Oracle Cloud Infrastructure v0.12.0, Mar 17 23

oci.AiAnomalyDetection.getDetectionModels

This data source provides the list of Models in Oracle Cloud Infrastructure Ai Anomaly Detection service.

Returns a list of Models.

Example Usage

using System.Collections.Generic;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModels = Oci.AiAnomalyDetection.GetDetectionModels.Invoke(new()
    {
        CompartmentId = @var.Compartment_id,
        DisplayName = @var.Model_display_name,
        ProjectId = oci_ai_anomaly_detection_project.Test_project.Id,
        State = @var.Model_state,
    });

});

package main

import (
	"github.com/pulumi/pulumi-oci/sdk/go/oci/AiAnomalyDetection"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := AiAnomalyDetection.GetDetectionModels(ctx, &aianomalydetection.GetDetectionModelsArgs{
			CompartmentId: _var.Compartment_id,
			DisplayName:   pulumi.StringRef(_var.Model_display_name),
			ProjectId:     pulumi.StringRef(oci_ai_anomaly_detection_project.Test_project.Id),
			State:         pulumi.StringRef(_var.Model_state),
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiAnomalyDetection.AiAnomalyDetectionFunctions;
import com.pulumi.oci.AiAnomalyDetection.inputs.GetDetectionModelsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var testModels = AiAnomalyDetectionFunctions.getDetectionModels(GetDetectionModelsArgs.builder()
            .compartmentId(var_.compartment_id())
            .displayName(var_.model_display_name())
            .projectId(oci_ai_anomaly_detection_project.test_project().id())
            .state(var_.model_state())
            .build());

    }
}

import pulumi
import pulumi_oci as oci

test_models = oci.AiAnomalyDetection.get_detection_models(compartment_id=var["compartment_id"],
    display_name=var["model_display_name"],
    project_id=oci_ai_anomaly_detection_project["test_project"]["id"],
    state=var["model_state"])

import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModels = oci.AiAnomalyDetection.getDetectionModels({
    compartmentId: _var.compartment_id,
    displayName: _var.model_display_name,
    projectId: oci_ai_anomaly_detection_project.test_project.id,
    state: _var.model_state,
});

variables:
  testModels:
    fn::invoke:
      Function: oci:AiAnomalyDetection:getDetectionModels
      Arguments:
        compartmentId: ${var.compartment_id}
        displayName: ${var.model_display_name}
        projectId: ${oci_ai_anomaly_detection_project.test_project.id}
        state: ${var.model_state}

Using getDetectionModels

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getDetectionModels(args: GetDetectionModelsArgs, opts?: InvokeOptions): Promise<GetDetectionModelsResult>
function getDetectionModelsOutput(args: GetDetectionModelsOutputArgs, opts?: InvokeOptions): Output<GetDetectionModelsResult>

def get_detection_models(compartment_id: Optional[str] = None,
                         display_name: Optional[str] = None,
                         filters: Optional[Sequence[_aianomalydetection.GetDetectionModelsFilter]] = None,
                         project_id: Optional[str] = None,
                         state: Optional[str] = None,
                         opts: Optional[InvokeOptions] = None) -> GetDetectionModelsResult
def get_detection_models_output(compartment_id: Optional[pulumi.Input[str]] = None,
                         display_name: Optional[pulumi.Input[str]] = None,
                         filters: Optional[pulumi.Input[Sequence[pulumi.Input[_aianomalydetection.GetDetectionModelsFilterArgs]]]] = None,
                         project_id: Optional[pulumi.Input[str]] = None,
                         state: Optional[pulumi.Input[str]] = None,
                         opts: Optional[InvokeOptions] = None) -> Output[GetDetectionModelsResult]

func GetDetectionModels(ctx *Context, args *GetDetectionModelsArgs, opts ...InvokeOption) (*GetDetectionModelsResult, error)
func GetDetectionModelsOutput(ctx *Context, args *GetDetectionModelsOutputArgs, opts ...InvokeOption) GetDetectionModelsResultOutput

> Note: This function is named GetDetectionModels in the Go SDK.

public static class GetDetectionModels 
{
    public static Task<GetDetectionModelsResult> InvokeAsync(GetDetectionModelsArgs args, InvokeOptions? opts = null)
    public static Output<GetDetectionModelsResult> Invoke(GetDetectionModelsInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetDetectionModelsResult> getDetectionModels(GetDetectionModelsArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

fn::invoke:
  function: oci:AiAnomalyDetection/getDetectionModels:getDetectionModels
  arguments:
    # arguments dictionary

