'''
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 enum import Enum
from typing import Optional, Dict, Any, Union, Tuple, List
import numpy as np
from pm4py.objects.log.obj import EventLog, Trace
from pm4py.util import constants
from pm4py.util import exec_utils, variants_util
from pm4py.util.xes_constants import DEFAULT_TIMESTAMP_KEY
from pm4py.objects.conversion.log import converter as log_converter
[docs]
class Parameters(Enum):
ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
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
MAX_NO_POINTS_SAMPLE = "max_no_of_points_to_sample"
KEEP_ONCE_PER_CASE = "keep_once_per_case"
[docs]
def get_language(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Union[
Dict[List[str], float], Dict[str, float]]:
"""
Gets the stochastic language of the log (from the variants)
Parameters
--------------
log
Event log
parameters
Parameters
Returns
--------------
dictio
Dictionary containing the stochastic language of the log
(variant associated to a number between 0 and 1; the sum is 1)
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
vars = get_variants(log, parameters=parameters)
vars = {variants_util.get_activities_from_variant(x): len(y) for x, y in vars.items()}
all_values_sum = sum(vars.values())
for x in vars:
vars[x] = vars[x] / all_values_sum
return vars
[docs]
def get_variants(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Union[
Dict[List[str], List[Trace]], Dict[str, List[Trace]]]:
"""
Gets a dictionary whose key is the variant and as value there
is the list of traces that share the variant
Parameters
----------
log
Trace log
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Attribute identifying the activity in the log
Returns
----------
variant
Dictionary with variant as the key and the list of traces as the value
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
variants_trace_idx = get_variants_from_log_trace_idx(log, parameters=parameters)
all_var = convert_variants_trace_idx_to_trace_obj(log, variants_trace_idx)
return all_var
[docs]
def get_variants_along_with_case_durations(log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Tuple[
Union[Dict[List[str], List[Trace]], Dict[str, List[Trace]]], np.array]:
"""
Gets a dictionary whose key is the variant and as value there
is the list of traces that share the variant
Parameters
----------
log
Trace log
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Attribute identifying the activity in the log
Returns
----------
variant
Dictionary with variant as the key and the list of traces as the value
"""
if parameters is None:
parameters = {}
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, DEFAULT_TIMESTAMP_KEY)
variants_trace_idx = get_variants_from_log_trace_idx(log, parameters=parameters)
all_var = convert_variants_trace_idx_to_trace_obj(log, variants_trace_idx)
all_durations = {}
for var in all_var:
all_durations[var] = []
for trace in all_var[var]:
if trace and timestamp_key in trace[-1] and timestamp_key in trace[0]:
all_durations[var].append((trace[-1][timestamp_key] - trace[0][timestamp_key]).total_seconds())
else:
all_durations[var].append(0)
all_durations[var] = np.array(all_durations[var])
return all_var, all_durations
[docs]
def get_variants_from_log_trace_idx(log, parameters=None):
"""
Gets a dictionary whose key is the variant and as value there
is the list of traces indexes that share the variant
Parameters
----------
log
Log
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Attribute identifying the activity in the log
Returns
----------
variant
Dictionary with variant as the key and the list of traces indexes as the value
"""
if parameters is None:
parameters = {}
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
variants = {}
for trace_idx, trace in enumerate(log):
variant = variants_util.get_variant_from_trace(trace, parameters=parameters)
if variant not in variants:
variants[variant] = []
variants[variant].append(trace_idx)
return variants
[docs]
def get_variants_sorted_by_count(variants):
"""
From the dictionary of variants returns an ordered list of variants
along with their count
Parameters
----------
variants
Dictionary with variant as the key and the list of traces as the value
Returns
----------
var_count
List of variant names along with their count
"""
var_count = []
for variant in variants:
var_count.append([variant, len(variants[variant])])
var_count = sorted(var_count, key=lambda x: (x[1], x[0]), reverse=True)
return var_count
[docs]
def convert_variants_trace_idx_to_trace_obj(log, variants_trace_idx):
"""
Converts variants expressed as trace indexes to trace objects
Parameters
-----------
log
Trace log object
variants_trace_idx
Variants associated to a list of belonging indexes
Returns
-----------
variants
Variants associated to a list of belonging traces
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG)
variants = {}
for key in variants_trace_idx:
variants[key] = []
for value in variants_trace_idx[key]:
variants[key].append(log[value])
return variants
