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+.. _ad_scoring_pipeline:
+Scoring Pipeline
+================
+Scoring Pipeline is a python app that can be used to perform ETL transformations followed by scoring on a deployed model, on a stream of records. The result is then either sent back to the client
+or is queued up on the kafka sink topic, depending upon the mode that the app was configured in at the time of initialization.
+
+Scoring pipeline can be initialized in 2 modes: Kafka streaming mode or REST endpoint streaming mode.
+
+This section covers instantiating a scoring pipeline in TAP.
+
+
+Create a Scoring Pipeline Instance from a broker in TAP
+-------------------------------------------------------
+
+In the TAP web site:
+
+1) Navigate to **Services -> Marketplace**.
+2) Select **scoring_pipeline** => **Create new isntance**.
+3) Fill in an instance name of your choice *(given below as **etlScoring**)*.
+4) You will be able to see your scoring pipeline under the Applications page and obtain its URL.
+5) Now call its REST endpoint to load the tar archive (advscore.tar) containing the **configuration file** (config.json) and the **python script** (test_script.py)to be executed.
+    curl -i -X POST -F file=@advscore.tar  "http://etlScoring.demotrustedanalytics.com"
+6) The configuration file needs the following fields in there:
+    "file_name" -- python script that needs to be executed on every streaming record
+    "func_name" -- name of the function in the python script that needs to be invoked
+    "input_schema" -- schema of the incoming streaming record
+    "src_topic" -- kafka topic from where to start consuming the records (in case of Kafka streaming) else this field should be empty
+    "sink_topic" -- kafka topic to which the app starts writing the predictions (in case of Kafka streaming) else this field should be empty
+
+    Note: For Kafka Streaming, both source and sink topics need to be specified
+
+Scoring Pipeline Config file template
+-------------------------------------
+
+Below is a sample json to configure scoring pipeline in kafka streaming mode:
+
+.. code::
+
+    { "file_name":"test_script.py", "func_name":"evaluate", "input_schema": {
+            "columns": [
+              {
+                "name":"field_0",
+                "type":"str"
+              },
+              {
+                "name":"field_1",
+                "type":"str"
+              },
+              .....
+
+              {
+                "name":"field_162",
+                "type":"str"
+              }
+            ]}, "src_topic":"input", "sink_topic":"output"}
+
+Below is a sample json to configure scoring pipeline in REST endpoint streaming mode:
+
+.. code::
+
+    { "file_name":"test_script.py", "func_name":"evaluate", "input_schema": {
+            "columns": [
+              {
+                "name":"field_0",
+                "type":"str"
+              },
+              {
+                "name":"field_1",
+                "type":"str"
+              },
+              .....
+
+              {
+                "name":"field_162",
+                "type":"str"
+              }
+            ]}, "src_topic":"", "sink_topic":""}
+
+
+
+Scoring Pipeline Python Script Example
+--------------------------------------
+
+
+from record import Record
+import atktypes as atk
+import numpy as np
+
+
+def add_numeric_time(row):
+    try:
+        x = row['field_0'].split(" ")
+        date = x[0]
+        time = x[1]
+        t = time.split(":")
+        numeric_time = float(t[0]) + float(t[1])/60.0+float(t[2])/3600.0
+        return [date, numeric_time]
+    except:
+        return ["None", None]
+
+def string_to_numeric(column_list):
+    def string_to_numeric2(row):
+        result = []
+        for column in column_list:
+            try:
+                result.append(float(row[column]))
+            except:
+                result.append(None)
+        return result
+    return string_to_numeric2
+
+def drop_null(column_list):
+    def drop_null2(row):
+        result = False
+        for col in column_list:
+            result = True if row[col] == None else result
+        return result
+    return drop_null2
+
+column_list = ['field_'+ str(x) for x in range(19,136) if np.mod(x,4)==3]
+new_columns_schema = [('num_' + x, atk.float64) for x in column_list]
+
+PCA_column_list = ['num_field_'+ str(x) for x in range(19,136) if np.mod(x,4)==3]
+
+y= ['field_'+str(i) for i in range(0, 163)]
+y.extend(['Venus'])
+y.extend(['Mercury'])
+
+def evaluate(record):
+    record.add_columns(add_numeric_time, [('date', str), ('numeric_time', atk.float64)])
+    record.add_columns(string_to_numeric(column_list), new_columns_schema)
+    record.rename_columns({'date':'Venus', 'numeric_time':'Mercury'})
+    record.drop_columns(y)
+    result = record.filter(drop_null(PCA_column_list))
+    print("result is %s" %result)
+    if not result:
+    	r = record.score("formosascoringengine2.demotrustedanalytics.com")
+    	return r
+
+
+6) If the scoring pipeline was configured to work with Kafka messaging queues then start streaming records to the source-topic.
+7) If the scoring pipeline was configured to use the REST endpoints, then you can post requests using curl command as follows:
+    curl -H "Content-type: application/json" -X POST -d '{"message": "4/3/2016 10:32, P0001,1,0.0001,....., 192,-4.1158,192,3.8264"}' http://etlscoring.demotrustedanalytics.com/v2/score
+
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