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")
# mine the decision tree given 'data' and 'target'
clf = tree.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
# visualize the decision tree
gviz = dt_vis.apply(
clf,
feature_names,
classes,
parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
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)
# mine the decision tree given 'data' and 'target'
clf = tree.DecisionTreeClassifier(max_depth=7)
clf.fit(data, target)
# visualize the decision tree
gviz = dt_vis.apply(
clf,
feature_names,
classes,
parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
dt_vis.view(gviz)
def execute_script():
# in this case, we obtain a decision tree by alignments on a specific decision point
log = xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes"))
net, im, fm = inductive_miner.apply(log)
# we need to specify a decision point. In this case, the place p_10 is a suitable decision point
clf, feature_names, classes = algorithm.get_decision_tree(log, net, im, fm, decision_point="p_10")
# we can visualize the decision tree
gviz = visualizer.apply(clf, feature_names, classes,
parameters={visualizer.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
visualizer.view(gviz)