from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.algo.decision_mining import algorithm
from pm4py.objects.conversion.process_tree import converter as process_tree_converter
from examples import examples_conf
import os
import importlib.util


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"))
    process_tree = inductive_miner.apply(log)
    net, im, fm = process_tree_converter.apply(process_tree)

    if importlib.util.find_spec("sklearn") and importlib.util.find_spec("graphviz"):
        # 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
        from pm4py.visualization.decisiontree import visualizer as visualizer
        gviz = visualizer.apply(clf, feature_names, classes,
                                parameters={visualizer.Variants.CLASSIC.value.Parameters.FORMAT: examples_conf.TARGET_IMG_FORMAT})
        visualizer.view(gviz)


if __name__ == "__main__":
    execute_script()