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97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
"""Minimal functional tests for the pure instrument layer.
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The model smoke test is the real integration gate. These tests cover the pure pieces the smoke test
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does not isolate cleanly: nominal vs ordinal reducers, frame canonicalization/keying, readout names,
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and packaged data integrity.
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"""
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from __future__ import annotations
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import unittest
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import numpy as np
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from tinymfv.data import CONFIGS, CONDITIONS, load_vignettes
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from tinymfv.instrument import (
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Instrument,
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canonicalize_to_forward,
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per_item_categorical,
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reduce_nominal,
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reduce_ordinal,
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)
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from tinymfv.readouts import expected_score, logit_contrast, logodds_agree
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class InstrumentFlowTest(unittest.TestCase):
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def test_nominal_flow_reduces_answer_categories_to_profile(self) -> None:
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instr = Instrument(
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"mfv", "salience", "nominal",
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["care", "fairness", "social"],
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["Care", "Fairness", "SocialNorms"],
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items=[],
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prefill='This is wrong because {"violation": "',
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answer_to_dim={"care": "Care", "fairness": "Fairness", "social": "SocialNorms"},
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)
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rows = [
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{"id": "a", "frame": "forward", "lp": np.log([0.80, 0.15, 0.05]),
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"p": np.array([0.80, 0.15, 0.05]), "pmass_allowed": 0.95,
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"dimension": None, "sign": 1, "human_label": None},
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{"id": "b", "frame": "forward", "lp": np.log([0.20, 0.50, 0.30]),
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"p": np.array([0.20, 0.50, 0.30]), "pmass_allowed": 0.90,
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"dimension": None, "sign": 1, "human_label": None},
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]
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items = per_item_categorical(rows, instr.kind)
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self.assertTrue(np.allclose(reduce_nominal(items, instr), [0.50, 0.325, 0.175]))
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self.assertAlmostEqual(items["a"]["pmass"], 0.95)
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self.assertEqual(items["a"]["n_frames"], 1)
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def test_ordinal_flow_canonicalizes_frames_then_keys_profile(self) -> None:
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instr = Instrument(
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"likert", "endorsement", "ordinal",
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["1", "2", "3", "4", "5"],
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["care", "authority"],
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items=[],
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prefill="(",
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)
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onehot = {d: np.eye(5)[d - 1] for d in range(1, 6)}
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rows = [
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{"id": "care", "frame": "forward", "lp": np.log(onehot[4] + 1e-9),
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"p": onehot[4], "pmass_allowed": 1.0,
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"dimension": "care", "sign": 1, "human_label": None},
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{"id": "authority", "frame": "inverted", "lp": np.log(onehot[5] + 1e-9),
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"p": onehot[5], "pmass_allowed": 1.0,
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"dimension": "authority", "sign": -1, "human_label": None},
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]
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items = per_item_categorical(rows, instr.kind)
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profile = reduce_ordinal(items, instr)
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self.assertTrue(np.array_equal(canonicalize_to_forward(onehot[5], "inverted", "ordinal"), onehot[1]))
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self.assertAlmostEqual(expected_score(items["care"]["p"], 5), 4.0)
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self.assertAlmostEqual(profile[0], 4.0)
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self.assertAlmostEqual(profile[1], 5.0)
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self.assertAlmostEqual(
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logit_contrast(items["care"]["lp"] + 100.0, 5),
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logit_contrast(items["care"]["lp"], 5),
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)
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self.assertTrue(np.isfinite(logodds_agree(items["care"]["lp"], 5)))
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def test_packaged_vignettes_have_aligned_conditions_and_labels(self) -> None:
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required_human = {
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"human_Care", "human_Fairness", "human_Loyalty", "human_Authority",
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"human_Sanctity", "human_Liberty", "human_SocialNorms",
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}
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for cfg in CONFIGS:
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rows = load_vignettes(cfg)
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self.assertGreaterEqual(len(rows), 100)
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self.assertEqual({r["set"] for r in rows}, {cfg})
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for row in rows:
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self.assertTrue({"id", "foundation", "foundation_coarse", *CONDITIONS} <= set(row))
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self.assertTrue(required_human <= set(row))
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self.assertGreater(sum(float(row[k]) for k in required_human), 0.0)
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if __name__ == "__main__":
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unittest.main()
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