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

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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]