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('')
Example #2
0
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')