Cloud AutoML V1 Client - Class ModelEvaluation (1.5.4)

Reference documentation and code samples for the Cloud AutoML V1 Client class ModelEvaluation.

Evaluation results of a model.

Generated from protobuf message google.cloud.automl.v1.ModelEvaluation

Namespace

Google \ Cloud \ AutoMl \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ classification_evaluation_metrics`	`Google\Cloud\AutoMl\V1\ClassificationEvaluationMetrics` Model evaluation metrics for image, text, video and tables classification. Tables problem is considered a classification when the target column is CATEGORY DataType.
`↳ translation_evaluation_metrics`	`Google\Cloud\AutoMl\V1\TranslationEvaluationMetrics` Model evaluation metrics for translation.
`↳ image_object_detection_evaluation_metrics`	`Google\Cloud\AutoMl\V1\ImageObjectDetectionEvaluationMetrics` Model evaluation metrics for image object detection.
`↳ text_sentiment_evaluation_metrics`	`Google\Cloud\AutoMl\V1\TextSentimentEvaluationMetrics` Evaluation metrics for text sentiment models.
`↳ text_extraction_evaluation_metrics`	`Google\Cloud\AutoMl\V1\TextExtractionEvaluationMetrics` Evaluation metrics for text extraction models.
`↳ name`	`string` Output only. Resource name of the model evaluation. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
`↳ annotation_spec_id`	`string` Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation. For Tables annotation specs in the dataset do not exist and this ID is always not set, but for CLASSIFICATION prediction_type-s the display_name field is used.
`↳ display_name`	`string` Output only. The value of display_name at the moment when the model was trained. Because this field returns a value at model training time, for different models trained from the same dataset, the values may differ, since display names could had been changed between the two model's trainings. For Tables CLASSIFICATION prediction_type-s distinct values of the target column at the moment of the model evaluation are populated here. The display_name is empty for the overall model evaluation.
`↳ create_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this model evaluation was created.
`↳ evaluated_example_count`	`int` Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model. For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the annotation_spec_id.

getClassificationEvaluationMetrics

Model evaluation metrics for image, text, video and tables classification.

Tables problem is considered a classification when the target column is CATEGORY DataType.

Returns
Type	Description
`Google\Cloud\AutoMl\V1\ClassificationEvaluationMetrics\|null`

hasClassificationEvaluationMetrics

setClassificationEvaluationMetrics

Model evaluation metrics for image, text, video and tables classification.

Tables problem is considered a classification when the target column is CATEGORY DataType.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\ClassificationEvaluationMetrics`

Returns
Type	Description
`$this`

getTranslationEvaluationMetrics

Model evaluation metrics for translation.

Returns
Type	Description
`Google\Cloud\AutoMl\V1\TranslationEvaluationMetrics\|null`

hasTranslationEvaluationMetrics

setTranslationEvaluationMetrics

Model evaluation metrics for translation.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\TranslationEvaluationMetrics`

Returns
Type	Description
`$this`

getImageObjectDetectionEvaluationMetrics

Model evaluation metrics for image object detection.

Returns
Type	Description
`Google\Cloud\AutoMl\V1\ImageObjectDetectionEvaluationMetrics\|null`

hasImageObjectDetectionEvaluationMetrics

setImageObjectDetectionEvaluationMetrics

Model evaluation metrics for image object detection.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\ImageObjectDetectionEvaluationMetrics`

Returns
Type	Description
`$this`

getTextSentimentEvaluationMetrics

Evaluation metrics for text sentiment models.

Returns
Type	Description
`Google\Cloud\AutoMl\V1\TextSentimentEvaluationMetrics\|null`

hasTextSentimentEvaluationMetrics

setTextSentimentEvaluationMetrics

Evaluation metrics for text sentiment models.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\TextSentimentEvaluationMetrics`

Returns
Type	Description
`$this`

getTextExtractionEvaluationMetrics

Evaluation metrics for text extraction models.

Returns
Type	Description
`Google\Cloud\AutoMl\V1\TextExtractionEvaluationMetrics\|null`

hasTextExtractionEvaluationMetrics

setTextExtractionEvaluationMetrics

Evaluation metrics for text extraction models.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\TextExtractionEvaluationMetrics`

Returns
Type	Description
`$this`

getName

Output only. Resource name of the model evaluation.

Format: projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}

Returns
Type	Description
`string`

setName

Output only. Resource name of the model evaluation.

Format: projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getAnnotationSpecId

Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation.

For Tables annotation specs in the dataset do not exist and this ID is always not set, but for CLASSIFICATION prediction_type-s the display_name field is used.

Returns
Type	Description
`string`

setAnnotationSpecId

Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation.

For Tables annotation specs in the dataset do not exist and this ID is always not set, but for CLASSIFICATION prediction_type-s the display_name field is used.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getDisplayName

Output only. The value of display_name at the moment when the model was trained. Because this field returns a value at model training time, for different models trained from the same dataset, the values may differ, since display names could had been changed between the two model's trainings. For Tables CLASSIFICATION prediction_type-s distinct values of the target column at the moment of the model evaluation are populated here.

The display_name is empty for the overall model evaluation.

Returns
Type	Description
`string`

setDisplayName

The display_name is empty for the overall model evaluation.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getCreateTime

Output only. Timestamp when this model evaluation was created.

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasCreateTime

clearCreateTime

setCreateTime

Output only. Timestamp when this model evaluation was created.

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getEvaluatedExampleCount

Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model.

For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the annotation_spec_id.

Returns
Type	Description
`int`

setEvaluatedExampleCount

Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model.

Parameter
Name	Description
`var`	`int`

Returns
Type	Description
`$this`

getMetrics

Returns
Type	Description
`string`