automl beta

language/automl/automl_natural_language_dataset.py

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+#!/usr/bin/env python
+
+# Copyright 2018 Google Inc. All Rights Reserved.
+#
+# 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.
+
+"""This application demonstrates how to perform basic operations on Dataset
+with the Google AutoML Natural Language API.
+
+For more information, see the tutorial page at
+https://cloud.google.com/natural-language/automl/docs/
+"""
+
+# [START automl_natural_language_import]
+import argparse
+import os
+
+from google.cloud import automl_v1beta1 as automl
+
+# [END automl_natural_language_import]
+
+
+# [START automl_natural_language_create_dataset]
+def create_dataset(project_id, compute_region, dataset_name, multilabel=False):
+    """Create a dataset.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        dataset_name: Name of the dataset.
+        multilabel: Type of the classification problem.
+            False - MULTICLASS, True - MULTILABEL.
+            Default is False.
+    """
+    client = automl.AutoMlClient()
+
+    # A resource that represents Google Cloud Platform location.
+    project_location = client.location_path(project_id, compute_region)
+
+    # Classification type is assigned based on multilabel value.
+    classification_type = "MULTICLASS"
+    if multilabel:
+        classification_type = "MULTILABEL"
+
+    # Specify the text classification type for the dataset.
+    dataset_metadata = {"classification_type": classification_type}
+
+    # Set dataset name and metadata.
+    my_dataset = {
+        "display_name": dataset_name,
+        "text_classification_dataset_metadata": dataset_metadata,
+    }
+
+    # Create a dataset with the dataset metadata in the region.
+    dataset = client.create_dataset(project_location, my_dataset)
+
+    # Display the dataset information.
+    print("Dataset name: {}".format(dataset.name))
+    print("Dataset id: {}".format(dataset.name.split("/")[-1]))
+    print("Dataset display name: {}".format(dataset.display_name))
+    print("Text classification dataset metadata:")
+    print("\t{}".format(dataset.text_classification_dataset_metadata))
+    print("Dataset example count: {}".format(dataset.example_count))
+    print("Dataset create time:")
+    print("\tseconds: {}".format(dataset.create_time.seconds))
+    print("\tnanos: {}".format(dataset.create_time.nanos))
+
+
+# [END automl_natural_language_create_dataset]
+
+
+# [START automl_natural_language_list_datasets]
+def list_datasets(project_id, compute_region, filter_):
+    """List all datasets.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        filter_: Filter expression.
+    """
+    client = automl.AutoMlClient()
+
+    # A resource that represents Google Cloud Platform location.
+    project_location = client.location_path(project_id, compute_region)
+
+    # List all the datasets available in the region by applying filter.
+    response = client.list_datasets(project_location, filter_)
+
+    print("List of datasets:")
+    for dataset in response:
+        # Display the dataset information.
+        print("Dataset name: {}".format(dataset.name))
+        print("Dataset id: {}".format(dataset.name.split("/")[-1]))
+        print("Dataset display name: {}".format(dataset.display_name))
+        print("Text classification dataset metadata:")
+        print("\t{}".format(dataset.text_classification_dataset_metadata))
+        print("Dataset example count: {}".format(dataset.example_count))
+        print("Dataset create time:")
+        print("\tseconds: {}".format(dataset.create_time.seconds))
+        print("\tnanos: {}".format(dataset.create_time.nanos))
+
+
+# [END automl_natural_language_list_datasets]
+
+
+# [START automl_natural_language_get_dataset]
+def get_dataset(project_id, compute_region, dataset_id):
+    """Get the dataset.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        dataset_id: Id of the dataset.
+    """
+    client = automl.AutoMlClient()
+
+    # Get the full path of the dataset
+    dataset_full_id = client.dataset_path(
+        project_id, compute_region, dataset_id
+    )
+
+    # Get complete detail of the dataset.
+    dataset = client.get_dataset(dataset_full_id)
+
+    # Display the dataset information.
+    print("Dataset name: {}".format(dataset.name))
+    print("Dataset id: {}".format(dataset.name.split("/")[-1]))
+    print("Dataset display name: {}".format(dataset.display_name))
+    print("Text classification dataset metadata:")
+    print("\t{}".format(dataset.text_classification_dataset_metadata))
+    print("Dataset example count: {}".format(dataset.example_count))
+    print("Dataset create time:")
+    print("\tseconds: {}".format(dataset.create_time.seconds))
+    print("\tnanos: {}".format(dataset.create_time.nanos))
+
+
+# [END automl_natural_language_get_dataset]
+
+
+# [START automl_natural_language_import_data]
+def import_data(project_id, compute_region, dataset_id, path):
+    """Import labeled items.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        dataset_id: ID of the dataset into which the training content are to
+                    be imported.
