'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import pandas as pd
from typing import Optional, Dict, Any
from enum import Enum
from pm4py.util import exec_utils, constants, xes_constants, pandas_utils
from pm4py.objects.log.util import dataframe_utils
from copy import copy
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
LEFT_SUFFIX = "left_suffix"
RIGHT_SUFFIX = "right_suffix"
INDEX_KEY = "index_key"
[docs]
def directly_follows_dataframe(
dataframe: pd.DataFrame, parameters: Optional[Dict[Any, Any]] = None
):
"""
Calculates the directly-follows dataframe (internal usage)
"""
if parameters is None:
parameters = {}
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
index_key = exec_utils.get_param_value(
Parameters.INDEX_KEY, parameters, constants.DEFAULT_INDEX_KEY
)
if not (hasattr(dataframe, "attrs") and dataframe.attrs):
# dataframe has not been initialized through format_dataframe
dataframe = pandas_utils.insert_index(dataframe, index_key)
dataframe.sort_values([case_id_key, timestamp_key, index_key])
dataframe = pandas_utils.insert_index(dataframe, index_key)
insert_parameters = copy(parameters)
insert_parameters["use_extremes_timestamp"] = True
dataframe = dataframe_utils.insert_artificial_start_end(
dataframe, parameters=insert_parameters
)
df_shifted = dataframe.shift(-1)
df_shifted.columns = [x + "_2" for x in df_shifted.columns]
dataframe = pandas_utils.concat([dataframe, df_shifted], axis=1)
dataframe = dataframe[
dataframe[case_id_key] == dataframe[case_id_key + "_2"]
]
return dataframe
[docs]
def merge_dataframes(
left_df: pd.DataFrame,
right_df: pd.DataFrame,
case_relations: pd.DataFrame,
parameters: Optional[Dict[Any, Any]] = None,
):
"""
Merge the two dataframes based on the provided case relations
Parameters
-----------------
left_df
First dataframe to merge
right_df
Second dataframe to merge
case_relations
Dictionary associating the cases of the first dataframe (column: case:concept:name_LEFT) to the
cases of the second dataframe (column: case:concept:name_RIGHT)
parameters
Parameters of the algorithm, including:
- Parameters.CASE_ID_KEY => the case ID
- Parameters.LEFT_SUFFIX => the suffix for the columns of the left dataframe
- Parameters.RIGHT_SUFFIX => the suffix for the columns of the right dataframe
Returns
------------------
merged_df
Merged dataframe
"""
if parameters is None:
parameters = {}
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
left_suffix = exec_utils.get_param_value(
Parameters.LEFT_SUFFIX, parameters, "_LEFT"
)
right_suffix = exec_utils.get_param_value(
Parameters.RIGHT_SUFFIX, parameters, "_RIGHT"
)
left_df = directly_follows_dataframe(left_df, parameters=parameters)
right_df = directly_follows_dataframe(right_df, parameters=parameters)
left_df = left_df.merge(
case_relations,
left_on=case_id_key,
right_on=case_id_key + left_suffix,
suffixes=("", ""),
)
del left_df[case_id_key + left_suffix]
# Rename the right-case column coming from relations to avoid clashing with
# the suffix that will be assigned to the right dataframe case column.
merge_key = case_id_key + right_suffix
if merge_key in left_df.columns:
renamed_merge_key = merge_key + "_REL"
left_df = left_df.rename(columns={merge_key: renamed_merge_key})
merge_key = renamed_merge_key
left_df = left_df.merge(
right_df,
left_on=merge_key,
right_on=case_id_key,
suffixes=(left_suffix, right_suffix),
)
if merge_key in left_df.columns and merge_key.endswith("_REL"):
del left_df[merge_key]
return left_df
