environment.

Overall, I think this project was well done and had a very interesting topic! Great job :)

[czwcandy]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• [x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• [x] Installation instructions: for the development version of the package and any non-standard dependencies in README.
• [x] Vignette(s) demonstrating major functionality that runs successfully locally.
• [x] Function Documentation: for all user-facing functions.
• [x] Examples for all user-facing functions.
• [x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• [x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

• [x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

• [x] The package name
• [ ] Badges for:
  • [ ] Continuous integration and test coverage,
  • [x] Docs building (if you have a documentation website),
  • [ ] A repostatus.org badge,
  • [x] Python versions supported,
  • [x] Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• [x] Short description of package goals.
• [x] Package installation instructions
• [x] Any additional setup required to use the package (authentication tokens, etc.)
• [x] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • [x] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• [x] Link to your documentation website.
• [ ] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• [ ] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

• [x] Package documentation is clear and easy to find and use.
• [x] The need for the package is clear
• [x] All functions have documentation and associated examples for use
• [x] The package is easy to install

Functionality

• [x] Installation: Installation succeeds as documented.
• [x] Functionality: Any functional claims of the software been confirmed.
• [x] Performance: Any performance claims of the software been confirmed.
• [x] Automated tests:
  • [ ] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [ ] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
  • [x] Package supports modern versions of Python and not End of life versions.
  • [x] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• [ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• [ ] A short summary describing the high-level functionality of the software
• [ ] Authors: A list of authors with their affiliations
• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
• [ ] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• [ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

---

Review Comments

1. Overall, it is a very useful package which can help relevant work more easily.
2. For ReadMe, It would be great to be more specific to explain the usefulness of such functions and give a difference between similar python build-in function, similarity of python build-in function.
3. And also if add a button for converge can help this package more clearly show on Readme.
4. Also, there is one test function cannot work well. Perhaps, there may exist some corner cases.
5. During instruction in Readme, it is not very clear for me to follow the guidelines to set up.
6. In a word, if can update Readme, this package would be great. But still it is a very good package. Great job guys! 👍