from pm4py.algo.organizational_mining.resource_profiles.variants import (
pandas,
log,
)
import pandas as pd
from pm4py.objects.log.obj import EventLog
from typing import Union, Optional, Dict, Any
from pm4py.util import pandas_utils
from datetime import datetime
[docs]
def distinct_activities(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> int:
"""
Number of distinct activities done by a resource in a given time interval [t1, t2)
Metric RBI 1.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
-----------------
distinct_activities
Distinct activities
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.distinct_activities(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.distinct_activities(
log_obj, t1, t2, r, parameters=parameters
)
[docs]
def activity_frequency(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
a: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
Fraction of completions of a given activity a, by a given resource r, during a given time slot, [t1, t2),
with respect to the total number of activity completions by resource r during [t1, t2)
Metric RBI 1.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
a
Activity
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.activity_frequency(
log_obj, t1, t2, r, a, parameters=parameters
)
else:
return log.activity_frequency(
log_obj, t1, t2, r, a, parameters=parameters
)
[docs]
def activity_completions(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> int:
"""
The number of activity instances completed by a given resource during a given time slot.
Metric RBI 2.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.activity_completions(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.activity_completions(
log_obj, t1, t2, r, parameters=parameters
)
[docs]
def case_completions(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> int:
"""
The number of cases completed during a given time slot in which a given resource was involved.
Metric RBI 2.2 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.case_completions(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.case_completions(log_obj, t1, t2, r, parameters=parameters)
[docs]
def fraction_case_completions(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The fraction of cases completed during a given time slot in which a given resource was involved with respect to the
total number of cases completed during the time slot.
Metric RBI 2.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.fraction_case_completions(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.fraction_case_completions(
log_obj, t1, t2, r, parameters=parameters
)
[docs]
def average_workload(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The average number of activities started by a given resource but not completed at a moment in time.
Metric RBI 2.4 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.average_workload(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.average_workload(log_obj, t1, t2, r, parameters=parameters)
[docs]
def multitasking(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The fraction of active time during which a given resource is involved in more than one activity with respect
to the resource's active time.
Metric RBI 3.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.multitasking(log_obj, t1, t2, r, parameters=parameters)
else:
return log.multitasking(log_obj, t1, t2, r, parameters=parameters)
[docs]
def average_duration_activity(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
a: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The average duration of instances of a given activity completed during a given time slot by a given resource.
Metric RBI 4.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
a
Activity
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.average_duration_activity(
log_obj, t1, t2, r, a, parameters=parameters
)
else:
return log.average_duration_activity(
log_obj, t1, t2, r, a, parameters=parameters
)
[docs]
def average_case_duration(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The average duration of cases completed during a given time slot in which a given resource was involved.
Metric RBI 4.4 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.average_case_duration(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.average_case_duration(
log_obj, t1, t2, r, parameters=parameters
)
[docs]
def interaction_two_resources(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r1: str,
r2: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The number of cases completed during a given time slot in which two given resources were involved.
Metric RBI 5.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r1
Resource 1
r2
Resource 2
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.interaction_two_resources(
log_obj, t1, t2, r1, r2, parameters=parameters
)
else:
return log.interaction_two_resources(
log_obj, t1, t2, r1, r2, parameters=parameters
)
[docs]
def social_position(
log_obj: Union[pd.DataFrame, EventLog],
t1: Union[datetime, str],
t2: Union[datetime, str],
r: str,
parameters: Optional[Dict[Any, Any]] = None,
) -> float:
"""
The fraction of resources involved in the same cases with a given resource during a given time slot with
respect to the total number of resources active during the time slot.
Metric RBI 5.2 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if pandas_utils.check_is_pandas_dataframe(log_obj):
return pandas.social_position(
log_obj, t1, t2, r, parameters=parameters
)
else:
return log.social_position(log_obj, t1, t2, r, parameters=parameters)
