feat: Added column_specs, training_encryption_spec_key_name, model_en…

…cryption_spec_key_name to AutoMLForecastingTrainingJob.init and various split methods to AutoMLForecastingTrainingJob.run (#647)

* Extracted column_names code from AutoMLTabularTrainingJob for reuse

* Added missing parameters to AutoMLForecast

* Fixed tests and added encryption spec

* Added missing docstrings

* Made _ColumnNamesDataset subclass _Dataset

* Fixed docstrings

* Moved transformations code out of column_names_dataset

* Minor fixes

* Cleanup

* Ran linter

* Fix lint issue

* Removed timestamp_split_column_name from AutoMLForecasting

* Cleaned up typehints

* Fixed test

* Ran linter

* Ran lint

* Added more docstrings for raising exceptions

google/cloud/aiplatform/datasets/__init__.py

@@ -16,6 +16,7 @@
 #
 
 from google.cloud.aiplatform.datasets.dataset import _Dataset
+from google.cloud.aiplatform.datasets.column_names_dataset import _ColumnNamesDataset
 from google.cloud.aiplatform.datasets.tabular_dataset import TabularDataset
 from google.cloud.aiplatform.datasets.time_series_dataset import TimeSeriesDataset
 from google.cloud.aiplatform.datasets.image_dataset import ImageDataset
@@ -25,6 +26,7 @@
 
 __all__ = (
     "_Dataset",
+    "_ColumnNamesDataset",
     "TabularDataset",
     "TimeSeriesDataset",
     "ImageDataset",

