from enum import Enum
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.util.business_hours import BusinessHours
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
[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(
log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
"""
Gets the service time per activity on an event log object
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 = {}
from statistics import mean, median
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,
)
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
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"
)
durations_dict = {}
activities = [ev[activity_key] for trace in log for ev in trace]
for act in activities:
durations_dict[act] = []
for trace in log:
for event in trace:
activity = event[activity_key]
if business_hours:
bh = BusinessHours(
event[start_timestamp_key],
event[timestamp_key],
business_hour_slots=business_hours_slots,
workcalendar=workcalendar,
)
durations_dict[activity].append(bh.get_seconds())
else:
start_time = event[start_timestamp_key].timestamp()
complete_time = event[timestamp_key].timestamp()
durations_dict[activity].append(complete_time - start_time)
from statistics import mean, median
for act in durations_dict:
if aggregation_measure == "median":
durations_dict[act] = median(durations_dict[act])
elif aggregation_measure == "min":
durations_dict[act] = min(durations_dict[act])
elif aggregation_measure == "max":
durations_dict[act] = max(durations_dict[act])
elif aggregation_measure == "sum":
durations_dict[act] = sum(durations_dict[act])
else:
durations_dict[act] = mean(durations_dict[act])
return durations_dict
