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
