As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1.types.dataset

# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import proto  # type: ignore

from google.cloud.automl_v1.types import image
from google.cloud.automl_v1.types import text
from google.cloud.automl_v1.types import translation
from google.protobuf import timestamp_pb2  # type: ignore


__protobuf__ = proto.module(package="google.cloud.automl.v1", manifest={"Dataset",},)


[docs]class Dataset(proto.Message): r"""A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. Attributes: translation_dataset_metadata (google.cloud.automl_v1.types.TranslationDatasetMetadata): Metadata for a dataset used for translation. image_classification_dataset_metadata (google.cloud.automl_v1.types.ImageClassificationDatasetMetadata): Metadata for a dataset used for image classification. text_classification_dataset_metadata (google.cloud.automl_v1.types.TextClassificationDatasetMetadata): Metadata for a dataset used for text classification. image_object_detection_dataset_metadata (google.cloud.automl_v1.types.ImageObjectDetectionDatasetMetadata): Metadata for a dataset used for image object detection. text_extraction_dataset_metadata (google.cloud.automl_v1.types.TextExtractionDatasetMetadata): Metadata for a dataset used for text extraction. text_sentiment_dataset_metadata (google.cloud.automl_v1.types.TextSentimentDatasetMetadata): Metadata for a dataset used for text sentiment. name (str): Output only. The resource name of the dataset. Form: ``projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`` display_name (str): Required. The name of the dataset to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. description (str): User-provided description of the dataset. The description can be up to 25000 characters long. example_count (int): Output only. The number of examples in the dataset. create_time (google.protobuf.timestamp_pb2.Timestamp): Output only. Timestamp when this dataset was created. etag (str): Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels (Sequence[google.cloud.automl_v1.types.Dataset.LabelsEntry]): Optional. The labels with user-defined metadata to organize your dataset. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. See https://goo.gl/xmQnxf for more information on and examples of labels. """ translation_dataset_metadata = proto.Field( proto.MESSAGE, number=23, oneof="dataset_metadata", message=translation.TranslationDatasetMetadata, ) image_classification_dataset_metadata = proto.Field( proto.MESSAGE, number=24, oneof="dataset_metadata", message=image.ImageClassificationDatasetMetadata, ) text_classification_dataset_metadata = proto.Field( proto.MESSAGE, number=25, oneof="dataset_metadata", message=text.TextClassificationDatasetMetadata, ) image_object_detection_dataset_metadata = proto.Field( proto.MESSAGE, number=26, oneof="dataset_metadata", message=image.ImageObjectDetectionDatasetMetadata, ) text_extraction_dataset_metadata = proto.Field( proto.MESSAGE, number=28, oneof="dataset_metadata", message=text.TextExtractionDatasetMetadata, ) text_sentiment_dataset_metadata = proto.Field( proto.MESSAGE, number=30, oneof="dataset_metadata", message=text.TextSentimentDatasetMetadata, ) name = proto.Field(proto.STRING, number=1,) display_name = proto.Field(proto.STRING, number=2,) description = proto.Field(proto.STRING, number=3,) example_count = proto.Field(proto.INT32, number=21,) create_time = proto.Field( proto.MESSAGE, number=14, message=timestamp_pb2.Timestamp, ) etag = proto.Field(proto.STRING, number=17,) labels = proto.MapField(proto.STRING, proto.STRING, number=39,)
__all__ = tuple(sorted(__protobuf__.manifest))