chore: Run black (#2703)

sdk/python/endpoints/online/data_collector/score.py

@@ -2,45 +2,51 @@
 import json
 from azureml.ai.monitoring import Collector
 
+
 def init():
-  global inputs_collector, outputs_collector, inputs_outputs_collector
-
-  # instantiate collectors with appropriate names, make sure align with deployment spec
-  inputs_collector = Collector(name='model_inputs')                    
-  outputs_collector = Collector(name='model_outputs')
-  inputs_outputs_collector = Collector(name='model_inputs_outputs') #note: this is used to enable Feature Attribution Drift
-
-def run(data): 
-  # json data: { "data" : {  "col1": [1,2,3], "col2": [2,3,4] } }
-  pdf_data = preprocess(json.loads(data))
-
-  # tabular data: {  "col1": [1,2,3], "col2": [2,3,4] }
-  input_df = pd.DataFrame(pdf_data)
-
-  # collect inputs data, store correlation_context
-  context = inputs_collector.collect(input_df)
-
-  # perform scoring with pandas Dataframe, return value is also pandas Dataframe
-  output_df = predict(input_df) 
-
-  # collect outputs data, pass in correlation_context so inputs and outputs data can be correlated later
-  outputs_collector.collect(output_df, context)
-
-  # create a dataframe with inputs/outputs joined - this creates a URI folder (not mltable) 
-  # input_output_df = input_df.merge(output_df, context)
-  input_output_df = input_df.join(output_df)
-
-  # collect both your inputs and output  
-  inputs_outputs_collector.collect(input_output_df, context)
-
-  return output_df.to_dict()
-
+    global inputs_collector, outputs_collector, inputs_outputs_collector
+
+    # instantiate collectors with appropriate names, make sure align with deployment spec
+    inputs_collector = Collector(name="model_inputs")
+    outputs_collector = Collector(name="model_outputs")
+    inputs_outputs_collector = Collector(
+        name="model_inputs_outputs"
+    )  # note: this is used to enable Feature Attribution Drift
+
+
+def run(data):
+    # json data: { "data" : {  "col1": [1,2,3], "col2": [2,3,4] } }
+    pdf_data = preprocess(json.loads(data))
+
+    # tabular data: {  "col1": [1,2,3], "col2": [2,3,4] }
+    input_df = pd.DataFrame(pdf_data)
+
+    # collect inputs data, store correlation_context
+    context = inputs_collector.collect(input_df)
+
+    # perform scoring with pandas Dataframe, return value is also pandas Dataframe
+    output_df = predict(input_df)
+
+    # collect outputs data, pass in correlation_context so inputs and outputs data can be correlated later
+    outputs_collector.collect(output_df, context)
+
+    # create a dataframe with inputs/outputs joined - this creates a URI folder (not mltable)
+    # input_output_df = input_df.merge(output_df, context)
+    input_output_df = input_df.join(output_df)
+
+    # collect both your inputs and output
+    inputs_outputs_collector.collect(input_output_df, context)
+
+    return output_df.to_dict()
+
+
 def preprocess(json_data):
-  # preprocess the payload to ensure it can be converted to pandas DataFrame
-  return json_data["data"]
+    # preprocess the payload to ensure it can be converted to pandas DataFrame
+    return json_data["data"]
+
 
 def predict(input_df):
-  # process input and return with outputs
-  ...
-  
-  return output_df
+    # process input and return with outputs
+    ...
+
+    return output_df