'''
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/>.
'''
from pm4py import util as pmutil
from pm4py.algo.discovery.dfg.variants import native, performance, freq_triples, case_attributes
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 enum import Enum
import pkgutil
from pm4py.util import constants
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
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
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
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 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 pkgutil.find_loader("pandas"):
import pandas
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics
from pm4py.objects.log.util import dataframe_utils
if isinstance(log, pandas.core.frame.DataFrame) and not variant == Variants.FREQ_TRIPLES:
log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_columns=[
timestamp_key])
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)