feat: adds function/method enhancements, demo samples

.github/CODEOWNERS

@@ -9,3 +9,6 @@
 
 # The python-samples-owners team is the default owner for samples
 /samples/**/*.py                       @dizcology @googleapis/python-samples-owners
+
+# The enhanced client library tests are owned by @telpirion
+/tests/unit/enhanced_library/*.py      @telpirion

google/cloud/aiplatform/helpers/__init__.py

@@ -0,0 +1,3 @@
+from google.cloud.aiplatform.helpers import value_converter
+
+__all__ = (value_converter,)

google/cloud/aiplatform/helpers/_decorators.py

@@ -0,0 +1,70 @@
+# 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.
+from __future__ import absolute_import
+from google.cloud.aiplatform.helpers import value_converter
+
+from proto.marshal import Marshal
+from proto.marshal.rules.struct import ValueRule
+from google.protobuf.struct_pb2 import Value
+
+
+class ConversionValueRule(ValueRule):
+    def to_python(self, value, *, absent: bool = None):
+        return super().to_python(value, absent=absent)
+
+    def to_proto(self, value):
+
+        # Need to check whether value is an instance
+        # of an enhanced type
+        if callable(getattr(value, "to_value", None)):
+            return value.to_value()
+        else:
+            return super().to_proto(value)
+
+
+def _add_methods_to_classes_in_package(pkg):
+    classes = dict(
+        [(name, cls) for name, cls in pkg.__dict__.items() if isinstance(cls, type)]
+    )
+
+    for class_name, cls in classes.items():
+        # Add to_value() method to class with docstring
+        setattr(cls, "to_value", value_converter.to_value)
+        cls.to_value.__doc__ = value_converter.to_value.__doc__
+
+        # Add from_value() method to class with docstring
+        setattr(cls, "from_value", _add_from_value_to_class(cls))
+        cls.from_value.__doc__ = value_converter.from_value.__doc__
+
+        # Add from_map() method to class with docstring
+        setattr(cls, "from_map", _add_from_map_to_class(cls))
+        cls.from_map.__doc__ = value_converter.from_map.__doc__
+
+
+def _add_from_value_to_class(cls):
+    def _from_value(value):
+        return value_converter.from_value(cls, value)
+
+    return _from_value
+
+
+def _add_from_map_to_class(cls):
+    def _from_map(map_):
+        return value_converter.from_map(cls, map_)
+
+    return _from_map
+
+
+marshal = Marshal(name="google.cloud.aiplatform.v1beta1")
+marshal.register(Value, ConversionValueRule(marshal=marshal))

google/cloud/aiplatform/helpers/value_converter.py

@@ -0,0 +1,60 @@
+# 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
+#
+#     https://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.
+from __future__ import absolute_import
+from google.protobuf.struct_pb2 import Value
+from google.protobuf import json_format
+from proto.marshal.collections.maps import MapComposite
+from proto.marshal import Marshal
+from proto import Message
+from proto.message import MessageMeta
+
+
+def to_value(self: Message) -> Value:
+    """Converts a message type to a :class:`~google.protobuf.struct_pb2.Value` object.
+
+    Args:
+      message: the message to convert
+
+    Returns:
+      the message as a :class:`~google.protobuf.struct_pb2.Value` object
+    """
+    tmp_dict = json_format.MessageToDict(self._pb)
+    return json_format.ParseDict(tmp_dict, Value())
+
+
+def from_value(cls: MessageMeta, value: Value) -> Message:
+    """Creates instance of class from a :class:`~google.protobuf.struct_pb2.Value` object.
+
+    Args:
+      value: a :class:`~google.protobuf.struct_pb2.Value` object
+
+    Returns:
+      Instance of class
+    """
+    value_dict = json_format.MessageToDict(value)
+    return json_format.ParseDict(value_dict, cls()._pb)
+
+
+def from_map(cls: MessageMeta, map_: MapComposite) -> Message:
+    """Creates instance of class from a :class:`~proto.marshal.collections.maps.MapComposite` object.
+
+    Args:
+      map_: a :class:`~proto.marshal.collections.maps.MapComposite` object
+
+    Returns:
+      Instance of class
+    """
+    marshal = Marshal(name="marshal")
+    pb = marshal.to_proto(Value, map_)
+    return from_value(cls, pb)

google/cloud/aiplatform/v1beta1/schema/predict/instance/__init__.py

@@ -14,6 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from google.cloud.aiplatform.helpers import _decorators
+import google.cloud.aiplatform.v1beta1.schema.predict.instance_v1beta1.types as pkg
 
 from google.cloud.aiplatform.v1beta1.schema.predict.instance_v1beta1.types.image_classification import (
     ImageClassificationPredictionInstance,
@@ -54,3 +56,4 @@
     "VideoClassificationPredictionInstance",
     "VideoObjectTrackingPredictionInstance",
 )
+_decorators._add_methods_to_classes_in_package(pkg)

