def process_ranking():
import numpy as np
from setting import ALL as ALL_SETTINGS
from Methods import ALL as ALL_METHODS
from experiments import result_exists
missing = []
table = {}
for s in ALL_SETTINGS:
for m in ALL_METHODS:
if m == "DIMAURO":
m = "Di Mauro"
accs = []
for d in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
if result_exists(d, m, s):
try:
results = get_scores(d, m, s)
accs.append(metric.ACCURACY.calculate(results))
except:
print(get_full_filename(d, m, s))
else:
missing.append((str(s),m,d))
table[(s, m)] = np.mean(accs)
res = {}
for s in ALL_SETTINGS:
i = 1
for acc in [(k, table[k]) for k in sorted(table.keys(), key=lambda l: table[l], reverse=True) if k[0] == s]:
if acc[0] not in res:
res[acc[0]] = []
res[acc[0]] = (i, acc[1])
i += 1
print(res)
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
line1 = []
line2 = []
for s in ALL_SETTINGS:
result = res[(s,m)]
if result[0] == 1:
line1.append("\\textbf{%i}" % result[0])
line2.append("\\textbf{\\emph{%.2f}}" % result[1])
else:
line1.append("%i" % result[0])
line2.append("\\emph{%.2f}" % result[1])
print("\\multirow{2}{*}{%s}" % m, "&", " & ".join(line1), "\\\\")
print("&", " & ".join(line2), "\\\\")
print("\\\\")
print()
print()
print("MISSING")
for m in missing:
print(m)
def process_standard():
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
results = get_scores(dataset, m, setting.STANDARD)
if results:
acc = metric.ACCURACY.calculate(results)
else:
acc = 0
print(acc, sep="\t")
print()
def check_variance_k_folds(k_fold=3):
check_setting = copy(setting.STANDARD)
check_setting.train_split = "k-fold"
check_setting.train_k = k_fold
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
results = get_scores("BPIC15_1", m, check_setting)
length_folds = len(results) // k_fold
accs = []
for i in range(k_fold-1):
accs.append(metric.ACCURACY.calculate(results[length_folds*i:length_folds*(i+1)]))
accs.append(metric.ACCURACY.calculate(results[length_folds*(k_fold-1):]))
accs.append(metric.ACCURACY.calculate(results))
print("\t\t".join([str(a) for a in accs]))
def process_split_cases():
split_cases = [True, False]
basic_setting = copy(setting.STANDARD)
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for split_case in split_cases:
basic_setting.split_cases = split_case
results = get_scores(dataset, m, basic_setting)
if results:
accs.append(metric.ACCURACY.calculate(results))
else:
accs.append(0)
print("\t\t".join([str(a) for a in accs]))
print()
def process_percentage():
train_percentages = [60, 66, 70, 80]
basic_setting = copy(setting.STANDARD)
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for percentage in train_percentages:
basic_setting.train_percentage = percentage
results = get_scores(dataset, m, basic_setting)
if results:
accs.append(metric.ACCURACY.calculate(results))
else:
accs.append(0)
print("\t\t".join([str(a) for a in accs]), sep="\t")
print()
def ranking_excel():
from setting import ALL as ALL_SETTINGS
from experiments import result_exists
for data in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
for method in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for setting in ALL_SETTINGS:
if result_exists(data, method, setting):
try:
results = get_scores(data, method, setting)
accs.append(metric.ACCURACY.calculate(results))
except:
print(get_full_filename(data, method, setting))
print("\t\t".join([str(a) for a in accs]))
print()
print()
def process_k():
ks = [3, 5, 10]
basic_setting = copy(setting.STANDARD)
basic_setting.train_split = "k-fold"
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for k in ks:
basic_setting.train_k = k
results = get_scores(dataset, m, basic_setting)
if results:
accs.append(metric.ACCURACY.calculate(results))
else:
accs.append(0)
print("\t\t".join([str(a) for a in accs]), sep="\t")
print()
def process_split():
splits = ["train-test", "test-train", "random", "k-fold"]
basic_setting = copy(setting.STANDARD)
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for split in splits:
basic_setting.train_split = split
if split == "k-fold":
basic_setting.train_k = 3
results = get_scores(dataset, m, basic_setting)
if results:
accs.append(metric.ACCURACY.calculate(results))
else:
accs.append(0)
print("\t\t".join([str(a) for a in accs]), sep="\t")
print()
def process_filter_cases():
filters = [None, 5]
basic_setting = copy(setting.STANDARD)
for dataset in ["Helpdesk", "BPIC12W", "BPIC12", "BPIC11", "BPIC15_1", "BPIC15_2", "BPIC15_3", "BPIC15_4", "BPIC15_5"]:
print()
for m in ["CAMARGO", "DBN", "Di Mauro", "LIN", "PASQUADIBISCEGLIE", "SDL", "TAX", "TAYMOURI"]:
accs = []
for filter in filters:
if filter == 5 and dataset == "Helpdesk":
filter = 3
basic_setting.filter_cases = filter
results = get_scores(dataset, m, basic_setting)
if results:
accs.append(metric.ACCURACY.calculate(results))
else:
accs.append(0)
print("\t\t".join([str(a) for a in accs]))
print()
