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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() | |