aqudem 0.1.0

Activity and Sequence Detection Performance Measures: A package to evaluate activity detection results, including the sequence of events given multiple activity types.

AquDeM

Activity and Sequence Detection Evaluation Metrics: A Comprehensive Tool for Event Log Comparison.

• Documentation: https://aqudem.readthedocs.io

Installation

pip install aqudem

Usage

import aqudem

aqu_context = aqudem.Context("ground_truth_log.xes", "detected_log.xes")

aqu_context.activity_names # get all activity names present in log
aqu_context.case_ids # get all case IDs present in log

aqu_context.cross_correlation() # aggregate over all cases and activites
aqu_context.event_analysis(activity_name="Pack", case_id="1") # filter on case and activity
aqu_context.two_set(activity_name="Pack") # filter on activity, aggregate over cases

For a more detailed description of the available methods, please refer to the rest of the documentation.

Preface

• Metrics to evaluate activity detection results

• Input: two XES files, one with the ground truth and one with the detection results

• Output: a set of metrics to evaluate the detection results

• Prerequisites for the input files: the XES files must…

  • … have a sampling_freq in Hz associated with each case (only detected file)

  • … have a concept:name attribute for each case (case ID)

  • … have a time:timestamp attribute for each event

  • … have an concept:name attribute for each event (activity name)

  • … have a lifecycle:transition attribute for each event

  • … each start event must have a corresponding complete event; and only these two types of events are relevant for the analysis currently

An ACTIVITY_METRIC is a metric that is calculated for each activity type in each case separately. Available ACTIVITY_METRICs are:

• Cross-Correlation

• Event Analysis by Ward et al. (2011)

• Two Set Metrics by Ward et al. (2011)

A SEQUENCE_METRIC is a metric that is calculated for each case separately. Available SEQUENCE_METRICs are:

• Damerau-Levenshtein Distance

• Levenshtein Distance

All metrics are also available in appropriately normalized versions. For requests that span multiple cases, the results are aggregated. The default and only aggregation method is currently the mean. For more detailed definitions of the metrics, please refer to the documentation.

History

0.1.0 (2024-06-19)

• First release on PyPI.