mirror of
https://github.com/saymrwulf/autoresearch-quantum.git
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saymrwulf
e13a3268c2
- 8 Jupyter notebooks across 3 learning plans (A: bottom-up, B: spiral, C: parallel tracks)
- Teaching toolkit (src/autoresearch_quantum/teaching/) with ipywidgets-based
quiz, predict_choice, reflect, and order widgets — visually distinct from code cells
- Fix spectator_z operator: was {1:'Z',2:'Z'} (IZZI, expectation=0), now {1:'Z',3:'Z'}
(ZIZI, expectation=+1 for ideal T-state, commutes with logical operators)
- Fix u_magic seed: swap phase arguments to match h_p and ry_rz preparations
- Fix double-display bug: widgets rendered twice when function returned the box
- Fix CLI override parser for negative integers and missing '=' validation
- Fix stabilizer detection quiz: ZZZZ detects X errors, not Z errors
- Add ties parameter to order() for questions with interchangeable items
- Expand test suite from 21 to 107 tests
- Update README with notebook instructions and project tree
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
"""Tests for scoring module — edge cases and registry."""
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from __future__ import annotations
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import pytest
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from autoresearch_quantum.models import EvaluationMetrics, QualityWeights, ScoreConfig
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from autoresearch_quantum.scoring.score import (
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SCORE_REGISTRY,
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score_metrics,
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weighted_acceptance_cost,
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)
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def test_score_all_zero_weights() -> None:
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metrics = EvaluationMetrics(acceptance_rate=0.5, two_qubit_count=10, depth=20)
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config = ScoreConfig(cheap_quality=QualityWeights()) # all zero weights
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score, quality, cost = weighted_acceptance_cost(metrics, "cheap", config)
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assert quality == 0.0
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assert score == 0.0
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def test_score_with_none_metrics() -> None:
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metrics = EvaluationMetrics(acceptance_rate=0.8)
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config = ScoreConfig(
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cheap_quality=QualityWeights(
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ideal_fidelity=1.0,
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noisy_fidelity=1.0,
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),
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)
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# ideal and noisy are None -> skipped
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score, quality, cost = weighted_acceptance_cost(metrics, "cheap", config)
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assert quality == 0.0
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def test_score_expensive_tier_uses_expensive_weights() -> None:
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metrics = EvaluationMetrics(
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logical_magic_witness=0.9,
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acceptance_rate=0.8,
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)
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config = ScoreConfig(
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cheap_quality=QualityWeights(logical_witness=0.0), # zero weight
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expensive_quality=QualityWeights(logical_witness=1.0), # full weight
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)
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score_cheap, _, _ = weighted_acceptance_cost(metrics, "cheap", config)
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score_exp, _, _ = weighted_acceptance_cost(metrics, "expensive", config)
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assert score_cheap == 0.0
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assert score_exp > 0.0
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def test_unknown_score_function_raises() -> None:
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metrics = EvaluationMetrics()
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config = ScoreConfig(name="nonexistent_scorer")
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with pytest.raises(ValueError, match="Unknown score function"):
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score_metrics(metrics, "cheap", config)
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