inspect-test-utils 1.1.1

Simple models and tasks for integration testing

inspect-test-utils

A small collection of tasks, scorers, and a simple model for use with the Inspect AI framework. It is designed to support integration/acceptance tests, demos, and reproductions by providing:

• Ready-made Tasks that exercise common evaluation patterns (simple generation, numeric closeness, failure injection, and sandbox configuration).
• Scorers for deterministic or parameterized scoring (including hardcoded outputs and a logarithmic closeness score).
• A hardcoded ModelAPI implementation that can deterministically emit tool calls and/or final answers, useful for testing tool-calling flows without hitting external APIs.

Passing arguments Most tasks and the hardcoded model accept parameters. With the Inspect CLI you can pass them via --task-arg and --model-arg repeatedly:

• Example: make the task generate 3 samples and set a numeric target for guessing: inspect eval inspect_test_utils/guess_number
  --task-arg sample_count=3
  --task-arg target=42.7
  --model hardcoded --model-arg answer=42.6

What’s included

• Tasks (inspect_test_utils.tasks)

  • say_hello(sample_count=1): Simple task; expects a response that includes "hello".
  • guess_number(sample_count=1, target="42.7"): Uses a logarithmic closeness scorer for numeric answers.
  • hardcoded_score(sample_count=10, hardcoded_score=None, hardcoded_score_by_sample_id_and_epoch=None): Scores are injected from parameters; useful for testing aggregations and edge cases (including NaN).
  • sometimes_fails_setup(sample_count=10, fail_setup_on_epochs=None, failure_rate=0.2): Randomly raises during setup via a failing solver; useful to test retry/resume behavior.
  • sometimes_fails_scoring(sample_count=10, fail_score_on_epochs=None, failure_rate=0.2): Randomly raises during scoring; useful to test scorer error handling.
  • configurable_sandbox(sample_count=1, cpu=0.5, memory="2G", storage="2G", gpu=None, gpu_model=None, allow_internet=False): A task with runtime configurable sandbox.

• Scorers (inspect_test_utils.scorers)

  • failing_scorer(fail_on_epochs=None, failure_rate=0.2): Raises errors at a controlled rate for selected epochs.
  • closeness_log(): Scores 1.0 for exact equality, otherwise 1/(1+log1p(relative_error)) for numeric strings.
  • hardcoded_scorer(hardcoded_score=None, hardcoded_score_by_sample_id_and_epoch=None): Returns pre-specified Score objects or looks them up by sample id and epoch.

• Model (inspect_test_utils.hardcoded)

  • hardcoded: A ModelAPI that can emit a sequence of tool calls (e.g., bash) for a number of repetitions and then submit a final answer. Parameters include:
    • answer: final answer string (default: "done").
    • repetitions: how many tool-call "turns" before submitting.
    • tool_calls: list of tool calls or shell strings (e.g., ["echo hi", "ls -la"]) to simulate; defaults to none.
    • delay: optional delay (seconds) before returning each model output.

Installation

pip install inspect-test-utils

License

MIT. See LICENSE.