feeed 1.3.2

Feature Extraction from Event Data

FEEED: Feature Extraction from Event Data [0]

The analysis of event data is largely influenced by the effective characterization of descriptors. These descriptors serve as the building blocks of our understanding, encapsulating the behavior described within the event data. In light of these considerations, we introduce FEEED (Feature Extraction for Event Data), an extendable tool for event data feature extraction. FEEED represents a significant advancement in event data behavior analysis, offering a range of features to empower analysts and data scientists in their pursuit of insightful, actionable, and understandable event data analysis. What sets FEEED apart is its unique capacity to act as a bridge between the worlds of data mining and process mining. In doing so, it promises to enhance the accuracy, comprehensiveness, and utility of characterizing event data for a diverse range of applications.

A video tutorial on how to use this tool can be found here. If you'd like to learn more about how it works, see References below.

Table of Contents

• Installation
• Usage
• Extending Features
• References

Installation

Requirements:

• Python > 3.9
• Java

To directly use meta feature extraction methods via import, install directly from PyPi with

pip install feeed

Run:

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes'))"

Usage

Features extracted by this tool stem from [2],[3],[4],[5]. Output data contains at least one feature with a feature_name and a corresponding value obtained by that feature's specific computation. The schema looks like this:

{
'log': 'Sepsis'
'feature_name': value
}

Every feature_name belongs to a feature_type, and a feature_type can comprise multiple features. The following Feature Types table presents the correspondence between feature_type and feature_name.

Feature types

Specific feature_names and feature_types can be selected as in the examples below:

Feature Type	Feature Names
simple_stats	n_traces, n_variants, ratio_variants_per_number_of_traces
trace_length	trace_len_min, trace_len_max, trace_len_mean, trace_len_median, trace_len_mode, trace_len_std, trace_len_variance, trace_len_q1, trace_len_q3, trace_len_iqr, trace_len_geometric_mean, trace_len_geometric_std, trace_len_harmonic_mean, trace_len_skewness, trace_len_kurtosis, trace_len_coefficient_variation, trace_len_entropy, trace_len_hist1, trace_len_hist2, trace_len_hist3, trace_len_hist4, trace_len_hist5, trace_len_hist6, trace_len_hist7, trace_len_hist8, trace_len_hist9, trace_len_hist10, trace_len_skewness_hist, trace_len_kurtosis_hist
trace_variant	ratio_most_common_variant, ratio_top_1_variants, ratio_top_5_variants, ratio_top_10_variants, ratio_top_20_variants, ratio_top_50_variants, ratio_top_75_variants, mean_variant_occurrence, std_variant_occurrence, skewness_variant_occurrence, kurtosis_variant_occurrence
activities	n_unique_activities, activities_min, activities_max, activities_mean, activities_median, activities_std, activities_variance, activities_q1, activities_q3, activities_iqr, activities_skewness, activities_kurtosis
start_activities	n_unique_start_activities, start_activities_min, start_activities_max, start_activities_mean, start_activities_median, start_activities_std, start_activities_variance, start_activities_q1, start_activities_q3, start_activities_iqr, start_activities_skewness, start_activities_kurtosis
end_activities	n_unique_end_activities, end_activities_min, end_activities_max, end_activities_mean, end_activities_median, end_activities_std, end_activities_variance, end_activities_q1, end_activities_q3, end_activities_iqr, end_activities_skewness, end_activities_kurtosis
eventropies[4]	eventropy_trace, eventropy_prefix, eventropy_prefix_flattened, eventropy_global_block, eventropy_global_block_flattened, eventropy_lempel_ziv, eventropy_lempel_ziv_flattened, eventropy_k_block_diff_1, eventropy_k_block_diff_3, eventropy_k_block_diff_5, eventropy_k_block_ratio_1, eventropy_k_block_ratio_3, eventropy_k_block_ratio_5, eventropy_knn_3, eventropy_knn_5, eventropy_knn_7
epa_based[5]	epa_variant_entropy, epa_normalized_variant_entropy, epa_sequence_entropy, epa_normalized_sequence_entropy, epa_sequence_entropy_linear_forgetting, epa_normalized_sequence_entropy_linear_forgetting, epa_sequence_entropy_exponential_forgetting, epa_normalized_sequence_entropy_exponential_forgetting
time_based	accumulated_time, execution_time, remaining_time, within_day (with each: min, max, mean, median, mode, std, variance, q1, q3, iqr, geometric_mean, geometric_std, harmonic_mean, skewness, kurtosis, coefficient_variation, entropy, skewness_hist, kurtosis_hist resulting in e.g. accumulated_time_min)

Examples

For the following examples we used Sepsis event data[1].