google/cloud/aiplatform/v1beta1/schema/predict/params/__init__.py

@@ -14,6 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from google.cloud.aiplatform.helpers import _decorators
+import google.cloud.aiplatform.v1beta1.schema.predict.params_v1beta1.types as pkg
 
 from google.cloud.aiplatform.v1beta1.schema.predict.params_v1beta1.types.image_classification import (
     ImageClassificationPredictionParams,
@@ -42,3 +44,4 @@
     "VideoClassificationPredictionParams",
     "VideoObjectTrackingPredictionParams",
 )
+_decorators._add_methods_to_classes_in_package(pkg)

google/cloud/aiplatform/v1beta1/schema/predict/prediction/__init__.py

@@ -14,6 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from google.cloud.aiplatform.helpers import _decorators
+import google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types as pkg
 
 from google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.classification import (
     ClassificationPredictionResult,
@@ -62,3 +64,4 @@
     "VideoClassificationPredictionResult",
     "VideoObjectTrackingPredictionResult",
 )
+_decorators._add_methods_to_classes_in_package(pkg)

google/cloud/aiplatform/v1beta1/schema/predict/prediction_v1beta1/types/text_sentiment.py

@@ -17,8 +17,10 @@
 
 import proto  # type: ignore
 
-
-from google.cloud.aiplatform.v1beta1.schema.predict.instance import text_sentiment_pb2 as gcaspi_text_sentiment  # type: ignore
+# DO NOT OVERWRITE FOLLOWING LINE: it was manually edited.
+from google.cloud.aiplatform.v1beta1.schema.predict.instance import (
+    TextSentimentPredictionInstance,
+)
 
 
 __protobuf__ = proto.module(
@@ -57,9 +59,7 @@ class Prediction(proto.Message):
         sentiment = proto.Field(proto.INT32, number=1)
 
     instance = proto.Field(
-        proto.MESSAGE,
-        number=1,
-        message=gcaspi_text_sentiment.TextSentimentPredictionInstance,
+        proto.MESSAGE, number=1, message=TextSentimentPredictionInstance,
     )
 
     prediction = proto.Field(proto.MESSAGE, number=2, message=Prediction,)

google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/__init__.py

@@ -14,6 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from google.cloud.aiplatform.helpers import _decorators
+import google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types as pkg
 
 from google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_forecasting import (
     AutoMlForecasting,
@@ -130,3 +132,4 @@
     "AutoMlVideoObjectTrackingInputs",
     "ExportEvaluatedDataItemsConfig",
 )
+_decorators._add_methods_to_classes_in_package(pkg)

google/cloud/aiplatform/v1beta1/schema/trainingjob/definition_v1beta1/types/automl_forecasting.py

@@ -78,14 +78,14 @@ class AutoMlForecastingInputs(proto.Message):
             function over the validation set.
 
             The supported optimization objectives:
-              "minimize-rmse" (default) - Minimize root-
+            "minimize-rmse" (default) - Minimize root-
             mean-squared error (RMSE).   "minimize-mae" -
             Minimize mean-absolute error (MAE).   "minimize-
             rmsle" - Minimize root-mean-squared log error
             (RMSLE).   "minimize-rmspe" - Minimize root-
             mean-squared percentage error (RMSPE).
             "minimize-wape-mae" - Minimize the combination
-            of weighted absolute     percentage error (WAPE)
+            of weighted absolute percentage error (WAPE)
             and mean-absolute-error (MAE).
         train_budget_milli_node_hours (int):
             Required. The train budget of creating this
@@ -418,11 +418,11 @@ class Period(proto.Message):
             unit (str):
                 The time granularity unit of this time
                 period. The supported unit are:
-                 "hour"
-                 "day"
-                 "week"
-                 "month"
-                 "year".
+                "hour"
+                "day"
+                "week"
+                "month"
+                "year".
             quantity (int):
                 The number of units per period, e.g. 3 weeks
                 or 2 months.

google/cloud/aiplatform/v1beta1/schema/trainingjob/definition_v1beta1/types/automl_tables.py

