This example shows how to access the 'match metrics' assocaited with a result from 51Degrees device detection. Match metrics are various properties that indicate the level of confidence that the supplied evidence corresponds to the result that has been returned.
This example is available in full on GitHub.
This example requires a local data file. Free data files can be acquired by pulling the submodules under this repository or from the device-detection-data GitHub repository.
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package fiftyone.devicedetection.examples.hash;
import fiftyone.devicedetection.DeviceDetectionPipelineBuilder;
import fiftyone.devicedetection.examples.ExampleBase;
import fiftyone.devicedetection.examples.ProgramBase;
import fiftyone.devicedetection.hash.engine.onpremise.data.DeviceDataHash;
import fiftyone.pipeline.core.data.FlowData;
import fiftyone.pipeline.core.flowelements.Pipeline;
import fiftyone.pipeline.engines.Constants;
import java.util.Map;
public class MatchMetrics extends ProgramBase {
public static void main(String[] args) throws Exception {
String dataFile = args.length > 0 ? args[0] :
getDefaultFilePath("51Degrees-LiteV4.1.hash").getAbsolutePath();
new Example(true).run(dataFile);
System.out.println("Complete. Press enter to exit.");
System.in.read();
}
public static class Example extends ExampleBase {
private final String mobileUserAgent =
"Mozilla/5.0 (Linux; Android 5.0; SM-G900P Build/LRX21T) " +
"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 " +
"Mobile Safari/537.36";
public Example(boolean printOutput) {
super(printOutput);
}
public void run(String dataFile) throws Exception {
println("Constructing pipeline with engine " +
"from file " + dataFile);
// Build a new Pipeline to use an on-premise Hash engine with the
// low memory performance profile.
Pipeline pipeline = new DeviceDetectionPipelineBuilder()
.useOnPremise(dataFile, false)
.setAutoUpdate(false)
// Prefer low memory profile where all data streamed from disk
// on-demand. Experiment with other profiles.
.setPerformanceProfile(Constants.PerformanceProfiles.LowMemory)
//.setPerformanceProfile(Constants.PerformanceProfiles.HighPerformance)
//.setPerformanceProfile(Constants.PerformanceProfiles.Balanced)
// Disable share usage for this example.
.setShareUsage(false)
// Include the IsMobile property in the results. Notice how the
// components related to OS, Browser and Crawler will not be
// provided in the results.
.setProperty("IsMobile")
// Uncomment BrowserName to include Browser component profile ID
// in the device ID value.
//.setProperty("BrowserName")
// If using the full on-premise data file this property will be
// present in the data file. See https://51degrees.com/pricing
.setProperty("HardwareName")
// Only use the predictive graph to better handle variances
// between the training data and the target User-Agent string.
// For a more detailed description of the differences between
// performance and predictive, see
// <a href="https://51degrees.com/documentation/4.1/_device_detection__hash.html#DeviceDetection_Hash_DataSetProduction_Performance">Hash Algorithm</a>
.setUsePredictiveGraph(true)
.setUsePerformanceGraph(false)
.build();
// Create a new FlowData instance ready to be populated with
// evidence for the Pipeline.
FlowData data = pipeline.createFlowData();
// Process a single HTTP User-Agent string to retrieve the values
// associated with the User-Agent for the selected properties.
data.addEvidence(
"header.user-agent",
mobileUserAgent)
.process();
DeviceDataHash device = data.get(DeviceDataHash.class);
println("User-Agent: " + mobileUserAgent);
// Obtain the matched User-Agent: the matched substrings in the
// User-Agent separated with underscored.
println("Matched User-Agent: " +
device.getUserAgents().getValue().get(0));
// Obtains the matched Device ID: the IDs of the matched profiles
// separated with hyphens. Notice how the value changes depending
// on the properties that are used with the builder. Profile IDs are
// replaced with zeros when there are no properties associated with
// the corresponding component available.
println("Id: " + device.getDeviceId().getValue());
// Obtain difference: The total difference in hash code values
// between the matched substrings and the actual substrings. The
// maximum difference to allow when finding a match can be set
// through the configuration structure.
println("Difference: " + device.getDifference().getValue());
// Obtain drift: The maximum drift for a matched substring from the
// character position where it was expected to be found. The maximum
// drift to allow when finding a match can be set through the
// configuration structure.
println("Drift: " + device.getDrift().getValue());
// Obtain iteration count: The number of iterations required to get
// the device offset in the devices collection in the graph of
// nodes. This is indicative of the time taken to fetch the result.
println("Iterations: " + device.getIterations().getValue());
// Output the method that was used to obtain the result. Play with
// the setUsePredictiveGraph and setUsePerformanceGraph values to
// see the different results.
println("Method: " + device.getMethod().getValue());
// Use the dictionary of key value pairs to output all the available
// values. Slightly less efficient than the strongly typed
// accessors.
Map<String, Object> entries = device.asKeyMap();
for (Map.Entry<String, Object> entry : entries.entrySet()) {
println(entry.getKey() + ": " + entry.getValue());
}
}
}
}