'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
[docs]def get_freq_triples(df, activity_key="concept:name", case_id_glue="case:concept:name", timestamp_key="time:timestamp",
sort_caseid_required=True, sort_timestamp_along_case_id=True):
"""
Gets the frequency triples out of a dataframe
Parameters
------------
df
Dataframe
activity_key
Activity key
case_id_glue
Case ID glue
timestamp_key
Timestamp key
sort_caseid_required
Determine if sort by case ID is required (default: True)
sort_timestamp_along_case_id
Determine if sort by timestamp is required (default: True)
Returns
-------------
freq_triples
Frequency triples from the dataframe
"""
import pandas as pd
if sort_caseid_required:
if sort_timestamp_along_case_id:
df = df.sort_values([case_id_glue, timestamp_key])
else:
df = df.sort_values(case_id_glue)
df_reduced = df[[case_id_glue, activity_key]]
# shift the dataframe by 1
df_reduced_1 = df_reduced.shift(-1)
# shift the dataframe by 2
df_reduced_2 = df_reduced.shift(-2)
# change column names to shifted dataframe
df_reduced_1.columns = [str(col) + '_2' for col in df_reduced_1.columns]
df_reduced_2.columns = [str(col) + '_3' for col in df_reduced_2.columns]
df_successive_rows = pd.concat([df_reduced, df_reduced_1, df_reduced_2], axis=1)
df_successive_rows = df_successive_rows[df_successive_rows[case_id_glue] == df_successive_rows[case_id_glue + '_2']]
df_successive_rows = df_successive_rows[df_successive_rows[case_id_glue] == df_successive_rows[case_id_glue + '_3']]
all_columns = set(df_successive_rows.columns)
all_columns = list(all_columns - set([activity_key, activity_key + '_2', activity_key + '_3']))
directly_follows_grouping = df_successive_rows.groupby([activity_key, activity_key + '_2', activity_key + '_3'])
if all_columns:
directly_follows_grouping = directly_follows_grouping[all_columns[0]]
freq_triples = directly_follows_grouping.size().to_dict()
return freq_triples