feat: remove in-container retraining, training is now mac-only

Made-with: Cursor

tests/test_mlx_filter.py

@@ -0,0 +1,86 @@
+"""
+MLXFilter 단위 테스트.
+Apple Silicon GPU(Metal)가 없는 환경에서는 스킵한다.
+"""
+import numpy as np
+import pandas as pd
+import pytest
+
+mlx = pytest.importorskip("mlx.core", reason="MLX 미설치")
+
+
+def _make_X(n: int = 4) -> pd.DataFrame:
+    rng = np.random.default_rng(0)
+    return pd.DataFrame(
+        {
+            "rsi": rng.uniform(20, 80, n),
+            "macd_hist": rng.uniform(-0.1, 0.1, n),
+            "bb_pct": rng.uniform(0, 1, n),
+            "ema_align": rng.choice([-1.0, 0.0, 1.0], n),
+            "stoch_k": rng.uniform(0, 100, n),
+            "stoch_d": rng.uniform(0, 100, n),
+            "atr_pct": rng.uniform(0.001, 0.05, n),
+            "vol_ratio": rng.uniform(0.5, 3.0, n),
+            "ret_1": rng.uniform(-0.01, 0.01, n),
+            "ret_3": rng.uniform(-0.02, 0.02, n),
+            "ret_5": rng.uniform(-0.03, 0.03, n),
+            "signal_strength": rng.integers(0, 6, n).astype(float),
+            "side": rng.choice([0.0, 1.0], n),
+        }
+    )
+
+
+def test_mlx_gpu_device():
+    """MLX가 GPU 디바이스를 기본으로 사용해야 한다."""
+    import mlx.core as mx
+
+    device = mx.default_device()
+    assert "gpu" in str(device)
+
+
+def test_mlx_filter_predict_shape_untrained():
+    """학습 전에도 predict_proba가 (N,) 형태를 반환해야 한다."""
+    from src.mlx_filter import MLXFilter
+
+    X = _make_X(4)
+    model = MLXFilter(input_dim=13, hidden_dim=32)
+    proba = model.predict_proba(X)
+    assert proba.shape == (4,)
+    assert np.all((proba >= 0.0) & (proba <= 1.0))
+
+
+def test_mlx_filter_fit_and_predict():
+    """학습 후 predict_proba가 유효한 확률값을 반환해야 한다."""
+    from src.mlx_filter import MLXFilter
+
+    n = 100
+    X = _make_X(n)
+    y = pd.Series(np.random.randint(0, 2, n))
+
+    model = MLXFilter(input_dim=13, hidden_dim=32, epochs=5, batch_size=32)
+    model.fit(X, y)
+    proba = model.predict_proba(X)
+
+    assert proba.shape == (n,)
+    assert np.all((proba >= 0.0) & (proba <= 1.0))
+
+
+def test_mlx_filter_save_load(tmp_path):
+    """저장 후 로드한 모델이 동일한 예측값을 반환해야 한다."""
+    from src.mlx_filter import MLXFilter
+
+    n = 50
+    X = _make_X(n)
+    y = pd.Series(np.random.randint(0, 2, n))
+
+    model = MLXFilter(input_dim=13, hidden_dim=32, epochs=3, batch_size=32)
+    model.fit(X, y)
+    proba_before = model.predict_proba(X)
+
+    save_path = tmp_path / "mlx_filter.weights"
+    model.save(save_path)
+
+    loaded = MLXFilter.load(save_path)
+    proba_after = loaded.predict_proba(X)
+
+    np.testing.assert_allclose(proba_before, proba_after, atol=1e-5)

tests/test_retrainer.py

@@ -1,35 +0,0 @@
-import pytest
-import json
-from pathlib import Path
-from unittest.mock import AsyncMock, MagicMock, patch
-from src.retrainer import Retrainer
-
-
-@pytest.mark.asyncio
-async def test_retrain_calls_train(tmp_path):
-    """재학습 시 train 함수가 호출되는지 확인"""
-    ml_filter = MagicMock()
-    r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
-
-    with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock) as mock_fetch, \
-         patch("src.retrainer.run_training", return_value=0.72) as mock_train, \
-         patch("src.retrainer.get_current_auc", return_value=0.65):
-        await r.retrain()
-
-    mock_fetch.assert_called_once()
-    mock_train.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_retrain_rollback_when_worse(tmp_path):
-    """새 모델이 기존보다 나쁘면 롤백"""
-    ml_filter = MagicMock()
-    r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
-
-    with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock), \
-         patch("src.retrainer.run_training", return_value=0.55), \
-         patch("src.retrainer.get_current_auc", return_value=0.70), \
-         patch("src.retrainer.rollback_model") as mock_rollback:
-        await r.retrain()
-
-    mock_rollback.assert_called_once()