Investigate issue 708

tests/lightgbm/test_lgbm_issue_2025.py

@@ -0,0 +1,84 @@
+import unittest
+import numpy as np
+
+
+class TestLightGBMIssue2025(unittest.TestCase):
+    def test_issue_708(self):
+        # https://github.com/onnx/onnxmltools/issues/708
+
+        import pprint
+        from datetime import datetime, timedelta
+        import pandas as pd
+        from lightgbm import LGBMRegressor
+        import onnx
+        import onnxmltools
+        import onnxruntime
+        from skl2onnx.common.data_types import FloatTensorType
+
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=30)
+        date_range = pd.date_range(start=start_date, end=end_date, freq="5min")
+        df_timestamps = pd.DataFrame(index=date_range)
+        N = len(df_timestamps)
+
+        used = pd.Series([0] * N, index=date_range)
+        used[(used.index.dayofweek <= 4) & (used.index.hour == 8)] = 1
+        used[(used.index.dayofweek <= 4) & (used.index.hour == 12)] = 2
+        used[(used.index.dayofweek <= 4) & (used.index.hour == 14)] = 3
+
+        y = pd.DataFrame(
+            {
+                "y": used,
+            },
+            index=date_range,
+        )
+        X = pd.DataFrame(
+            {
+                "sin_day_of_week": np.sin(2 * np.pi * date_range.dayofweek / 7),
+                "cos_day_of_week": np.cos(2 * np.pi * date_range.dayofweek / 7),
+                "sin_hour_of_day": np.sin(2 * np.pi * date_range.hour / 24),
+                "cos_hour_of_day": np.cos(2 * np.pi * date_range.hour / 24),
+            },
+            index=date_range,
+        )
+        X.columns = [f"f{i}" for i in range(X.shape[1])]
+
+        lgb_model = LGBMRegressor(
+            objective="quantile",  # Use quantile loss
+            alpha=0.95,  # Quantile for the loss (default is median: 0.5)
+            n_estimators=1,  # Number of boosting iterations
+            max_depth=2,  # Maximum tree depth
+        )
+        lgb_model.fit(X, y)
+
+        init_types = [("float_input", FloatTensorType([None, X.shape[1]]))]
+
+        onnx_model_lgmb = onnxmltools.convert_lightgbm(
+            lgb_model, initial_types=init_types
+        )
+        onnx.save(onnx_model_lgmb, "test_issue_708.onnx")
+
+        lgb_predictions = lgb_model.predict(X)
+
+        lgbm_sess = onnxruntime.InferenceSession(
+            onnx_model_lgmb.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        loaded_lgb_predictions = lgbm_sess.run(
+            output_names=["variable"],
+            input_feed={"float_input": X.to_numpy().astype(np.float32)},
+        )[0]
+        disc = []
+        for i, (features, x, y) in enumerate(
+            zip(
+                X.values.astype(np.float32),
+                lgb_predictions,
+                loaded_lgb_predictions.ravel(),
+            )
+        ):
+            if abs(x - y) > 1e-5:
+                disc.append((i, features, x, np.float32(x), y))
+        assert not disc, f"Discrepancies: {pprint.pformat(disc)}"
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)