The following arguments are supported:

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters List<GetDetectionModelsFilter>
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters []GetDetectionModelsFilter
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<GetDetectionModelsFilter>
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId string: The ID of the compartment in which to list resources.
displayName string: A filter to return only resources that match the entire display name given.
filters GetDetectionModelsFilter[]
projectId string: The ID of the project for which to list the objects.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartment_id str: The ID of the compartment in which to list resources.
display_name str: A filter to return only resources that match the entire display name given.
filters GetDetectionModelsFilter]
project_id str: The ID of the project for which to list the objects.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<Property Map>
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

getDetectionModels Result

The following output properties are available:

CompartmentId string: The OCID for the model's compartment.
Id string: The provider-assigned unique ID for this managed resource.
ModelCollections List<GetDetectionModelsModelCollection>: The list of model_collection.
DisplayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
Filters List<GetDetectionModelsFilter>
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.

CompartmentId string: The OCID for the model's compartment.
Id string: The provider-assigned unique ID for this managed resource.
ModelCollections []GetDetectionModelsModelCollection: The list of model_collection.
DisplayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
Filters []GetDetectionModelsFilter
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.

compartmentId String: The OCID for the model's compartment.
id String: The provider-assigned unique ID for this managed resource.
modelCollections List<GetDetectionModelsModelCollection>: The list of model_collection.
displayName String: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
filters List<GetDetectionModelsFilter>
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.

compartmentId string: The OCID for the model's compartment.
id string: The provider-assigned unique ID for this managed resource.
modelCollections GetDetectionModelsModelCollection[]: The list of model_collection.
displayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
filters GetDetectionModelsFilter[]
projectId string: The OCID of the project to associate with the model.
state string: The state of the model.

compartment_id str: The OCID for the model's compartment.
id str: The provider-assigned unique ID for this managed resource.
model_collections GetDetectionModelsModelCollection]: The list of model_collection.
display_name str: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
filters GetDetectionModelsFilter]
project_id str: The OCID of the project to associate with the model.
state str: The state of the model.

compartmentId String: The OCID for the model's compartment.
id String: The provider-assigned unique ID for this managed resource.
modelCollections List<Property Map>: The list of model_collection.
displayName String: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
filters List<Property Map>
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.

Supporting Types

GetDetectionModelsFilter

Name string
Values List<string>
Regex bool

Name string
Values []string
Regex bool

name String
values List<String>
regex Boolean

name string
values string[]
regex boolean

name str
values Sequence[str]
regex bool

name String
values List<String>
regex Boolean

GetDetectionModelsModelCollection

Items List<GetDetectionModelsModelCollectionItem>

Items []GetDetectionModelsModelCollectionItem

items List<GetDetectionModelsModelCollectionItem>

items GetDetectionModelsModelCollectionItem[]

items GetDetectionModelsModelCollectionItem]

items List<Property Map>

GetDetectionModelsModelCollectionItem

CompartmentId string: The ID of the compartment in which to list resources.
DefinedTags Dictionary<string, object>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags Dictionary<string, object>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: The OCID of the model that is immutable on creation.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelTrainingDetails List<GetDetectionModelsModelCollectionItemModelTrainingDetail>: Specifies the details of the MSET model during the create call.
ModelTrainingResults List<GetDetectionModelsModelCollectionItemModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
SystemTags Dictionary<string, object>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TimeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

CompartmentId string: The ID of the compartment in which to list resources.
DefinedTags map[string]interface{}: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags map[string]interface{}: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: The OCID of the model that is immutable on creation.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelTrainingDetails []GetDetectionModelsModelCollectionItemModelTrainingDetail: Specifies the details of the MSET model during the create call.
ModelTrainingResults []GetDetectionModelsModelCollectionItemModelTrainingResult: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
SystemTags map[string]interface{}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TimeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId String: The ID of the compartment in which to list resources.
definedTags Map<String,Object>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String,Object>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: The OCID of the model that is immutable on creation.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelTrainingDetails List<GetDetectionModelsModelCollectionItemModelTrainingDetail>: Specifies the details of the MSET model during the create call.
modelTrainingResults List<GetDetectionModelsModelCollectionItemModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags Map<String,Object>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated String: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId string: The ID of the compartment in which to list resources.
definedTags {[key: string]: any}: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description string: A short description of the Model.
displayName string: A filter to return only resources that match the entire display name given.
freeformTags {[key: string]: any}: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id string: The OCID of the model that is immutable on creation.
lifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelTrainingDetails GetDetectionModelsModelCollectionItemModelTrainingDetail[]: Specifies the details of the MSET model during the create call.
modelTrainingResults GetDetectionModelsModelCollectionItemModelTrainingResult[]: Specifies the details for an Anomaly Detection model trained with MSET.
projectId string: The ID of the project for which to list the objects.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags {[key: string]: any}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

