fix: lint issues

samples/snippets/explain_tabular_sample.py

@@ -0,0 +1,68 @@
+# 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.
+
+# [START aiplatform_explain_tabular_sample]
+from typing import Dict
+
+from google.cloud import aiplatform
+from google.protobuf import json_format
+from google.protobuf.struct_pb2 import Value
+
+
+def explain_tabular_sample(
+    project: str,
+    endpoint_id: str,
+    instance_dict: Dict,
+    location: str = "us-central1",
+    api_endpoint: str = "us-central1-prediction-aiplatform.googleapis.com",
+):
+    client_options = {"api_endpoint": api_endpoint}
+    # 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.PredictionServiceClient(client_options=client_options)
+    # The format of each instance should conform to the deployed model's prediction input schema.
+    instance = json_format.ParseDict(instance_dict, Value())
+    instances = [instance]
+    # tabular models do not have additional parameters
+    parameters_dict = {}
+    parameters = json_format.ParseDict(parameters_dict, Value())
+    endpoint = client.endpoint_path(
+        project=project, location=location, endpoint=endpoint_id
+    )
+    response = client.explain(
+        endpoint=endpoint, instances=instances, parameters=parameters
+    )
+    print("response")
+    print(" deployed_model_id:", response.deployed_model_id)
+    explanations = response.explanations
+    for explanation in explanations:
+        print(" explanation")
+        # Feature attributions.
+        attributions = explanation.attributions
+        for attribution in attributions:
+            print("  attribution")
+            print("   baseline_output_value:", attribution.baseline_output_value)
+            print("   instance_output_value:", attribution.instance_output_value)
+            print("   output_display_name:", attribution.output_display_name)
+            print("   approximation_error:", attribution.approximation_error)
+            print("   output_name:", attribution.output_name)
+            output_index = attribution.output_index
+            for output_index in output_index:
+                print("   output_index:", output_index)
+    predictions = response.predictions
+    for prediction in predictions:
+        print(" prediction:", dict(prediction))
+
+
+# [END aiplatform_explain_tabular_sample]