\r\n

51Degrees Device Detection Python  4.4

Device Detection services for 51Degrees Pipeline

Search

Documentation

onpremise/offlineprocessing.py

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

 header.user-agent: 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36'

 header.sec-ch-ua: '" Not A;Brand";v="99", "Chromium";v="98", "Google Chrome";v="98"'

 header.sec-ch-ua-full-version: '"98.0.4758.87"'

 header.sec-ch-ua-mobile: '?0'

 header.sec-ch-ua-platform: '"Android"'

We create a device detection pipeline to read the data and find out about the associated device, we write this data to a YAML formatted output stream.

As well as explaining the basic operation of off line processing using the defaults, for advanced operation this example can be used to experiment with tuning device detection for performance and predictive power using Performance Profile, Graph and Difference and Drift settings.

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
• ruamel

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

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

 # Copyright 2025 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.

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

 

 

 

 from pathlib import Path

 import sys

 from fiftyone_devicedetection.devicedetection_pipelinebuilder import DeviceDetectionPipelineBuilder

 from fiftyone_devicedetection_examples.example_utils import ExampleUtils

 from fiftyone_pipeline_core.logger import Logger

 from fiftyone_devicedetection_shared.example_constants import LITE_DATAFILE_NAME

 from fiftyone_devicedetection_shared.example_constants import EVIDENCE_FILE_NAME

 from ruamel.yaml import YAML

 

 class OfflineProcessing():

  def run(self, data_file, evidence_yaml, logger, output):

  """!

  Process a YAML representation of evidence - and create a YAML output containing

  the processed evidence.

  @param data_file: The path to the device detection data file

  @param evidence_yaml: File containing the yaml representation of the evidence to process

  @param logger: Logger to use within the pipeline

  @param output: Output file to write results to

  """

 

  # In this example, we use the DeviceDetectionPipelineBuilder

  # and configure it in code. For more information about

  # pipelines in general see the documentation at

  # https://51degrees.com/documentation/_concepts__configuration__builders__index.html

  pipeline = DeviceDetectionPipelineBuilder(

  data_file_path = data_file,

  # We use the low memory profile as its performance is

  # sufficient for this example. See the documentation for

  # more detail on this and other configuration options:

  # https://51degrees.com/documentation/_device_detection__features__performance_options.html

  # https://51degrees.com/documentation/_features__automatic_datafile_updates.html

  # https://51degrees.com/documentation/_features__usage_sharing.html

  performance_profile = "LowMemory",

  # inhibit sharing usage for this test, usually this

  # should be set "true"

  # In general, off line processing usage should NOT be shared back to 51Degrees.

  # This is because it will not contain the full set of information that is

  # required by our data processing back-end and will be discarded.

  # If you specifically want to share data that is being processed off line

  # in order to help us improve detection of new devices/browsers/etc, then

  # this additional data will need to be collected and included as evidence

  # to the Pipeline. See

  # https://51degrees.com/documentation/_features__usage_sharing.html#Low_Level_Usage_Sharing

  # for more details on this.

  usage_sharing = False,

  # Inhibit auto-update of the data file for this example

  auto_update = False,

  licence_keys = "").add_logger(logger).build()

 

  records = 0

  yaml = YAML()

  yaml_data = yaml.load_all(evidence_yaml)

 

  try:

  # Keep going as long as we have more document records.

  for evidence in yaml_data:

  # Output progress.

  records = records + 1

  if (records % 100 == 0):

  logger.log("info", f"Processed {records} records")

 

  # write the yaml document separator

  print("---", file = output)

  # Pass the record to the pipeline as evidence so that it can be analyzed

  headers = {}

  for key in evidence:

  headers[f"header.{key}"] = evidence[key]

 

  self.analyseEvidence(headers, pipeline, output, yaml)

  except BaseException as err:

  # We can't read the evidence values, so cant write them to the output. Will just

  # have to skip this entry.

  logger.log("error", err)

 

  # write the yaml document end marker

  print("...", file = output)

 

  ExampleUtils.check_data_file(pipeline, logger)

 

  def analyseEvidence(self, evidence, pipeline, output, yaml):

  # FlowData is a data structure that is used to convey information required for

  # detection and the results of the detection through the pipeline.

  # Information required for detection is called "evidence" and usually consists

  # of a number of HTTP Header field values, in this case represented by a

  # dictionary of header name/value entries.

  data = pipeline.create_flowdata()

  # Add the evidence values to the flow data

  data.evidence.add_from_dict(evidence)

  # Process the flow data.

  data.process()

 

  device = data.device

 

  values = {}

  # Add the evidence values to the output

  for key in evidence:

  values[key] = evidence[key]

  # Now add the values that we want to store against the record.

  values["device.ismobile"] = device.ismobile.value() if device.ismobile.has_value() else "Unknown"

  values["device.platformname"] = ExampleUtils.get_human_readable(device, "platformname")

  values["device.platformversion"] = ExampleUtils.get_human_readable(device, "platformversion")

  values["device.browsername"] = ExampleUtils.get_human_readable(device, "browsername")

  values["device.browserversion"] = ExampleUtils.get_human_readable(device, "browserversion")

  # DeviceId is a unique identifier for the combination of hardware, operating

  # system, browser and crawler that has been detected.

  # Our device detection solution uses machine learning to find the optimal

  # way to identify devices based on the real-world evidence values that we

  # observe each day.

  # As this changes over time, the result of detection can potentially change

  # as well. By storing the device id, we can use this as a lookup in future

  # rather than performing detection with the original evidence again.

  # Do this by passing an evidence entry with:

  # key = query.51D_ProfileIds

  # value = [the device id]

  # This is much faster and avoids the potential for getting a different

  # result.

  values["device.deviceid"] = ExampleUtils.get_human_readable(device, "deviceid")

  yaml.dump(values, output)

 

 def main(argv):

  # In this example, by default, the 51degrees "Lite" file needs to be

  # somewhere in the project space, or you may specify another file as

  # a command line parameter.

  #

  # Note that the Lite data file is only used for illustration, and has

  # limited accuracy and capabilities.

  # Find out about the Enterprise data file on our pricing page:

  # https://51degrees.com/pricing

  data_file = argv[0] if len(argv) > 0 else ExampleUtils.find_file(LITE_DATAFILE_NAME)

  # This file contains the 20,000 most commonly seen combinations of header values

  # that are relevant to device detection. For example, User-Agent and UA-CH headers.

  evidence_file = argv[1] if len(argv) > 1 else ExampleUtils.find_file(EVIDENCE_FILE_NAME)

  # Finally, get the location for the output file. Use the same location as the

  # evidence if a path is not supplied on the command line.

  output_file = argv[2] if len(argv) > 2 else Path.joinpath(Path(evidence_file).absolute().parent, "offline-processing-output.yml")

 

  # Configure a logger to output to the console.

  logger = Logger(min_level="info")

 

  if (data_file != None):

  with open(output_file, "w") as output:

  with open(evidence_file, "r") as input:

  OfflineProcessing().run(data_file, input, logger, output)

  logger.log("info",

  f"Processing complete. See results in: '{output_file}'")

  else:

  logger.log("error",

  "Failed to find a device detection data file. Make sure the " +

  "device-detection-data submodule has been updated by running " +

  "`git submodule update --recursive`.")

 

 if __name__ == "__main__":

  main(sys.argv[1:])

Generated by doxygen 1.8.15