Group 16 - Knn model

[billwan96]

Submitting Author: Bill Wan(@billwan96) All current maintainers: (@zhang-shizhe, @sho-i98, @weiranzhao97) Package Name: pyknnmodel: A KNN model for binary classification Repository Link: https://github.com/UBC-MDS/Group16_knnmodel Version submitted: v2.0.0 Editor: @ttimbers Reviewer 1: Hayley
Reviewer 2: Nasim Reviewer 3: Kittipong Reviewer 4: Jing

Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

---

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[yhan178]

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Estimated hours spent reviewing: 1.5

---

Review Comments

Nice work. Your documentation (README, vignettes) in general is clear and easy to follow. The package can be install and run smoothly.

I only have some minor revision suggestions on the documentation.

• Since this is a version before publishing the package on pypi etc., so users should be able to install the package from github. It would be better to have the instruction on how to do that: $ git clone https://github.com/UBC-MDS/Group16_knnmodel.git , just before your "Installation" instruction point 1 of the README.

• I follow your "Usage" section of the README, but in the second last line, it throws an error message NameError: name 'evaluate' is not defined, because your have not imported your evaluate function in the beginning. After adding from pkg_pyknnclassifier.evaluate import evaluate, everything in the "Usage" section works fine.

• In the "Functions" section of the README, it is better to use backticks to differentiate between function code and plain text to enhance clarity.

• By the time I am reviewing your repo, there is a file named scaling.py, which was created 2 weeks ago in your root directory. This file seems unnecessary and does not align with the Python package structure.

• In the "Narrative" section of your vignettes, the term "some transformation" seems a bit unclear to users. It would be better to rephrase and clarify this term, for instance, "If your data involves some categorical features, please convert them into a numerical format using appropriate encoding techniques before using our package".

• In the vignettes, "Data-Loading" section, the species' Latin names should be italic: Iris Setosa and Iris Versicolor. Also in this section, no code is involved. It seems the code chunk

  path_to_training = "../data/iris_train.csv"
  path_to_validation = "../data/iris_valid.csv"
  target_column = "Species"
  X_train, y_train = data_loading(path_to_training, target_column)

should be embedded in this section rather than the "Preprocessing" section.

[Jing-19]

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• [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.
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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

The User Guide is well-written and easy to follow for someone unfamiliar with the KNN classifier. I was able to follow the instructions to download the package and use it locally. The coverage is good and all the tests are passed. I like the icon made for the package, which is very creative! Some minor changes I suggest below to make your user guide flow more smoothly:

1. There is rich detail in the README which is good for the user to get a full understanding of the package. For an added layer of clarity, consider wrapping each function name within backticks (`) to differentiate them more clearly in the text.
2. Still in the README, scaling down the logo could refine the visual balance of the guide.
3. Upon inspecting the root directory, I noticed the scaling.py file is currently located there. It might be more in keeping with conventional project structure to move this file into the src/ directory, to maintain a clean separation of concerns.
4. The initial cell of the example usage file contains a version print statement (print(pkg_pyknnclassifier.__version__)). It could be beneficial to relocate this to a separate cell to maintain the import cell's focus, or it might be streamlined away if deemed non-essential.
5. In the example file, referencing the iris dataset towards the end of the example file is a wise choice.
6. Also in the example file, displaying the head of the dataset in a table format would give readers a quick snapshot of the data structure at a glance.
7. Additionally, pruning any commented-out code could tidy up the examples, ensuring that each line serves a purpose.

[nassimgha]

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).

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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.

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

The README should include, from top to bottom:

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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.)

• [X] Short description of package goals.
• [X] Package installation instructions
• [X] Any additional setup required to use the package (authentication tokens, etc.)
• [ ] 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)
• [ ] 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.
• [ ] 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:
  • [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

Hi, overall it looks great and works without any issues. A few suggestions for improvements though:

1. On readme file, the installation part, it is better to mention this here as a part of the installation guide that the user needs to clone the repository to their local machine by running: "git clone ... " before running poetry install. Testing section on readme file: Line coverage can also be added here. The example provided on readme file could also include a short demonstration of the results.
2. When running the usage example, I got an error as follows: "NameError: name 'evaluate' is not defined". Also, we needed to read the toy data from data folder inside the repository, which seems needed, but for the short example, maybe a toy dataset in the code could help avoid any errors regarding reading data from another file.
3. In vignettes, there are codes that are commented out, specifically the line excluding the id column. I think it is better if you delete the lines that are not needed for better readability.

[jokittipong]

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),
  • [ ] 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
• [ ] Any additional setup required to use the package (authentication tokens, etc.)
• [ ] 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

• [ ] 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.
  • [ ] 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.
  • [ ] 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

I've got some suggestions on how to improve your package:

1. There is some mistypo(s) on README.(@Functions: This pacakge...)

2. The installation instruction doesn't correctly instruct an user to install the package. You skip the step to install poetry inside the conda environment pyknnclassifier, you should add pip install poetry before give us instruction to run poetry install or give instruction like If you dont have poetry installed in your base environment, you can follow the installation guide(with provided link) for poetry.

3. It would be great if you add on the instruction on how to clone your repository to user's local machine. such as Clone the github repository using: git clone https://github.com/UBC-MDS/Group16_knnmodel.git

4. No consistency in the code block, some has the $, some has not. In my opinion, you should cut off $ because when an user copies your command from the README, an user doesn't need to erase the $ before running a command line.

5. Your instruction to run the tests is not provided good details on your tests. In my opinion, it would be better if you provided the tests via poetry: poetry run pytest tests/ --cov=src/pkg_pyknnclassifier/ instead of pytest tests/

6. The test coverage report shown that there are some code which haven't been covered by the tests on calculate_distance.py (75%)(branch cover 67%), evaluate.py (97%)(branch cover 97%), and scaling.py(73%)(branch cover 71%).

7. From the Usage example in README, First I can't run the line:

   accuracy_result = evaluate(target, pred, metric='accuracy') Traceback (most recent call last): File "", line 1, in NameError: name 'evaluate' is not defined

As you forgot to provide the import line on evaluate function in the example, so you should add this line in example: from pkg_pyknnclassifier.evaluate import evaluate

8. In the vignettes (example.ipynb): For the data-loading: in my opinion, You should cut off the "Preprocessing" head line or using different font size , as it may confused an user because you have tried to explain your package function, not the process on data wrangling For the Scaling explanation: it would be great if you provide some briefly on how you scale from the function. For the Prediction: it would be better if you print out the prediction to show us the form of prediction you give from the package, as you did in evaluate.

Anyways, thanks for your hard work!!! Also, I really love how you put the big logo picture at the top and some small logo on each topic. It makes your README look really nice and fun to follow your instruction.

P.S. I have not checked the box about following PEP 8 guidlines as I'm not able to tell and I think you don't linting set up it and it would be nice if you provide a separated link to your read the docs page, even you have posted the badge that can give access to read the docs page.