ModelArgs

data class ModelArgs(val catalogId: Output<String>? = null, val displayName: Output<String>? = null, val dryRun: Output<Boolean>? = null, val filteringOption: Output<ModelFilteringOption>? = null, val location: Output<String>? = null, val modelFeaturesConfig: Output<GoogleCloudRetailV2ModelModelFeaturesConfigArgs>? = null, val name: Output<String>? = null, val optimizationObjective: Output<String>? = null, val periodicTuningState: Output<ModelPeriodicTuningState>? = null, val project: Output<String>? = null, val trainingState: Output<ModelTrainingState>? = null, val type: Output<String>? = null) : ConvertibleToJava<ModelArgs>

Creates a new model.

Constructors

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fun ModelArgs(catalogId: Output<String>? = null, displayName: Output<String>? = null, dryRun: Output<Boolean>? = null, filteringOption: Output<ModelFilteringOption>? = null, location: Output<String>? = null, modelFeaturesConfig: Output<GoogleCloudRetailV2ModelModelFeaturesConfigArgs>? = null, name: Output<String>? = null, optimizationObjective: Output<String>? = null, periodicTuningState: Output<ModelPeriodicTuningState>? = null, project: Output<String>? = null, trainingState: Output<ModelTrainingState>? = null, type: Output<String>? = null)

Functions

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open override fun toJava(): ModelArgs

Properties

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val catalogId: Output<String>? = null
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val displayName: Output<String>? = null

The display name of the model. Should be human readable, used to display Recommendation Models in the Retail Cloud Console Dashboard. UTF-8 encoded string with limit of 1024 characters.

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val dryRun: Output<Boolean>? = null

Optional. Whether to run a dry run to validate the request (without actually creating the model).

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Optional. If RECOMMENDATIONS_FILTERING_ENABLED, recommendation filtering by attributes is enabled for the model.

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val location: Output<String>? = null
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Optional. Additional model features config.

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val name: Output<String>? = null

The fully qualified resource name of the model. Format: projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id} catalog_id has char limit of 50. recommendation_model_id has char limit of 40.

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val optimizationObjective: Output<String>? = null

Optional. The optimization objective e.g. cvr. Currently supported values: ctr, cvr, revenue-per-order. If not specified, we choose default based on model type. Default depends on type of recommendation: recommended-for-you =>ctr others-you-may-like =>ctr frequently-bought-together =>revenue_per_order This field together with optimization_objective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimization_objective = ctr), you receive an error 400 if you try to create/update a recommendation with this set of knobs.

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Optional. The state of periodic tuning. The period we use is 3 months - to do a one-off tune earlier use the TuneModel method. Default value is PERIODIC_TUNING_ENABLED.

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val project: Output<String>? = null
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val trainingState: Output<ModelTrainingState>? = null

Optional. The training state that the model is in (e.g. TRAINING or PAUSED). Since part of the cost of running the service is frequency of training - this can be used to determine when to train model in order to control cost. If not specified: the default value for CreateModel method is TRAINING. The default value for UpdateModel method is to keep the state the same as before.

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val type: Output<String>? = null

The type of model e.g. home-page. Currently supported values: recommended-for-you, others-you-may-like, frequently-bought-together, page-optimization, similar-items, buy-it-again, on-sale-items, and recently-viewed(readonly value). This field together with optimization_objective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimization_objective = ctr), you receive an error 400 if you try to create/update a recommendation with this set of knobs.