remove stale tests

Author: ahosler

tests/operators/forecast/test_datasets.py

@@ -186,7 +186,7 @@ def test_pandas_to_historical(model):
         check_output_for_errors(output_data_path)
 
 
-@pytest.mark.parametrize("model", MODELS[:-2])
+@pytest.mark.parametrize("model", ["prophet", "neuralprophet"])
 def test_pandas_to_historical_test(model):
     df = pd.read_csv(f"{DATASET_PREFIX}dataset4.csv")
     df_train = df[:-PERIODS]

tests/operators/forecast/test_errors.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 from unittest.mock import patch
 
-# Copyright (c) 2023, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2023, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import yaml
@@ -405,15 +405,21 @@ def test_0_series(operator_setup, model):
     yaml_i["spec"].pop("target_category_columns")
     yaml_i["spec"]["generate_explanations"] = True
     run_yaml(tmpdirname=tmpdirname, yaml_i=yaml_i, output_data_path=output_data_path)
-    output_files = ['forecast.csv', 'metrics.csv', 'test_metrics.csv',
-                    'report.html', 'local_explanation.csv', 'global_explanation.csv']
+    output_files = [
+        "forecast.csv",
+        "metrics.csv",
+        "test_metrics.csv",
+        "report.html",
+        "local_explanation.csv",
+        "global_explanation.csv",
+    ]
     if model == "autots":
         # explanations are not supported for autots
         output_files.remove("local_explanation.csv")
         output_files.remove("global_explanation.csv")
     for file in output_files:
         file_path = os.path.join(output_data_path, file)
-        with open(file_path, 'r', encoding='utf-8') as cur_file:
+        with open(file_path, "r", encoding="utf-8") as cur_file:
             content = cur_file.read()
             assert "Series 1" not in content, f"'Series 1' found in file: {file}"
     yaml_i["spec"].pop("additional_data")
@@ -467,59 +473,59 @@ def test_invalid_dates(operator_setup, model):
         )
 
 
-def test_disabling_outlier_treatment(operator_setup):
-    tmpdirname = operator_setup
-    NUM_ROWS = 100
-    hist_data_0 = pd.concat(
-        [
-            HISTORICAL_DATETIME_COL[: NUM_ROWS - HORIZON],
-            TARGET_COL[: NUM_ROWS - HORIZON],
-        ],
-        axis=1,
-    )
-    outliers = [1000, -800]
-    hist_data_0.at[40, "Sales"] = outliers[0]
-    hist_data_0.at[75, "Sales"] = outliers[1]
-    historical_data_path, additional_data_path, test_data_path = setup_artificial_data(
-        tmpdirname, hist_data_0
-    )
-
-    yaml_i, output_data_path = populate_yaml(
-        tmpdirname=tmpdirname,
-        model="arima",
-        historical_data_path=historical_data_path,
-    )
-    yaml_i["spec"].pop("target_category_columns")
-    yaml_i["spec"].pop("additional_data")
+# def test_disabling_outlier_treatment(operator_setup):
+#     tmpdirname = operator_setup
+#     NUM_ROWS = 100
+#     hist_data_0 = pd.concat(
+#         [
+#             HISTORICAL_DATETIME_COL[: NUM_ROWS - HORIZON],
+#             TARGET_COL[: NUM_ROWS - HORIZON],
+#         ],
+#         axis=1,
+#     )
+#     outliers = [1000, -800]
+#     hist_data_0.at[40, "Sales"] = outliers[0]
+#     hist_data_0.at[75, "Sales"] = outliers[1]
+#     historical_data_path, additional_data_path, test_data_path = setup_artificial_data(
+#         tmpdirname, hist_data_0
+#     )
 
-    # running default pipeline where outlier will be treated
-    run_yaml(
-        tmpdirname=tmpdirname,
-        yaml_i=yaml_i,
-        output_data_path=output_data_path,
-        test_metrics_check=False,
-    )
-    forecast_without_outlier = pd.read_csv(f"{tmpdirname}/results/forecast.csv")
-    input_vals_without_outlier = set(forecast_without_outlier["input_value"])
-    assert all(
-        item not in input_vals_without_outlier for item in outliers
-    ), "forecast file should not contain any outliers"
+#     yaml_i, output_data_path = populate_yaml(
+#         tmpdirname=tmpdirname,
+#         model="arima",
+#         historical_data_path=historical_data_path,
+#     )
+#     yaml_i["spec"].pop("target_category_columns")
+#     yaml_i["spec"].pop("additional_data")
 
-    # switching off outlier_treatment
-    preprocessing_steps = {"missing_value_imputation": True, "outlier_treatment": False}
-    preprocessing = {"enabled": True, "steps": preprocessing_steps}
-    yaml_i["spec"]["preprocessing"] = preprocessing
-    run_yaml(
-        tmpdirname=tmpdirname,
-        yaml_i=yaml_i,
-        output_data_path=output_data_path,
-        test_metrics_check=False,
-    )
-    forecast_with_outlier = pd.read_csv(f"{tmpdirname}/results/forecast.csv")
-    input_vals_with_outlier = set(forecast_with_outlier["input_value"])
-    assert all(
-        item in input_vals_with_outlier for item in outliers
-    ), "forecast file should contain all the outliers"
+#     # running default pipeline where outlier will be treated
+#     run_yaml(
+#         tmpdirname=tmpdirname,
+#         yaml_i=yaml_i,
+#         output_data_path=output_data_path,
+#         test_metrics_check=False,
+#     )
+#     forecast_without_outlier = pd.read_csv(f"{tmpdirname}/results/forecast.csv")
+#     input_vals_without_outlier = set(forecast_without_outlier["input_value"])
+#     assert all(
+#         item not in input_vals_without_outlier for item in outliers
+#     ), "forecast file should not contain any outliers"
+
+#     # switching off outlier_treatment
+#     preprocessing_steps = {"missing_value_imputation": True, "outlier_treatment": False}
+#     preprocessing = {"enabled": True, "steps": preprocessing_steps}
+#     yaml_i["spec"]["preprocessing"] = preprocessing
+#     run_yaml(
+#         tmpdirname=tmpdirname,
+#         yaml_i=yaml_i,
+#         output_data_path=output_data_path,
+#         test_metrics_check=False,
+#     )
+#     forecast_with_outlier = pd.read_csv(f"{tmpdirname}/results/forecast.csv")
+#     input_vals_with_outlier = set(forecast_with_outlier["input_value"])
+#     assert all(
+#         item in input_vals_with_outlier for item in outliers
+#     ), "forecast file should contain all the outliers"
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -705,11 +711,15 @@ def test_arima_automlx_errors(operator_setup, model):
     if model not in ["autots"]:  # , "lgbforecast"
         if yaml_i["spec"].get("explanations_accuracy_mode") != "AUTOMLX":
             global_fn = f"{tmpdirname}/results/global_explanation.csv"
-            assert os.path.exists(global_fn), f"Global explanation file not found at {report_path}"
+            assert os.path.exists(
+                global_fn
+            ), f"Global explanation file not found at {report_path}"
             assert not pd.read_csv(global_fn, index_col=0).empty
 
         local_fn = f"{tmpdirname}/results/local_explanation.csv"
-        assert os.path.exists(local_fn), f"Local explanation file not found at {report_path}"
+        assert os.path.exists(
+            local_fn
+        ), f"Local explanation file not found at {report_path}"
         assert not pd.read_csv(local_fn).empty