pm4py.algo.discovery.dfg.variants.performance module#

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class pm4py.algo.discovery.dfg.variants.performance.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

AGGREGATION_MEASURE = 'aggregationMeasure'#

BUSINESS_HOURS = 'business_hours'#

BUSINESS_HOUR_SLOTS = 'business_hour_slots'#

WORKCALENDAR = 'workcalendar'#

pm4py.algo.discovery.dfg.variants.performance.apply(log: EventLog | EventStream, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Tuple[str, str], float][source]#

pm4py.algo.discovery.dfg.variants.performance.performance(log: EventLog | EventStream, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Tuple[str, str], float][source]#

Measure performance between couples of attributes in the DFG graph.

Parameters:

log – Log
parameters –

Possible parameters passed to the algorithms:
aggregationMeasure -> performance aggregation measure (min, max, mean, median) activity_key -> Attribute to use as activity timestamp_key -> Attribute to use as timestamp
- 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. The following list defines that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00:
```
[
    (7 * 60 * 60, 17 * 60 * 60),
    ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
    ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
]
```

Returns:

dfg – DFG graph

Return type:

dict

pm4py.algo.discovery.dfg.variants.performance module#

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