ivanhalim/bayesopt

Optimal Asset Distribution based on Bayesian Optimization

bayesopt's Issues

Final Project Feedback

Rubric

(Reordered from PDF rubric to reflect the order I evaluate items)

• Overview/README: Meets Expectations

  💯 Very thorough README - I love the plots along with all the usage examples.
  A direct link to the PDF report would be nice.

• Report - clarity: Meets Expectations

• Report - conciseness: Does Not Meet Expectations 👎

  11 pages >> 3 page limit

• Presentation: Not required

• Project Organization: Meets Expectations

  💯

• Project Reproducibilty: Meets Expectations

  R Markdown files Knit for me!

• Code Correctness: Meets Expectations

  Some informal testing by comparing across algorithms and with known functions.

• Code Documentation: Meets Expectations

• Code Efficiency: Meets Expectations

• Code Style: Meets Expectations

• Functions - Ease of use: Exceeds Expectations 👍

  Formal documentation with roxygen.
  Good use of defaults for details arguments.

• Consistency of Effort: Does Not Meet Expectations

  All work done in one week.

• Response to Feedback: Meets Expectations

Other feedback

@IvanHalim This is huge chunk of work and I'm really impressed with how it turned out! Just a little disappointed it took until very close to the deadline to see progress in the repo.

Final Submission Checklist

@IvanHalim here is a checklist of things to do before submitting your project, due Sunday Dec 6th at midnight.

• Review project structure

  It's good time to review your file and folder organization. Remember group files into folders based on their purpose, and both folder and file names should be self-explanatory. Follow conventions where it makes sense (e.g. R function definitions in an R/ folder, data in a data/ folder).

  Double-check your reorganization doesn't break any code. Searching for old file names and paths in RStudio using Edit -> Find in Files, can be a useful technique to uncover places you've referenced folders or files. You should also check any .Rmd files Knit without error, and include a Knit .md version in the repo.

• Write your report

  Consider me (Charlotte) the audience for your report. I want to know what you set out to do, what you did, and what you learned. But, you might also attempt to write this to be suitable for someone outside of the class (i.e. a potential employer).

  Consider this report a summary document. You will likely have other files in your project that document the technical details of the things you did to complete the project. It is OK to refer a reader of the report to other files in your repository, but the reader shouldn’t have to read anything else to understand the primary goals and results of your project.

  You can also use this report to address rubric items. Make sure to point out anything you did to "Exceed Expectations".

  • Your report should be less than 3 pages, including figures, but excluding references.
  • A PDF, or HTML, version of the report should be included in your repository.

 

• Update your repository README

  When I'm grading I'll visit this first.
  Your repo README should include:

  • Your name
  • A title for your project
  • A brief (2-5 sentence) description of the project
  • Links to both the written report and (if applicable) presentation slides
  • Pointers to someone who wants to reproduce your work, e.g.:
    • required packages
    • locations of important code files, and any order they need to be run in,
    • the primary functions, and how should they be used

 

• Double-check your README

  Make sure you visit your repository homepage, and scroll down to the README. Check the README appears as it should, and click on any links to ensure they work.

• Submit your project on canvas, by providing the URL of your project repository: https://github.com/IvanHalim/Bayesian-Optimization.