'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import pandas as pd
from enum import Enum
from pm4py.util import exec_utils, constants, xes_constants, pandas_utils
from pm4py.util.business_hours import soj_time_business_hours_diff
from typing import Optional, Dict, Any, Union
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
AGGREGATION_MEASURE = "aggregationMeasure"
BUSINESS_HOURS = "business_hours"
BUSINESS_HOUR_SLOTS = "business_hour_slots"
WORKCALENDAR = "workcalendar"
DIFF_KEY = "@@diff"
[docs]
def apply(dataframe: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, float]:
"""
Gets the service time per activity on a Pandas dataframe
Parameters
--------------
dataframe
Pandas dataframe
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => activity key
- Parameters.START_TIMESTAMP_KEY => start timestamp key
- Parameters.TIMESTAMP_KEY => timestamp key
- Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time.
Default: False
- Parameters.BUSINESS_HOURS_SLOTS =>
work schedule of the company, provided as a list of tuples where each tuple represents one time slot of business
hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g.
[
(7 * 60 * 60, 17 * 60 * 60),
((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
]
meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00
- Parameters.AGGREGATION_MEASURE => performance aggregation measure (sum, min, max, mean, median)
Returns
--------------
soj_time_dict
Service time dictionary
"""
if parameters is None:
parameters = {}
business_hours = exec_utils.get_param_value(Parameters.BUSINESS_HOURS, parameters, False)
business_hours_slots = exec_utils.get_param_value(Parameters.BUSINESS_HOUR_SLOTS, parameters, constants.DEFAULT_BUSINESS_HOUR_SLOTS)
workcalendar = exec_utils.get_param_value(Parameters.WORKCALENDAR, parameters, constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE,
parameters, "mean")
if business_hours:
dataframe[DIFF_KEY] = dataframe.apply(
lambda x: soj_time_business_hours_diff(x[start_timestamp_key], x[timestamp_key], business_hours_slots, workcalendar), axis=1)
else:
dataframe[DIFF_KEY] = pandas_utils.get_total_seconds(dataframe[timestamp_key] - dataframe[start_timestamp_key])
dataframe = dataframe.reset_index()
column = dataframe.groupby(activity_key)[DIFF_KEY]
if aggregation_measure == "median":
ret_dict = column.median().to_dict()
elif aggregation_measure == "min":
ret_dict = column.min().to_dict()
elif aggregation_measure == "max":
ret_dict = column.max().to_dict()
elif aggregation_measure == "sum":
ret_dict = column.sum().to_dict()
else:
ret_dict = column.mean().to_dict()
# assure to avoid problems with np.float64, by using the Python float type
for el in ret_dict:
ret_dict[el] = float(ret_dict[el])
return ret_dict
