ArrayEqual Evaluation Function

Compares a student's response array against an answer array, checking whether all elements are within the supplied tolerances. Uses numpy.allclose under the hood, so arrays of any regular shape (1D, 2D, etc.) are supported.

Table of Contents

• ArrayEqual Evaluation Function
  • Table of Contents
  • Repository Structure
  • Function Reference
    • Inputs
    • Parameters
    • Output
    • Error handling
    • Examples
  • Development
    • Running tests locally
  • How it works
    • Docker & Amazon Web Services (AWS)
    • Middleware Functions
    • GitHub Actions
  • Pre-requisites
  • Contact

Repository Structure

app/
__init__.py
evaluation.py # Main evaluation_function implementation
evaluation_tests.py # Unit tests for evaluation_function
requirements.txt # Python dependencies (numpy, evaluation-function-utils)
Dockerfile # Docker image definition for AWS Lambda
docs/
user.md # User-facing documentation
dev.md # Developer notes
.github/
workflows/
test-lint.yml # Lint and test on pull requests
staging-deploy.yml # Deploy to staging on push to main
production-deploy.yml # Manual production deployment
pre_production_tests.yml # Pre-production database validation tests
config.json # Evaluation function name ("arrayEqual")

Function Reference

Inputs

Field	Type	Description
response	array	Student's submitted array
answer	array	Correct answer array
params	object	Optional parameters (see below)

Both response and answer are parsed with np.array(dtype=np.float32).

Parameters

Parameter	Type	Default	Description
atol	number	0	Absolute tolerance — maximum allowed absolute difference between elements
rtol	number	0	Relative tolerance — maximum allowed relative difference between elements
feedback_for_incorrect_response	string	none	If set, replaces the default feedback message for all incorrect responses

atol and rtol map directly to the corresponding parameters of numpy.allclose.

Output

{
"is_correct": true
}

When the response is incorrect:

{
"is_correct": false,
"feedback": "<message>"
}

Error handling

• If answer contains non-numeric or empty values an exception is raised (the question is misconfigured).
• If response contains non-numeric values, is_correct: false is returned with the feedback "Only numbers are permitted.".
• If response contains empty fields, is_correct: false is returned with the feedback "Response has at least one empty field.".

Examples

Exact match (no tolerance)

{
"response": [1, 2, 3],
"answer": [1, 2, 3],
"params": {}
}

{ "is_correct": true }

2D array with absolute tolerance

{
"response": [[1, 2], [3, 4]],
"answer": [[1, 2], [3, 4.05]],
"params": { "atol": 0.1 }
}

{ "is_correct": true }

Custom feedback for incorrect response

{
"response": [[1, 1], [1, 1]],
"answer": [[1, 1], [1, 0]],
"params": { "feedback_for_incorrect_response": "Check the last element of the second row." }
}

{ "is_correct": false, "feedback": "Check the last element of the second row." }

Development

Running tests locally

python -m venv .venv
source .venv/bin/activate
pip install pytest -r app/requirements.txt
pytest --tb=auto -v

How it works

The function is built on top of a custom base layer, BaseEvaluationFunctionLayer, which provides tooling, tests and schema checking common to all evaluation functions.

Docker & Amazon Web Services (AWS)

The evaluation function runs on AWS Lambda inside a Docker container. Docker bundles the app and all its dependencies into a single image, giving full control over the Python version and packages used. For more information on Docker, read this introduction to containerisation. To learn more about AWS Lambda, click here.

Middleware Functions

Middleware provided by BaseEvaluationFunctionLayer handles request parsing, input validation and response formatting, so this repository only needs to implement the core comparison logic in evaluation.py.

GitHub Actions

Three automated pipelines are configured under .github/workflows/:

• test-lint.yml — runs on every pull request; lints with flake8 and runs the unit test suite.
• staging-deploy.yml — runs on every push to main; builds the Docker image and deploys to the staging environment after tests pass.
• production-deploy.yml — manually triggered; runs database validation tests then promotes the image to production.

Pre-requisites

To develop locally you will need:

• Python 3.8 or higher
• git CLI or GitHub Desktop
• A code editor (VS Code, PyCharm, etc.)
• Docker (optional — only needed to build/test the image locally)

Contact

For questions or issues, open a GitHub issue or visit the lambda-feedback organisation.