remove 3.9, merge testcase to reduce runtime

Author: codeloop

.github/workflows/run-forecast-explainer-tests.yml

@@ -26,7 +26,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v4
@@ -56,4 +56,4 @@ jobs:
           pip install -r test-requirements-operators.txt
           pip install "oracle-automlx[forecasting]>=25.1.1"
           pip install pandas>=2.2.0
-          python -m pytest -v -p no:warnings --durations=5 tests/operators/forecast/test_explainers.py
+          python -m pytest -v -p no:warnings --durations=5 --asyncio-default-fixture-loop-scope=function tests/operators/forecast/test_explainers.py

tests/operators/forecast/test_explainers.py

@@ -30,7 +30,7 @@
 
 MODELS = [
     "arima",
-    "automlx",
+    # "automlx",
     "prophet",
     "neuralprophet",
 ]
@@ -140,9 +140,10 @@ def setup_test_data(model, freq, num_series, horizon=5, num_points=100, seed=42,
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("freq", ["D", "W", "M", "H", "T"])
 @pytest.mark.parametrize("num_series", [1, 3])
-def test_explanations_output(model, freq, num_series):
+def test_explanations_output_and_columns(model, freq, num_series):
     """
     Test the global and local explanations for different models, frequencies, and number of series.
+    Also test that the explanation output contains all the columns from the additional dataset.
 
     Parameters:
     - model: The forecasting model to use.
@@ -156,7 +157,7 @@ def test_explanations_output(model, freq, num_series):
     if model == "neuralprophet":
         pytest.skip("Skipping 'neuralprophet' model as it takes a long time to finish")
 
-    _, _, operator_config = setup_test_data(model, freq, num_series)
+    _, additional, operator_config = setup_test_data(model, freq, num_series)
 
     results = forecast_operate(operator_config)
 
@@ -176,33 +177,6 @@ def test_explanations_output(model, freq, num_series):
         not (local_explanations == 0).all().all()
     ), "Local explanations contain only 0 values"
 
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("freq", ["D", "W", "M", "H", "T"])
-@pytest.mark.parametrize("num_series", [1, 3])
-def test_explanations_columns(model, freq, num_series):
-    """
-    Test that the explanation output contains all the columns from the additional dataset.
-
-    Parameters:
-    - model: The forecasting model to use.
-    - freq: Frequency of the datetime column.
-    - num_series: Number of different time series to generate.
-    """
-    if model == "automlx" and freq == "T":
-        pytest.skip(
-            "Skipping 'T' frequency for 'automlx' model. automlx requires data with a frequency of at least one hour"
-        )
-    if model == "neuralprophet":
-        pytest.skip("Skipping 'neuralprophet' model as it takes a long time to finish")
-
-    _, additional, operator_config = setup_test_data(model, freq, num_series)
-
-    results = forecast_operate(operator_config)
-
-    global_explanations = results.get_global_explanations()
-    local_explanations = results.get_local_explanations()
-
     additional_columns = additional.columns.tolist()
     for column in additional_columns:
         assert column in global_explanations.columns, f"Column {column} missing in global explanations"