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:

1 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'

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

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

4 header.sec-ch-ua-mobile: '?0'

5 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
pyyaml

1 # *********************************************************************

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

4 # Caversham, Reading, Berkshire, United Kingdom RG4 7BY.

5 #

6 # This Original Work is licensed under the European Union Public Licence (EUPL)

7 # v.1.2 and is subject to its terms as set out below.

8 #

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

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

11 #

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

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

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

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

16 #

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

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

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

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

21 # ********************************************************************

22

23

52

53 from pathlib import Path

54 import sys

55 import yaml

56 from fiftyone_devicedetection.devicedetection_pipelinebuilder import DeviceDetectionPipelineBuilder

57 from fiftyone_devicedetection_examples.example_utils import ExampleUtils

58 from fiftyone_pipeline_core.logger import Logger

59

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

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

62 # a command line parameter.

63 #

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

65 # limited accuracy and capabilities.

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

67 # https://51degrees.com/pricing

68 LITE_V_4_1_HASH = "51Degrees-LiteV4.1.hash"

69

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

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

72 EVIDENCE = "20000 Evidence Records.yml"

73

74 class OfflineProcessing():

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

76 """!

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

78 the processed evidence.

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

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

81 @param logger: Logger to use within the pipeline

82 @param output: Output file to write results to

83 """

84

85 # In this example, we use the DeviceDetectionPipelineBuilder

86 # and configure it in code. For more information about

87 # pipelines in general see the documentation at

88 # http://51degrees.com/documentation/4.3/_concepts__configuration__builders__index.html

89 pipeline = DeviceDetectionPipelineBuilder(

90 data_file_path = data_file,

91 # We use the low memory profile as its performance is

92 # sufficient for this example. See the documentation for

93 # more detail on this and other configuration options:

94 # http://51degrees.com/documentation/4.3/_device_detection__features__performance_options.html

95 # http://51degrees.com/documentation/4.3/_features__automatic_datafile_updates.html

96 # http://51degrees.com/documentation/4.3/_features__usage_sharing.html

97 performance_profile = "LowMemory",

98 # inhibit sharing usage for this test, usually this

99 # should be set "true"

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

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

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

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

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

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

106 # to the Pipeline. See

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

108 # for more details on this.

109 usage_sharing = False,

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

111 auto_update = False,

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

113

114 records = 0

115 yaml_data = yaml.safe_load_all(evidence_yaml)

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

117 for evidence in yaml_data:

118 # Output progress.

119 records = records + 1

120 if (records % 100 == 0):

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

122

123 # write the yaml document separator

124 print("---", file = output)

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

126 headers = {}

127 for key in evidence:

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

129

130 self.analyseEvidence(headers, pipeline, output)

131 # write the yaml document end marker

132 print("...", file = output)

133

134 ExampleUtils.check_data_file(pipeline, logger)

135

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

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

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

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

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

141 # dictionary of header name/value entries.

142 data = pipeline.create_flowdata()

143 # Add the evidence values to the flow data

144 data.evidence.add_from_dict(evidence)

145 # Process the flow data.

146 data.process()

147

148 device = data.device

149

150 values = {}

151 # Add the evidence values to the output

152 for key in evidence:

153 values[key] = evidence[key]

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

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

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

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

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

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

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

161 # system, browser and crawler that has been detected.

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

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

164 # observe each day.

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

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

167 # rather than performing detection with the original evidence again.

168 # Do this by passing an evidence entry with:

169 # key = query.51D_ProfileIds

170 # value = [the device id]

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

172 # result.

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

174 yaml.dump(values, output)

175

176 def main(argv):

177 # Use the supplied path for the data file or find the lite

178 # file that is included in the repository.

179 data_file = argv[0] if len(argv) > 0 else ExampleUtils.find_file(LITE_V_4_1_HASH)

180 # Do the same for the yaml evidence file.

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

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

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

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

185

186 # Configure a logger to output to the console.

187 logger = Logger(min_level="info")

188

189 if (data_file != None):

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

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

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

193 logger.log("info",

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

195 else:

196 logger.log("error",

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

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

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

200

201 if __name__ == "__main__":

202 main(sys.argv[1:])

51Degrees Device Detection Python 4.4

onpremise/offlineprocessing.py