pm4py.algo.organizational_mining.resource_profiles.variants package#
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
Submodules#
pm4py.algo.organizational_mining.resource_profiles.variants.log module#
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
- class pm4py.algo.organizational_mining.resource_profiles.variants.log.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- RESOURCE_KEY = 'pm4py:param:resource_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- pm4py.algo.organizational_mining.resource_profiles.variants.log.distinct_activities(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- distinct_activities
Distinct activities
- pm4py.algo.organizational_mining.resource_profiles.variants.log.activity_frequency(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, a: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
- a
Activity
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.activity_completions(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.case_completions(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.fraction_case_completions(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.average_workload(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.multitasking(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.average_duration_activity(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, a: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
- a
Activity
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.average_case_duration(log: EventLog, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.interaction_two_resources(log: EventLog, t1: datetime | str, t2: datetime | str, r1: str, r2: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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
Event log
- t1
Left interval
- t2
Right interval
- r1
Resource 1
- r2
Resource 2
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.log.social_position(log: EventLog, t1_0: datetime | str, t2_0: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1_0
Left interval
- t2_0
Right interval
- r
Resource
Returns#
- metric
Value of the metric
pm4py.algo.organizational_mining.resource_profiles.variants.pandas module#
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
- class pm4py.algo.organizational_mining.resource_profiles.variants.pandas.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- RESOURCE_KEY = 'pm4py:param:resource_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.distinct_activities(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- distinct_activities
Distinct activities
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.activity_frequency(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, a: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
- a
Activity
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.activity_completions(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.case_completions(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) int [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.fraction_case_completions(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_workload(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.multitasking(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_duration_activity(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, a: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
- a
Activity
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_case_duration(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.interaction_two_resources(df: DataFrame, t1: datetime | str, t2: datetime | str, r1: str, r2: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r1
Resource 1
- r2
Resource 2
Returns#
- metric
Value of the metric
- pm4py.algo.organizational_mining.resource_profiles.variants.pandas.social_position(df: DataFrame, t1: datetime | str, t2: datetime | str, r: str, parameters: Dict[str | Parameters, Any] | None = None) float [source]#
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#
- df
Dataframe
- t1
Left interval
- t2
Right interval
- r
Resource
Returns#
- metric
Value of the metric