Oracle Cloud Infrastructure v1.16.1, Nov 22 23

Oracle Cloud Infrastructure v1.16.1 published on Wednesday, Nov 22, 2023 by Pulumi

oci.AiAnomalyDetection.getDetectionModel

Explore with Pulumi AI

Oracle Cloud Infrastructure v1.16.1 published on Wednesday, Nov 22, 2023 by Pulumi

Example Usage

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

return await Deployment.RunAsync(() => 
{
    var testModel = Oci.AiAnomalyDetection.GetDetectionModel.Invoke(new()
    {
        ModelId = oci_ai_anomaly_detection_model.Test_model.Id,
    });

});

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.GetDetectionModel(ctx, &aianomalydetection.GetDetectionModelArgs{
			ModelId: oci_ai_anomaly_detection_model.Test_model.Id,
		}, 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.GetDetectionModelArgs;
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 testModel = AiAnomalyDetectionFunctions.getDetectionModel(GetDetectionModelArgs.builder()
            .modelId(oci_ai_anomaly_detection_model.test_model().id())
            .build());

    }
}

import pulumi
import pulumi_oci as oci

test_model = oci.AiAnomalyDetection.get_detection_model(model_id=oci_ai_anomaly_detection_model["test_model"]["id"])

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

const testModel = oci.AiAnomalyDetection.getDetectionModel({
    modelId: oci_ai_anomaly_detection_model.test_model.id,
});

variables:
  testModel:
    fn::invoke:
      Function: oci:AiAnomalyDetection:getDetectionModel
      Arguments:
        modelId: ${oci_ai_anomaly_detection_model.test_model.id}

Using getDetectionModel

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 getDetectionModel(args: GetDetectionModelArgs, opts?: InvokeOptions): Promise<GetDetectionModelResult>
function getDetectionModelOutput(args: GetDetectionModelOutputArgs, opts?: InvokeOptions): Output<GetDetectionModelResult>

def get_detection_model(model_id: Optional[str] = None,
                        opts: Optional[InvokeOptions] = None) -> GetDetectionModelResult
def get_detection_model_output(model_id: Optional[pulumi.Input[str]] = None,
                        opts: Optional[InvokeOptions] = None) -> Output[GetDetectionModelResult]

func GetDetectionModel(ctx *Context, args *GetDetectionModelArgs, opts ...InvokeOption) (*GetDetectionModelResult, error)
func GetDetectionModelOutput(ctx *Context, args *GetDetectionModelOutputArgs, opts ...InvokeOption) GetDetectionModelResultOutput

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

public static class GetDetectionModel 
{
    public static Task<GetDetectionModelResult> InvokeAsync(GetDetectionModelArgs args, InvokeOptions? opts = null)
    public static Output<GetDetectionModelResult> Invoke(GetDetectionModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetDetectionModelResult> getDetectionModel(GetDetectionModelArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

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

The following arguments are supported:

ModelId string: The OCID of the Model.

ModelId string: The OCID of the Model.

modelId String: The OCID of the Model.

modelId string: The OCID of the Model.

model_id str: The OCID of the Model.

modelId String: The OCID of the Model.

getDetectionModel Result

The following output properties are available:

CompartmentId string: The OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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.
ModelId string
ModelTrainingDetails List<GetDetectionModelModelTrainingDetail>: Specifies the details of the MSET model during the create call.
ModelTrainingResults List<GetDetectionModelModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.
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 OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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.
ModelId string
ModelTrainingDetails []GetDetectionModelModelTrainingDetail: Specifies the details of the MSET model during the create call.
ModelTrainingResults []GetDetectionModelModelTrainingResult: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.
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 OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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.
modelId String
modelTrainingDetails List<GetDetectionModelModelTrainingDetail>: Specifies the details of the MSET model during the create call.
modelTrainingResults List<GetDetectionModelModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.
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 OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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.
modelId string
modelTrainingDetails GetDetectionModelModelTrainingDetail[]: Specifies the details of the MSET model during the create call.
modelTrainingResults GetDetectionModelModelTrainingResult[]: Specifies the details for an Anomaly Detection model trained with MSET.
projectId string: The OCID of the project to associate with the model.
state string: The state of the model.
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 OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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_id str
model_training_details GetDetectionModelModelTrainingDetail]: Specifies the details of the MSET model during the create call.
model_training_results GetDetectionModelModelTrainingResult]: Specifies the details for an Anomaly Detection model trained with MSET.
project_id str: The OCID of the project to associate with the model.
state str: The state of the model.
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 OCID for the model's compartment.
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 user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
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.
modelId String
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 OCID of the project to associate with the model.
state String: The state of the model.
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.

Supporting Types

GetDetectionModelModelTrainingDetail

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.

GetDetectionModelModelTrainingResult

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<GetDetectionModelModelTrainingResultRowReductionDetail>: 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<GetDetectionModelModelTrainingResultSignalDetail>: 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 []GetDetectionModelModelTrainingResultRowReductionDetail: 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 []GetDetectionModelModelTrainingResultSignalDetail: 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<GetDetectionModelModelTrainingResultRowReductionDetail>: 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<GetDetectionModelModelTrainingResultSignalDetail>: 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 GetDetectionModelModelTrainingResultRowReductionDetail[]: 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 GetDetectionModelModelTrainingResultSignalDetail[]: 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 GetDetectionModelModelTrainingResultRowReductionDetail]: 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 GetDetectionModelModelTrainingResultSignalDetail]: 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.

GetDetectionModelModelTrainingResultRowReductionDetail

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

GetDetectionModelModelTrainingResultSignalDetail

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 v1.16.1 published on Wednesday, Nov 22, 2023 by Pulumi

pulumi/pulumi-oci

oci.AiAnomalyDetection.getDetectionModel

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Example Usage

Using getDetectionModel

getDetectionModel Result

Supporting Types

GetDetectionModelModelTrainingDetail

GetDetectionModelModelTrainingResult

GetDetectionModelModelTrainingResultRowReductionDetail

GetDetectionModelModelTrainingResultSignalDetail

Package Details

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On this page