'''
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
'''
from pm4py.util import xes_constants
from pm4py.util import constants
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics
from pm4py.util import exec_utils, pandas_utils
from pm4py.algo.discovery.causal import algorithm as causal_discovery
from enum import Enum
from typing import Optional, Dict, Any, Union
import pandas as pd
[docs]
class Outputs(Enum):
DFG = "dfg"
SEQUENCE = "sequence"
PARALLEL = "parallel"
START_ACTIVITIES = "start_activities"
END_ACTIVITIES = "end_activities"
ACTIVITIES = "activities"
SKIPPABLE = "skippable"
ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening"
MIN_TRACE_LENGTH = "min_trace_length"
TRACE = "trace"
[docs]
class Parameters(Enum):
SORT_REQUIRED = "sort_required"
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
INDEX_KEY = "index_key"
DEFAULT_SORT_REQUIRED = True
DEFAULT_INDEX_KEY = "@@index"
[docs]
def apply(
df: pd.DataFrame,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, Any]:
"""
Discovers a footprint object from a dataframe
(the footprints of the dataframe are returned)
Parameters
--------------
df
Dataframe
parameters
Parameters of the algorithm
Returns
--------------
footprints_obj
Footprints object
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
)
caseid_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY, parameters, None
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
sort_required = exec_utils.get_param_value(
Parameters.SORT_REQUIRED, parameters, DEFAULT_SORT_REQUIRED
)
index_key = exec_utils.get_param_value(
Parameters.INDEX_KEY, parameters, DEFAULT_INDEX_KEY
)
df = df[[caseid_key, activity_key, timestamp_key]]
if sort_required:
df = pandas_utils.insert_index(df, index_key)
if start_timestamp_key is not None:
df = df.sort_values(
[caseid_key, start_timestamp_key, timestamp_key, index_key]
)
else:
df = df.sort_values([caseid_key, timestamp_key, index_key])
grouped_df = df.groupby(caseid_key)
dfg = df_statistics.get_dfg_graph(
df,
measure="frequency",
activity_key=activity_key,
case_id_glue=caseid_key,
timestamp_key=timestamp_key,
sort_caseid_required=False,
sort_timestamp_along_case_id=False,
start_timestamp_key=start_timestamp_key,
)
activities = set(pandas_utils.format_unique(df[activity_key].unique()))
start_activities = set(
pandas_utils.format_unique(grouped_df.first()[activity_key].unique())
)
end_activities = set(
pandas_utils.format_unique(grouped_df.last()[activity_key].unique())
)
parallel = {(x, y) for (x, y) in dfg if (y, x) in dfg}
sequence = set(
causal_discovery.apply(dfg, causal_discovery.Variants.CAUSAL_ALPHA)
)
ret = {}
ret[Outputs.DFG.value] = dfg
ret[Outputs.SEQUENCE.value] = sequence
ret[Outputs.PARALLEL.value] = parallel
ret[Outputs.ACTIVITIES.value] = activities
ret[Outputs.START_ACTIVITIES.value] = start_activities
ret[Outputs.END_ACTIVITIES.value] = end_activities
ret[Outputs.MIN_TRACE_LENGTH.value] = int(grouped_df.size().min())
return ret
