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feat(components): Add sliced evaluation metrics support for custom and unstructured AutoML models in evaluation pipeline 

PiperOrigin-RevId: 573331226
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Jason Dai 2023-10-13 15:15:56 -07:00 committed by Google Cloud Pipeline Components maintainers
parent ea93979eed
commit 0487f9a8b1
2 changed files with 8 additions and 1 deletions
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1
components/google-cloud/RELEASE.md
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@ -3,6 +3,7 @@
* Support `incremental_train_base_model`, `parent_model`, `is_default_version`, `model_version_aliases`, `model_version_description` in `AutoMLImageTrainingJobRunOp`.
* Add `preview.automl.vision` and `DataConverterJobOp`.
* Set display names for `preview.llm` pipelines.
* Add sliced evaluation metrics support for custom and unstructured AutoML models in evaluation pipeline.
## Release 2.4.1
* Disable caching for LLM pipeline tasks that store temporary artifacts.

8
components/google-cloud/google_cloud_pipeline_components/v1/model_evaluation/evaluation_automl_unstructure_data_pipeline.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, NamedTuple
from typing import Any, List, NamedTuple
from google_cloud_pipeline_components import _placeholders
from google_cloud_pipeline_components._implementation.model import GetVertexModelOp
@ -43,6 +43,7 @@ def evaluation_automl_unstructure_data_classification_pipeline( # pylint: disab
batch_predict_max_replica_count: int = 10,
batch_predict_accelerator_type: str = '',
batch_predict_accelerator_count: int = 0,
slicing_specs: List[Any] = [], # pylint: disable=g-bare-generic
evaluation_prediction_label_column: str = '',
evaluation_prediction_score_column: str = '',
evaluation_class_labels: List[str] = [], # pylint: disable=g-bare-generic
@ -81,6 +82,7 @@ def evaluation_automl_unstructure_data_classification_pipeline( # pylint: disab
batch_predict_max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set.
batch_predict_accelerator_type: The type of accelerator(s) that may be attached to the machine as per `batch_predict_accelerator_count`. Only used if `batch_predict_machine_type` is set. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_accelerator_count: The number of accelerators to attach to the `batch_predict_machine_type`. Only used if `batch_predict_machine_type` is set.
slicing_specs: List of `google.cloud.aiplatform_v1.types.ModelEvaluationSlice.SlicingSpec`. When provided, compute metrics for each defined slice. See sample code in https://cloud.google.com/vertex-ai/docs/pipelines/model-evaluation-component For more details on configuring slices, see https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ModelEvaluationSlice.
evaluation_prediction_label_column: The column name of the field containing classes the model is scoring. Formatted to be able to find nested columns, delimited by `.`.
evaluation_prediction_score_column: The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by `.`.
evaluation_class_labels: Required for classification prediction type. The list of class names for the target_field_name, in the same order they appear in a file in batch_predict_gcs_source_uris. For instance, if the target_field_name could be either `1` or `0`, then the class_labels input will be ["1", "0"].
@ -173,6 +175,7 @@ def evaluation_automl_unstructure_data_classification_pipeline( # pylint: disab
encryption_spec_key_name=encryption_spec_key_name,
force_runner_mode=force_runner_mode,
model=get_model_task.outputs['model'],
slicing_specs=slicing_specs,
)
# Import the evaluation result to Vertex AI.
@ -373,6 +376,7 @@ def evaluation_automl_unstructure_data_pipeline( # pylint: disable=dangerous-de
batch_predict_max_replica_count: int = 10,
batch_predict_accelerator_type: str = '',
batch_predict_accelerator_count: int = 0,
slicing_specs: List[Any] = [], # pylint: disable=g-bare-generic
evaluation_prediction_label_column: str = '',
evaluation_prediction_score_column: str = '',
evaluation_class_labels: List[str] = [], # pylint: disable=g-bare-generic
@ -409,6 +413,7 @@ def evaluation_automl_unstructure_data_pipeline( # pylint: disable=dangerous-de
batch_predict_max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set.
batch_predict_accelerator_type: The type of accelerator(s) that may be attached to the machine as per `batch_predict_accelerator_count`. Only used if `batch_predict_machine_type` is set. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_accelerator_count: The number of accelerators to attach to the `batch_predict_machine_type`. Only used if `batch_predict_machine_type` is set.
slicing_specs: List of `google.cloud.aiplatform_v1.types.ModelEvaluationSlice.SlicingSpec`. When provided, compute metrics for each defined slice. See sample code in https://cloud.google.com/vertex-ai/docs/pipelines/model-evaluation-component For more details on configuring slices, see https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ModelEvaluationSlice.
evaluation_prediction_label_column: The column name of the field containing classes the model is scoring. Formatted to be able to find nested columns, delimited by `.`.
evaluation_prediction_score_column: The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by `.`.
evaluation_class_labels: Required for classification prediction type. The list of class names for the target_field_name, in the same order they appear in a file in batch_predict_gcs_source_uris. For instance, if the target_field_name could be either `1` or `0`, then the class_labels input will be ["1", "0"].
@ -442,6 +447,7 @@ def evaluation_automl_unstructure_data_pipeline( # pylint: disable=dangerous-de
batch_predict_max_replica_count=batch_predict_max_replica_count,
batch_predict_accelerator_type=batch_predict_accelerator_type,
batch_predict_accelerator_count=batch_predict_accelerator_count,
slicing_specs=slicing_specs,
evaluation_prediction_label_column=evaluation_prediction_label_column,
evaluation_prediction_score_column=evaluation_prediction_score_column,
evaluation_class_labels=evaluation_class_labels,