intial commit rcf

Author: codeloop

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

@@ -21,6 +21,7 @@ class NonTimeADSupportedModels(str, metaclass=ExtendedEnumMeta):
 
     OneClassSVM = "oneclasssvm"
     IsolationForest = "isolationforest"
+    RandomCutForest = "randomcutforest"
     # TODO : Add DBScan
     # DBScan = "dbscan"
 

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

@@ -15,6 +15,7 @@
 from .base_model import AnomalyOperatorBaseModel
 from .isolationforest import IsolationForestOperatorModel
 from .oneclasssvm import OneClassSVMOperatorModel
+from .randomcutforest import RandomCutForestOperatorModel
 
 
 class UnSupportedModelError(Exception):
@@ -52,6 +53,7 @@ class AnomalyOperatorModelFactory:
     _NonTime_MAP = {
         NonTimeADSupportedModels.OneClassSVM: OneClassSVMOperatorModel,
         NonTimeADSupportedModels.IsolationForest: IsolationForestOperatorModel,
+        NonTimeADSupportedModels.RandomCutForest: RandomCutForestOperatorModel,
         # TODO: Add DBScan model for non time based anomaly
         # NonTimeADSupportedModels.DBScan: DBScanOperatorModel,
     }

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

@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+
+# 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/
+
+import numpy as np
+import pandas as pd
+
+from ads.common.decorator.runtime_dependency import runtime_dependency
+from ads.opctl.operator.lowcode.anomaly.const import OutputColumns
+
+from .anomaly_dataset import AnomalyOutput
+from .base_model import AnomalyOperatorBaseModel
+
+
+class RandomCutForestOperatorModel(AnomalyOperatorBaseModel):
+    """
+    Class representing Random Cut Forest Anomaly Detection operator model.
+    """
+
+    @runtime_dependency(
+        module="rrcf",
+        err_msg=(
+            "Please run `pip install rrcf` to "
+            "install the required dependencies for RandomCutForest."
+        ),
+    )
+    def _build_model(self) -> AnomalyOutput:
+        from rrcf import RCTree
+
+        model_kwargs = self.spec.model_kwargs
+        # map the output as per anomaly dataset class, 1: outlier, 0: inlier
+        self.outlier_map = {1: 0, -1: 1}
+
+        anomaly_output = AnomalyOutput(date_column="index")
+        #TODO: PDB
+        import pdb
+
+        pdb.set_trace()
+
+        for target, df in self.datasets.full_data_dict.items():
+            model = RCTree(**model_kwargs)
+            model.fit(df)
+            y_pred = model.predict(df)
+            y_pred = np.vectorize(self.outlier_map.get)(y_pred)
+
+            scores = model.score_samples(df)
+
+            index_col = df.columns[0]
+
+            anomaly = pd.DataFrame(
+                {index_col: df[index_col], OutputColumns.ANOMALY_COL: y_pred}
+            ).reset_index(drop=True)
+            score = pd.DataFrame(
+                {"index": df[index_col], OutputColumns.SCORE_COL: scores}
+            ).reset_index(drop=True)
+
+            anomaly_output.add_output(target, anomaly, score)
+
+        return anomaly_output
+
+    def _generate_report(self):
+        """Generates the report."""
+        import report_creator as rc
+
+        other_sections = [
+            rc.Heading("Selected Models Overview", level=2),
+            rc.Text(
+                "The following tables provide information regarding the chosen model."
+            ),
+        ]
+
+        model_description = rc.Text(
+            "The Random Cut Forest (RCF) is an unsupervised machine learning algorithm that is used for anomaly detection."
+            " It works by building an ensemble of binary trees (random cut trees) and using them to compute anomaly scores for data points."
+        )
+
+        return (
+            model_description,
+            other_sections,
+        )

ads/opctl/operator/lowcode/anomaly/schema.yaml

@@ -363,6 +363,7 @@ spec:
         - auto
         - oneclasssvm
         - isolationforest
+        - randomcutforest
       meta:
         description: "The model to be used for anomaly detection"
 

pyproject.toml

@@ -175,6 +175,8 @@ anomaly  = [
   "autots",
   "oracledb",
   "report-creator==1.0.9",
+  "rrcf==0.4.4",
+  "scikit-learn"
 ]
 recommender = [
   "oracle_ads[opctl]",

test-requirements-operators.txt

@@ -1,5 +1,6 @@
 -r test-requirements.txt
 -e ".[forecast]"
+-e ".[anomaly]"
 -e ".[recommender]"
 -e ".[feature-store-marketplace]"
 plotly