Example 1:

Passing sublist of feature_names, e.g. ['start_activities_min', 'end_activities_max'], to get a list of values for those features only.

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes',  ['start_activities_min', 'end_activities_max']))"

outputs

SUCCESSFULLY: 2 features for SEPSIS.xes took 0:00:00.342855 sec.
{'log': 'SEPSIS', 'start_activities_min': 6, 'end_activities_max': 393}

Example 2:

Passing sublist of feature_types, e.g. ['start_activities'], to get a list of values for the feature type 'start_activities' only

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes',  ['start_activities']))"

outputs

SUCCESSFULLY: 12 features for Sepsis.xes took 0:00:01.946063 sec.
{'log': 'Sepsis', 'n_unique_start_activities': 6, 'start_activities_iqr': 9.25, 'start_activities_kurtosis': 1.199106773708694, 'start_activities_max': 995, 'start_activities_mean': 175.0, 'start_activities_median': 12.0, 'start_activities_min': 6, 'start_activities_q1': 7.75, 'start_activities_q3': 17.0, 'start_activities_skewness': 1.7883562472303318, 'start_activities_std': 366.73787187399483, 'start_activities_variance': 134496.66666666666}

Example 3:

By not passing any list of feature_types to get the full list of all feature values for all feature_types, from the shell

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes'))"

For the order of feature_names as in Feature Type table:

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes', ['n_traces', 'n_variants', 'ratio_variants_per_number_of_traces', 'trace_len_min', 'trace_len_max', 'trace_len_mean', 'trace_len_median', 'trace_len_mode', 'trace_len_std', 'trace_len_variance', 'trace_len_q1', 'trace_len_q3', 'trace_len_iqr', 'trace_len_geometric_mean', 'trace_len_geometric_std', 'trace_len_harmonic_mean', 'trace_len_skewness', 'trace_len_kurtosis', 'trace_len_coefficient_variation', 'trace_len_entropy', 'trace_len_hist1', 'trace_len_hist2', 'trace_len_hist3', 'trace_len_hist4', 'trace_len_hist5', 'trace_len_hist6', 'trace_len_hist7', 'trace_len_hist8', 'trace_len_hist9', 'trace_len_hist10', 'trace_len_skewness_hist', 'trace_len_kurtosis_hist', 'ratio_most_common_variant', 'ratio_top_1_variants', 'ratio_top_5_variants', 'ratio_top_10_variants', 'ratio_top_20_variants', 'ratio_top_50_variants', 'ratio_top_75_variants', 'mean_variant_occurrence', 'std_variant_occurrence', 'skewness_variant_occurrence', 'kurtosis_variant_occurrence', 'n_unique_activities', 'activities_min', 'activities_max', 'activities_mean', 'activities_median', 'activities_std', 'activities_variance', 'activities_q1', 'activities_q3', 'activities_iqr', 'activities_skewness', 'activities_kurtosis', 'n_unique_start_activities', 'start_activities_min', 'start_activities_max', 'start_activities_mean', 'start_activities_median', 'start_activities_std', 'start_activities_variance', 'start_activities_q1', 'start_activities_q3', 'start_activities_iqr', 'start_activities_skewness', 'start_activities_kurtosis', 'n_unique_end_activities', 'end_activities_min', 'end_activities_max', 'end_activities_mean', 'end_activities_median', 'end_activities_std', 'end_activities_variance', 'end_activities_q1', 'end_activities_q3', 'end_activities_iqr', 'end_activities_skewness', 'end_activities_kurtosis', 'eventropy_trace', 'eventropy_prefix', 'eventropy_global_block', 'eventropy_lempel_ziv', 'eventropy_k_block_diff_1', 'eventropy_k_block_diff_3', 'eventropy_k_block_diff_5', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_3', 'eventropy_k_block_ratio_5', 'eventropy_knn_3', 'eventropy_knn_5', 'eventropy_knn_7', 'epa_variant_entropy', 'epa_normalized_variant_entropy', 'epa_sequence_entropy', 'epa_normalized_sequence_entropy', 'epa_sequence_entropy_linear_forgetting', 'epa_normalized_sequence_entropy_linear_forgetting', 'epa_sequence_entropy_exponential_forgetting', 'epa_normalized_sequence_entropy_exponential_forgetting', 'accumulated_time', 'execution_time', 'remaining_time', 'within_day']))"

