def act_sim_med(var_list_1, var_list_2, log1, log2, freq_thres, num, 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
:return: the distance matrix between 2 sublogs in which each element is the distance between two variants.
'''
if parameters is None:
parameters = {}
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']
dist_matrix = np.zeros((max_len, min_len))
max_per_var = np.zeros(max_len)
max_freq = 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 = np.zeros(min_len)
df_1 = occu_var_act(max_var[i])
for j in range(min_len):
df_2 = occu_var_act(min_var[j])
df = pd.merge(df_1, df_2, how='outer', on='var').fillna(0)
# cosine similarity is used to calculate trace similarity
dist_vec[j] = (pdist(np.array([df['freq_x'].values, df['freq_y'].values]), 'cosine')[0])
dist_matrix[i][j] = 1 - dist_vec[j]
if (j == min_len - 1):
med_loc = np.argsort(dist_vec)[len(dist_vec) // 2]
max_freq[i] = var_count_max.iloc[i] * var_count_min.iloc[med_loc]
max_per_var[i] = dist_vec[med_loc] * max_freq[i]
# single linkage
dist = np.sum(max_per_var) / np.sum(max_freq)
return dist
def act_sim(var_list_1,
var_list_2,
log1,
log2,
freq_thres,
num,
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
:return: the distance matrix between 2 sublogs in which each element is the distance between two variants.
'''
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']
dist_matrix = np.zeros((max_len, min_len))
max_per_var = np.zeros(max_len)
max_freq = np.zeros(max_len)
col_sum = 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 = np.zeros(min_len)
df_1 = occu_var_act(max_var[i])
for j in range(min_len):
df_2 = occu_var_act(min_var[j])
df = pd.merge(df_1, df_2, how='outer', on='var').fillna(0)
# cosine similarity is used to calculate trace similarity
dist_vec[j] = (pdist(
np.array([df['freq_x'].values, df['freq_y'].values]),
'cosine')[0])
dist_matrix[i][j] = dist_vec[j]
if (single):
if abs(dist_vec[j]) <= 1e-6:
max_freq[
i] = var_count_max.iloc[i] * var_count_min.iloc[j]
max_per_var[i] = dist_vec[j] * max_freq[i]
break
elif j == (min_len - 1):
max_loc_col = np.argmin(dist_vec)
max_freq[i] = var_count_max.iloc[
i] * var_count_min.iloc[max_loc_col]
max_per_var[i] = dist_vec[max_loc_col] * max_freq[i]
else:
col_sum[i] += dist_vec[j] * var_count_max.iloc[
i] * var_count_min.iloc[j]
if single:
dist = np.sum(max_per_var) / np.sum(max_freq)
else:
vmax_vec = (var_count_max.values).reshape(-1, 1)
vmin_vec = (var_count_min.values).reshape(1, -1)
vec_sum = np.sum(np.dot(vmax_vec, vmin_vec))
dist = np.sum(col_sum) / vec_sum
return dist
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(np.dot(vmax_vec, vmin_vec))
dist = (np.sum(col_sum_act) * alpha + np.sum(col_sum_suc) * (1 - alpha)) / vec_sum
return dist