test case for fine-tuned model

services/inference/tests/test_inference.py

@@ -15,6 +15,7 @@
     "ttm-r1": {"context_length": 512, "prediction_length": 96},
     "ttm-1024-96-r1": {"context_length": 1024, "prediction_length": 96},
     "ttm-r2": {"context_length": 512, "prediction_length": 96},
+    "ttm-r2-etth-finetuned": {"context_length": 512, "prediction_length": 96},
     "ttm-1024-96-r2": {"context_length": 1024, "prediction_length": 96},
     "ttm-1536-96-r2": {"context_length": 1536, "prediction_length": 96},
     "ibm/test-patchtst": {"context_length": 512, "prediction_length": 96},
@@ -434,6 +435,64 @@ def test_zero_shot_forecast_inference_no_timestamp(ts_data):
     print(df_out[0].head())
 
 
+@pytest.mark.parametrize(
+    "ts_data",
+    [
+        "ttm-r2-etth-finetuned",
+    ],
+    indirect=True,
+)
+def test_finetuned_model_inference(ts_data):
+    test_data, params = ts_data
+    id_columns = params["id_columns"]
+    model_id = params["model_id"]
+
+    prediction_length = 96
+
+    # test single
+    test_data_ = test_data[test_data[id_columns[0]] == "a"].copy()
+    encoded_data = encode_data(test_data_, params["timestamp_column"])
+
+    msg = {
+        "model_id": model_id,
+        "parameters": {
+            # "prediction_length": params["prediction_length"],
+        },
+        "schema": {
+            "timestamp_column": params["timestamp_column"],
+            "id_columns": params["id_columns"],
+            "target_columns": params["target_columns"],
+        },
+        "data": encoded_data,
+        "future_data": {},
+    }
+
+    out = get_inference_response(msg)
+    assert "Attempted to use a fine-tuned model with a different schema" in out.text
+
+    test_data_ = test_data_.drop(columns=params["timestamp_column"])
+    msg = {
+        "model_id": model_id,
+        "parameters": {
+            # "prediction_length": params["prediction_length"],
+        },
+        "schema": {
+            "timestamp_column": params["timestamp_column"],
+            "id_columns": params["id_columns"],
+            "target_columns": ["OT"],
+            "freq": "1h",
+            "conditional_columns": [c for c in params["target_columns"] if c != "OT"],
+        },
+        "data": encoded_data,
+        "future_data": {},
+    }
+
+    df_out = get_inference_response(msg)
+    assert len(df_out) == 1
+    assert df_out[0].shape[0] == prediction_length
+    print(df_out[0].head())
+
+
 @pytest.mark.parametrize(
     "ts_data",
     [