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add merlion ad
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‎THIRD_PARTY_LICENSES.txt

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* Source code: https://github.com/kLabUM/rrcf
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* Project home: https://github.com/kLabUM/rrcf
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Merlion
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* Copyright 2021 Salesforce.com Inc
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* License: BSD-3 Clause License
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* Source code: https://github.com/salesforce/Merlion
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* Project Home: https://github.com/salesforce/Merlion
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=============================== Licenses ===============================
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------------------------------------------------------------------------

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

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# Copyright (c) 2023 Oracle and/or its affiliates.
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# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
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import random
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from ads.common.extended_enum import ExtendedEnumMeta
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from ads.opctl.operator.lowcode.common.const import DataColumns
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from merlion.models.anomaly import autoencoder, deep_point_anomaly_detector, isolation_forest, spectral_residual, windstats, windstats_monthly
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from merlion.models.anomaly.change_point import bocpd
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from merlion.models.forecast import prophet
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class SupportedModels(str, metaclass=ExtendedEnumMeta):
@@ -14,6 +18,7 @@ class SupportedModels(str, metaclass=ExtendedEnumMeta):
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AutoMLX = "automlx"
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AutoTS = "autots"
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Auto = "auto"
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MerilonAD = "merlion_ad"
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# TODS = "tods"
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class NonTimeADSupportedModels(str, metaclass=ExtendedEnumMeta):
@@ -56,6 +61,84 @@ class TODSSubModels(str, metaclass=ExtendedEnumMeta):
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}
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class MerlionADSubmodels(str, metaclass=ExtendedEnumMeta):
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"""Supported Merlion AD sub models."""
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# point anomaly
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AUTOENCODER = "autoencoder"
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DAGMM = "dagmm"
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DBL = "dbl"
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DEEP_POINT_ANOMALY_DETECTOR = "deep_point_anomaly_detector"
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ISOLATION_FOREST = "isolation_forest"
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LOF = "lof"
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LSTM_ED = "lstm_ed"
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# RANDOM_CUT_FOREST = "random_cut_forest"
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SPECTRAL_RESIDUAL = "spectral_residual"
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STAT_RESIDUAL = "stat_residual"
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VAE = "vae"
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WINDSTATS = "windstats"
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WINDSTATS_MONTHLY = "windstats_monthly"
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ZMS = "zms"
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# forecast_based
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ARIMA = "arima"
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ETS = "ets"
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MSES = "mses"
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PROPHET = "prophet"
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SARIMA = "sarima"
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#changepoint
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BOCPD = "bocpd"
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MERLIONAD_IMPORT_MODEL_MAP = {
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MerlionADSubmodels.AUTOENCODER: ".autoendcoder",
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MerlionADSubmodels.DAGMM: ".dagmm",
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MerlionADSubmodels.DBL: ".dbl",
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MerlionADSubmodels.DEEP_POINT_ANOMALY_DETECTOR: ".deep_point_anomaly_detector",
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MerlionADSubmodels.ISOLATION_FOREST: ".isolation_forest",
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MerlionADSubmodels.LOF: ".lof",
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MerlionADSubmodels.LSTM_ED: ".lstm_ed",
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# MerlionADSubmodels.RANDOM_CUT_FOREST: ".random_cut_forest",
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MerlionADSubmodels.SPECTRAL_RESIDUAL: ".spectral_residual",
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MerlionADSubmodels.STAT_RESIDUAL: ".stat_residual",
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MerlionADSubmodels.VAE: ".vae",
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MerlionADSubmodels.WINDSTATS: ".windstats",
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MerlionADSubmodels.WINDSTATS_MONTHLY: ".windstats_monthly",
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MerlionADSubmodels.ZMS: ".zms",
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MerlionADSubmodels.ARIMA: ".forecast_based.arima",
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MerlionADSubmodels.ETS: ".forecast_based.ets",
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MerlionADSubmodels.MSES: ".forecast_based.mses",
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MerlionADSubmodels.PROPHET: ".forecast_based.prophet",
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MerlionADSubmodels.SARIMA: ".forecast_based.sarima",
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MerlionADSubmodels.BOCPD: ".change_point.bocpd",
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}
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MERLIONAD_MODEL_MAP = {
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MerlionADSubmodels.AUTOENCODER: "AutoEncoder",
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MerlionADSubmodels.DAGMM: "DAGMM",
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MerlionADSubmodels.DBL: "DynamicBaseline",
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MerlionADSubmodels.DEEP_POINT_ANOMALY_DETECTOR: "DeepPointAnomalyDetector",
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MerlionADSubmodels.ISOLATION_FOREST: "IsolationForest",
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MerlionADSubmodels.LOF: "LOF",
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MerlionADSubmodels.LSTM_ED: "LSTMED",
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# MerlionADSubmodels.RANDOM_CUT_FOREST: "RandomCutForest",
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MerlionADSubmodels.SPECTRAL_RESIDUAL: "SpectralResidual",
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MerlionADSubmodels.STAT_RESIDUAL: "StatThreshold",
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MerlionADSubmodels.VAE: "VAE",
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MerlionADSubmodels.WINDSTATS: "WindStats",
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MerlionADSubmodels.WINDSTATS_MONTHLY: "MonthlyWindStats",
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MerlionADSubmodels.ZMS: "ZMS",
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MerlionADSubmodels.ARIMA: "ArimaDetector",
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MerlionADSubmodels.ETS: "ETSDetector",
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MerlionADSubmodels.MSES: "MSESDetector",
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MerlionADSubmodels.PROPHET: "ProphetDetector",
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MerlionADSubmodels.SARIMA: "SarimaDetector",
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MerlionADSubmodels.BOCPD: "BOCPD",
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}
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class SupportedMetrics(str, metaclass=ExtendedEnumMeta):
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UNSUPERVISED_UNIFY95 = "unsupervised_unify95"
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UNSUPERVISED_UNIFY95_LOG_LOSS = "unsupervised_unify95_log_loss"
@@ -94,5 +177,6 @@ class OutputColumns(str, metaclass=ExtendedEnumMeta):
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Series = DataColumns.Series
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MERLION_DEFAULT_MODEL = "prophet"
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TODS_DEFAULT_MODEL = "ocsvm"
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SUBSAMPLE_THRESHOLD = 1000

