Oracle Cloud Infrastructure v1.41.0, Jun 19 24

Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

oci.AiAnomalyDetection.getDetectionModels

Explore with Pulumi AI

Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

Example Usage

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

const testModels = oci.AiAnomalyDetection.getDetectionModels({
    compartmentId: compartmentId,
    displayName: modelDisplayName,
    projectId: testProject.id,
    state: modelState,
});

import pulumi
import pulumi_oci as oci

test_models = oci.AiAnomalyDetection.get_detection_models(compartment_id=compartment_id,
    display_name=model_display_name,
    project_id=test_project["id"],
    state=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: compartmentId,
			DisplayName:   pulumi.StringRef(modelDisplayName),
			ProjectId:     pulumi.StringRef(testProject.Id),
			State:         pulumi.StringRef(modelState),
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

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

return await Deployment.RunAsync(() => 
{
    var testModels = Oci.AiAnomalyDetection.GetDetectionModels.Invoke(new()
    {
        CompartmentId = compartmentId,
        DisplayName = modelDisplayName,
        ProjectId = testProject.Id,
        State = modelState,
    });

});

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(compartmentId)
            .displayName(modelDisplayName)
            .projectId(testProject.id())
            .state(modelState)
            .build());

    }
}

variables:
  testModels:
    fn::invoke:
      Function: oci:AiAnomalyDetection:getDetectionModels
      Arguments:
        compartmentId: ${compartmentId}
        displayName: ${modelDisplayName}
        projectId: ${testProject.id}
        state: ${modelState}

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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.GetDetectionModelsModelCollectionItemModelTrainingDetail]: Specifies the details of the MSET model during the create call.
model_training_results Sequence[aianomalydetection.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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.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.

Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

pulumi/pulumi-oci

oci.AiAnomalyDetection.getDetectionModels

On this page

On this page

Example Usage

Using getDetectionModels

getDetectionModels Result

Supporting Types

GetDetectionModelsFilter

GetDetectionModelsModelCollection

GetDetectionModelsModelCollectionItem

GetDetectionModelsModelCollectionItemModelTrainingDetail

GetDetectionModelsModelCollectionItemModelTrainingResult

GetDetectionModelsModelCollectionItemModelTrainingResultRowReductionDetail

GetDetectionModelsModelCollectionItemModelTrainingResultSignalDetail

Package Details

On this page

On this page