pip install wqet-grader

Simple grading:

import wqet_grader

submission = "My Answer"
wqet_grader.grade('Sample Assessment', 'Question 1', submission)

Object grading:

import wqet_grader

arg1 = 3.14
arg2 = "something else"
wqet_grader.grade_object('Sample Assessment', 'Question 1', arg1=arg1, arg2=arg2)

Supported objects:

• file Binary file
• pandas_dataframe Pandas DataFrame object
• sklearn_model SKLearn pipeline/model
• float, int, string, dict, list - Primative types

You can load a directory of grading algorithm modules, by running the following command:

./scripts/load_and_run fixtures/content

This will automatically restart the server when any changes to the Grading API code or content is detected. Reporting to the WQET platform is disabled and additional debug logging is enabled on the console.

The sample notebook of this content can be run as follows:

cd fixtures/notebooks
./run.sh

Then open: http://127.0.0.1:8888/lab/tree/sample-questions.ipynb?token=test

You can spin up a grading server for a directory of content, using the following code:

from wqet_grader.utils import set_grading_content_path
set_grading_content_path("my-curriculum-folder")

from wqet_grader.server import app
app.run(host="localhost", port=2400, debug=True, threaded=False)

Through environment variables:

• GRADING_API_URL - URL of grading API
• VM_TOKEN - Temporary token for user authentication
• SCORE_OUTPUT_FORMAT - Format of score output in notebook: json or html (defaults to JSON)

Requirements:

• Python 3.6+.

Setup your Virtual Environment:

make venv

To run unit tests:

make test

make package.build
make package.release