QA-pass round 3: C1 mechanism reframe, F1 decoys, ladder + 4 sensitiv… #6

	name: CI

	on:
	push:
	branches: ["**"]
	pull_request:
	branches: ["**"]

	env:
	# Pin hash seed so tests that guard against hash()-based seeding are
	# deterministic and correctly distinguish hash() from hashlib.md5.
	PYTHONHASHSEED: "0"

	jobs:
	test:
	name: pytest (Python 3.11)
	runs-on: ubuntu-latest

	steps:
	- name: Checkout aedt-mspe-experiments
	uses: actions/checkout@v4

	- name: Set up Python 3.11
	uses: actions/setup-python@v5
	with:
	python-version: "3.11"

	- name: Clone upstream toolkit (aedt-fairness-audit) at pinned SHA
	run: \|
	PINNED_SHA="0951db99e3385fd235bd12fe5ba411251f4eba40"
	git clone https://github.com/chadmarkey/aedt-fairness-audit.git "$HOME/aedt-fairness-audit"
	git -C "$HOME/aedt-fairness-audit" fetch --depth 1 origin "$PINNED_SHA"
	git -C "$HOME/aedt-fairness-audit" checkout "$PINNED_SHA"
	# Verify the SHA actually landed where we expect (defensive).
	ACTUAL_SHA=$(git -C "$HOME/aedt-fairness-audit" rev-parse HEAD)
	if [ "$ACTUAL_SHA" != "$PINNED_SHA" ]; then
	echo "ERROR: toolkit checkout is at $ACTUAL_SHA, expected $PINNED_SHA" >&2
	exit 1
	fi
	echo "Upstream toolkit pinned at $PINNED_SHA"

	- name: Install dependencies
	run: \|
	python -m pip install --upgrade pip
	pip install \
	numpy \
	pandas \
	scipy \
	scikit-learn \
	matplotlib \
	pytest
	# scikit-learn is required because the upstream toolkit's
	# audit/metrics.py imports sklearn.linear_model.LogisticRegression.
	# sentence-transformers is intentionally excluded — tests run offline
	# and do not require SBERT model downloads.

	- name: Run tests
	run: \|
	pytest tests/ -v --tb=short

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