recommender-system/exp_only_DT.py at master · shikhizada/recommender-system · GitHub

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import pm4py
import os
import shutil
import csv
import pdb
from src.dataset_manager.datasetManager import DatasetManager
from src.enums.ConstraintChecker import ConstraintChecker
from src.machine_learning.utils import *
from src.machine_learning import *
import pandas as pd
from pm4py.objects.log.util import dataframe_utils
from pm4py.objects.conversion.log import converter as log_converter


if __name__ == "__main__":
        # ================ inputs ================
        support_threshold = 0.6
        output_dir = "media/output/result_dio"
        train_log_path = "media/input/log/train.xes"
        test_log_path = "media/input/log/test.xes"
        dataset_folder = "media/input/processed_benchmark_event_logs"
        datasets_names = ["bpic2011_f1", "bpic2011_f2", "bpic2011_f3", "bpic2011_f4",
                          "bpic2015_1_f2", "bpic2015_2_f2", "bpic2015_3_f2", "bpic2015_4_f2",
                          "bpic2015_5_f2", "bpic2017_accepted", "bpic2017_cancelled",
                          "bpic2017_refused", "bpic2012_cancelled",
                          "bpic2012_accepted", "bpic2012_declined",
                          "hospital_billing_2", "hospital_billing_3", "Production",
                          "sepsis_cases_1", "sepsis_cases_2", "sepsis_cases_4", "traffic_fines_1"]
        datasets_names = ["traffic_fines_1", "sepsis_cases_2", "hospital_billing_3", "Production"]
        datasets_names = ["traffic_fines_1", "sepsis_cases_2", "hospital_billing_3", "Production",
                          "bpic2011_f1", "bpic2017_accepted", "sepsis_cases_1", "Production"]

        checkers = {"existence": [ConstraintChecker.EXISTENCE,
            ConstraintChecker.ABSENCE,
            ConstraintChecker.INIT,
            ConstraintChecker.EXACTLY],
                    "choice": [ConstraintChecker.EXISTENCE,
            ConstraintChecker.ABSENCE,
            ConstraintChecker.INIT,
            ConstraintChecker.EXACTLY,
            ConstraintChecker.CHOICE,
            ConstraintChecker.EXCLUSIVE_CHOICE],
                    "positive relations": [ConstraintChecker.EXISTENCE,
            ConstraintChecker.ABSENCE,
            ConstraintChecker.INIT,
            ConstraintChecker.EXACTLY,
            ConstraintChecker.CHOICE,
            ConstraintChecker.EXCLUSIVE_CHOICE,
            ConstraintChecker.RESPONDED_EXISTENCE,
            ConstraintChecker.RESPONSE,
            ConstraintChecker.ALTERNATE_RESPONSE,
            ConstraintChecker.CHAIN_RESPONSE,
            ConstraintChecker.PRECEDENCE,
            ConstraintChecker.ALTERNATE_PRECEDENCE,
            ConstraintChecker.CHAIN_PRECEDENCE],
                    "negative relations": [ConstraintChecker.EXISTENCE,
            ConstraintChecker.ABSENCE,
            ConstraintChecker.INIT,
            ConstraintChecker.EXACTLY,
            ConstraintChecker.CHOICE,
            ConstraintChecker.EXCLUSIVE_CHOICE,
            ConstraintChecker.RESPONDED_EXISTENCE,
            ConstraintChecker.RESPONSE,
            ConstraintChecker.ALTERNATE_RESPONSE,
            ConstraintChecker.CHAIN_RESPONSE,
            ConstraintChecker.PRECEDENCE,
            ConstraintChecker.ALTERNATE_PRECEDENCE,
            ConstraintChecker.CHAIN_PRECEDENCE,
            ConstraintChecker.NOT_RESPONDED_EXISTENCE,
            ConstraintChecker.NOT_RESPONSE,
            ConstraintChecker.NOT_CHAIN_RESPONSE,
            ConstraintChecker.NOT_PRECEDENCE,
            ConstraintChecker.NOT_CHAIN_PRECEDENCE]}

        constr_family_list = ["existence", "choice", "positive relations", "negative relations"]

        rules = {
            "vacuous_satisfaction": True,
            "activation": "", # e.g. A.attr > 6
            "correlation": "", # e.g. T.attr < 12
            "n": {
                ConstraintChecker.EXISTENCE: 1,
                ConstraintChecker.ABSENCE: 1,
                ConstraintChecker.EXACTLY: 1,
            }
        }

        # ================ inputs ================

        # recreate ouput folder
        #shutil.rmtree("media/output", ignore_errors=True)
        #os.makedirs(os.path.join(output_dir))

        # generate rules
        rules["activation"] = generate_rules(rules["activation"])
        rules["correlation"] = generate_rules(rules["correlation"])

        with open(os.path.join(output_dir, "only_DT_res.csv"), mode='w') as out_file:
            writer = csv.writer(out_file, delimiter=',')
            writer.writerow(["dataset_name"] + constr_family_list)
            # read the datasets
            for dataset_name in datasets_names:

                dataset_manager = DatasetManager(dataset_name.lower())
                data = dataset_manager.read_dataset(os.path.join(os.getcwd(), dataset_folder))

                # determine min and max (truncated) prefix lengths
                min_prefix_length = 1
                if "traffic_fines" in dataset_name:
                    max_prefix_length = 10
                elif "bpic2017" in dataset_name:
                    max_prefix_length = min(20, dataset_manager.get_pos_case_length_quantile(data, 0.90))
                else:
                    max_prefix_length = min(40, dataset_manager.get_pos_case_length_quantile(data, 0.90))

                # split into training and test
                train_ratio = 0.999
                train_df, test_df = dataset_manager.split_data_strict(data, train_ratio, split="temporal")
                train_df = train_df.rename(columns={dataset_manager.timestamp_col: 'time:timestamp', dataset_manager.case_id_col: 'case:concept:name', dataset_manager.activity_col: 'concept:name'})
                test_df = test_df.rename(columns={dataset_manager.timestamp_col: 'time:timestamp', dataset_manager.case_id_col: 'case:concept:name', dataset_manager.activity_col: 'concept:name'})
                train_log = log_converter.apply(train_df)
                test_log = log_converter.apply(test_df)

                labeling = {
                    "type": LabelType.TRACE_CATEGORICAL_ATTRIBUTES,
                    "threshold_type": "",
                    "target": TraceLabel.TRUE,  # lower than a threshold considered as True
                    "trace_lbl_attr": dataset_manager.label_col,
                    "trace_label": dataset_manager.pos_label,
                    "custom_threshold": 0.0
                }
                prefixing = {
                    "type": PrefixType.ONLY,
                    "length": 1
                }

                # generate recommendations and evaluation
                dataset_result = []


                for constr_family in constr_family_list:
                    print(f"<--- DATASET: {dataset_name}, CONSTRAINTS: {constr_family} --->")

                    f1_score = test_dt(test_log=test_log, train_log=train_log, labeling=labeling,
                                       prefixing=prefixing,
                                       support_threshold=support_threshold,
                                       checkers=checkers[constr_family],
                                       rules=rules)
                    print(f"{f1_score}")
                    dataset_result.append(f1_score)

                writer.writerow([dataset_name] + dataset_result)