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

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#!/usr/bin/env python
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# -*- coding: utf-8 -*--
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# Copyright (c) 2023, 2024 Oracle and/or its affiliates.
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# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
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import importlib
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from collections import defaultdict
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import numpy as np
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import pandas as pd
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from merlion.utils import TimeSeries
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from ads.common.decorator.runtime_dependency import runtime_dependency
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from ads.opctl.operator.lowcode.anomaly.const import (
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MERLION_DEFAULT_MODEL,
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MERLIONAD_IMPORT_MODEL_MAP,
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MERLIONAD_MODEL_MAP,
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OutputColumns,
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)
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from tests.integration import other
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from .anomaly_dataset import AnomalyOutput
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from .base_model import AnomalyOperatorBaseModel
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class AnomalyMerlionOperatorModel(AnomalyOperatorBaseModel):
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"""Class representing Merlion Anomaly Detection operator model."""
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@runtime_dependency(
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module="merlion",
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err_msg=(
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"Please run `pip3 install salesforce-merlion[all]` to "
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"install the required packages."
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),
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)
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def _get_config_model(self, model_list):
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"""
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Returns a dictionary with model names as keys and a list of model config and model object as values.
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Parameters
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----------
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model_list : list
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A list of model names.
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Returns
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-------
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dict
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A dictionary with model names as keys and a list of model config and model object as values.
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"""
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model_config_map = {}
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for model_name in model_list:
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model_module = importlib.import_module(
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name=MERLIONAD_IMPORT_MODEL_MAP.get(model_name),
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package="merlion.models.anomaly",
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)
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model_config = getattr(
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model_module, MERLIONAD_MODEL_MAP.get(model_name) + "Config"
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)
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model = getattr(model_module, MERLIONAD_MODEL_MAP.get(model_name))
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model_config_map[model_name] = [model_config, model]
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return model_config_map
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def _build_model(self) -> AnomalyOutput:
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"""
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Builds a Merlion anomaly detection model and trains it using the given data.
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Parameters
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----------
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None
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Returns
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-------
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AnomalyOutput
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An AnomalyOutput object containing the anomaly detection results.
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"""
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model_kwargs = self.spec.model_kwargs
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anomaly_output = AnomalyOutput(date_column="index")
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anomaly_threshold = model_kwargs.get("anomaly_threshold", 95)
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model_config_map = {}
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if model_kwargs.get("sub_model", None):
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model_config_map = self._get_config_model(model_kwargs.get("sub_model"))
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else:
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from merlion.models.anomaly.forecast_based.prophet import ( # noqa: I001
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ProphetDetector,
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ProphetDetectorConfig,
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)
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model_config_map[MERLION_DEFAULT_MODEL] = [
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ProphetDetectorConfig,
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ProphetDetector,
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]
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date_column = self.spec.datetime_column.name
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anomaly_output = AnomalyOutput(date_column=date_column)
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# model_objects = defaultdict(list)
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for target, df in self.datasets.full_data_dict.items():
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data = df.set_index(date_column)
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data = TimeSeries.from_pd(data)
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for model_name, (model_config, model) in model_config_map.items():
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model_config = model_config(**self.spec.model_kwargs)
