'''
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.util import xes_constants as xes
from pm4py.util import exec_utils
from pm4py.algo.enhancement.sna.parameters import Parameters
[docs]def apply(log, parameters=None):
"""
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.
"""
import numpy as np
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)
activities = dict(log[activity_key].value_counts())
resources = dict(log[resource_key].value_counts())
activity_resource_couples = dict(log.groupby([resource_key, activity_key]).size())
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]