add to test cases for ad

Author: codeloop

ads/opctl/operator/lowcode/anomaly/model/base_model.py

@@ -173,7 +173,7 @@ def _test_data_evaluate_metrics(self, anomaly_output, test_data, elapsed_time):
 
         for cat in anomaly_output.list_categories():
             output = anomaly_output.category_map[cat][0]
-            date_col = self.spec.datetime_column.name
+            date_col = self.spec.datetime_column.name  if self.spec.datetime_column else "index"
 
             test_data_i = test_data.get_data_for_series(cat)
 

ads/opctl/operator/lowcode/anomaly/model/isolationforest.py

@@ -35,13 +35,13 @@ def _build_model(self) -> AnomalyOutput:
 
         for target, df in self.datasets.full_data_dict.items():
             model = IsolationForest(**model_kwargs)
-            model.fit(df[self.spec.target_column].values.reshape(-1, 1))
+            model.fit(df)
             y_pred = np.vectorize(self.outlier_map.get)(
-                model.predict(df[self.spec.target_column].values.reshape(-1, 1))
+                model.predict(df)
             )
 
             scores = model.score_samples(
-                df[self.spec.target_column].values.reshape(-1, 1)
+                df
             )
 
             index_col = df.columns[0]

ads/opctl/operator/lowcode/anomaly/model/oneclasssvm.py

@@ -35,13 +35,13 @@ def _build_model(self) -> AnomalyOutput:
 
         for target, df in self.datasets.full_data_dict.items():
             model = OneClassSVM(**model_kwargs)
-            model.fit(df[self.spec.target_column].values.reshape(-1, 1))
+            model.fit(df)
             y_pred = np.vectorize(self.outlier_map.get)(
-                model.predict(df[self.spec.target_column].values.reshape(-1, 1))
+                model.predict(df)
             )
 
             scores = model.score_samples(
-                df[self.spec.target_column].values.reshape(-1, 1)
+                df
             )
 
             index_col = df.columns[0]

tests/operators/anomaly/test_anomaly_simple.py

@@ -3,6 +3,7 @@
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+from ads.opctl.operator.lowcode.anomaly.const import NonTimeADSupportedModels
 import yaml
 import subprocess
 import pandas as pd
@@ -16,7 +17,7 @@
 from ads.opctl.operator.cmd import run
 
 
-MODELS = ["autots"]  # "automlx",
+MODELS = ["autots", "oneclasssvm", "isolationforest"]  # "automlx",
 
 # Mandatory YAML parameters
 TEMPLATE_YAML = {
@@ -71,7 +72,8 @@ def test_artificial_big(model):
         df_i = pd.DataFrame(
             d1, columns=[TARGET_COLUMN, "extra reg 1", "extra reg 2"]
         )  # columns=[f"sensor {i}", f"extra reg {i}-1", f"extra reg {i}-2"]  # Uncomment for wide format
-        df_i[DATETIME_COLUMN] = yr_in_30_min
+        if model not in NonTimeADSupportedModels.values():
+            df_i[DATETIME_COLUMN] = yr_in_30_min
         df_i[TARGET_CATEGORY_COLUMN] = f"GHNI007894032{i}"
         all_data.append(df_i)
 
@@ -90,7 +92,10 @@ def test_artificial_big(model):
         yaml_i["spec"]["output_directory"]["url"] = output_dirname
         yaml_i["spec"]["target_column"] = TARGET_COLUMN
         yaml_i["spec"]["target_category_columns"] = [TARGET_CATEGORY_COLUMN]
-        yaml_i["spec"]["datetime_column"]["name"] = DATETIME_COLUMN
+        if model in NonTimeADSupportedModels.values():
+            del yaml_i["spec"]["datetime_column"]
+        else:
+            yaml_i["spec"]["datetime_column"]["name"] = DATETIME_COLUMN
 
         # run(yaml_i, backend="operator.local", debug=False)
 
@@ -131,6 +136,8 @@ def test_artificial_small(model):
         yaml_i["spec"]["input_data"]["url"] = input_data
         yaml_i["spec"]["output_directory"]["url"] = output_dirname
         yaml_i["spec"]["contamination"] = 0.3
+        if model in NonTimeADSupportedModels.values():
+            del yaml_i["spec"]["datetime_column"]
 
         # run(yaml_i, debug=False)
 
@@ -162,8 +169,9 @@ def test_validation(model):
         np.concatenate([np.zeros(100), np.ones(2), np.zeros(100)], axis=0),
         columns=["anomaly"],
     )
-    d = d.reset_index().rename({"index": "ds"}, axis=1)
-    anomaly_col["ds"] = d["ds"]
+    if model not in NonTimeADSupportedModels.values():
+        d = d.reset_index().rename({"index": "ds"}, axis=1)
+        anomaly_col["ds"] = d["ds"]
     v = d.copy()
     v["anomaly"] = anomaly_col["anomaly"]
     with tempfile.TemporaryDirectory() as tmpdirname:
@@ -185,6 +193,8 @@ def test_validation(model):
         yaml_i["spec"]["test_data"] = {"url": test_data}
         yaml_i["spec"]["output_directory"]["url"] = output_dirname
         yaml_i["spec"]["contamination"] = 0.05
+        if model in NonTimeADSupportedModels.values():
+            del yaml_i["spec"]["datetime_column"]
 
         # run(yaml_i, backend="operator.local", debug=False)
         with open(anomaly_yaml_filename, "w") as f:
@@ -207,7 +217,10 @@ def test_load_datasets(model, data_dict):
         yaml_i = deepcopy(TEMPLATE_YAML)
         yaml_i["spec"]["model"] = model
         yaml_i["spec"]["input_data"]["url"] = data_dict["url"]
-        yaml_i["spec"]["datetime_column"]["name"] = data_dict["dt_col"]
+        if model in NonTimeADSupportedModels.values():
+            del yaml_i["spec"]["datetime_column"]
+        else:
+            yaml_i["spec"]["datetime_column"]["name"] = data_dict["dt_col"]
         yaml_i["spec"]["output_directory"]["url"] = output_dirname
 
         # run(yaml_i, backend="operator.local", debug=False)