@@ -61,7 +61,7 @@ class AutoMlTablesInputs(proto.Message):
             produce.   "classification" - Predict one out of
             multiple target values is
             picked for each row.
-              "regression" - Predict a value based on its
+            "regression" - Predict a value based on its
             relation to other values.                  This
             type is available only to columns that contain
             semantically numeric values, i.e. integers or
@@ -87,22 +87,22 @@ class AutoMlTablesInputs(proto.Message):
             the prediction type. If the field is not set, a
             default objective function is used.
             classification (binary):
-              "maximize-au-roc" (default) - Maximize the
+            "maximize-au-roc" (default) - Maximize the
             area under the receiver
             operating characteristic (ROC) curve.
             "minimize-log-loss" - Minimize log loss.
-              "maximize-au-prc" - Maximize the area under
+            "maximize-au-prc" - Maximize the area under
             the precision-recall curve.   "maximize-
             precision-at-recall" - Maximize precision for a
             specified
             recall value.   "maximize-recall-at-precision" -
             Maximize recall for a specified
             precision value.
             classification (multi-class):
-              "minimize-log-loss" (default) - Minimize log
+            "minimize-log-loss" (default) - Minimize log
             loss.
             regression:
-              "minimize-rmse" (default) - Minimize root-
+            "minimize-rmse" (default) - Minimize root-
             mean-squared error (RMSE).   "minimize-mae" -
             Minimize mean-absolute error (MAE).   "minimize-
             rmsle" - Minimize root-mean-squared log error

samples/snippets/create_training_pipeline_image_classification_sample.py

@@ -14,8 +14,8 @@
 
 # [START aiplatform_create_training_pipeline_image_classification_sample]
 from google.cloud import aiplatform
-from google.protobuf import json_format
-from google.protobuf.struct_pb2 import Value
+from google.cloud.aiplatform.v1beta1.schema.trainingjob import definition
+ModelType = definition.AutoMlImageClassificationInputs().ModelType
 
 
 def create_training_pipeline_image_classification_sample(
@@ -30,13 +30,14 @@ def create_training_pipeline_image_classification_sample(
     # Initialize client that will be used to create and send requests.
     # This client only needs to be created once, and can be reused for multiple requests.
     client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
-    training_task_inputs_dict = {
-        "multiLabel": True,
-        "modelType": "CLOUD",
-        "budgetMilliNodeHours": 8000,
-        "disableEarlyStopping": False,
-    }
-    training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
+
+    icn_training_inputs = definition.AutoMlImageClassificationInputs(
+        multi_label=True,
+        model_type=ModelType.CLOUD,
+        budget_milli_node_hours=8000,
+        disable_early_stopping=False
+    )
+    training_task_inputs = icn_training_inputs.to_value()
 
     training_pipeline = {
         "display_name": display_name,

samples/snippets/predict_image_classification_sample.py

@@ -16,8 +16,9 @@
 import base64
 
 from google.cloud import aiplatform
-from google.protobuf import json_format
-from google.protobuf.struct_pb2 import Value
+from google.cloud.aiplatform.v1beta1.schema.predict import instance
+from google.cloud.aiplatform.v1beta1.schema.predict import params
+from google.cloud.aiplatform.v1beta1.schema.predict import prediction
 
 
 def predict_image_classification_sample(
@@ -36,25 +37,29 @@ def predict_image_classification_sample(
 
     # The format of each instance should conform to the deployed model's prediction input schema.
     encoded_content = base64.b64encode(file_content).decode("utf-8")
-    instance_dict = {"content": encoded_content}
 
-    instance = json_format.ParseDict(instance_dict, Value())
-    instances = [instance]
-    # See gs://google-cloud-aiplatform/schema/predict/params/image_classification_1.0.0.yaml for the format of the parameters.
-    parameters_dict = {"confidence_threshold": 0.5, "max_predictions": 5}
-    parameters = json_format.ParseDict(parameters_dict, Value())
+    instance_obj = instance.ImageClassificationPredictionInstance(
+        content=encoded_content)
+
+    instance_val = instance_obj.to_value()
+    instances = [instance_val]
+
+    params_obj = params.ImageClassificationPredictionParams(
+        confidence_threshold=0.5, max_predictions=5)
+
     endpoint = client.endpoint_path(
         project=project, location=location, endpoint=endpoint_id
     )
     response = client.predict(
-        endpoint=endpoint, instances=instances, parameters=parameters
+        endpoint=endpoint, instances=instances, parameters=params_obj
     )
     print("response")
-    print(" deployed_model_id:", response.deployed_model_id)
+    print("\tdeployed_model_id:", response.deployed_model_id)
     # See gs://google-cloud-aiplatform/schema/predict/prediction/classification.yaml for the format of the predictions.
     predictions = response.predictions
-    for prediction in predictions:
-        print(" prediction:", dict(prediction))
+    for prediction_ in predictions:
+        prediction_obj = prediction.ClassificationPredictionResult.from_map(prediction_)
+        print(prediction_obj)
 
 
 # [END aiplatform_predict_image_classification_sample]

samples/snippets/predict_image_classification_sample_test.py

@@ -31,4 +31,4 @@ def test_ucaip_generated_predict_image_classification_sample(capsys):
     )
 
     out, _ = capsys.readouterr()
-    assert 'string_value: "daisy"' in out
+    assert 'deployed_model_id:' in out