google/cloud/aiplatform/datasets/column_names_dataset.py

@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2021 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 csv
+import logging
+from typing import List, Optional, Set
+from google.auth import credentials as auth_credentials
+
+from google.cloud import bigquery
+from google.cloud import storage
+
+from google.cloud.aiplatform import utils
+from google.cloud.aiplatform import datasets
+
+
+class _ColumnNamesDataset(datasets._Dataset):
+    @property
+    def column_names(self) -> List[str]:
+        """Retrieve the columns for the dataset by extracting it from the Google Cloud Storage or
+        Google BigQuery source.
+
+        Returns:
+            List[str]
+                A list of columns names
+
+        Raises:
+            RuntimeError: When no valid source is found.
+        """
+
+        self._assert_gca_resource_is_available()
+
+        metadata = self._gca_resource.metadata
+
+        if metadata is None:
+            raise RuntimeError("No metadata found for dataset")
+
+        input_config = metadata.get("inputConfig")
+
+        if input_config is None:
+            raise RuntimeError("No inputConfig found for dataset")
+
+        gcs_source = input_config.get("gcsSource")
+        bq_source = input_config.get("bigquerySource")
+
+        if gcs_source:
+            gcs_source_uris = gcs_source.get("uri")
+
+            if gcs_source_uris and len(gcs_source_uris) > 0:
+                # Lexicographically sort the files
+                gcs_source_uris.sort()
+
+                # Get the first file in sorted list
+                # TODO(b/193044977): Return as Set instead of List
+                return list(
+                    self._retrieve_gcs_source_columns(
+                        project=self.project,
+                        gcs_csv_file_path=gcs_source_uris[0],
+                        credentials=self.credentials,
+                    )
+                )
+        elif bq_source:
+            bq_table_uri = bq_source.get("uri")
+            if bq_table_uri:
+                # TODO(b/193044977): Return as Set instead of List
+                return list(
+                    self._retrieve_bq_source_columns(
+                        project=self.project,
+                        bq_table_uri=bq_table_uri,
+                        credentials=self.credentials,
+                    )
+                )
+
+        raise RuntimeError("No valid CSV or BigQuery datasource found.")
+
+    @staticmethod
+    def _retrieve_gcs_source_columns(
+        project: str,
+        gcs_csv_file_path: str,
+        credentials: Optional[auth_credentials.Credentials] = None,
+    ) -> Set[str]:
+        """Retrieve the columns from a comma-delimited CSV file stored on Google Cloud Storage
+
+        Example Usage:
+
+            column_names = _retrieve_gcs_source_columns(
+                "project_id",
+                "gs://example-bucket/path/to/csv_file"
+            )
+
+            # column_names = {"column_1", "column_2"}
+
+        Args:
+            project (str):
+                Required. Project to initiate the Google Cloud Storage client with.
+            gcs_csv_file_path (str):
+                Required. A full path to a CSV files stored on Google Cloud Storage.
+                Must include "gs://" prefix.
+            credentials (auth_credentials.Credentials):
+                Credentials to use to with GCS Client.
+        Returns:
+            Set[str]
+                A set of columns names in the CSV file.
+
+        Raises:
+            RuntimeError: When the retrieved CSV file is invalid.
+        """
+
+        gcs_bucket, gcs_blob = utils.extract_bucket_and_prefix_from_gcs_path(
+            gcs_csv_file_path
+        )
+        client = storage.Client(project=project, credentials=credentials)
+        bucket = client.bucket(gcs_bucket)
+        blob = bucket.blob(gcs_blob)
+
+        # Incrementally download the CSV file until the header is retrieved
+        first_new_line_index = -1
+        start_index = 0
+        increment = 1000
+        line = ""
+
+        try:
+            logger = logging.getLogger("google.resumable_media._helpers")
+            logging_warning_filter = utils.LoggingFilter(logging.INFO)
+            logger.addFilter(logging_warning_filter)
+
+            while first_new_line_index == -1:
+                line += blob.download_as_bytes(
+                    start=start_index, end=start_index + increment - 1
+                ).decode("utf-8")
+
+                first_new_line_index = line.find("\n")
+                start_index += increment
+
+            header_line = line[:first_new_line_index]
+
+            # Split to make it an iterable
+            header_line = header_line.split("\n")[:1]
+
+            csv_reader = csv.reader(header_line, delimiter=",")
+        except (ValueError, RuntimeError) as err:
+            raise RuntimeError(
+                "There was a problem extracting the headers from the CSV file at '{}': {}".format(
+                    gcs_csv_file_path, err
+                )
+            )
+        finally:
+            logger.removeFilter(logging_warning_filter)
+
+        return set(next(csv_reader))
+
+    @staticmethod
+    def _get_bq_schema_field_names_recursively(
+        schema_field: bigquery.SchemaField,
+    ) -> Set[str]:
+        """Retrieve the name for a schema field along with ancestor fields.
+        Nested schema fields are flattened and concatenated with a ".".
+        Schema fields with child fields are not included, but the children are.
+
+        Args:
+            project (str):
+                Required. Project to initiate the BigQuery client with.
+            bq_table_uri (str):
+                Required. A URI to a BigQuery table.
+                Can include "bq://" prefix but not required.
+            credentials (auth_credentials.Credentials):
+                Credentials to use with BQ Client.
+
+        Returns:
+            Set[str]
+                A set of columns names in the BigQuery table.
+        """
+
+        ancestor_names = {
+            nested_field_name
+            for field in schema_field.fields
+            for nested_field_name in _ColumnNamesDataset._get_bq_schema_field_names_recursively(
+                field
+            )
+        }
+
+        # Only return "leaf nodes", basically any field that doesn't have children
+        if len(ancestor_names) == 0:
+            return {schema_field.name}
+        else:
+            return {f"{schema_field.name}.{name}" for name in ancestor_names}
+
+    @staticmethod
+    def _retrieve_bq_source_columns(
+        project: str,
+        bq_table_uri: str,
+        credentials: Optional[auth_credentials.Credentials] = None,
+    ) -> Set[str]:
+        """Retrieve the column names from a table on Google BigQuery
+        Nested schema fields are flattened and concatenated with a ".".
+        Schema fields with child fields are not included, but the children are.
+
+        Example Usage:
+
+            column_names = _retrieve_bq_source_columns(
+                "project_id",
+                "bq://project_id.dataset.table"
+            )
+
+            # column_names = {"column_1", "column_2", "column_3.nested_field"}
+
+        Args:
+            project (str):
+                Required. Project to initiate the BigQuery client with.
+            bq_table_uri (str):
+                Required. A URI to a BigQuery table.
+                Can include "bq://" prefix but not required.
+            credentials (auth_credentials.Credentials):
+                Credentials to use with BQ Client.
+
+        Returns:
+            Set[str]
+                A set of column names in the BigQuery table.
+        """
+
+        # Remove bq:// prefix
+        prefix = "bq://"
+        if bq_table_uri.startswith(prefix):
+            bq_table_uri = bq_table_uri[len(prefix) :]
+
+        client = bigquery.Client(project=project, credentials=credentials)
+        table = client.get_table(bq_table_uri)
+        schema = table.schema
+
+        return {
+            field_name
+            for field in schema
+            for field_name in _ColumnNamesDataset._get_bq_schema_field_names_recursively(
+                field
+            )
+        }