IMPORTANT: PUT YOUR NETID IN THE FILE netid in the root directory of the assignment.
This is used to put the autograder output into Canvas. Please don't put someone else's netid
here, we will check. The netid should be placed into that file by itself as the only line,
with lower case letters and no carriage returns.
The line that is marked NETID_GOES_HERE should be REPLACED with YOUR netid.
IF YOU DON'T PUT YOUR NETID INTO THAT FILE PROPERLY YOU WILL NOT RECIEVE CREDIT FOR YOUR WORK!
In this assignment, the goals are:
- to set up a working programming environment using either
condaorvirtualenvenvironments. - to get acclimated to the "pull, commit, push" development cycle for git. All programming assignments in this course will be submitted via Github (all free-response quesitons will be submitted via Canvas).
First, let's clone this repository. We'll use git for all submissions in this class. New to git? Not to worry, it's quite easy! Here's a helpful guide.
To clone this repository install GIT on your computer and copy the link of the repository (find above at "Clone or Download") and enter in the command line:
git clone YOUR-LINK
Alternatively, just look at the link in your address bar if you're viewing this README in your submission repository in a browser. Once cloned, cd into the cloned repository. Every assignment has some files that you edit to complete it.
What to do for this assignment is detailed in problems.md. To pass the test cases you will edit code.py.
You should also create a separate .pdf file with answers to the questions in problems.md. This file should be submitted on Canvas.
Do not edit anything in the tests directory. Files can be added to tests but files that exist already cannot be edited.
This course uses Python 3. Python 2 will not work for these assignments and all assignments will be graded with Python 3 on our end. Python 2 is leaving the data science programming stack on Jan 1 2020.
The easiest way to get setup is to use miniconda.
Install the appropriate version of miniconda for your operating system (take care to select the Python 3 version). After miniconda is installed, you should be able to run conda. If you get an error (e.g. -bash: conda: command not found), make sure to source your bash file afterwards (source ~/.bash_profile worked for Prem) or if on Windows, restart your terminal.
Now let's create a virtual environment. Virtual environments are a simple way to isolate all the dependencies for a particular project, making it easy to work on multiple projects at once without them interfering with each other (e.g. conflicting versions of libraries between projects). To make sure your environment matches the testing environment that we use for grading exactly, it's best to make a new environment for each assignment in this course. Here's the command:
conda create -n hw0-setup python
hw0-setup is the name for the environment and it can be replaced for each assignment (e.g. for the next homework, this would be hw1-decision-tree or something similar). The name can be whatever you want, just make sure to remember it!
Once the environment is created you should activate it with:
conda activate hw0-setup
Your shell might look something like (hw0-setup) stark:nucs349 prem. The environment name is in parenthesis, indicating that it is active. Now let's install the requirements for this assignment. Do this by navigating to the top root of this folder on the terminal and running, with an activated conda environment:
pip install -r requirements.txt
Great! Your environment is all set up. Do this for every assignment in the course.
All assignments are submitted via Github in this class. Once you've accepted this assignment via the Github classroom link, it made a repository of the form https://github.com/nucs349/hw0-setup-\[your github username\]. In the first part of this README, you cloned the repository to your local machine to develop on.
To make changes, simply open or create some file in your local version. If you created a file, you have to do:
git add [new_file_name]
to make git track the file. If you edited an already tracked file, you don't have to add it. Then:
git commit -am [commit_message]
will commit the change. commit_message is something that describes the type of change you made. Good commit messages are descriptive, easy to understand, and correspond well with the actual changes made. Finally:
git pull origin master
git push origin master
will pull the remote code and then push the commit to the repository on Github.
The code that is on the Github server (not the version on your local machine) is the code we will run our tests on. If you don't push a commit, we won't see it and we won't grade it.
Every assignment comes with a testing suite that is used for autograding. This compares every function you implement with the output of a solution made by the course instructors. The test cases are transparent in input/output and can be inspected in the tests directory in the assignment's repository. If you pass all the test cases, you will get 100% on the autograded portion of the assignment. If you pass 9/10 test cases, you will get 90% on the autograded portion. 8/10 = 80%. And so on. However, for some assignments, there will be additional private test cases that are only known by the instructors. This is to avoid you overfitting to the distributed test cases. The distributed test cases are mostly to guide and help you with development.
The test cases can be run with:
python -m pytest -s
at the root directory of the assignment repository. This gives output that looks something like:
========== test session starts ===========
platform darwin -- Python 3.7.2, pytest-4.1.0, py-1.7.0, pluggy-0.8.1
rootdir: /Users/prem/teaching/nucs349/hw0-setup-pseeth, inifile:
collected 1 item
tests/test_assignment.py F [100%]
================ FAILURES ================
____________ test_sum_numbers ____________
def test_sum_numbers():
from code import sum_numbers
> assert sum_numbers(3, 5) == 8
E assert None == 8
E + where None = <function sum_numbers at 0x10f62de18>(3, 5)
tests/test_assignment.py:3: AssertionError
======== 1 failed in 0.04 seconds ========
Parsing this output we see we have failed one test: test_sum_numbers. Let's try to make this test pass by implementing the related function.
Do that now by editing "code.py".
Then re-run the tests to see if they passed! If they did, you'll see something like this:
========== test session starts ===========
platform darwin -- Python 3.7.2, pytest-4.1.0, py-1.7.0, pluggy-0.8.1
rootdir: /Users/prem/teaching/nucs349/hw0-setup-pseeth, inifile:
collected 1 item
tests/test_assignment.py . [100%]
======== 1 passed in 0.03 seconds ========
CODE: The code that is on the Github server (not the version on your local machine) as of the deadline is the code we will run our tests on. If you don't push a commit, we won't see it and we won't grade it.
FREE RESPONSE: For your free response answers, you must submit a .pdf file on Canvas. We will grade the last thing you put on Canvas by the deadline. You don't get credit for having the free response in your code repository on github.
Please make sure first that something in this document doesn't already address your issue! If you still have problems, simply open an issue on the starter code repository for this assignment here. Someone from the teaching staff will get back to you through there!
That's all! The workflow for every assignment in this class will work something like this.