compartment_id str: The ID of the compartment in which to list resources.
defined_tags Mapping[str, Any]: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description str: A short description of the Model.
display_name str: A filter to return only resources that match the entire display name given.
freeform_tags Mapping[str, Any]: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id str: The OCID of the model that is immutable on creation.
lifecycle_details str: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
model_training_details GetDetectionModelsModelCollectionItemModelTrainingDetail]: Specifies the details of the MSET model during the create call.
model_training_results GetDetectionModelsModelCollectionItemModelTrainingResult]: Specifies the details for an Anomaly Detection model trained with MSET.
project_id str: The ID of the project for which to list the objects.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
system_tags Mapping[str, Any]: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
time_created str: The time the the Model was created. An RFC3339 formatted datetime string.
time_updated str: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId String: The ID of the compartment in which to list resources.
definedTags Map<Any>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<Any>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: The OCID of the model that is immutable on creation.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelTrainingDetails List<Property Map>: Specifies the details of the MSET model during the create call.
modelTrainingResults List<Property Map>: Specifies the details for an Anomaly Detection model trained with MSET.
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags Map<Any>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated String: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string.

GetDetectionModelsModelCollectionItemModelTrainingDetail

AlgorithmHint string: User can choose specific algorithm for training.
DataAssetIds List<string>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
TargetFap double: A target model accuracy metric user provides as their requirement
TrainingFraction double: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
WindowSize int: Window size defined during training or deduced by the algorithm.

AlgorithmHint string: User can choose specific algorithm for training.
DataAssetIds []string: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
TargetFap float64: A target model accuracy metric user provides as their requirement
TrainingFraction float64: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
WindowSize int: Window size defined during training or deduced by the algorithm.

algorithmHint String: User can choose specific algorithm for training.
dataAssetIds List<String>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap Double: A target model accuracy metric user provides as their requirement
trainingFraction Double: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize Integer: Window size defined during training or deduced by the algorithm.

algorithmHint string: User can choose specific algorithm for training.
dataAssetIds string[]: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap number: A target model accuracy metric user provides as their requirement
trainingFraction number: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize number: Window size defined during training or deduced by the algorithm.

algorithm_hint str: User can choose specific algorithm for training.
data_asset_ids Sequence[str]: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
target_fap float: A target model accuracy metric user provides as their requirement
training_fraction float: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
window_size int: Window size defined during training or deduced by the algorithm.

algorithmHint String: User can choose specific algorithm for training.
dataAssetIds List<String>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap Number: A target model accuracy metric user provides as their requirement
trainingFraction Number: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize Number: Window size defined during training or deduced by the algorithm.

GetDetectionModelsModelCollectionItemModelTrainingResult

Fap double: Accuracy metric for a signal.
IsTrainingGoalAchieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
Mae double
MaxInferenceSyncRows int
MultivariateFap double: The model accuracy metric on timestamp level.
Rmse double
RowReductionDetails List<GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
SignalDetails List<GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail>: The list of signal details.
Warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
WindowSize int: Window size defined during training or deduced by the algorithm.

Fap float64: Accuracy metric for a signal.
IsTrainingGoalAchieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
Mae float64
MaxInferenceSyncRows int
MultivariateFap float64: The model accuracy metric on timestamp level.
Rmse float64
RowReductionDetails []GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
SignalDetails []GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail: The list of signal details.
Warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
WindowSize int: Window size defined during training or deduced by the algorithm.

fap Double: Accuracy metric for a signal.
isTrainingGoalAchieved Boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae Double
maxInferenceSyncRows Integer
multivariateFap Double: The model accuracy metric on timestamp level.
rmse Double
rowReductionDetails List<GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails List<GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail>: The list of signal details.
warning String: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize Integer: Window size defined during training or deduced by the algorithm.