or in a python script

from feeed.feature_extractor import extract_features

features = extract_features("test_data/Sepsis.xes")

outputs

SUCCESSFULLY: 179 features for Sepsis.xes took 0:00:14.875727 sec.
{'log': 'Sepsis', 'n_traces': 1050, 'n_variants': 846, 'ratio_variants_per_number_of_traces': 0.8057142857142857, 'trace_len_coefficient_variation': 0.7916391922924689, 'trace_len_entropy': 6.769403523350811, 'trace_len_geometric_mean': 12.281860759040903, 'trace_len_geometric_std': 1.7464004837799154, 'trace_len_harmonic_mean': 10.47731701485374, 'trace_len_hist1': 0.048613291470434326, 'trace_len_hist10': 0.00010465724751439027, 'trace_len_hist2': 0.005285190999476714, 'trace_len_hist3': 0.0005756148613291472, 'trace_len_hist4': 0.0002093144950287807, 'trace_len_hist5': 0.00010465724751439036, 'trace_len_hist6': 0.0, 'trace_len_hist7': 5.232862375719522e-05, 'trace_len_hist8': 0.0, 'trace_len_hist9': 0.0, 'trace_len_iqr': 7.0, 'trace_len_kurtosis': 87.0376906898399, 'trace_len_kurtosis_hist': 4.931206347805768, 'trace_len_max': 185, 'trace_len_mean': 14.48952380952381, 'trace_len_median': 13.0, 'trace_len_min': 3, 'trace_len_mode': 8, 'trace_len_q1': 9.0, 'trace_len_q3': 16.0, 'trace_len_skewness': 7.250526815880918, 'trace_len_skewness_hist': 2.6128507781562513, 'trace_len_std': 11.470474925273926, 'trace_len_variance': 131.57179501133788, 'kurtosis_variant_occurrence': 217.44268017168216, 'mean_variant_occurrence': 1.2411347517730495, 'ratio_most_common_variant': 0.03333333333333333, 'ratio_top_10_variants': 0.2742857142857143, 'ratio_top_1_variants': 0.12, 'ratio_top_20_variants': 0.35523809523809524, 'ratio_top_50_variants': 0.5971428571428572, 'ratio_top_5_variants': 0.21523809523809523, 'ratio_top_75_variants': 0.7980952380952381, 'skewness_variant_occurrence': 13.637101374069475, 'std_variant_occurrence': 1.7594085182491936, 'activities_iqr': 983.5, 'activities_kurtosis': 1.05777753209275, 'activities_max': 3383, 'activities_mean': 950.875, 'activities_median': 788.0, 'activities_min': 6, 'activities_q1': 101.75, 'activities_q3': 1085.25, 'activities_skewness': 1.3912385607018212, 'activities_std': 1008.5815457239935, 'activities_variance': 1017236.734375, 'n_unique_activities': 16, 'n_unique_start_activities': 6, 'start_activities_iqr': 9.25, 'start_activities_kurtosis': 1.199106773708694, 'start_activities_max': 995, 'start_activities_mean': 175.0, 'start_activities_median': 12.0, 'start_activities_min': 6, 'start_activities_q1': 7.75, 'start_activities_q3': 17.0, 'start_activities_skewness': 1.7883562472303318, 'start_activities_std': 366.73787187399483, 'start_activities_variance': 134496.66666666666, 'end_activities_iqr': 39.5, 'end_activities_kurtosis': 2.5007579343413617, 'end_activities_max': 393, 'end_activities_mean': 75.0, 'end_activities_median': 32.5, 'end_activities_min': 2, 'end_activities_q1': 14.0, 'end_activities_q3': 53.5, 'end_activities_skewness': 2.004413358907822, 'end_activities_std': 112.91400014423114, 'end_activities_variance': 12749.57142857143, 'n_unique_end_activities': 14, 'eventropy_global_block': 14.501, 'eventropy_global_block_flattened': 14.655, 'eventropy_k_block_diff_1': 3.238, 'eventropy_k_block_diff_3': 1.712, 'eventropy_k_block_diff_5': 1.104, 'eventropy_k_block_ratio_1': 3.238, 'eventropy_k_block_ratio_3': 2.262, 'eventropy_k_block_ratio_5': 1.871, 'eventropy_knn_3': 4.956, 'eventropy_knn_5': 4.49, 'eventropy_knn_7': 4.191, 'eventropy_lempel_ziv': 1.727, 'eventropy_lempel_ziv_flattened': 1.888, 'eventropy_prefix': 10.227, 'eventropy_prefix_flattened': 10.595, 'eventropy_trace': 9.334, 'epa_normalized_sequence_entropy': 0.5223430410751398, 'epa_normalized_sequence_entropy_exponential_forgetting': 0.29950463593968696, 'epa_normalized_sequence_entropy_linear_forgetting': 