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51Degrees Device Detection Python  4.4

Device Detection services for 51Degrees Pipeline

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onpremise/performance.py

Provides an example of processing a CSV file containing evidence for device detection. There are 20,000 examples in the supplied file of evidence representing HTTP Headers. For example:

We create a device detection pipeline to read the data and output the time taken and information about about the detection.

Requesting properties from a single component reduces detection time compared with requesting properties from multiple components. If you don't specify any properties to detect, then all properties are detected.

Please review performance options and hash dataset options for more information about adjusting performance.

This example is available in full on GitHub.

This example requires a local data file. The free 'Lite' data file can be acquired by pulling the git submodules under this repository (run `git submodule update --recursive`) or from the device-detection-data GitHub repository.

The Lite data file is only used for illustration, and has limited accuracy and capabilities. Find out about the more capable data files that are available on our pricing page

Required PyPi Dependencies:

• fiftyone_devicedetection

 # *********************************************************************

 # This Original Work is copyright of 51 Degrees Mobile Experts Limited.

 # Copyright 2023 51 Degrees Mobile Experts Limited, Davidson House,

 # Forbury Square, Reading, Berkshire, United Kingdom RG1 3EU.

 #

 # This Original Work is licensed under the European Union Public Licence

 # (EUPL) v.1.2 and is subject to its terms as set out below.

 #

 # If a copy of the EUPL was not distributed with this file, You can obtain

 # one at https://opensource.org/licenses/EUPL-1.2.

 #

 # The 'Compatible Licences' set out in the Appendix to the EUPL (as may be

 # amended by the European Commission) shall be deemed incompatible for

 # the purposes of the Work and the provisions of the compatibility

 # clause in Article 5 of the EUPL shall not apply.

 #

 # If using the Work as, or as part of, a network application, by

 # including the attribution notice(s) required under Article 5 of the EUPL

 # in the end user terms of the application under an appropriate heading,

 # such notice(s) shall fulfill the requirements of that article.

 # *********************************************************************

 

 

 import argparse

 import csv

 import json

 import time

 import multiprocessing as mp

 

 from fiftyone_devicedetection_examples.example_utils import ExampleUtils

 # This example goes through a CSV of 20000 user agents and processes them, returning the time and information about the matches

 from fiftyone_devicedetection_onpremise.devicedetection_onpremise_pipelinebuilder import DeviceDetectionOnPremisePipelineBuilder

 

 # Here we make a function that processes a user agent

 # And returns if it is a mobile device

 

 def process_user_agent(user_agent):

 

  # First we create the flowdata using the global pipeline

  flowdata = pipeline.create_flowdata() # pylint: disable=used-before-assignment

 

  # Here we add the user agent as evidence

  flowdata.evidence.add("header.user-agent", user_agent)

 

  # We process the flowdata to get the results

  flowdata.process()

 

  # To check whether the User-Agent is a mobile device we look at the ismobile

  # property inside the Device Detection Engine

 

  # first we check if this has a meaningful result

 

  if flowdata.device.ismobile.has_value():

  return flowdata.device.ismobile.value()

  else:

  return None

 

 def process_user_agent_list(data_file, user_agent_list, list_number, output, skip=False):

  global pipeline

  pipeline = DeviceDetectionOnPremisePipelineBuilder(

  data_file_path=data_file,

  licence_keys="",

  performance_profile='MaxPerformance',

  add_javascript_builder=False,

  restricted_properties=["ismobile"],

  usage_sharing=False,

  auto_update=False).build()

 

  results = {

  "mobile": 0,

  "notmobile": 0,

  "unknown": 0

  }

  for user_agent in user_agent_list:

  if skip:

  break

  result = process_user_agent(user_agent[0])

  if(result == None):

  results["unknown"] += 1

  if(result == True):

  results["mobile"] += 1

  if(result == False):

  results["notmobile"] += 1

 

  output.put(results, list_number)

 

 # Run the process

 def run(data_file, skip = False):

  # Make a queue to store the results in

 

  output = mp.Queue()

 

  # Create processes

  processes = []

 

  for x in range(threads): # pylint: disable=used-before-assignment

  processes.append(mp.Process(target=process_user_agent_list,

  args=(data_file, split_lists[x], x, output, skip))) # pylint: disable=used-before-assignment

 

  # Start timer

 

  t0 = time.time()

 

  for p in processes:

  p.start()

 

  # Exit the completed processes

  for p in processes:

  p.join()

 

  # Get process results from the output queue

  results = [output.get() for p in processes]

 

  t1 = time.time()

  total = t1-t0

 

  return {"time": total, "result": results}

 

 if __name__ == "__main__":

  ap = argparse.ArgumentParser(description='Run detection benchmark.')

  ap.add_argument('-d', '--data_file', default='', help='Path to data file')

  ap.add_argument('-u', '--user_agents_file', default='src/fiftyone_devicedetection_onpremise/cxx/device-detection-data/20000 User Agents.csv', help='Path to user agents evidence file')

  ap.add_argument('-j', '--json_output', default='', help='Output results in JSON format')

  args = ap.parse_args()

  if args.data_file == "":

  args.data_file = ExampleUtils.find_file("51Degrees-LiteV4.1.hash")

 

  # First we read the contents of the 20000 user agents file as a list

  with open(args.user_agents_file, newline='') as file:

  reader = csv.reader(file)

  user_agents = list(reader)

 

  number_of_user_agents = len(user_agents)

 

  print("Processing " + str(number_of_user_agents) + " user agents")

 

  # Now we make a function that returns results of the user agent matching

 

  threads = mp.cpu_count()

 

  print("Using " + str(threads) + " threads")

 

  chunk_size = int(number_of_user_agents / threads)

 

  # Split lists by number of threads

  split_lists = [user_agents[x:x+chunk_size]

  for x in range(0, len(user_agents), chunk_size)]

 

  calibration = run(args.data_file, skip=True)

 

  real = run(args.data_file, skip=False)

 

  real_time = real["time"]

 

  print("Total time (seconds): " + str(real_time) + " seconds")

  print("Time per user agent (ms): " + str((real_time / number_of_user_agents) * 1000))

 

  if args.json_output != "":

  results = {

  "DetectionsPerSecond": 1.0 / (real_time / number_of_user_agents),

  "MsPerDetection": real_time * 1000 / number_of_user_agents

  }

  with open(args.json_output, "w") as file:

  print(json.dumps(results), file = file)

 

  final_result = {

  "mobile": 0,

  "notmobile": 0,

  "unknown": 0

  }

 

  for result in real["result"]:

  final_result["unknown"] += result["unknown"]

  final_result["mobile"] += result["mobile"]

  final_result["notmobile"] += result["notmobile"]

 

  print("Results", final_result)

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