added validation data metrics and updated report, inliers, outliers generation

ads/opctl/operator/lowcode/anomaly/const.py

@@ -48,7 +48,32 @@ class SupportedMetrics(str, metaclass=ExtendedEnumMeta):
     UNSUPERVISED_UNIFY95 = "unsupervised_unify95"
     UNSUPERVISED_UNIFY95_LOG_LOSS = "unsupervised_unify95_log_loss"
     UNSUPERVISED_N1_EXPERTS = "unsupervised_n-1_experts"
-
+    RECALL = "Recall"
+    PRECISION = "Precision"
+    ACCURACY = "Accuracy"
+    F1_SCORE = "f1_score"
+    FP = "False Positive"
+    FN = "False Negative"
+    TP = "True Positive"
+    TN = "True Negative"
+    ROC_AUC = "ROC_AUC"
+    PRC_AUC = "PRC_AUC"
+    MCC = "MCC"
+    MEAN_RECALL = "Mean Recall"
+    MEAN_PRECISION = "Mean Precision"
+    MEAN_ACCURACY = "Mean Accuracy"
+    MEAN_F1_SCORE = "Mean f1_score"
+    MEAN_ROC_AUC = "Mean ROC_AUC"
+    MEAN_PRC_AUC = "Mean PRC_AUC"
+    MEAN_MCC = "Mean MCC"
+    MEDIAN_RECALL = "Median Recall"
+    MEDIAN_PRECISION = "Median Precision"
+    MEDIAN_ACCURACY = "Median Accuracy"
+    MEDIAN_F1_SCORE = "Median f1_score"
+    MEDIAN_ROC_AUC = "Median ROC_AUC"
+    MEDIAN_PRC_AUC = "Median PRC_AUC"
+    MEDIAN_MCC = "Median MCC"
+    ELAPSED_TIME = "Elapsed Time"
 
 class OutputColumns(str, metaclass=ExtendedEnumMeta):
     ANOMALY_COL = "anomaly"

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

@@ -69,8 +69,9 @@ def _load_data(self, spec):
 
 
 class AnomalyOutput:
-    def __init__(self):
+    def __init__(self, date_column):
         self.category_map = dict()
+        self.date_column = date_column
 
     def add_output(self, category: str, anomalies: pd.DataFrame, scores: pd.DataFrame):
         self.category_map[category] = (anomalies, scores)
@@ -83,15 +84,29 @@ def get_scores_by_cat(self, category: str):
 
     def get_inliers_by_cat(self, category: str, data: pd.DataFrame):
         anomaly = self.get_anomalies_by_cat(category)
+        scores = self.get_scores_by_cat(category)
         inlier_indices = anomaly.index[anomaly[OutputColumns.ANOMALY_COL] == 0]
-
-        return data.iloc[inlier_indices]
+        inliers = data.iloc[inlier_indices]
+        if scores is not None and not scores.empty:
+            inliers = pd.merge(
+                inliers,
+                scores,
+                on=self.date_column,
+                how='inner')
+        return inliers
 
     def get_outliers_by_cat(self, category: str, data: pd.DataFrame):
         anomaly = self.get_anomalies_by_cat(category)
+        scores = self.get_scores_by_cat(category)
         outliers_indices = anomaly.index[anomaly[OutputColumns.ANOMALY_COL] == 1]
-
-        return data.iloc[outliers_indices]
+        outliers = data.iloc[outliers_indices]
+        if scores is not None and not scores.empty:
+            outliers = pd.merge(
+                outliers,
+                scores,
+                on=self.date_column,
+                how='inner')
+        return outliers
 
     def get_inliers(self, full_data_dict):
         inliers = pd.DataFrame()
@@ -128,3 +143,6 @@ def get_scores(self, target_category_columns):
             score[target_category_columns[0]] = category
             scores = pd.concat([scores, score], axis=0, ignore_index=True)
         return scores
+
+    def get_num_anomalies_by_cat(self, category: str):
+        return (self.category_map[category][0][OutputColumns.ANOMALY_COL] == 1).sum()

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

@@ -7,6 +7,7 @@
 import pandas as pd
 
 from ads.common.decorator.runtime_dependency import runtime_dependency
+from .anomaly_dataset import AnomalyOutput
 
 from .base_model import AnomalyOperatorBaseModel
 from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
@@ -22,11 +23,34 @@ class AutoMLXOperatorModel(AnomalyOperatorBaseModel):
         ),
     )
     def _build_model(self) -> pd.DataFrame:
-        est = automl.Pipeline(task='anomaly_detection')
+
+
+        date_column = self.spec.datetime_column.name
         dataset = self.datasets
-        est.fit(dataset.data, y=None)
-        y_pred = est.predict(dataset.data)
-        dataset.data[OutputColumns.ANOMALY_COL] = y_pred
+
+        full_data_dict = dataset.full_data_dict
+
+        anomaly_output = AnomalyOutput(date_column=date_column)
+
+        # Iterate over the full_data_dict items
+        for target, df in full_data_dict.items():
+            est = automl.Pipeline(task='anomaly_detection')
+            est.fit(df, y=None)
+            y_pred = est.predict(df)
+            scores = est.predict_proba(df)
+
+            anomaly = pd.DataFrame({
+                date_column: df[date_column],
+                OutputColumns.ANOMALY_COL: y_pred
+            })
+            score = pd.DataFrame({
+                date_column: df[date_column],
+                OutputColumns.SCORE_COL: [item[1] for item in scores]
+            })
+            anomaly_output.add_output(target, anomaly, score)
+
+        return anomaly_output
+
 
     def _generate_report(self):
         import datapane as dp

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

@@ -48,13 +48,7 @@ def _build_model(self) -> AnomalyOutput:
 
         full_data_dict = dataset.full_data_dict
 
-        target_category_column = (
-            self.spec.target_category_columns[0]
-            if self.spec.target_category_columns is not None
-            else None
-        )
-
-        anomaly_output = AnomalyOutput()
+        anomaly_output = AnomalyOutput(date_column=date_column)
 
         # Iterate over the full_data_dict items
         for target, df in full_data_dict.items():
@@ -70,6 +64,7 @@ def _build_model(self) -> AnomalyOutput:
                     columns={score.columns.values[0]: OutputColumns.SCORE_COL},
                     inplace=True,
                 )
+                score = 1-score
                 score = score.reset_index(drop=False)
 
                 col = anomaly.columns.values[0]