Feature Extraction for Event Data
Features extracted by this tool stem from [2],[3],[4],[5]. A video tutorial on how to use this tool can be found here.
Requirements:
- Python > 3.9
- Java
To directly use meta feature extraction methods via import
pip install feeedRun:
python -c "from feeed.feature_extractor import extract_features; print(extract_features('test_data/Sepsis.xes'))"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.
Specific feature_names and feature_types can be selected as in the examples below:
| Feature Type | Feature Names |
|---|---|
| simple_stats | n_traces, n_unique_traces, ratio_unique_traces_per_trace |
| 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) |
For the following examples we used Sepsis event data[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}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}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_unique_traces', 'ratio_unique_traces_per_trace', '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_unique_traces': 846, 'ratio_unique_traces_per_trace': 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}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).
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.
- Clone this repo to your local machine using
git clone git@github.com:lmu-dbs/feeed.git - Include the new module containing the
new_featurecomputation infeeed/, resulting infeeed/new_feature.py(e.g.feeed/time.py). - Import the new class
NewFeaturesinfeeed/feature_extractor.py(e.g.from .time import TimeBased as time_based) - To call the new class and methods, include the new
new_feature_type(e.g. "time_based") in the list offeeed/feature_extractor.py.- Include the
new_feature_typein NEW_FEATURE_TYPE
- Include the
- Include the new
new_feature_type(e.g. "time_based") and itsfeature_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_lengthAfter 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']))"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! :)
- Mannhardt, Felix (2016): Sepsis Cases - Event Log. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460
- 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.
- 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.
- 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.
- 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.