+        path: Google Cloud Storage URIs.
+              Target files must be in AutoML Natural Language CSV format.
+    """
+    client = automl.AutoMlClient()
+
+    # Get the full path of the dataset.
+    dataset_full_id = client.dataset_path(
+        project_id, compute_region, dataset_id
+    )
+
+    # Get the multiple Google Cloud Storage URIs.
+    input_uris = path.split(",")
+    input_config = {"gcs_source": {"input_uris": input_uris}}
+
+    # Import the dataset from the input URI.
+    response = client.import_data(dataset_full_id, input_config)
+
+    print("Processing import...")
+    # synchronous check of operation status.
+    print("Data imported. {}".format(response.result()))
+
+
+# [END automl_natural_language_import_data]
+
+
+# [START automl_natural_language_export_data]
+def export_data(project_id, compute_region, dataset_id, output_uri):
+    """Export a dataset to a Google Cloud Storage bucket.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        dataset_id: Id of the dataset to which will be exported.
+        output_uri: Google Cloud Storage URI for the export directory.
+    """
+    client = automl.AutoMlClient()
+
+    # Get the full path of the dataset.
+    dataset_full_id = client.dataset_path(
+        project_id, compute_region, dataset_id
+    )
+
+    # Set the output URI
+    output_config = {"gcs_destination": {"output_uri_prefix": output_uri}}
+
+    # Export the data to the output URI.
+    response = client.export_data(dataset_full_id, output_config)
+
+    print("Processing export...")
+    # synchronous check of operation status.
+    print("Data exported. {}".format(response.result()))
+
+
+# [END automl_natural_language_export_data]
+
+
+# [START automl_natural_language_delete_dataset]
+def delete_dataset(project_id, compute_region, dataset_id):
+    """Delete a dataset.
+    Args:
+        project_id: Id of the project.
+        compute_region: Region name.
+        dataset_id: Id of the dataset.
+    """
+    client = automl.AutoMlClient()
+
+    # Get the full path of the dataset.
+    dataset_full_id = client.dataset_path(
+        project_id, compute_region, dataset_id
+    )
+
+    # Delete a dataset.
+    response = client.delete_dataset(dataset_full_id)
+
+    # synchronous check of operation status.
+    print("Dataset deleted. {}".format(response.result()))
+
+
+# [END automl_natural_language_delete_dataset]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    subparsers = parser.add_subparsers(dest="command")
+
+    create_dataset_parser = subparsers.add_parser(
+        "create_dataset", help=create_dataset.__doc__
+    )
+    create_dataset_parser.add_argument("dataset_name")
+    create_dataset_parser.add_argument(
+        "multilabel", nargs="?", choices=["False", "True"], default="False"
+    )
+
+    list_datasets_parser = subparsers.add_parser(
+        "list_datasets", help=list_datasets.__doc__
+    )
+    list_datasets_parser.add_argument(
+        "filter_", nargs="?", default="text_classification_dataset_metadata:*"
+    )
+
+    get_dataset_parser = subparsers.add_parser(
+        "get_dataset", help=get_dataset.__doc__
+    )
+    get_dataset_parser.add_argument("dataset_id")
+
+    import_data_parser = subparsers.add_parser(
+        "import_data", help=import_data.__doc__
+    )
+    import_data_parser.add_argument("dataset_id")
+    import_data_parser.add_argument("path")
+
+    export_data_parser = subparsers.add_parser(
+        "export_data", help=export_data.__doc__
+    )
+    export_data_parser.add_argument("dataset_id")
+    export_data_parser.add_argument("output_uri")
+
+    delete_dataset_parser = subparsers.add_parser(
+        "delete_dataset", help=delete_dataset.__doc__
+    )
+    delete_dataset_parser.add_argument("dataset_id")
+
+    project_id = os.environ["PROJECT_ID"]
+    compute_region = os.environ["REGION_NAME"]
+
+    args = parser.parse_args()
+
+    if args.command == "create_dataset":
+        multilabel = True if args.multilabel == "True" else False
+        create_dataset(
+            project_id, compute_region, args.dataset_name, multilabel
+        )
+    if args.command == "list_datasets":
+        list_datasets(project_id, compute_region, args.filter_)
+    if args.command == "get_dataset":
+        get_dataset(project_id, compute_region, args.dataset_id)
+    if args.command == "import_data":
+        import_data(project_id, compute_region, args.dataset_id, args.path)
+    if args.command == "export_data":
+        export_data(
+            project_id, compute_region, args.dataset_id, args.output_uri
+        )
+    if args.command == "delete_dataset":
+        delete_dataset(project_id, compute_region, args.dataset_id)