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import os
from pm4py.util import ml_utils
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.objects.log.util import get_class_representation
from pm4py.algo.transformation.log_to_features import algorithm as log_to_features
from examples import examples_conf
import importlib.util
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "roadtraffic50traces.xes")
# log_path = os.path.join("..", "tests", "input_data", "receipt.xes")
log = xes_importer.apply(log_path)
# now, it is possible to get a default representation of an event log
data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED)
# gets classes representation by final concept:name value (end activity)
target, classes = get_class_representation.get_class_representation_by_str_ev_attr_value_value(log, "concept:name")
if importlib.util.find_spec("sklearn") and importlib.util.find_spec("graphviz"):
# mine the decision tree given 'data' and 'target'
clf = ml_utils.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
# visualize the decision tree
from pm4py.visualization.decisiontree import visualizer as dt_vis
gviz = dt_vis.apply(clf, feature_names, classes, parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: examples_conf.TARGET_IMG_FORMAT})
dt_vis.view(gviz)
# gets classes representation by trace duration (threshold between the two classes = 200D)
target, classes = get_class_representation.get_class_representation_by_trace_duration(log, 2 * 8640000)
if importlib.util.find_spec("sklearn") and importlib.util.find_spec("graphviz"):
# mine the decision tree given 'data' and 'target'
clf = ml_utils.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
# visualize the decision tree
from pm4py.visualization.decisiontree import visualizer as dt_vis
gviz = dt_vis.apply(clf, feature_names, classes, parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: examples_conf.TARGET_IMG_FORMAT})
dt_vis.view(gviz)
if __name__ == "__main__":
execute_script()
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