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run_thesis_experiments_massive.py
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"""
This file is part of Interactive Process Drift (IPDD) Framework.
IPDD is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
IPDD is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with IPDD. If not, see <https://www.gnu.org/licenses/>.
"""
from components.adaptive.detectors import SelectDetector, ConceptDriftDetector
from ipdd_massive import run_massive_fixed_controlflow, run_massive_adaptive_controlflow_trace_by_trace, \
calculate_metrics_massive, run_massive_adaptive_controlflow_windowing
def define_change_points_dataset1(inter_drift_distance):
actual_change_points = []
for i in range(inter_drift_distance, inter_drift_distance * 10, inter_drift_distance):
actual_change_points.append(i)
return actual_change_points
class Dataset1Configuration:
###############################################################
# Information about the data for performing the experiments
###############################################################
dataset_name = 'dataset1'
input_path = 'datasets/dataset1'
lognames2500 = [
'cb2.5k.xes',
'cd2.5k.xes',
'cf2.5k.xes',
'cm2.5k.xes',
'cp2.5k.xes',
# 'fr2.5k.xes', # not used because IPDD only detects structural drifts in the control-flow perspective
'IOR2.5k.xes',
'IRO2.5k.xes',
'lp2.5k.xes',
'OIR2.5k.xes',
'ORI2.5k.xes',
'pl2.5k.xes',
'pm2.5k.xes',
're2.5k.xes',
'RIO2.5k.xes',
'ROI2.5k.xes',
'rp2.5k.xes',
'sw2.5k.xes',
]
lognames5000 = [
'cb5k.xes',
'cd5k.xes',
'cf5k.xes',
'cm5k.xes',
'cp5k.xes',
# 'fr5k.xes', # not used because IPDD only detects structural drifts in the control-flow perspective
'IOR5k.xes',
'IRO5k.xes',
'lp5k.xes',
'OIR5k.xes',
'ORI5k.xes',
'pl5k.xes',
'pm5k.xes',
're5k.xes',
'RIO5k.xes',
'ROI5k.xes',
'rp5k.xes',
'sw5k.xes',
]
lognames7500 = [
'cb7.5k.xes',
'cd7.5k.xes',
'cf7.5k.xes',
'cm7.5k.xes',
'cp7.5k.xes',
# 'fr7.5k.xes', # not used because IPDD only detects structural drifts in the control-flow perspective
'IOR7.5k.xes',
'IRO7.5k.xes',
'lp7.5k.xes',
'OIR7.5k.xes',
'ORI7.5k.xes',
'pl7.5k.xes',
'pm7.5k.xes',
're7.5k.xes',
'RIO7.5k.xes',
'ROI7.5k.xes',
'rp7.5k.xes',
'sw7.5k.xes',
]
lognames10000 = [
'cb10k.xes',
'cd10k.xes',
'cf10k.xes',
'cm10k.xes',
'cp10k.xes',
# 'fr10k.xes', # not used because IPDD only detects structural drifts in the control-flow perspective
'IOR10k.xes',
'IRO10k.xes',
'lp10k.xes',
'OIR10k.xes',
'ORI10k.xes',
'pl10k.xes',
'pm10k.xes',
're10k.xes',
'RIO10k.xes',
'ROI10k.xes',
'rp10k.xes',
'sw10k.xes',
]
lognames = lognames2500 + lognames5000 + lognames7500 + lognames10000
windows = [i for i in range(25, 301, 25)]
detectors = [
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.05}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.1}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.3}),
]
###############################################################
# Information for calculating evaluation metrics
###############################################################
actual_change_points = {
'2.5k': define_change_points_dataset1(250),
'5k': define_change_points_dataset1(500),
'7.5k': define_change_points_dataset1(750),
'10k': define_change_points_dataset1(1000)
}
number_of_instances = {
'2.5k': 2500,
'5k': 5000,
'7.5k': 7500,
'10k': 10000
}
###############################################################
# Plot specific information
###############################################################
# For defining the correct order for the legend of the plots_thesis
order_legend = [1, 2, 3, 0]
class Dataset2Configuration:
###############################################################
# Information about the data for performing the experiments
###############################################################
dataset_name = 'dataset2'
input_path = 'datasets/dataset2'
lognames3000 = [
'cb3k.xes',
'cd3k.xes',
'cf3k.xes',
'cm3k.xes',
'cp3k.xes',
'IOR3k.xes',
'IRO3k.xes',
