Spaces:
Running
Running
| ''' | |
| This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de). | |
| PM4Py is free software: you can redistribute it and/or modify | |
| it under the terms of the GNU General Public License as published by | |
| the Free Software Foundation, either version 3 of the License, or | |
| (at your option) any later version. | |
| PM4Py is distributed in the hope that it will be useful, | |
| but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| GNU General Public License for more details. | |
| You should have received a copy of the GNU General Public License | |
| along with PM4Py. If not, see <https://www.gnu.org/licenses/>. | |
| ''' | |
| from enum import Enum | |
| from pm4py.util import constants | |
| from pm4py.util import exec_utils, pandas_utils | |
| from pm4py.util import xes_constants as xes | |
| from pm4py.util.constants import CASE_CONCEPT_NAME | |
| from typing import Optional, Dict, Any, Union, List | |
| import pandas as pd | |
| class Parameters(Enum): | |
| ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY | |
| TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY | |
| CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY | |
| ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY | |
| PARAMETER_SAMPLE_SIZE = "sample_size" | |
| SORT_LOG_REQUIRED = "sort_log_required" | |
| def apply(dataframe: pd.DataFrame, list_activities: List[str], sample_size: int, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]: | |
| """ | |
| Finds the performance spectrum provided a dataframe | |
| and a list of activities | |
| Parameters | |
| ------------- | |
| dataframe | |
| Dataframe | |
| list_activities | |
| List of activities interesting for the performance spectrum (at least two) | |
| sample_size | |
| Size of the sample | |
| parameters | |
| Parameters of the algorithm, including: | |
| - Parameters.ACTIVITY_KEY | |
| - Parameters.TIMESTAMP_KEY | |
| - Parameters.CASE_ID_KEY | |
| Returns | |
| ------------- | |
| points | |
| Points of the performance spectrum | |
| """ | |
| if parameters is None: | |
| parameters = {} | |
| import pandas as pd | |
| import numpy as np | |
| case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, CASE_CONCEPT_NAME) | |
| activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY) | |
| timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY) | |
| sort_log_required = exec_utils.get_param_value(Parameters.SORT_LOG_REQUIRED, parameters, True) | |
| dataframe = dataframe[[case_id_glue, activity_key, timestamp_key]] | |
| dataframe[activity_key] = dataframe[activity_key].astype("string") | |
| dataframe = dataframe[dataframe[activity_key].isin(list_activities)] | |
| dataframe = pandas_utils.insert_index(dataframe, constants.DEFAULT_EVENT_INDEX_KEY) | |
| if sort_log_required: | |
| dataframe = dataframe.sort_values([case_id_glue, timestamp_key, constants.DEFAULT_EVENT_INDEX_KEY]) | |
| dataframe[timestamp_key] = dataframe[timestamp_key].astype(np.int64) / 10 ** 9 | |
| def key(k, n): | |
| return k + str(n) | |
| # create a dataframe with all needed columns to check for the activities pattern | |
| dfs = [dataframe.add_suffix(str(i)).shift(-i) for i in range(len(list_activities))] | |
| dataframe = pandas_utils.concat(dfs, axis=1) | |
| # keep only rows that belong to exactly one case | |
| for i in range(len(list_activities) - 1): | |
| dataframe = dataframe[dataframe[key(case_id_glue, i)] == dataframe[key(case_id_glue, i + 1)]] | |
| column_list = [key(activity_key, i) for i in range(len(list_activities))] | |
| pattern = "".join(list_activities) | |
| # keep only rows that have the desired activities pattern | |
| matches = dataframe[np.equal(dataframe[column_list].agg(''.join, axis=1), pattern)] | |
| if len(matches) > sample_size: | |
| matches = matches.sample(n=sample_size) | |
| filt_col_names = [timestamp_key + str(i) for i in range(len(list_activities))] | |
| points = pandas_utils.to_dict_records(matches) | |
| points = [[p[tk] for tk in filt_col_names] for p in points] | |
| points = sorted(points, key=lambda x: x[0]) | |
| return points | |