Updated with Azure ML refresh changes

IoT Edge anomaly detection tutorial/iot_score.py

@@ -2,92 +2,34 @@
 # with the init and run functions needed to 
 # operationalize the anomaly detection sample
 
-# Import data collection library. Only supported for docker mode.
-# Functionality will be ignored when package isn't found
-try:
-    from azureml.datacollector import ModelDataCollector
-except ImportError:
-    print("Data collection is currently only supported in docker mode. May be disabled for local mode.")
-    # Mocking out model data collector functionality
-    class ModelDataCollector(object):
-        def nop(*args, **kw): pass
-        def __getattr__(self, _): return self.nop
-        def __init__(self, *args, **kw): return None
-    pass
+import pickle
+import json
+import pandas
+from sklearn.externals import joblib
+from sklearn.linear_model import Ridge
+from azureml.core.model import Model
 
-import os
-
-# Prepare the web service definition by authoring
-# init() and run() functions. Test the functions
-# before deploying the web service.
 def init():
-    global inputs_dc, prediction_dc
-    from sklearn.externals import joblib
-
-    # load the model file
     global model
-    model = joblib.load('model.pkl')
-
-    inputs_dc = ModelDataCollector("model.pkl", identifier="inputs")
-    prediction_dc = ModelDataCollector("model.pkl", identifier="prediction")
+    # note here "sklearn_regression_model.pkl" is the name of the model registered under
+    # this is a different behavior than before when the code is run locally, even though the code is the same.
+    model_path = Model.get_model_path('model.pkl')
+    # deserialize the model file back into a sklearn model
+    model = joblib.load(model_path)
 
+# note you can pass in multiple rows for scoring
 def run(input_str):
-    import json
-    import pandas
-
-    input_json = json.loads(input_str)
-
-    input_df = pandas.DataFrame([[input_json['machine']['temperature'], \
-        input_json['machine']['pressure'], \
-        input_json['ambient']['temperature'], \
-        input_json['ambient']['humidity'], \
-        ]])
-
-    print(input_df)
-    inputs_dc.collect(input_df)
-
-    pred = model.predict(input_df)
-    prediction_dc.collect(pred)
-
-    print("Prediction is ", pred[0])
-
+    try:
+        input_json = json.loads(input_str)
+        input_df = pandas.DataFrame([[input_json['machine']['temperature'],input_json['machine']['pressure'],input_json['ambient']['temperature'],input_json['ambient']['humidity']]])
+        pred = model.predict(input_df)
+        print("Prediction is ", pred[0])
+    except Exception as e:
+        result = str(e)
+        
     if pred[0] == '1':
         input_json['anomaly']=True
     else:
         input_json['anomaly']=False
-
+        
     return [json.dumps(input_json)]
-
-def main():
-  from azureml.api.schema.dataTypes import DataTypes
-  from azureml.api.schema.sampleDefinition import SampleDefinition
-  from azureml.api.realtime.services import generate_schema
-  import pandas
-  import json
-  
-  # Anomaly
-  df = pandas.DataFrame(data=[[33.66995566, 2.44341267, 21.39450979, 26]], columns=['machine_temperature', \
-    'machine_pressure','ambient_temperature','ambient_humidity'])
-
-  # Turn on data collection debug mode to view output in stdout
-  os.environ["AML_MODEL_DC_DEBUG"] = 'true'
-
-  # Test the output of the functions
-  init()
-
-  # Anomaly
-  #input1 = '{ "machine": { "temperature": 33.66995566, "pressure": 2.44341267 }, \
-  #      "ambient": { "temperature": 21.39450979, "humidity": 26 },\
-  #      "timeCreated": "2017-10-27T18:14:02.4911177Z" }'
-  
-  # Normal
-  input1 = '{ "machine": { "temperature": 31.16469009, "pressure": 2.158002669 }, \
-        "ambient": { "temperature": 21.17794693, "humidity": 25 },\
-        "timeCreated": "2017-10-27T18:14:02.4911177Z" }'
-
-  print("Result: " + run(input1))
-  
-  inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
-  
-if __name__ == "__main__":
-    main()