Provides an example of processing a YAML file containing evidence for device detection.
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.
Required npm Dependencies:
- fiftyone.pipeline.core
- fiftyone.pipeline.engines
- fiftyone.pipeline.engines.fiftyone
- fiftyone.devicedetection.onpremise
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const LineReader = require('n-readlines');
const path = require('path');
const require51 = (requestedPackage) => {
try {
return require(path.join(__dirname, '/../../../node_modules/', requestedPackage));
} catch (e) {
return require(path.join(__dirname, '/../../../../', requestedPackage));
}
};
require51('fiftyone.devicedetection.onpremise').DeviceDetectionOnPremisePipelineBuilder;
// 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:
// /pricing
const LITE_V_4_1_HASH = '51Degrees-LiteV4.1.hash';
// 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.
const EVIDENCE = '20000 Evidence Records.yml';
// Check if files exists
const fs = require('fs');
const yaml = require('js-yaml');
const analyzeEvidence = async function (evidence, pipeline, outputFile, outputFunc) {
// 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
// Object of header name/value entries.
const data = pipeline.createFlowData();
const document = {};
// Add the evidence values to the output and add it to output object
// at the same time
for (const [key, value] of Object.entries(evidence)) {
if (value === null) {
data.evidence.add(key, '');
} else {
data.evidence.add(key, value.toString());
}
document[key] = value;
}
await data.process();
// Now add the values that we want to store against the record.
const device = data.device;
// 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.
outputFunc(outputFile, yaml.dump(document));
};
const run = async function (dataFile, evidenceFile, outputFile, outputFunc) {
// In this example, we use the DeviceDetectionOnPremisePipelineBuilder
// and configure it in code. For more information about
// pipelines in general see the documentation at
// /documentation/_concepts__configuration__builders__index.html
dataFile,
// 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:
// /documentation/_device_detection__features__performance_options.html
// /documentation/_features__automatic_datafile_updates.html
// /documentation/_features__usage_sharing.html
performanceProfile: 'LowMemory',
// Inhibit sharing usage for this example.
// 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
// /documentation/_features__usage_sharing.html#Low_Level_Usage_Sharing
// for more details on this.
shareUsage: false,
// inhibit auto-update of the data file for this test
autoUpdate: false,
updateOnStart: false,
fileSystemWatcher: false
}).build();
// Create lines reader for the evidence file
const liner = new LineReader(evidenceFile);
let buffer, utf8Line, evidence;
let records = 0;
// Start reading evidence from file
let document = '';
while ((buffer = liner.next())) {
utf8Line = buffer.toString('utf8').trim();
if ((utf8Line.match(/^---/) || utf8Line.match(/^\.\.\./)) && document) {
// Output progress
records++;
if (records % 1000 === 0) {
console.log(`Processed ${records} records`);
}
// Write the yaml document separator
outputFunc(outputFile, '---\n');
// Load a yaml document
evidence = yaml.load(document);
await analyzeEvidence(evidence, pipeline, outputFile, outputFunc);
// Stop processing if reach end of file
if (utf8Line.match(/^\.\.\./)) {
break;
}
// Move the document separator to the string buffer
document = `${utf8Line}\n`;
} else {
document += `${utf8Line}\n`;
}
}
// Write the yaml document end marker
outputFunc(outputFile, '...\n');
ExampleUtils.checkDataFile(pipeline, dataFile);
console.log(`Processing complete. See results in: '${outputFile}'`);
};
// Don't run the server if under TEST
if (process.env.JEST_WORKER_ID === undefined) {
const args = process.argv.slice(2);
// Use the supplied path for the data file or find the lite
// file that is included in the repository.
// Do the same for the yaml evidence file
const evidenceFile = args.length > 1 ? args[1] : ExampleUtils.findFile(EVIDENCE);
// 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.
const outputFile = args.length > 2
? args[2]
: path.join(path.dirname(path.resolve(evidenceFile)), 'offline-processing-output.yml');
if (dataFile !== undefined) {
// Create output file or overwrite the existing one
fs.writeFileSync(outputFile, '');
run(
dataFile,
evidenceFile,
outputFile,
(output, content) => { fs.appendFileSync(output, content); });
} else {
console.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`.');
}
};
module.exports = {
run
};
Definition dataExtension.js:23
static getValueHelper(elementData, propertyName)
Helper function to read property values from flowData.
Definition dataExtension.js:31
Definition deviceDetectionOnPremisePipelineBuilder.js:35
Definition fiftyone.devicedetection.onpremise/examples/onpremise/exampleUtils.js:36