oci.AiAnomalyDetection.getDetectionModel
Explore with Pulumi AI
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
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:
- Model
Id string The OCID of the Model.
- Model
Id string The OCID of the Model.
- model
Id String The OCID of the Model.
- model
Id string The OCID of the Model.
- model_
id str The OCID of the Model.
- model
Id String The OCID of the Model.
getDetectionModel Result
The following output properties are available:
- Compartment
Id string The OCID for the model's compartment.
- 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.
- Display
Name string A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
- 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.
- Lifecycle
Details 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.
- Model
Id string - Model
Training List<GetDetails Detection Model Model Training Detail> Specifies the details of the MSET model during the create call.
- Model
Training List<GetResults Detection Model Model Training Result> Specifies the details for an Anomaly Detection model trained with MSET.
- Project
Id string The OCID of the project to associate with the model.
- State string
The state of the model.
- Dictionary<string, object>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example:
{"orcl-cloud.free-tier-retained": "true"}
- Time
Created string The time the the Model was created. An RFC3339 formatted datetime string.
- Time
Updated string The time the Model was updated. An RFC3339 formatted datetime string.
- Compartment
Id string The OCID for the model's compartment.
- 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.
- Display
Name string A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
- 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.
- Lifecycle
Details 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.
- Model
Id string - Model
Training []GetDetails Detection Model Model Training Detail Specifies the details of the MSET model during the create call.
- Model
Training []GetResults Detection Model Model Training Result Specifies the details for an Anomaly Detection model trained with MSET.
- Project
Id string The OCID of the project to associate with the model.
- State string
The state of the model.
- map[string]interface{}
Usage of system tag keys. These predefined keys are scoped to namespaces. Example:
{"orcl-cloud.free-tier-retained": "true"}
- Time
Created string The time the the Model was created. An RFC3339 formatted datetime string.
- Time
Updated string The time the Model was updated. An RFC3339 formatted datetime string.
- compartment
Id String The OCID for the model's compartment.
- 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.
- display
Name String A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
- 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.
- lifecycle
Details 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.
- model
Id String - model
Training List<GetDetails Detection Model Model Training Detail> Specifies the details of the MSET model during the create call.
- model
Training List<GetResults Detection Model Model Training Result> Specifies the details for an Anomaly Detection model trained with MSET.
- project
Id String The OCID of the project to associate with the model.
- state String
The state of the model.
- Map<String,Object>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created String The time the the Model was created. An RFC3339 formatted datetime string.
- time
Updated String The time the Model was updated. An RFC3339 formatted datetime string.
- compartment
Id string The OCID for the model's compartment.
- {[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.
- display
Name string A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
- {[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.
- lifecycle
Details 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.
- model
Id string - model
Training GetDetails Detection Model Model Training Detail[] Specifies the details of the MSET model during the create call.
- model
Training GetResults Detection Model Model Training Result[] Specifies the details for an Anomaly Detection model trained with MSET.
- project
Id string The OCID of the project to associate with the model.
- state string
The state of the model.
- {[key: string]: any}
Usage of system tag keys. These predefined keys are scoped to namespaces. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created string The time the the Model was created. An RFC3339 formatted datetime string.
- time
Updated string The time the Model was updated. An RFC3339 formatted datetime string.
- compartment_
id str The OCID for the model's compartment.
- 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.
- 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_ Getdetails Detection Model Model Training Detail] Specifies the details of the MSET model during the create call.
- model_
training_ Getresults Detection Model Model Training Result] 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.
- 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.
- compartment
Id String The OCID for the model's compartment.
- 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.
- display
Name String A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
- 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.
- lifecycle
Details 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.
- model
Id String - model
Training List<Property Map>Details Specifies the details of the MSET model during the create call.
- model
Training List<Property Map>Results Specifies the details for an Anomaly Detection model trained with MSET.
- project
Id String The OCID of the project to associate with the model.
- state String
The state of the model.
- Map<Any>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example:
{"orcl-cloud.free-tier-retained": "true"}
- time
Created String The time the the Model was created. An RFC3339 formatted datetime string.
- time
Updated String The time the Model was updated. An RFC3339 formatted datetime string.
Supporting Types
GetDetectionModelModelTrainingDetail
- Algorithm
Hint string User can choose specific algorithm for training.
- Data
Asset List<string>Ids 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 double A target model accuracy metric user provides as their requirement
- Training
Fraction double 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.
- Algorithm
Hint string User can choose specific algorithm for training.
- Data
Asset []stringIds 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 float64 A target model accuracy metric user provides as their requirement
- Training
Fraction float64 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.
- algorithm
Hint String User can choose specific algorithm for training.
- data
Asset List<String>Ids 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 Double A target model accuracy metric user provides as their requirement
- training
Fraction Double Fraction of total data that is used for training the model. The remaining is used for validation of the model.
- window
Size Integer Window size defined during training or deduced by the algorithm.
- algorithm
Hint string User can choose specific algorithm for training.
- data
Asset string[]Ids 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 number A target model accuracy metric user provides as their requirement
- training
Fraction number Fraction of total data that is used for training the model. The remaining is used for validation of the model.
- window
Size number Window size defined during training or deduced by the algorithm.
- algorithm_
hint str User can choose specific algorithm for training.
- data_
asset_ Sequence[str]ids 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.
