docs: add samples from tables/automl

.github/CODEOWNERS

@@ -0,0 +1,8 @@
+# Code owners file.
+# This file controls who is tagged for review for any given pull request.
+#
+# For syntax help see:
+# https://help.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners#codeowners-syntax
+
+
+/samples/**/*.py                           @telpirion @sirtorry @googleapis/python-samples-owners

google/cloud/automl_v1beta1/tables/tables_client.py

@@ -2762,6 +2762,7 @@ def batch_predict(
         region=None,
         credentials=None,
         inputs=None,
+        params={},
         **kwargs
     ):
         """Makes a batch prediction on a model. This does _not_ require the
@@ -2828,6 +2829,9 @@ def batch_predict(
                 The `model` instance you want to predict with . This must be
                 supplied if `model_display_name` or `model_name` are not
                 supplied.
+            params (Optional[dict]):
+                Additional domain-specific parameters for the predictions,
+                any string must be up to 25000 characters long.
 
         Returns:
             google.api_core.operation.Operation:
@@ -2886,7 +2890,7 @@ def batch_predict(
             )
 
         op = self.prediction_client.batch_predict(
-            model_name, input_request, output_request, **kwargs
+            model_name, input_request, output_request, params, **kwargs
         )
         self.__log_operation_info("Batch predict", op)
         return op

