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import os
import unittest
import importlib.util
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
class DecisionTreeTest(unittest.TestCase):
def test_decisiontree_evattrvalue(self):
if importlib.util.find_spec("sklearn"):
from pm4py.util import ml_utils
from pm4py.visualization.decisiontree import visualizer as dt_vis
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log_path = os.path.join("input_data", "roadtraffic50traces.xes")
log = xes_importer.apply(log_path)
data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
"num_tr_attr": [], "num_ev_attr": ["amount"]})
target, classes = get_class_representation.get_class_representation_by_str_ev_attr_value_value(log,
"concept:name")
clf = ml_utils.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
gviz = dt_vis.apply(clf, feature_names, classes,
parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
del gviz
def test_decisiontree_traceduration(self):
if importlib.util.find_spec("sklearn"):
from pm4py.util import ml_utils
from pm4py.visualization.decisiontree import visualizer as dt_vis
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log_path = os.path.join("input_data", "roadtraffic50traces.xes")
log = xes_importer.apply(log_path)
data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
"num_tr_attr": [], "num_ev_attr": ["amount"]})
target, classes = get_class_representation.get_class_representation_by_trace_duration(log, 2 * 8640000)
clf = ml_utils.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
gviz = dt_vis.apply(clf, feature_names, classes,
parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
del gviz
if __name__ == "__main__":
unittest.main()
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