'lp3k.xes',
'OIR3k.xes',
'ORI3k.xes',
'pl3k.xes',
'pm3k.xes',
're3k.xes',
'RIO3k.xes',
'ROI3k.xes',
'rp3k.xes',
'sw3k.xes',
]
lognames4500 = [
'cb4.5k.xes',
'cd4.5k.xes',
'cf4.5k.xes',
'cm4.5k.xes',
'cp4.5k.xes',
'IOR4.5k.xes',
'IRO4.5k.xes',
'lp4.5k.xes',
'OIR4.5k.xes',
'ORI4.5k.xes',
'pl4.5k.xes',
'pm4.5k.xes',
're4.5k.xes',
'RIO4.5k.xes',
'ROI4.5k.xes',
'rp4.5k.xes',
'sw4.5k.xes',
]
lognames8000 = [
'cb8k.xes',
'cd8k.xes',
'cf8k.xes',
'cm8k.xes',
'cp8k.xes',
'IOR8k.xes',
'IRO8k.xes',
'lp8k.xes',
'OIR8k.xes',
'ORI8k.xes',
'pl8k.xes',
'pm8k.xes',
're8k.xes',
'RIO8k.xes',
'ROI8k.xes',
'rp8k.xes',
'sw8k.xes',
]
lognames = lognames3000 + lognames4500 + lognames8000
windows = [i for i in range(25, 301, 25)]
detectors = [
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.05}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.1}),
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.3}),
]
# sample for testing one specific scenario
# lognames = ['cb3k.xes']
# windows = [100]
# detectors = [SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002})]
###############################################################
# Information for calculating evaluation metrics
###############################################################
actual_change_points = {
'3k': [250, 750, 1500, 2500],
'4.5k': [250, 750, 1500, 2500, 3250, 3750, 4000],
'8k': [250, 750, 1500, 2500, 3250, 3750, 4000, 4500, 5250, 6250, 7000, 7500, 7750],
}
number_of_instances = {
'3k': 3000,
'4.5k': 4500,
'8k': 8000,
}
class RealDatasetConfiguration:
###############################################################
# Information about the data for performing the experiments
###############################################################
dataset_name = 'italian_help_desk_company'
input_path = 'datasets/real'
lognames = ['italian_help_desk_company.xes']
# windows = [i for i in range(25, 301, 25)]
# detectors = [
# SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002}),
# SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.05}),
# SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.1}),
# SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.3}),
# ]
windows = [100]
detectors = [
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002})]
class DatasetTestConfiguration:
###############################################################
# Information about the data for performing the experiments
###############################################################
dataset_name = 'dataset_test'
input_path = 'datasets/dataset2'
lognames = ['cb3k.xes']
windows = [100]
detectors = [
SelectDetector.get_detector_instance(ConceptDriftDetector.ADWIN.name, parameters={'delta': 0.002}),
]
###############################################################
# Information for calculating evaluation metrics
###############################################################
actual_change_points = {
'3k': [250, 750, 1500, 2500],
'4.5k': [250, 750, 1500, 2500, 3250, 3750, 4000],
'8k': [250, 750, 1500, 2500, 3250, 3750, 4000, 4500, 5250, 6250, 7000, 7500, 7750],
}
number_of_instances = {
'3k': 3000,
'4.5k': 4500,
'8k': 8000,
}
if __name__ == '__main__':
dataset1 = Dataset1Configuration()
run_massive_fixed_controlflow(dataset1, evaluate=True)
run_massive_adaptive_controlflow_trace_by_trace(dataset1, evaluate=True)
run_massive_adaptive_controlflow_windowing(dataset1, evaluate=True)
dataset2 = Dataset2Configuration()
run_massive_fixed_controlflow(dataset2, evaluate=True)
run_massive_adaptive_controlflow_trace_by_trace(dataset2, evaluate=True)
run_massive_adaptive_controlflow_windowing(dataset2, evaluate=True)
real_dataset = RealDatasetConfiguration()
# run_massive_fixed_controlflow(real_dataset)
run_massive_adaptive_controlflow_trace_by_trace(real_dataset, save_model_svg=True)
# run_massive_adaptive_controlflow_windowing(real_dataset)
# for testing
# dataset = DatasetTestConfiguration()
# run_massive_adaptive_controlflow_trace_by_trace(dataset, evaluate=True)