feat(question_gen): add ActionRecognitionStrategy with 6 SubPatterns

Self-contained strategy targeting 6 Agent failure modes in Action Recognition: premature_evidence_anchoring, temporal_reasoning_failure, semantic_rigidity, fine_grained_visual_action, cross_segment_entity_tracking, evidence_gap_confabulation. - L2 default sampling (upgrade from L3) with 3 patterns overriding to L1 - Weighted random SubPattern selection (0.20/0.20/0.15/0.15/0.15/0.15) - Each SubPattern includes instruction, examples, distractor rules - Satisfies TaskTypeStrategy Protocol without extending BaseTaskTypeStrategy - 32 unit tests covering all properties, definitions, and selection behavior Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-07-14 06:49:03 -04:00
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+"""ActionRecognitionStrategy 单元测试。
+
+验证策略属性、SubPattern 定义、加权随机选择行为。
+"""
+
+from __future__ import annotations
+
+import random
+from collections import Counter
+
+import pytest
+
+from app.question_gen.families import SamplingConstraint
+from app.question_gen.strategy import SubPattern, TaskTypeStrategy
+from app.question_gen.strategy_action_recognition import (
+    AR_SUB_PATTERNS,
+    ActionRecognitionStrategy,
+)
+
+# ---------------------------------------------------------------------------
+# SubPattern 分组
+# ---------------------------------------------------------------------------
+
+_L1_PATTERN_NAMES = frozenset(
+    {
+        "premature_evidence_anchoring",
+        "temporal_reasoning_failure",
+        "cross_segment_entity_tracking",
+    }
+)
+_L2_PATTERN_NAMES = frozenset(
+    {
+        "semantic_rigidity",
+        "fine_grained_visual_action",
+        "evidence_gap_confabulation",
+    }
+)
+_ALL_PATTERN_NAMES = _L1_PATTERN_NAMES | _L2_PATTERN_NAMES
+
+
+class TestStrategyProperties:
+    """策略静态属性与 Protocol 符合性。"""
+
+    @pytest.fixture()
+    def strategy(self) -> ActionRecognitionStrategy:
+        """创建策略实例。"""
+        return ActionRecognitionStrategy()
+
+    def test_implements_protocol(self, strategy: ActionRecognitionStrategy) -> None:
+        """ActionRecognitionStrategy 满足 TaskTypeStrategy Protocol。"""
+        assert isinstance(strategy, TaskTypeStrategy)
+
+    def test_task_type(self, strategy: ActionRecognitionStrategy) -> None:
+        """task_type 为 'Action Recognition'。"""
+        assert strategy.task_type == "Action Recognition"
+
+    def test_strategy_name(self, strategy: ActionRecognitionStrategy) -> None:
+        """strategy_name 为 'ACTION_RECOGNITION'。"""
+        assert strategy.strategy_name == "ACTION_RECOGNITION"
+
+    def test_skill_target(self, strategy: ActionRecognitionStrategy) -> None:
+        """skill_target 为 'M1_AR'。"""
+        assert strategy.skill_target == "M1_AR"
+
+    def test_sampling_level(self, strategy: ActionRecognitionStrategy) -> None:
+        """sampling_level 为 2（L2）。"""
+        assert strategy.sampling_level == 2
+
+    def test_sampling_constraint(self, strategy: ActionRecognitionStrategy) -> None:
+        """采样约束匹配设计值。"""
+        expected = SamplingConstraint(
+            min_subtitles=3,
+            min_l3_nodes=5,
+            require_frames=True,
+            cross_l2_span=True,
+        )
+        assert strategy.sampling_constraint == expected
+
+    def test_prompt_template(self, strategy: ActionRecognitionStrategy) -> None:
+        """prompt_template 为 'action_recognition.md'。"""
+        assert strategy.prompt_template == "action_recognition.md"
+
+    def test_leak_probe_template(self, strategy: ActionRecognitionStrategy) -> None:
+        """leak_probe_template 为 'gate_leak_retrieval.md'。"""
+        assert strategy.leak_probe_template == "gate_leak_retrieval.md"
+
