feat: pre-flight filter of untrainable task types before gate

tests/unit/test_harness_checkpoint.py

@@ -157,6 +157,7 @@ class _FakeConfig:
     diag_size: int = 30
     val_size: int = 50
     batch_correct_ratio: float = 0.5
+    trainable_min_units: int = 8
     edit_budget_start: int = 6
     edit_budget_end: int = 3
     early_stop_patience: int = 3
@@ -289,6 +290,7 @@ class TestFingerprintStructuralVsDecision:
             "diag_size",
             "val_size",
             "batch_correct_ratio",
+            "trainable_min_units",
         }
         decision = {
             "edit_budget_start",

tests/unit/test_harness_config.py

@@ -40,6 +40,7 @@ def _valid_kwargs() -> dict:
         "batch_size": 15,
         "min_class_per_batch": 2,
         "eval_min_per_class": 2,
+        "trainable_min_units": 8,
         "early_stop_patience": 8,
         "test_size": 60,
         "use_slow_momentum": True,

tests/unit/test_harness_pools.py

@@ -317,6 +317,7 @@ class TestBuildOrLoadPoolsFrozen:
             batch_size=15,
             min_class_per_batch=2,
             eval_min_per_class=2,
+            trainable_min_units=8,
             early_stop_patience=4,
             test_size=10,
             use_slow_momentum=True,
@@ -870,6 +871,7 @@ class TestRunHoldoutEvalConfig:
             batch_size=15,
             min_class_per_batch=2,
             eval_min_per_class=2,
+            trainable_min_units=8,
             early_stop_patience=4,
             test_size=30,
             use_slow_momentum=True,
@@ -920,6 +922,7 @@ class TestRunHoldoutEvalConfig:
             batch_size=15,
             min_class_per_batch=2,
             eval_min_per_class=2,
+            trainable_min_units=8,
             early_stop_patience=4,
             test_size=30,
             use_slow_momentum=True,

tests/unit/test_harness_runner.py

@@ -20,6 +20,7 @@ from app.harness.runner import (
     _build_comparison_pairs,
     _compute_total_steps,
     _fallback_summary,
+    _filter_untrainable_types,
     _format_applied_edits,
     _guard_infra_failures,
     _outcome_to_quadrant_pairs,
@@ -401,6 +402,58 @@ class TestShouldEarlyStop:
         assert state.epochs_since_best_improved == 2
 
 
+class TestFilterUntrainableTypes:
+    """_filter_untrainable_types 可训练性预检纯函数。"""
+
+    def test_untrainable_types_filtered_before_gate(self) -> None:
+        """val<eval_min_per_class 或 非test单元<trainable_min_units 的题型被剔除。"""
+        # 题型 A：diag=5 + val=5 → units=10、val=5，可训
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(5)]
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(5)]
+        # 题型 B：val=0（<eval_min_per_class）不可训
+        diag += [_FakeQuestion(question_id=f"B-d{i}", task_type="B") for i in range(2)]
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools,
+            task_types=["A", "B"],
+            eval_min_per_class=2,
+            trainable_min_units=8,
+        )
+
+        assert {q.task_type for q in new_pools.diagnosis} == {"A"}
+        assert {q.task_type for q in new_pools.validation} == {"A"}
+        assert new_types == ["A"]
+
+    def test_units_below_threshold_filtered(self) -> None:
+        """val 达标但 diag+val 单元数 < trainable_min_units 的题型被剔除。"""
+        # 题型 C：val=2（>=2）但 units=2+0=... 补 diag 使总数不足
+        val = [_FakeQuestion(question_id=f"C-v{i}", task_type="C") for i in range(2)]
+        diag = [_FakeQuestion(question_id="C-d0", task_type="C")]  # units=3 < 8
+        pools = _FakePools(diagnosis=diag, validation=val, test=[])
+
+        new_pools, new_types = _filter_untrainable_types(
+            pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+        )
+
+        assert new_pools.diagnosis == []
+        assert new_pools.validation == []
+        assert new_types == []
+
+    def test_test_pool_untouched(self) -> None:
+        """test 池不参与过滤（继续报告全题型准确率）。"""
+        val = [_FakeQuestion(question_id=f"A-v{i}", task_type="A") for i in range(8)]
+        diag = [_FakeQuestion(question_id=f"A-d{i}", task_type="A") for i in range(8)]
+        test = [_FakeQuestion(question_id="B-t0", task_type="B")]
+        pools = _FakePools(diagnosis=diag, validation=val, test=test)
+
+        new_pools, _ = _filter_untrainable_types(
+            pools, task_types=None, eval_min_per_class=2, trainable_min_units=8
+        )
+
+        assert new_pools.test == test
+
+
 # =========================================================================
 # 13c: Probation 数据结构测试
 # =========================================================================
@@ -690,6 +743,7 @@ class TestRunnerFactoryInjection:
             "batch_size": 5,
             "min_class_per_batch": 2,
             "eval_min_per_class": 2,
+            "trainable_min_units": 8,
             "early_stop_patience": 3,
             "test_size": 10,
             "use_slow_momentum": False,

tests/unit/test_runner_diag_tree_inject.py

@@ -54,6 +54,7 @@ def _base_config(workspace_dir: Path, store_dir: Path) -> RunConfig:
         batch_size=5,
         min_class_per_batch=2,
         eval_min_per_class=2,
+        trainable_min_units=8,
         early_stop_patience=3,
         test_size=10,
         use_slow_momentum=False,