You can install the development version of pm4py with:
Then, automatically install the pm4py package in a virtual or Conda environment:
See the 'reticulate' documentation for more information on the available options or how to specify an existing Python environment: https://rstudio.github.io/reticulate/
To facilitate getting stable results and to reduce the number of regressions due to API changes in PM4Py, this package is built against a fixed PM4Py version that is defined in the file
R/version.R. We also adopt the versioning schema of the PM4Py project for this R package. So, the R package version
1.1.19 will install the PM4Py version
1.1.19. In case of fixes required to the R package itself, for example, for bugs or adopting new features, we will add a suffix
-rev to the version to indicate the change. Of course, nothing prevents you from manually overriding the synchronisation between the PM4Py version and the R PM4Py package version.
library(pm4py) # Most of the data structures are converted in their bupaR equivalents library(bupaR) # As Inductive Miner of PM4PY is not life-cycle aware, keep only `complete` events: patients_completes <- patients[patients$registration_type == "complete", ] # Discovery with Inductive Miner pn <- discovery_inductive(patients_completes) # This results in an auto-converted bupaR Petri net and markings str(pn) class(pn$petrinet) # Render with bupaR render_PN(pn$petrinet) # Render with PM4PY and DiagrammeR library(DiagrammeR) viz <- reticulate::import("pm4py.visualization.petrinet") # Convert back to Python py_pn <- r_to_py(pn$petrinet) class(py_pn) # Render to DOT with PMP4Y dot <- viz$factory$apply(py_pn)$source grViz(diagram = dot) # Compute alignment alignment <- conformance_alignment(patients_completes, pn$petrinet, pn$initial_marking, pn$final_marking) # # Alignment is returned in long format as data frame head(alignment) # Evaluate model quality quality <- evaluation_all(patients_completes, pn$petrinet, pn$initial_marking, pn$final_marking)
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