Create scikit-xg-predict.py (#1422)

* Create scikit-xg-predict.py

This predict_json method uses exactly the same code as in https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/ml_engine/online_prediction/predict.py#L28. The only difference is that the definition of instances at line 29 is updated to be more appropriate for scikit and xgb. Kathy provided the updated definition.

* Lint tweaks

* Trailing whitespace removed.

Author: thedriftofwords

ml_engine/online_prediction/scikit-xg-predict.py

@@ -0,0 +1,53 @@
+
+# Copyright 2018 Google Inc. All Rights Reserved.
+#
+# 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.
+
+"""Examples of using the Cloud ML Engine's online prediction service,
+   modified for scikit-learn and XGBoost."""
+
+import googleapiclient.discovery
+
+
+# [START predict_json]
+def predict_json(project, model, instances, version=None):
+    """Send json data to a deployed model for prediction.
+    Args:
+        project (str): project where the Cloud ML Engine Model is deployed.
+        model (str): model name.
+        instances ([[float]]): List of input instances, where each input
+                   instance is a list of floats.
+        version: str, version of the model to target.
+    Returns:
+        Mapping[str: any]: dictionary of prediction results defined by the
+            model.
+    """
+    # Create the ML Engine service object.
+    # To authenticate set the environment variable
+    # GOOGLE_APPLICATION_CREDENTIALS=<path_to_service_account_file>
+    service = googleapiclient.discovery.build('ml', 'v1')
+    name = 'projects/{}/models/{}'.format(project, model)
+
+    if version is not None:
+        name += '/versions/{}'.format(version)
+
+    response = service.projects().predict(
+        name=name,
+        body={'instances': instances}
+    ).execute()
+
+    if 'error' in response:
+        raise RuntimeError(response['error'])
+
+    return response['predictions']
+# [END predict_json]