0.21936523360299368, 'epa_normalized_variant_entropy': 0.6957588422064969, 'epa_sequence_entropy': 76528.6794749776, 'epa_sequence_entropy_exponential_forgetting': 43880.53919110408, 'epa_sequence_entropy_linear_forgetting': 32139.284589305265, 'epa_variant_entropy': 40624.49329803771, 'accumulated_time_min': 0.0, 'accumulated_time_max': 36488789.0, 'accumulated_time_mean': 396893.5456158801, 'accumulated_time_median': 11924.0, 'accumulated_time_mode': 0.0, 'accumulated_time_std': 1603193.2693230412, 'accumulated_time_variance': 2570228658802.701, 'accumulated_time_q1': 1138.5, 'accumulated_time_q3': 273793.5, 'accumulated_time_iqr': 272655.0, 'accumulated_time_geometric_mean': 10904.332835327954, 'accumulated_time_geometric_std': 44.90292804116573, 'accumulated_time_harmonic_mean': 0.0, 'accumulated_time_skewness': 11.401470845961647, 'accumulated_time_kurtosis': 172.5725804780399, 'accumulated_time_coefficient_variation': 4.039353340541942, 'accumulated_time_entropy': 7.7513093893416505, 'accumulated_time_skewness_hist': 2.6663623098416838, 'accumulated_time_kurtosis_hist': 5.1101603988544575, 'execution_time_min': 0.0, 'execution_time_max': 36051318.0, 'execution_time_mean': 169759.47397134217, 'execution_time_median': 188.0, 'execution_time_mode': 0.0, 'execution_time_std': 1442884.0333929851, 'execution_time_variance': 2081914333820.4087, 'execution_time_q1': 0.0, 'execution_time_q3': 18623.25, 'execution_time_iqr': 18623.25, 'execution_time_geometric_mean': 199.88320191111325, 'execution_time_geometric_std': 127.92792986844444, 'execution_time_harmonic_mean': 0.0, 'execution_time_skewness': 14.528527518337812, 'execution_time_kurtosis': 250.488253204707, 'execution_time_coefficient_variation': 8.499578843161146, 'execution_time_entropy': 6.221052534222753, 'execution_time_skewness_hist': 2.666603580180752, 'execution_time_kurtosis_hist': 5.110914600502133, 'remaining_time_min': 0.0, 'remaining_time_max': 36488789.0, 'remaining_time_mean': 2796232.825161036, 'remaining_time_median': 619470.0, 'remaining_time_mode': 0.0, 'remaining_time_std': 5281078.119895241, 'remaining_time_variance': 27889786108436.258, 'remaining_time_q1': 202862.5, 'remaining_time_q3': 2487420.0, 'remaining_time_iqr': 2284557.5, 'remaining_time_geometric_mean': 224736.22203397762, 'remaining_time_geometric_std': 70.1715364379747, 'remaining_time_harmonic_mean': 0.0, 'remaining_time_skewness': 3.1659682263680318, 'remaining_time_kurtosis': 11.666720436340661, 'remaining_time_coefficient_variation': 1.8886403422401359, 'remaining_time_entropy': 8.55331137332654, 'remaining_time_skewness_hist': 2.61693528788402, 'remaining_time_kurtosis_hist': 4.950830339077765, 'within_day_min': 0.0, 'within_day_max': 86390.0, 'within_day_mean': 41330.543183909555, 'within_day_median': 37800.0, 'within_day_mode': 21600.0, 'within_day_std': 20590.894075207798, 'within_day_variance': 423984918.8164276, 'within_day_q1': 23113.75, 'within_day_q3': 57600.0, 'within_day_iqr': 34486.25, 'within_day_geometric_mean': 35069.233548115764, 'within_day_geometric_std': 1.9726454507370417, 'within_day_harmonic_mean': 0.0, 'within_day_skewness': 0.3603519661740256, 'within_day_kurtosis': -0.9142275965359778, 'within_day_coefficient_variation': 0.49820042247168106, 'within_day_entropy': 9.501009299480838, 'within_day_skewness_hist': 1.7511033515349685, 'within_day_kurtosis_hist': 2.6115894228132266}

Extending Features

This tutorial is for extending this tool to include additional features (e.g. time-based). As an example for this tutorial, we focus on the example of time-based features. The feeed/time.py script contains the class TimeBased, which extracts features from timestamps. FEEED focuses and extracts features of the whole log only (e.g., time within the day).