+    def test_extra_gates_empty(self, strategy: ActionRecognitionStrategy) -> None:
+        """extra_gates 返回空列表。"""
+        assert strategy.extra_gates(None) == []
+
+
+class TestSubPatternDefinitions:
+    """SubPattern 定义的完整性与正确性。"""
+
+    def test_ar_sub_patterns_count(self) -> None:
+        """AR_SUB_PATTERNS 包含 6 个子模式。"""
+        assert len(AR_SUB_PATTERNS) == 6
+
+    def test_ar_sub_patterns_is_tuple(self) -> None:
+        """AR_SUB_PATTERNS 为 tuple 类型。"""
+        assert isinstance(AR_SUB_PATTERNS, tuple)
+
+    def test_all_are_sub_pattern_instances(self) -> None:
+        """所有元素均为 SubPattern 实例。"""
+        for sp in AR_SUB_PATTERNS:
+            assert isinstance(sp, SubPattern)
+
+    def test_all_names_present(self) -> None:
+        """6 个子模式名称完整覆盖。"""
+        names = {sp.name for sp in AR_SUB_PATTERNS}
+        assert names == _ALL_PATTERN_NAMES
+
+    def test_l1_patterns_have_level_override_1(self) -> None:
+        """L1 子模式的 sampling_level_override 为 1。"""
+        for sp in AR_SUB_PATTERNS:
+            if sp.name in _L1_PATTERN_NAMES:
+                assert sp.sampling_level_override == 1, (
+                    f"{sp.name} 应为 L1 (override=1)，实际 {sp.sampling_level_override}"
+                )
+
+    def test_l2_patterns_have_no_level_override(self) -> None:
+        """L2 子模式的 sampling_level_override 为 None。"""
+        for sp in AR_SUB_PATTERNS:
+            if sp.name in _L2_PATTERN_NAMES:
+                assert sp.sampling_level_override is None, (
+                    f"{sp.name} 应为 L2 (override=None)，实际 {sp.sampling_level_override}"
+                )
+
+    def test_weights_sum_to_one(self) -> None:
+        """所有子模式权重之和为 1.0。"""
+        total = sum(sp.weight for sp in AR_SUB_PATTERNS)
+        assert abs(total - 1.0) < 1e-9
+
+    def test_individual_weights(self) -> None:
+        """各子模式权重匹配设计值。"""
+        weight_map = {sp.name: sp.weight for sp in AR_SUB_PATTERNS}
+        assert abs(weight_map["premature_evidence_anchoring"] - 0.20) < 1e-9
+        assert abs(weight_map["temporal_reasoning_failure"] - 0.20) < 1e-9
+        assert abs(weight_map["semantic_rigidity"] - 0.15) < 1e-9
+        assert abs(weight_map["fine_grained_visual_action"] - 0.15) < 1e-9
+        assert abs(weight_map["cross_segment_entity_tracking"] - 0.15) < 1e-9
+        assert abs(weight_map["evidence_gap_confabulation"] - 0.15) < 1e-9
+
+    def test_all_have_nonempty_instruction(self) -> None:
+        """每个子模式有非空 instruction。"""
+        for sp in AR_SUB_PATTERNS:
+            assert sp.instruction.strip(), f"{sp.name} instruction 为空"
+
+    def test_all_have_nonempty_distractor_rules(self) -> None:
+        """每个子模式有非空 distractor_rules。"""
+        for sp in AR_SUB_PATTERNS:
+            assert sp.distractor_rules.strip(), f"{sp.name} distractor_rules 为空"
+
+    def test_all_have_positive_examples(self) -> None:
+        """每个子模式有至少 1 个正面示例。"""
+        for sp in AR_SUB_PATTERNS:
+            assert len(sp.positive_examples) >= 1, f"{sp.name} 缺少 positive_examples"
+
+    def test_all_have_negative_examples(self) -> None:
+        """每个子模式有至少 1 个反面示例。"""
+        for sp in AR_SUB_PATTERNS:
+            assert len(sp.negative_examples) >= 1, f"{sp.name} 缺少 negative_examples"
+
+    def test_positive_examples_have_required_keys(self) -> None:
+        """正面示例包含 question, answer, why 字段。"""
+        required = {"question", "answer", "why"}
+        for sp in AR_SUB_PATTERNS:
+            for ex in sp.positive_examples:
+                missing = required - set(ex.keys())
+                assert not missing, f"{sp.name} 正面示例缺少字段: {missing}"
+
+    def test_negative_examples_have_required_keys(self) -> None:
+        """反面示例包含 question, why 字段。"""
+        required = {"question", "why"}
