docs: add samples from tables/automl (#54)

* Tables Notebooks [(#2090)](GoogleCloudPlatform/python-docs-samples#2090) * initial commit * update census * update notebooks * remove the reference to a bug [(#2100)](GoogleCloudPlatform/python-docs-samples#2100) as the bug has been fixed in the public client lib * delete this file. [(#2102)](GoogleCloudPlatform/python-docs-samples#2102) * rename file name [(#2103)](GoogleCloudPlatform/python-docs-samples#2103) * trying to fix images [(#2101)](GoogleCloudPlatform/python-docs-samples#2101) * remove typo in installation [(#2110)](GoogleCloudPlatform/python-docs-samples#2110) * Rename census_income_prediction.ipynb to getting_started_notebook.ipynb [(#2115)](GoogleCloudPlatform/python-docs-samples#2115) renaming the notebooks as Getting Started (will be in sync with the doc). It will be great if the folder could be renamed too * added back missing file package import [(#2150)](GoogleCloudPlatform/python-docs-samples#2150) * added back missing file import [(#2145)](GoogleCloudPlatform/python-docs-samples#2145) * remove incorrect reference to Iris dataset [(#2203)](GoogleCloudPlatform/python-docs-samples#2203) * conversion to jupyter/colab [(#2340)](GoogleCloudPlatform/python-docs-samples#2340) plus bug fixes * updated for the Jupyter support [(#2337)](GoogleCloudPlatform/python-docs-samples#2337) * updated readme for support Jupyter [(#2336)](GoogleCloudPlatform/python-docs-samples#2336) to approve with the updated notebook supporting jupyter * conversion to jupyer/colab [(#2339)](GoogleCloudPlatform/python-docs-samples#2339) plus bug fixes * conversion of notebook for jupyter/Colab [(#2338)](GoogleCloudPlatform/python-docs-samples#2338) conversion of the notebook to support both Jupyter and Colab + bug fixes * [BLOCKED] AutoML Tables: Docs samples updated to use new (pending) client [(#2276)](GoogleCloudPlatform/python-docs-samples#2276) * AutoML Tables: Docs samples updated to use new (pending) client * Linter warnings * add product recommendation for automl tables notebook [(#2257)](GoogleCloudPlatform/python-docs-samples#2257) * added colab filtering notebook * update to tables client * update readme * tell user to restart kernel for automl * AutoML Tables: Notebook samples updated to use new tables client [(#2424)](GoogleCloudPlatform/python-docs-samples#2424) * fix users bug and emphasize kernal restart [(#2407)](GoogleCloudPlatform/python-docs-samples#2407) * fix problems with automl docs [(#2501)](GoogleCloudPlatform/python-docs-samples#2501) Today when we try to use the function `batch_predict` follow the docs we receive and error saying: `the paramaters should be a pandas.Dataframe` it’s happens because the first parameter of the function `batch_predict` is a pandas.Dataframe. To solve this problem we need to use de named parameters of python. * Fix typo in GCS URI parameter [(#2459)](GoogleCloudPlatform/python-docs-samples#2459) * fix: fix tables notebook links and bugs [(#2601)](GoogleCloudPlatform/python-docs-samples#2601) * feat(tables): update samples to show explainability [(#2523)](GoogleCloudPlatform/python-docs-samples#2523) * show xai * local feature importance * use updated client * use fixed library * use new model * Auto-update dependencies. [(#2005)](GoogleCloudPlatform/python-docs-samples#2005) * Auto-update dependencies. * Revert update of appengine/flexible/datastore. * revert update of appengine/flexible/scipy * revert update of bigquery/bqml * revert update of bigquery/cloud-client * revert update of bigquery/datalab-migration * revert update of bigtable/quickstart * revert update of compute/api * revert update of container_registry/container_analysis * revert update of dataflow/run_template * revert update of datastore/cloud-ndb * revert update of dialogflow/cloud-client * revert update of dlp * revert update of functions/imagemagick * revert update of functions/ocr/app * revert update of healthcare/api-client/fhir * revert update of iam/api-client * revert update of iot/api-client/gcs_file_to_device * revert update of iot/api-client/mqtt_example * revert update of language/automl * revert update of run/image-processing * revert update of vision/automl * revert update testing/requirements.txt * revert update of vision/cloud-client/detect * revert update of vision/cloud-client/product_search * revert update of jobs/v2/api_client * revert update of jobs/v3/api_client * revert update of opencensus * revert update of translate/cloud-client * revert update to speech/cloud-client Co-authored-by: Kurtis Van Gent <[email protected]> Co-authored-by: Doug Mahugh <[email protected]> * Update dependency google-cloud-automl to v0.10.0 [(#3033)](GoogleCloudPlatform/python-docs-samples#3033) Co-authored-by: Bu Sun Kim <[email protected]> Co-authored-by: Leah E. Cole <[email protected]> * Simplify noxfile setup. [(#2806)](GoogleCloudPlatform/python-docs-samples#2806) * chore(deps): update dependency requests to v2.23.0 * Simplify noxfile and add version control. * Configure appengine/standard to only test Python 2.7. * Update Kokokro configs to match noxfile. * Add requirements-test to each folder. * Remove Py2 versions from everything execept appengine/standard. * Remove conftest.py. * Remove appengine/standard/conftest.py * Remove 'no-sucess-flaky-report' from pytest.ini. * Add GAE SDK back to appengine/standard tests. * Fix typo. * Roll pytest to python 2 version. * Add a bunch of testing requirements. * Remove typo. * Add appengine lib directory back in. * Add some additional requirements. * Fix issue with flake8 args. * Even