l = Log(file_path)
# variants_count = case_statistics.get_variant_statistics(l.log)
# variants_count = \
# sorted(variants_count,
# key=lambda x: x['count'],
# reverse=True)
# print('')
# print(variants_count)
# print('')
l.filter_variants(Log.INTENSE_FILTERING)
# dfg_discovery.apply(l.log)
dfg = DFG(l.log)
Visualizer.dfg_visualizer(dfg.dfg, l.log)
# variants_count = case_statistics.get_variant_statistics(l.log)
# variants_count = \
# sorted(variants_count,
# key=lambda x: x['count'],
# reverse=True)
# print('')
# print(variants_count)
# print('')
df_time = df_time[(df_time['duration'] < lower_bound) |
(df_time['duration'] > upper_bound)]
df_time.count()
df_time.reset_index(level=0, inplace=True)
df['case:concept:name'].nunique()
key = ['case:concept:name']
i1 = df.set_index(key).index
i2 = df_time.set_index(key).index
df = df[~i1.isin(i2)]
df['case:concept:name'].nunique()
# log = log_converter.apply(df_log)
l = Log(df_log=df)
l.filter_variants(1)
dfg = DFG(l.log,
parameters={parameters.Parameters.AGGREGATION_MEASURE: 'mean'},
variant=dfg_discovery.Variants.PERFORMANCE)
print(dfg.dfg)
Visualizer.dfg_visualizer(dfg.dfg,
l.log,
variant=dfg_visualization.Variants.PERFORMANCE)
print('teste')