test: add 129 tests for critical untested code (core, CoSTEER, factor_coder, model_coder, qlib pipeline)

test/qlib/test_model_coder.py

@@ -0,0 +1,182 @@
+"""Tests for model_coder — ModelTask, shape/value evaluators, config."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+PROJECT_ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+
+# =============================================================================
+# ModelTask
+# =============================================================================
+
+
+class TestModelTask:
+    def test_construction_fields(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        t = ModelTask(
+            name="m1",
+            description="desc",
+            architecture="LSTM",
+            hyperparameters={"lr": 0.001},
+            training_hyperparameters={"epochs": 10},
+            formulation="y = f(x)",
+            variables={"x": "feature"},
+            model_type="TimeSeries",
+        )
+        assert t.name == "m1"
+        assert t.description == "desc"
+        assert t.architecture == "LSTM"
+        assert t.hyperparameters == {"lr": 0.001}
+        assert t.training_hyperparameters == {"epochs": 10}
+        assert t.formulation == "y = f(x)"
+        assert t.variables == {"x": "feature"}
+        assert t.model_type == "TimeSeries"
+        assert t.base_code is None
+
+    def test_get_task_information(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        t = ModelTask(
+            name="m1", description="desc", architecture="LSTM",
+            hyperparameters={}, training_hyperparameters={},
+            model_type="Tabular",
+        )
+        info = t.get_task_information()
+        assert "name: m1" in info
+        assert "architecture: LSTM" in info
+        assert "model_type: Tabular" in info
+
+    def test_get_task_information_with_optional_fields(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        t = ModelTask(
+            name="m2", description="d2", architecture="GRU",
+            hyperparameters={}, training_hyperparameters={},
+            formulation="f1", variables={"v": 1}, model_type="Graph",
+        )
+        info = t.get_task_information()
+        assert "formulation: f1" in info
+        assert "variables: {'v': 1}" in info
+
+    def test_get_task_brief_information(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        t = ModelTask(
+            name="m1", description="desc", architecture="LSTM",
+            hyperparameters={"lr": 0.01}, training_hyperparameters={"epochs": 5},
+        )
+        info = t.get_task_brief_information()
+        assert "name: m1" in info
+        assert "architecture: LSTM" in info
+        assert "hyperparameters" in info
+
+    def test_from_dict(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        d = {
+            "name": "m3", "description": "d3", "architecture": "TCN",
+            "hyperparameters": {}, "training_hyperparameters": {},
+        }
+        t = ModelTask.from_dict(d)
+        assert t.name == "m3"
+
+    def test_repr(self):
+        from rdagent.components.coder.model_coder.model import ModelTask
+        t = ModelTask(
+            name="mymodel", description="d", architecture="LSTM",
+            hyperparameters={}, training_hyperparameters={},
+        )
+        assert "ModelTask" in repr(t)
+        assert "mymodel" in repr(t)
+
+
+# =============================================================================
+# Shape/Value evaluators (eva_utils)
+# =============================================================================
+
+
+class TestShapeEvaluator:
+    def test_correct_shape(self):
+        from rdagent.components.coder.model_coder.eva_utils import shape_evaluator
+        msg, ok = shape_evaluator(np.ones((32, 10)), target_shape=(32, 10))
+        assert ok is True
+        assert "correct" in msg.lower()
+
+    def test_incorrect_shape(self):
+        from rdagent.components.coder.model_coder.eva_utils import shape_evaluator
+        msg, ok = shape_evaluator(np.ones((32, 5)), target_shape=(32, 10))
+        assert ok is False
+        assert "incorrect" in msg.lower()
+
+    def test_none_prediction(self):
+        from rdagent.components.coder.model_coder.eva_utils import shape_evaluator
+        msg, ok = shape_evaluator(None, target_shape=(32, 10))
+        assert ok is False
+
+    def test_none_target_shape(self):
+        from rdagent.components.coder.model_coder.eva_utils import shape_evaluator
+        msg, ok = shape_evaluator(np.ones((3,)), target_shape=None)
+        assert ok is False
+
+    def test_float_array(self):
+        from rdagent.components.coder.model_coder.eva_utils import shape_evaluator
+        msg, ok = shape_evaluator(np.array([1.0, 2.0]), target_shape=(2,))
+        assert ok is True
+
+
+class TestValueEvaluator:
+    def test_none_prediction(self):
+        from rdagent.components.coder.model_coder.eva_utils import value_evaluator
+        msg, ok = value_evaluator(None, np.ones((3,)))
+        assert ok is False
+
+    def test_none_target(self):
+        from rdagent.components.coder.model_coder.eva_utils import value_evaluator
+        msg, ok = value_evaluator(np.ones((3,)), None)
+        assert ok is False
+
+    def test_small_difference_passes(self):
+        from rdagent.components.coder.model_coder.eva_utils import value_evaluator
+        msg, ok = value_evaluator(
+            np.array([1.0, 2.0, 3.0]),
+            np.array([1.0, 2.0, 3.01]),
+        )
+        assert bool(ok) is True  # diff < 0.1
+
+    def test_large_difference_fails(self):
+        from rdagent.components.coder.model_coder.eva_utils import value_evaluator
+        msg, ok = value_evaluator(
+            np.array([1.0, 2.0]),
+            np.array([10.0, 20.0]),
+        )
+        assert bool(ok) is False  # diff > 0.1
+
+
+# =============================================================================
+# ModelCoSTEERSettings
+# =============================================================================
+
+
+class TestModelCoSTEERSettings:
+    def test_default_env_type(self):
+        from rdagent.components.coder.model_coder.conf import ModelCoSTEERSettings
+        s = ModelCoSTEERSettings()
+        assert s.env_type == "conda"
+
+    def test_singleton(self):
+        from rdagent.components.coder.model_coder.conf import MODEL_COSTEER_SETTINGS
+        from rdagent.components.coder.model_coder.conf import ModelCoSTEERSettings
+        assert isinstance(MODEL_COSTEER_SETTINGS, ModelCoSTEERSettings)
+
+    def test_get_model_env_runs(self):
+        from rdagent.components.coder.model_coder.conf import get_model_env
+        # May succeed (conda available) or fail — either way, test the code path
+        try:
+            env = get_model_env()
+            assert env is not None
+        except Exception:
+            pass  # expected if docker/conda not available