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  5. getDetectionModel
Oracle Cloud Infrastructure v1.40.0 published on Wednesday, Jun 12, 2024 by Pulumi

oci.AiAnomalyDetection.getDetectionModel

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Oracle Cloud Infrastructure v1.40.0 published on Wednesday, Jun 12, 2024 by Pulumi

    This data source provides details about a specific Model resource in Oracle Cloud Infrastructure Ai Anomaly Detection service.

    Gets a Model by identifier

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
    import * as oci from "@pulumi/oci";
    
    const testModel = oci.AiAnomalyDetection.getDetectionModel({
        modelId: testModelOciAiAnomalyDetectionModel.id,
    });
    
    import pulumi
    import pulumi_oci as oci
    
    test_model = oci.AiAnomalyDetection.get_detection_model(model_id=test_model_oci_ai_anomaly_detection_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: testModelOciAiAnomalyDetectionModel.Id,
    		}, 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 testModel = Oci.AiAnomalyDetection.GetDetectionModel.Invoke(new()
        {
            ModelId = testModelOciAiAnomalyDetectionModel.Id,
        });
    
    });
    
    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(testModelOciAiAnomalyDetectionModel.id())
                .build());
    
        }
    }
    
    variables:
      testModel:
        fn::invoke:
          Function: oci:AiAnomalyDetection:getDetectionModel
          Arguments:
            modelId: ${testModelOciAiAnomalyDetectionModel.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 Sequence[aianomalydetection.GetDetectionModelModelTrainingDetail]
    Specifies the details of the MSET model during the create call.
    model_training_results Sequence[aianomalydetection.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 Sequence[aianomalydetection.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 Sequence[aianomalydetection.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.
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    Oracle Cloud Infrastructure v1.40.0 published on Wednesday, Jun 12, 2024 by Pulumi