- algorithm
Hint String User can choose specific algorithm for training.
- data
Asset List<String>Ids 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 Number A target model accuracy metric user provides as their requirement
- training
Fraction Number Fraction of total data that is used for training the model. The remaining is used for validation of the model.
- window
Size Number Window size defined during training or deduced by the algorithm.
GetDetectionModelModelTrainingResult
- Fap double
Accuracy metric for a signal.
- Is
Training boolGoal Achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- Mae double
- Max
Inference intSync Rows - Multivariate
Fap double The model accuracy metric on timestamp level.
- Rmse double
- Row
Reduction List<GetDetails Detection Model Model Training Result Row Reduction Detail> 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 List<GetDetection Model Model Training Result Signal Detail> The list of signal details.
- Warning string
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 float64
Accuracy metric for a signal.
- Is
Training boolGoal Achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- Mae float64
- Max
Inference intSync Rows - Multivariate
Fap float64 The model accuracy metric on timestamp level.
- Rmse float64
- Row
Reduction []GetDetails Detection Model Model Training Result Row Reduction Detail 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 []GetDetection Model Model Training Result Signal Detail The list of signal details.
- Warning string
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 Double
Accuracy metric for a signal.
- is
Training BooleanGoal Achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- mae Double
- max
Inference IntegerSync Rows - multivariate
Fap Double The model accuracy metric on timestamp level.
- rmse Double
- row
Reduction List<GetDetails Detection Model Model Training Result Row Reduction Detail> 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 List<GetDetection Model Model Training Result Signal Detail> The list of signal details.
- warning String
A warning message to explain the reason when targetFap cannot be achieved for trained model
- window
Size Integer Window size defined during training or deduced by the algorithm.
- fap number
Accuracy metric for a signal.
- is
Training booleanGoal Achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- mae number
- max
Inference numberSync Rows - multivariate
Fap number The model accuracy metric on timestamp level.
- rmse number
- row
Reduction GetDetails Detection Model Model Training Result Row Reduction Detail[] 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 GetDetection Model Model Training Result Signal Detail[] The list of signal details.
- warning string
A warning message to explain the reason when targetFap cannot be achieved for trained model
- window
Size number Window size defined during training or deduced by the algorithm.
- fap float
Accuracy metric for a signal.
- is_
training_ boolgoal_ achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- mae float
- max_
inference_ intsync_ rows - multivariate_
fap float The model accuracy metric on timestamp level.
- rmse float
- row_
reduction_ Getdetails Detection Model Model Training Result Row Reduction Detail] 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 GetDetection Model Model Training Result Signal Detail] 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.
- is
Training BooleanGoal Achieved A boolean value to indicate if train goal/targetFap is achieved for trained model
- mae Number
- max
Inference NumberSync Rows - multivariate
Fap Number The model accuracy metric on timestamp level.
- rmse Number
- row
Reduction List<Property Map>Details 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 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
- window
Size Number Window size defined during training or deduced by the algorithm.
GetDetectionModelModelTrainingResultRowReductionDetail
- Is
Reduction boolEnabled A boolean value to indicate if row reduction is applied
- Reduction
Method string Method for row reduction:
- DELETE_ROW - delete rows with equal intervals
- AVERAGE_ROW - average multiple rows to one row
- Reduction
Percentage double A percentage to reduce data size down to on top of original data
- Is
Reduction boolEnabled A boolean value to indicate if row reduction is applied
- Reduction
Method string Method for row reduction:
- DELETE_ROW - delete rows with equal intervals
- AVERAGE_ROW - average multiple rows to one row
- Reduction
Percentage float64 A percentage to reduce data size down to on top of original data
- is
Reduction BooleanEnabled A boolean value to indicate if row reduction is applied
- reduction
Method String Method for row reduction:
- DELETE_ROW - delete rows with equal intervals
- AVERAGE_ROW - average multiple rows to one row
- reduction
Percentage Double A percentage to reduce data size down to on top of original data
- is
Reduction booleanEnabled A boolean value to indicate if row reduction is applied
- reduction
Method string Method for row reduction:
- DELETE_ROW - delete rows with equal intervals
- AVERAGE_ROW - average multiple rows to one row
- reduction
Percentage number A percentage to reduce data size down to on top of original data
- is_
reduction_ boolenabled 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
- is
Reduction BooleanEnabled A boolean value to indicate if row reduction is applied
- reduction
Method String Method for row reduction:
- DELETE_ROW - delete rows with equal intervals
- AVERAGE_ROW - average multiple rows to one row
- reduction
Percentage 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.
- Is
Quantized 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.
- Mvi
Ratio double The ratio of missing values in a signal filled/imputed by the IDP algorithm.
- Signal
Name 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.
- Is
Quantized 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.
- Mvi
Ratio float64 The ratio of missing values in a signal filled/imputed by the IDP algorithm.
- Signal
Name 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.
- is
Quantized 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.
- mvi
Ratio Double The ratio of missing values in a signal filled/imputed by the IDP algorithm.
- signal
Name 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.
- is
Quantized 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.
- mvi
Ratio number The ratio of missing values in a signal filled/imputed by the IDP algorithm.
- signal
Name 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.
- is
Quantized 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.
- mvi
Ratio Number The ratio of missing values in a signal filled/imputed by the IDP algorithm.
- signal
Name 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.