more requirements. * Readd appengine conftest.py. * Add a few more requirements. * Even more Appengine requirements. * Add webtest for appengine/standard/mailgun. * Add some additional requirements. * Add workaround for issue with mailjet-rest. * Add responses for appengine/standard/mailjet. Co-authored-by: Renovate Bot <[email protected]> * chore: some lint fixes [(#3750)](GoogleCloudPlatform/python-docs-samples#3750) * automl: tables code sample clean-up [(#3571)](GoogleCloudPlatform/python-docs-samples#3571) * delete unused tables_dataset samples * delete args code associated with unused automl_tables samples * delete tests associated with unused automl_tables samples * restore get_dataset method/yargs without region tagging * Restore update_dataset methodsa without region tagging Co-authored-by: Takashi Matsuo <[email protected]> Co-authored-by: Leah E. Cole <[email protected]> * add example of creating AutoML Tables client with non-default endpoint ('new' sdk) [(#3929)](GoogleCloudPlatform/python-docs-samples#3929) * add example of creating client with non-default endpoint * more test file cleanup * move connectivity print stmt out of test fn Co-authored-by: Leah E. Cole <[email protected]> Co-authored-by: Torry Yang <[email protected]> * Replace GCLOUD_PROJECT with GOOGLE_CLOUD_PROJECT. [(#4022)](GoogleCloudPlatform/python-docs-samples#4022) * chore(deps): update dependency google-cloud-automl to v1 [(#4127)](GoogleCloudPlatform/python-docs-samples#4127) This PR contains the following updates: | Package | Update | Change | |---|---|---| | [google-cloud-automl](https://togithub.com/googleapis/python-automl) | major | `==0.10.0` -> `==1.0.1` | --- ### Release Notes <details> <summary>googleapis/python-automl</summary> ### [`v1.0.1`](https://togithub.com/googleapis/python-automl/blob/master/CHANGELOG.md#101-httpswwwgithubcomgoogleapispython-automlcomparev100v101-2020-06-18) [Compare Source](https://togithub.com/googleapis/python-automl/compare/v0.10.0...v1.0.1) </details> --- ### Renovate configuration :date: **Schedule**: At any time (no schedule defined). :vertical_traffic_light: **Automerge**: Disabled by config. Please merge this manually once you are satisfied. :recycle: **Rebasing**: Never, or you tick the rebase/retry checkbox. :no_bell: **Ignore**: Close this PR and you won't be reminded about this update again. --- - [ ] If you want to rebase/retry this PR, check this box --- This PR has been generated by [WhiteSource Renovate](https://renovate.whitesourcesoftware.com). View repository job log [here](https://app.renovatebot.com/dashboard#GoogleCloudPlatform/python-docs-samples). * [tables/automl] fix: update the csv file and the dataset name [(#4188)](GoogleCloudPlatform/python-docs-samples#4188) fixes #4177 fixes #4178 * samples: Automl table batch test [(#4267)](GoogleCloudPlatform/python-docs-samples#4267) * added rtest req.txt * samples: added automl batch predict test * added missing package * Update tables/automl/batch_predict_test.py Co-authored-by: Bu Sun Kim <[email protected]> Co-authored-by: Bu Sun Kim <[email protected]> * samples: fixed wrong format on GCS input Uri [(#4270)](GoogleCloudPlatform/python-docs-samples#4270) ## Description Current predict sample indicates that it can multiples GCS URI inputs but it should be singular. ## Checklist - [X] Please **merge** this PR for me once it is approved. * chore(deps): update dependency pytest to v5.4.3 [(#4279)](GoogleCloudPlatform/python-docs-samples#4279) * chore(deps): update dependency pytest to v5.4.3 * specify pytest for python 2 in appengine Co-authored-by: Leah Cole <[email protected]> * Update automl_tables_predict.py with batch_predict_bq sample [(#4142)](GoogleCloudPlatform/python-docs-samples#4142) Added a new method `batch_predict_bq` demonstrating running batch_prediction using BigQuery. Added notes in comments about asynchronicity for `batch_predict` method. The region `automl_tables_batch_predict_bq` will be used on cloud.google.com (currently both sections for GCS and BigQuery use the same sample code which is incorrect). Fixes #4141 Note: It's a good idea to open an issue first for discussion. - [x] Please **merge** this PR for me once it is approved. * Update dependency pytest to v6 [(#4390)](GoogleCloudPlatform/python-docs-samples#4390) * chore: exclude notebooks * chore: update templates * chore: add codeowners and fix tests * chore: ignore warnings from sphinx * chore: fix tables client * test: fix unit tests Co-authored-by: Torry Yang <[email protected]> Co-authored-by: florencep <[email protected]> Co-authored-by: Mike Burton <[email protected]> Co-authored-by: Lars Wander <[email protected]> Co-authored-by: Michael Hu <[email protected]> Co-authored-by: Michael Hu <[email protected]> Co-authored-by: Alefh Sousa <[email protected]> Co-authored-by: DPEBot <[email protected]> Co-authored-by: Kurtis Van Gent <[email protected]> Co-authored-by: Doug Mahugh <[email protected]> Co-authored-by: WhiteSource Renovate <[email protected]> Co-authored-by: Leah E. Cole <[email protected]> Co-authored-by: Takashi Matsuo <[email protected]> Co-authored-by: Anthony <[email protected]> Co-authored-by: Amy <[email protected]> Co-authored-by: Mike <[email protected]> Co-authored-by: Leah Cole <[email protected]> Co-authored-by: Sergei Dorogin <[email protected]>
wwilusz · Aug 13, 2020 · 083c3e1 · 083c3e1
commit 083c3e1
Show file tree

Hide file tree

Showing 12 changed files with 1,648 additions and 0 deletions.
diff --git a/automl_tables_dataset.py b/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)