samples/tables/automl_tables_dataset.py

@@ -0,0 +1,306 @@
+#!/usr/bin/env python
+
+# Copyright 2019 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.
+
+"""This application demonstrates how to perform basic operations on dataset
+with the Google AutoML Tables API.
+
+For more information, the documentation at
+https://cloud.google.com/automl-tables/docs.
+"""
+
+import argparse
+import os
+
+
+def create_dataset(project_id, compute_region, dataset_display_name):
+    """Create a dataset."""
+    # [START automl_tables_create_dataset]
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # dataset_display_name = 'DATASET_DISPLAY_NAME_HERE'
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    # Create a dataset with the given display name
+    dataset = client.create_dataset(dataset_display_name)
+
+    # 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("Dataset metadata:")
+    print("\t{}".format(dataset.tables_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_tables_create_dataset]
+
+    return dataset
+
+
+def list_datasets(project_id, compute_region, filter_=None):
+    """List all datasets."""
+    result = []
+    # [START automl_tables_list_datasets]
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # filter_ = 'filter expression here'
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    # List all the datasets available in the region by applying filter.
+    response = client.list_datasets(filter_=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))
+        metadata = dataset.tables_dataset_metadata
+        print(
+            "Dataset primary table spec id: {}".format(
+                metadata.primary_table_spec_id
+            )
+        )
+        print(
+            "Dataset target column spec id: {}".format(
+                metadata.target_column_spec_id
+            )
+        )
+        print(
+            "Dataset target column spec id: {}".format(
+                metadata.target_column_spec_id
+            )
+        )
+        print(
+            "Dataset weight column spec id: {}".format(
+                metadata.weight_column_spec_id
+            )
+        )
+        print(
+            "Dataset ml use column spec id: {}".format(
+                metadata.ml_use_column_spec_id
+            )
+        )
+        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))
+        print("\n")
+
+        # [END automl_tables_list_datasets]
+        result.append(dataset)
+
+    return result
+
+
+def get_dataset(project_id, compute_region, dataset_display_name):
+    """Get the dataset."""
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # dataset_display_name = 'DATASET_DISPLAY_NAME_HERE'
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    # Get complete detail of the dataset.
+    dataset = client.get_dataset(dataset_display_name=dataset_display_name)
+
+    # 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("Dataset metadata:")
+    print("\t{}".format(dataset.tables_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))
+
+    return dataset
+
+
+def import_data(project_id, compute_region, dataset_display_name, path):
+    """Import structured data."""
+    # [START automl_tables_import_data]
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # dataset_display_name = 'DATASET_DISPLAY_NAME'
+    # path = 'gs://path/to/file.csv' or 'bq://project_id.dataset.table_id'
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    response = None
+    if path.startswith("bq"):
+        response = client.import_data(
+            dataset_display_name=dataset_display_name, bigquery_input_uri=path
+        )
+    else:
+        # Get the multiple Google Cloud Storage URIs.
+        input_uris = path.split(",")
+        response = client.import_data(
+            dataset_display_name=dataset_display_name,
+            gcs_input_uris=input_uris,
+        )
+
+    print("Processing import...")
+    # synchronous check of operation status.
+    print("Data imported. {}".format(response.result()))
+
+    # [END automl_tables_import_data]
+
+
+def update_dataset(
+    project_id,
+    compute_region,
+    dataset_display_name,
+    target_column_spec_name=None,
+    weight_column_spec_name=None,
+    test_train_column_spec_name=None,
+):
+    """Update dataset."""
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # dataset_display_name = 'DATASET_DISPLAY_NAME_HERE'
+    # target_column_spec_name = 'TARGET_COLUMN_SPEC_NAME_HERE' or None
+    # weight_column_spec_name = 'WEIGHT_COLUMN_SPEC_NAME_HERE' or None
+    # test_train_column_spec_name = 'TEST_TRAIN_COLUMN_SPEC_NAME_HERE' or None
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    if target_column_spec_name is not None:
+        response = client.set_target_column(
+            dataset_display_name=dataset_display_name,
+            column_spec_display_name=target_column_spec_name,
+        )
+        print("Target column updated. {}".format(response))
+    if weight_column_spec_name is not None:
+        response = client.set_weight_column(
+            dataset_display_name=dataset_display_name,
+            column_spec_display_name=weight_column_spec_name,
+        )
+        print("Weight column updated. {}".format(response))
+    if test_train_column_spec_name is not None:
+        response = client.set_test_train_column(
+            dataset_display_name=dataset_display_name,
+            column_spec_display_name=test_train_column_spec_name,
+        )
+        print("Test/train column updated. {}".format(response))
+
+
+def delete_dataset(project_id, compute_region, dataset_display_name):
+    """Delete a dataset"""
+    # [START automl_tables_delete_dataset]
+    # TODO(developer): Uncomment and set the following variables
+    # project_id = 'PROJECT_ID_HERE'
+    # compute_region = 'COMPUTE_REGION_HERE'
+    # dataset_display_name = 'DATASET_DISPLAY_NAME_HERE
+
+    from google.cloud import automl_v1beta1 as automl
+
+    client = automl.TablesClient(project=project_id, region=compute_region)
+
+    # Delete a dataset.
+    response = client.delete_dataset(dataset_display_name=dataset_display_name)
+
+    # synchronous check of operation status.
+    print("Dataset deleted. {}".format(response.result()))
+    # [END automl_tables_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")
+
+    list_datasets_parser = subparsers.add_parser(
+        "list_datasets", help=list_datasets.__doc__
+    )
+    list_datasets_parser.add_argument("--filter_")
+
+    get_dataset_parser = subparsers.add_parser(
+        "get_dataset", help=get_dataset.__doc__
+    )
+    get_dataset_parser.add_argument("--dataset_display_name")
+
+    import_data_parser = subparsers.add_parser(
+        "import_data", help=import_data.__doc__
+    )
+    import_data_parser.add_argument("--dataset_display_name")
+    import_data_parser.add_argument("--path")
+
+    update_dataset_parser = subparsers.add_parser(
+        "update_dataset", help=update_dataset.__doc__
+    )
+    update_dataset_parser.add_argument("--dataset_display_name")
+    update_dataset_parser.add_argument("--target_column_spec_name")
+    update_dataset_parser.add_argument("--weight_column_spec_name")
+    update_dataset_parser.add_argument("--ml_use_column_spec_name")
+
+    delete_dataset_parser = subparsers.add_parser(
+        "delete_dataset", help=delete_dataset.__doc__
+    )
+    delete_dataset_parser.add_argument("--dataset_display_name")
+
+    project_id = os.environ["PROJECT_ID"]
+    compute_region = os.environ["REGION_NAME"]
+
+    args = parser.parse_args()
+    if args.command == "create_dataset":
+        create_dataset(project_id, compute_region, args.dataset_name)
+    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_display_name)
+    if args.command == "import_data":
+        import_data(
+            project_id, compute_region, args.dataset_display_name, args.path
+        )
+    if args.command == "update_dataset":
+        update_dataset(
+            project_id,
+            compute_region,
+            args.dataset_display_name,
+            args.target_column_spec_name,
+            args.weight_column_spec_name,
+            args.ml_use_column_spec_name,
+        )
+    if args.command == "delete_dataset":
+        delete_dataset(project_id, compute_region, args.dataset_display_name)