Assumptions and conditions

To include new features in this repo, first consider the following:

• Clarifying whether the proposed feature is on event-log-level instead of a single event, trace, or activity level.
• Next, check for dependency and Python version compatibility with this current repo (see setup.py).

If both conditions apply, move on to implementation.

Implementing any NewFeatures class

• Clone this repo to your local machine using git clone [e-mail]:lmu-dbs/feeed.git
• Include the new module containing the new_feature computation in feeed/, resulting in feeed/new_feature.py (e.g. feeed/time.py).
• Import the new class NewFeatures in feeed/feature_extractor.py (e.g. from .time import TimeBased as time_based)
  • NewFeatures should inherit from Feature to use extract.
  • Input for NewFeatures should support event-logs, as in pm4py.
  • Ensure output of the NewFeatures class is a dict of the sort: {"new_feature_name_1": value1, "new_feature_name_2": value2}.
• To call the new class and methods, include the new new_feature_type (e.g. "time_based") in the list of feeed/feature_extractor.py.
  • Include the new_feature_type in NEW_FEATURE_TYPE
• Include the new new_feature_type (e.g. "time_based") and its feature_namess (e.g. "accumulated_time_geometric_mean") in the Feature Type table.

Below, see an example of pseudo-code of how to implement a new (generic) feature extraction class. Note that cls is the class object of the form <class 'feeed.new_feature_type.NewFeatures>; **kwargs points to e.g. log attributes, which can also be replaced by simply log, if desired; and summarize() needs to be implemented depending on the feature level, meaning if arr_values is on a log level, summarize is the identity function:

import inspect

from .feature import Feature

class NewFeatures(Feature):
    def __init__(self, feature_names='new_feature_type'):
    self.feature_type="new_feature_type"
    self.available_class_methods = dict(inspect.getmembers(NewFeatures, predicate=inspect.ismethod))
    if self.feature_type in feature_names:
        self.feature_names = [*self.available_class_methods.keys()]
    else:
        self.feature_names = feature_names

    def helper_function(log):
        return len(log)*2

    @classmethod
    def new_feature_name_1(cls, **kwargs):
        double_length = NewFeatures.helper_funtion(log)
        return kwargs["event_attribute"] ** double_length

    @classmethod
    def new_feature_name_2(cls, **kwargs):
        double_length = NewFeatures.helper_funtion(log)
        return kwargs["event_attribute"] + double_length

Testing the new implementation

After implementing the new feature; including it in the list of feeed/feature_extractor.py and importing the new method accordingly, you can quickly test it by running the:

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/SEPSIS.xes', ['new_feature_type']))"

and

python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/SEPSIS.xes', ['new_feature_name_1', 'new_feature_name_2']))"

Update documentation

When updating results and table in the documentation, please note that the features are sorted by their type and types are sorted chronologically.

Finally, consider submitting a pull request to our repository. We are looking forward to your new features! :)

Acknowledgements

We thank Anirudh and Nikolina R. for assistance with the maintenance and automation of this repository, which greatly improved its usability.

References

0. Maldonado, A., Tavares, G.M., Oyamada, R.S., Ceravolo, P., & Seidl, T. (2023). FEEED: Feature Extraction from Event Data. ICPM Doctoral Consortium / Demo.
1. Mannhardt, Felix (2016): Sepsis Cases - Event Log. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460
2. G. M. Tavares, S. Barbon Junior, E. Damiani, and P. Ceravolo, “Selecting optimal trace clustering pipelines with meta-learning,” in Intelligent Systems, J. C. Xavier-Junior and R. A. Rios, Eds. Cham: Springer International Publishing, 2022, pp. 150–164.
3. S. B. Jr., P. Ceravolo, R. S. Oyamada, and G. M. Tavares, “Trace encoding in process mining: a survey and benchmarking,” Engineering Applications of Artificial Intelligence, 2023.
4. C. O. Back, S. Debois, and T. Slaats, “Entropy as a measure of log variability,” Journal on Data Semantics, vol. 8, no. 2, Jun 2019.
5. A. Augusto, J. Mendling, M. Vidgof, and B. Wurm, “The connection between process complexity of event sequences and models discovered by process mining,” Information Sciences, vol. 598, pp. 196–215, 2022.