+        for sp in AR_SUB_PATTERNS:
+            for ex in sp.negative_examples:
+                missing = required - set(ex.keys())
+                assert not missing, f"{sp.name} 反面示例缺少字段: {missing}"
+
+    def test_all_constraint_overrides_none(self) -> None:
+        """所有子模式的 constraint_override 为 None。"""
+        for sp in AR_SUB_PATTERNS:
+            assert sp.constraint_override is None, f"{sp.name} constraint_override 应为 None"
+
+
+class TestSelectSubPattern:
+    """select_sub_pattern 加权随机选择行为。"""
+
+    @pytest.fixture()
+    def strategy(self) -> ActionRecognitionStrategy:
+        """创建策略实例。"""
+        return ActionRecognitionStrategy()
+
+    def test_returns_sub_pattern(self, strategy: ActionRecognitionStrategy) -> None:
+        """select_sub_pattern 返回 SubPattern（不是 None）。"""
+        rng = random.Random(42)
+        result = strategy.select_sub_pattern(rng)
+        assert isinstance(result, SubPattern)
+
+    def test_deterministic_with_same_seed(self, strategy: ActionRecognitionStrategy) -> None:
+        """相同种子产生相同结果。"""
+        results_a = [strategy.select_sub_pattern(random.Random(99)).name for _ in range(20)]
+        results_b = [strategy.select_sub_pattern(random.Random(99)).name for _ in range(20)]
+        assert results_a == results_b
+
+    def test_covers_all_patterns(self, strategy: ActionRecognitionStrategy) -> None:
+        """足够多次采样覆盖全部 6 个子模式。"""
+        rng = random.Random(12345)
+        seen = {strategy.select_sub_pattern(rng).name for _ in range(500)}
+        assert seen == _ALL_PATTERN_NAMES
+
+    def test_distribution_roughly_matches_weights(
+        self, strategy: ActionRecognitionStrategy
+    ) -> None:
+        """采样分布大致匹配权重（容差 +-0.05）。"""
+        rng = random.Random(42)
+        n = 5000
+        counter: Counter[str] = Counter()
+        for _ in range(n):
+            counter[strategy.select_sub_pattern(rng).name] += 1
+
+        weight_map = {sp.name: sp.weight for sp in AR_SUB_PATTERNS}
+        for name, expected_w in weight_map.items():
+            observed_ratio = counter[name] / n
+            assert abs(observed_ratio - expected_w) < 0.05, (
+                f"{name}: expected ~{expected_w:.2f}, got {observed_ratio:.3f}"
+            )
+
+
+class TestBuildPromptContext:
+    """build_prompt_context 输出验证。"""
+
+    @pytest.fixture()
+    def strategy(self) -> ActionRecognitionStrategy:
+        """创建策略实例。"""
+        return ActionRecognitionStrategy()
+
+    def test_returns_dict_with_required_keys(self, strategy: ActionRecognitionStrategy) -> None:
+        """返回包含 family_name, prompt_template, sub_pattern 的字典。"""
+        sp = AR_SUB_PATTERNS[0]
+        ctx = strategy.build_prompt_context(material=None, sub_pattern=sp)
+        assert "family_name" in ctx
+        assert "prompt_template" in ctx
+        assert "sub_pattern" in ctx
+
+    def test_family_name_value(self, strategy: ActionRecognitionStrategy) -> None:
+        """family_name 为 ACTION_RECOGNITION。"""
+        sp = AR_SUB_PATTERNS[0]
+        ctx = strategy.build_prompt_context(material=None, sub_pattern=sp)
+        assert ctx["family_name"] == "ACTION_RECOGNITION"
+
+    def test_prompt_template_value(self, strategy: ActionRecognitionStrategy) -> None:
+        """prompt_template 为 action_recognition.md。"""
+        sp = AR_SUB_PATTERNS[0]
+        ctx = strategy.build_prompt_context(material=None, sub_pattern=sp)
+        assert ctx["prompt_template"] == "action_recognition.md"
+
+    def test_sub_pattern_name(self, strategy: ActionRecognitionStrategy) -> None:
+        """sub_pattern 字段等于子模式名称。"""
+        for sp in AR_SUB_PATTERNS:
+            ctx = strategy.build_prompt_context(material=None, sub_pattern=sp)
+            assert ctx["sub_pattern"] == sp.name