'''
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 import util as pmutil
from pm4py.algo.discovery.dfg.variants import (
native,
performance,
freq_triples,
case_attributes,
clean,
)
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.util import xes_constants as xes_util
from pm4py.util import exec_utils
from pm4py.util import constants, pandas_utils
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics
import pandas as pd
[docs]
class Parameters(Enum):
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
[docs]
class Variants(Enum):
NATIVE = native
FREQUENCY = native
PERFORMANCE = performance
FREQUENCY_GREEDY = native
PERFORMANCE_GREEDY = performance
FREQ_TRIPLES = freq_triples
CASE_ATTRIBUTES = case_attributes
CLEAN = clean # 'novel' replacement for native
DFG_NATIVE = Variants.NATIVE
DFG_FREQUENCY = Variants.FREQUENCY
DFG_PERFORMANCE = Variants.PERFORMANCE
DFG_FREQUENCY_GREEDY = Variants.FREQUENCY_GREEDY
DFG_PERFORMANCE_GREEDY = Variants.PERFORMANCE_GREEDY
FREQ_TRIPLES = Variants.FREQ_TRIPLES
DFG_CLEAN = Variants.CLEAN
DEFAULT_VARIANT = Variants.NATIVE
VERSIONS = {
DFG_NATIVE,
DFG_FREQUENCY,
DFG_PERFORMANCE,
DFG_FREQUENCY_GREEDY,
DFG_PERFORMANCE_GREEDY,
FREQ_TRIPLES,
}
[docs]
def apply(
log: Union[EventLog, EventStream, pd.DataFrame],
parameters: Optional[Dict[Any, Any]] = None,
variant=DEFAULT_VARIANT,
) -> Dict[Tuple[str, str], float]:
"""
Calculates DFG graph (frequency or performance) starting from a log
Parameters
----------
log
Log
parameters
Possible parameters passed to the algorithms:
Parameters.AGGREGATION_MEASURE -> performance aggregation measure (min, max, mean, median)
Parameters.ACTIVITY_KEY -> Attribute to use as activity
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp
variant
Variant of the algorithm to use, possible values:
- Variants.NATIVE
- Variants.FREQUENCY
- Variants.FREQUENCY_GREEDY
- Variants.PERFORMANCE
- Variants.PERFORMANCE_GREEDY
- Variants.FREQ_TRIPLES
Returns
-------
dfg
DFG graph
"""
if variant == Variants.CLEAN and pandas_utils.check_is_pandas_dataframe(
log
):
return clean.apply(log, parameters)
elif variant is None:
variant = Variants.NATIVE
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_util.DEFAULT_NAME_KEY
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY, parameters, None
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY, parameters, xes_util.DEFAULT_TIMESTAMP_KEY
)
case_id_glue = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, pmutil.constants.CASE_CONCEPT_NAME
)
if (
pandas_utils.check_is_pandas_dataframe(log)
and not variant == Variants.FREQ_TRIPLES
):
dfg_frequency, dfg_performance = df_statistics.get_dfg_graph(
log,
measure="both",
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
start_timestamp_key=start_timestamp_key,
)
if variant in [Variants.PERFORMANCE, Variants.PERFORMANCE_GREEDY]:
return dfg_performance
else:
return dfg_frequency
return exec_utils.get_variant(variant).apply(
log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG),
parameters=parameters,
)
