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Description:

aqudem 0.1.1

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), and each case must have the same sampling frequency
… have a concept:name attribute for each case (case ID), with a matching case ID in both files (ground truth and detected)
… 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; activity executions with a duration of exactly zero are removed

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

0.1.1 (2024-06-19)

Added additional validations and checks for the input logs, with helpful tips in errors in case of non-compliance.
Minor bug fixes.

0.1.0 (2024-06-19)

First release on PyPI.