'''
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 collections import Counter
import numpy as np
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY
METRIC_NORMALIZATION = "metric_normalization"
[docs]def apply(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> List[Any]:
"""
Calculates the Joint Activities / Similar Task metric
Parameters
------------
log
Log
parameters
Possible parameters of the algorithm
Returns
-----------
tuple
Tuple containing the metric matrix and the resources list. Moreover, last boolean indicates that the metric is
directed.
"""
from scipy.stats import pearsonr
if parameters is None:
parameters = {}
resource_key = exec_utils.get_param_value(Parameters.RESOURCE_KEY, parameters, xes.DEFAULT_RESOURCE_KEY)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
stream = log_converter.apply(log, variant=log_converter.TO_EVENT_STREAM, parameters={"deepcopy": False, "include_case_attributes": False})
activities = Counter(event[activity_key] for event in stream)
resources = Counter(event[resource_key] for event in stream)
activity_resource_couples = Counter((event[resource_key], event[activity_key]) for event in stream)
activities_keys = sorted(list(activities.keys()))
resources_keys = sorted(list(resources.keys()))
rsc_act_matrix = np.zeros((len(resources_keys), len(activities_keys)))
for arc in activity_resource_couples.keys():
i = resources_keys.index(arc[0])
j = activities_keys.index(arc[1])
rsc_act_matrix[i, j] += activity_resource_couples[arc]
metric_matrix = np.zeros((len(resources_keys), len(resources_keys)))
for i in range(rsc_act_matrix.shape[0]):
vect_i = rsc_act_matrix[i, :]
for j in range(rsc_act_matrix.shape[0]):
if not i == j:
vect_j = rsc_act_matrix[j, :]
r, p = pearsonr(vect_i, vect_j)
metric_matrix[i, j] = r
return [metric_matrix, resources_keys, False]