Source code for pm4py.algo.enhancement.sna.variants.log.jointactivities

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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 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. """ 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]