Source code for pm4py.algo.clustering.trace_attribute_driven.variants.sim_calc

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import pandas as pd
import numpy as np
from pm4py.algo.clustering.trace_attribute_driven.variants import act_dist_calc, suc_dist_calc
from pm4py.algo.clustering.trace_attribute_driven.util import filter_subsets
from scipy.spatial.distance import pdist
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants

[docs]class Parameters(Enum): ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY SINGLE = "single" BINARIZE = "binarize" POSITIVE = "positive" LOWER_PERCENT = "lower_percent"
[docs]def inner_prod_calc(df): innerprod = ((df.loc[:, 'freq_x']) * (df.loc[:, 'freq_y'])).sum() sqrt_1 = np.sqrt(((df.loc[:, 'freq_x']) ** 2).sum()) sqrt_2 = np.sqrt(((df.loc[:, 'freq_y']) ** 2).sum()) return innerprod, sqrt_1, sqrt_2
[docs]def dist_calc(var_list_1, var_list_2, log1, log2, freq_thres, num, alpha, parameters=None): ''' this function compare the activity similarity between two sublogs via the two lists of variants. :param var_list_1: lists of variants in sublog 1 :param var_list_2: lists of variants in sublog 2 :param freq_thres: same as sublog2df() :param log1: input sublog1 of sublog2df(), which must correspond to var_list_1 :param log2: input sublog2 of sublog2df(), which must correspond to var_list_2 :param alpha: the weight parameter between activity similarity and succession similarity, which belongs to (0,1) :param parameters: state which linkage method to use :return: the similarity value between two sublogs ''' if parameters is None: parameters = {} single = exec_utils.get_param_value(Parameters.SINGLE, parameters, False) if len(var_list_1) >= len(var_list_2): max_len = len(var_list_1) min_len = len(var_list_2) max_var = var_list_1 min_var = var_list_2 var_count_max = filter_subsets.sublog2df(log1, freq_thres, num)['count'] var_count_min = filter_subsets.sublog2df(log2, freq_thres, num)['count'] else: max_len = len(var_list_2) min_len = len(var_list_1) max_var = var_list_2 min_var = var_list_1 var_count_max = filter_subsets.sublog2df(log2, freq_thres, num)['count'] var_count_min = filter_subsets.sublog2df(log1, freq_thres, num)['count'] # act max_per_var_act = np.zeros(max_len) max_freq_act = np.zeros(max_len) col_sum_act = np.zeros(max_len) # suc max_per_var_suc = np.zeros(max_len) col_sum_suc = np.zeros(max_len) max_freq_suc = np.zeros(max_len) if var_list_1 == var_list_2: print("Please give different variant lists!") else: for i in range(max_len): dist_vec_act = np.zeros(min_len) dist_vec_suc = np.zeros(min_len) df_1_act = act_dist_calc.occu_var_act(max_var[i]) df_1_suc = suc_dist_calc.occu_var_suc(max_var[i], parameters={"binarize": True}) for j in range(min_len): df_2_act = act_dist_calc.occu_var_act(min_var[j]) df_2_suc = suc_dist_calc.occu_var_suc(min_var[j], parameters={"binarize": True}) df_act = pd.merge(df_1_act, df_2_act, how='outer', on='var').fillna(0) df_suc = pd.merge(df_1_suc, df_2_suc, how='outer', on='direct_suc').fillna(0) dist_vec_act[j] = (pdist(np.array([df_act['freq_x'].values, df_act['freq_y'].values]), 'cosine')[0]) dist_vec_suc[j] = (pdist(np.array([df_suc['freq_x'].values, df_suc['freq_y'].values]), 'cosine')[0]) if (single): if (abs(dist_vec_act[j]) <= 1e-8) and (abs(dist_vec_suc[j]) <= 1e-6): # ensure both are 1 max_freq_act[i] = var_count_max.iloc[i] * var_count_min.iloc[j] max_freq_suc[i] = max_freq_act[i] max_per_var_act[i] = dist_vec_act[j] * max_freq_act[i] max_per_var_suc[i] = dist_vec_suc[j] * max_freq_suc[i] break elif j == (min_len - 1): max_loc_col_act = np.argmin(dist_vec_act) # location of max value max_loc_col_suc = np.argmin(dist_vec_suc) # location of max value max_freq_act[i] = var_count_max.iloc[i] * var_count_min.iloc[max_loc_col_act] max_freq_suc[i] = var_count_max.iloc[i] * var_count_min.iloc[max_loc_col_suc] max_per_var_act[i] = dist_vec_act[max_loc_col_act] * max_freq_act[i] max_per_var_suc[i] = dist_vec_suc[max_loc_col_suc] * max_freq_suc[i] else: col_sum_act[i] += dist_vec_act[j] * var_count_max.iloc[i] * var_count_min.iloc[j] col_sum_suc[i] += dist_vec_suc[j] * var_count_max.iloc[i] * var_count_min.iloc[j] if (single): # single linkage dist_act = np.sum(max_per_var_act) / np.sum(max_freq_act) dist_suc = np.sum(max_per_var_suc) / np.sum(max_freq_suc) dist = dist_act * alpha + dist_suc * (1 - alpha) else: vmax_vec = (var_count_max.values).reshape(-1, 1) vmin_vec = (var_count_min.values).reshape(1, -1) vec_sum = np.sum(, vmin_vec)) dist = (np.sum(col_sum_act) * alpha + np.sum(col_sum_suc) * (1 - alpha)) / vec_sum return dist