fap number: Accuracy metric for a signal.
isTrainingGoalAchieved boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae number
maxInferenceSyncRows number
multivariateFap number: The model accuracy metric on timestamp level.
rmse number
rowReductionDetails GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail[]: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail[]: The list of signal details.
warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize number: Window size defined during training or deduced by the algorithm.

fap float: Accuracy metric for a signal.
is_training_goal_achieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae float
max_inference_sync_rows int
multivariate_fap float: The model accuracy metric on timestamp level.
rmse float
row_reduction_details GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail]: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signal_details GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail]: The list of signal details.
warning str: A warning message to explain the reason when targetFap cannot be achieved for trained model
window_size int: Window size defined during training or deduced by the algorithm.

fap Number: Accuracy metric for a signal.
isTrainingGoalAchieved Boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae Number
maxInferenceSyncRows Number
multivariateFap Number: The model accuracy metric on timestamp level.
rmse Number
rowReductionDetails List<Property Map>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails List<Property Map>: The list of signal details.
warning String: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize Number: Window size defined during training or deduced by the algorithm.

GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail

IsReductionEnabled bool

A boolean value to indicate if row reduction is applied

ReductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

ReductionPercentage double

A percentage to reduce data size down to on top of original data

IsReductionEnabled bool

A boolean value to indicate if row reduction is applied

ReductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

ReductionPercentage float64

A percentage to reduce data size down to on top of original data

isReductionEnabled Boolean

A boolean value to indicate if row reduction is applied

reductionMethod String

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage Double

A percentage to reduce data size down to on top of original data

isReductionEnabled boolean

A boolean value to indicate if row reduction is applied

reductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage number

A percentage to reduce data size down to on top of original data

is_reduction_enabled bool

A boolean value to indicate if row reduction is applied

reduction_method str

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reduction_percentage float

A percentage to reduce data size down to on top of original data

isReductionEnabled Boolean

A boolean value to indicate if row reduction is applied

reductionMethod String

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage Number

A percentage to reduce data size down to on top of original data

GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail

Details string

detailed information for a signal.

Fap double

Accuracy metric for a signal.

IsQuantized bool

A boolean value to indicate if a signal is quantized or not.

Max double

Max value within a signal.

Min double

Min value within a signal.

MviRatio double

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

SignalName string

The name of a signal.

Status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

Std double

Standard deviation of values within a signal.

Details string

detailed information for a signal.

Fap float64

Accuracy metric for a signal.

IsQuantized bool

A boolean value to indicate if a signal is quantized or not.

Max float64

Max value within a signal.

Min float64

Min value within a signal.

MviRatio float64

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

SignalName string

The name of a signal.

Status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

Std float64

Standard deviation of values within a signal.

details String

detailed information for a signal.

fap Double

Accuracy metric for a signal.

isQuantized Boolean

A boolean value to indicate if a signal is quantized or not.

max Double

Max value within a signal.

min Double

Min value within a signal.

mviRatio Double

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName String

The name of a signal.

status String

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std Double

Standard deviation of values within a signal.

details string

detailed information for a signal.

fap number

Accuracy metric for a signal.

isQuantized boolean

A boolean value to indicate if a signal is quantized or not.

max number

Max value within a signal.

min number

Min value within a signal.

mviRatio number

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName string

The name of a signal.

status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std number

Standard deviation of values within a signal.

details str

detailed information for a signal.

fap float

Accuracy metric for a signal.

is_quantized bool

A boolean value to indicate if a signal is quantized or not.

max float

Max value within a signal.

min float

Min value within a signal.

mvi_ratio float

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signal_name str

The name of a signal.

status str

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std float

Standard deviation of values within a signal.

details String

detailed information for a signal.

fap Number

Accuracy metric for a signal.

isQuantized Boolean

A boolean value to indicate if a signal is quantized or not.

max Number

Max value within a signal.

min Number

Min value within a signal.

mviRatio Number

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName String

The name of a signal.

status String

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std Number

Standard deviation of values within a signal.

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

oci.AiAnomalyDetection.getDetectionModels

Example Usage

Using getDetectionModels

getDetectionModels Result

Supporting Types

GetDetectionModelsFilter

GetDetectionModelsModelCollection

GetDetectionModelsModelCollectionItem

GetDetectionModelsModelCollectionItemModelTrainingDetail

GetDetectionModelsModelCollectionItemModelTrainingResult

GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail

GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail

Package Details

Contents