Source code for pm4py.objects.conversion.log.variants.df_to_event_log_nv

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from pm4py.objects.log.obj import EventLog, Trace, Event
from pm4py.util import xes_constants as xes
from pm4py.util import constants as pm4_constants

RETURN_VARIANTS = 'return_variants'

[docs]def apply(df, parameters=None): """ Convert a dataframe into a log containing N case per variant (only control-flow perspective is considered) Parameters ------------- df Dataframe parameters Parameters of the algorithm Returns ------------- log Event log """ from pm4py.statistics.traces.generic.pandas import case_statistics if parameters is None: parameters = {} return_variants = parameters[RETURN_VARIANTS] if RETURN_VARIANTS in parameters else False case_glue = parameters[ pm4_constants.PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else pm4_constants.CASE_CONCEPT_NAME activity_key = parameters[ pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY variant_stats = case_statistics.get_variant_statistics(df, parameters=parameters) log = EventLog() all_variants_log = {} for vd in variant_stats: variant = vd['variant'].split(pm4_constants.DEFAULT_VARIANT_SEP) variant_count = vd[case_glue] trace = Trace() for activity in variant: event = Event() event[activity_key] = activity trace.append(event) all_variants_log[vd['variant']] = [] for i in range(variant_count): log.append(trace) all_variants_log[vd['variant']].append(len(log) - 1) if return_variants: return log, all_variants_log return log