Comments (1)
Hi,
An improvement have been added to the 'develop' branch in the log_conversion.apply method, that converts a Pandas dataframe into an event logs prior to check if other types of conversions are needed.
The change is then propagated to the following algorithms:
-Alpha Miner
-Inductive Miner
-DFG
-Transition systems
-Conformance (alignments and token-based replay)
-Evaluation (general factory, replay fitness, precision, generalization)
-Petri net decoration
Moreover, Alpha Miner and Inductive Miner have been entitled to use the customized, faster, version for the extraction of the DFG directly from the Pandas dataframe.
from pm4py-core.
Related Issues (20)
- Token-based replay KeyError in `get_diagnostics_dataframe` HOT 2
- How to remove black rectangle (hidden transition)? HOT 1
- Durations for DFG based on prior events HOT 1
- Support on Root Cause analysis HOT 3
- EventLog Object HOT 2
- Unclear status of format_dataframe function HOT 2
- Can't manage to format the duration within nodes HOT 3
- Time range filter not working with timezone-naive timestamps HOT 2
- dfg created using dfg_visualizer not detecting the starting activity of the process. I am using correlation miner without case id. HOT 1
- pm4py.write.write_bpmn(...) produces invalid BPMN schema HOT 2
- pm4py.llm.openai_query() does not work HOT 1
- Trouble Generating Log File with 'Extensive' Playout Variant in Petri Net Simulation from .pmml File HOT 1
- GPU-based process mining HOT 3
- pm4py.convert.convert_to_petri_net(bpmn) not support inclusiveGateway HOT 1
- Missing PEP 561-compliancy HOT 1
- OCDFG: Visualization issue when having only one activity to show HOT 1
- Suggestion: Integration of Azure OpenAI API HOT 1
- Duplicated sequence from activity to Exclusive Gateway in BPMN visualization HOT 7
- Convert_to_process_tree not working HOT 3
- Method get_event_attribute_values does not get attributes. HOT 4
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from pm4py-core.