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model = model(model_config)
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scores = model.train(train_data=data, anomaly_labels=None)
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try:
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y_pred = model.get_anomaly_label(data)
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y_pred =(y_pred.to_pd().reset_index()["anom_score"] > 0).astype(int)
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except Exception as e:
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y_pred = (
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scores.to_pd().reset_index()["anom_score"]
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> np.percentile(
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scores.to_pd().reset_index()["anom_score"], anomaly_threshold
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)
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).astype(int)
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index_col = df.columns[0]
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anomaly = pd.DataFrame(
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{index_col: df[index_col], OutputColumns.ANOMALY_COL: y_pred}
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).reset_index(drop=True)
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score = pd.DataFrame(
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{
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index_col: df[index_col],
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OutputColumns.SCORE_COL: scores.to_pd().reset_index()[
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"anom_score"
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],
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}
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).reset_index(drop=True)
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# model_objects[model_name].append(model)
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anomaly_output.add_output(target, anomaly, score)
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return anomaly_output
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def _generate_report(self):
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"""Genreates a report for the model."""
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import report_creator as rc
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other_sections = [
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rc.Heading("Selected Models Overview", level=2),
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rc.Text(
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"The following tables provide information regarding the chosen model."
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),
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]
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model_description = rc.Text(
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"The Merlion anomaly detection model is a full-stack automated machine learning system for anomaly detection."
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)
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return (
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model_description,
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other_sections,
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)

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

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from ..const import NonTimeADSupportedModels, SupportedModels
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from ..operator_config import AnomalyOperatorConfig
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from .anomaly_dataset import AnomalyDatasets
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from .anomaly_merlion import AnomalyMerlionOperatorModel
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from .automlx import AutoMLXOperatorModel
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from .autots import AutoTSOperatorModel
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SupportedModels.AutoMLX: AutoMLXOperatorModel,
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# SupportedModels.TODS: TODSOperatorModel,
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SupportedModels.AutoTS: AutoTSOperatorModel,
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SupportedModels.MerilonAD: AnomalyMerlionOperatorModel
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}
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_NonTime_MAP = {

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

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# Set tree parameters
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num_trees = model_kwargs.get("num_trees", 200)
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shingle_size = model_kwargs.get("shingle_size", None)
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anomaly_threshold = model_kwargs.get("anamoly_threshold", 95)
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anomaly_threshold = model_kwargs.get("anomaly_threshold", 95)
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for target, df in self.datasets.full_data_dict.items():
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try:

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

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- oneclasssvm
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- isolationforest
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- randomcutforest
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- merlion_ad
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meta:
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description: "The model to be used for anomaly detection"
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‎pyproject.toml

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"oracledb",
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"report-creator==1.0.9",
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"rrcf==0.4.4",
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"scikit-learn"
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"scikit-learn",
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"salesforce-merlion[all]==2.0.4"
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]
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recommender = [
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"oracle_ads[opctl]",

‎tests/operators/anomaly/test_anomaly_simple.py

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for d in DATASETS:
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parameters_short.append((m, d))
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MODELS = ["autots", "oneclasssvm", "isolationforest", "randomcutforest"]
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MODELS = ["autots", "oneclasssvm", "isolationforest", "randomcutforest", "merlion_ad"]
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@pytest.mark.parametrize("model", ["autots"])
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def test_artificial_big(model):

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