def long_table(num=2, title=("符号", "说明")):
data_table = LongTable("l " * num)
data_table.add_hline()
data_table.add_hline()
data_table.add_row(title)
data_table.add_hline()
data_table.end_table_header()
data_table.add_hline()
data_table.end_table_footer()
data_table.add_hline()
data_table.add_hline()
data_table.end_table_last_footer()
return data_table
def test_table():
# Tabular
t = Tabular(table_spec='|c|c|', data=None, pos=None, width=2)
t.add_hline(start=None, end=None)
t.add_row((1, 2), escape=False, strict=True, mapper=[bold])
t.add_row(1, 2, escape=False, strict=True, mapper=[bold])
# MultiColumn/MultiRow.
t.add_row((MultiColumn(size=2, align='|c|', data='MultiColumn'), ),
strict=True)
# One multiRow-cell in that table would not be proper LaTeX,
# so strict is set to False
t.add_row((MultiRow(size=2, width='*', data='MultiRow'), ), strict=False)
repr(t)
# TabularX
tabularx = Tabularx(table_spec='X X X',
width_argument=NoEscape(r"\textwidth"))
tabularx.add_row(["test1", "test2", "test3"])
# Long Table
longtable = LongTable(table_spec='c c c')
longtable.add_row(["test", "test2", "test3"])
longtable.end_table_header()
# Colored Tabu
coloredtable = Tabu(table_spec='X[c] X[c]')
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabu with 'spread'
coloredtable = Tabu(table_spec='X[c] X[c]', spread="1in")
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabu with 'to'
coloredtable = Tabu(table_spec='X[c] X[c]', to="5in")
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabularx
coloredtable = Tabularx(table_spec='X[c] X[c]')
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Column
column = ColumnType("R", "X", r"\raggedleft", parameters=2)
repr(column)
def test_table():
# Tabular
t = Tabular(table_spec='|c|c|', data=None, pos=None, width=2)
t.add_hline(start=None, end=None)
t.add_row((1, 2), escape=False, strict=True, mapper=[bold])
t.add_row(1, 2, escape=False, strict=True, mapper=[bold])
# MultiColumn/MultiRow.
t.add_row((MultiColumn(size=2, align='|c|', data='MultiColumn'),),
strict=True)
# One multiRow-cell in that table would not be proper LaTeX,
# so strict is set to False
t.add_row((MultiRow(size=2, width='*', data='MultiRow'),), strict=False)
repr(t)
# TabularX
tabularx = Tabularx(table_spec='X X X',
width_argument=NoEscape(r"\textwidth"))
tabularx.add_row(["test1", "test2", "test3"])
# Long Table
longtable = LongTable(table_spec='c c c')
longtable.add_row(["test", "test2", "test3"])
longtable.end_table_header()
# Colored Tabu
coloredtable = Tabu(table_spec='X[c] X[c]')
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabu with 'spread'
coloredtable = Tabu(table_spec='X[c] X[c]', spread="1in")
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabu with 'to'
coloredtable = Tabu(table_spec='X[c] X[c]', to="5in")
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Colored Tabularx
coloredtable = Tabularx(table_spec='X[c] X[c]')
coloredtable.add_row(["test", "test2"], color="gray", mapper=bold)
# Column
column = ColumnType("R", "X", r"\raggedleft", parameters=2)
repr(column)
def make_table_of_symbols(symbols: List[str], units: List[str],
descriptions: List[str]):
table_symbols_content = LongTable("l l l")
table_symbols_content.add_hline()
table_symbols_content.add_row(["Symbol", "Unit", "Description"])
table_symbols_content.add_hline()
table_symbols_content.end_table_header()
table_symbols_content.add_hline()
table_symbols_content.add_row(
(MultiColumn(3, align='r', data='Continued on Next Page'), ))
table_symbols_content.add_hline()
table_symbols_content.end_table_footer()
table_symbols_content.add_hline()
table_symbols_content.add_row((MultiColumn(3, align='r', data='***End'), ))
table_symbols_content.add_hline()
table_symbols_content.end_table_last_footer()
for i in range(len(symbols)):
try:
symbol = py2tex(symbols[i], print_latex=False,
print_formula=False).replace('$', '')
except:
symbol = symbols[i]
try:
unit = py2tex(units[i],
print_latex=False,
print_formula=False,
simplify_fractions=True).replace('$', '')
except:
unit = units[i]
table_symbols_content.add_row([
NoEscape(f'${symbol}$'),
NoEscape(f'${unit}$'),
NoEscape(descriptions[i])
])
table_symbols_content.add_hline()
return table_symbols_content
def make_table(headers: List, *args):
table = LongTable('|l| l| l| p{10cm}|')
table.add_hline()
table.add_row([
NoEscape(f'\\textbf{{\\textcolor{{black}}{{{i}}}}}')
for i in headers
])
table.add_hline()
table.end_table_header()
table.add_hline()
table.add_row((MultiColumn(4, align='r',
data='Continued on next page'), ))
# table.add_hline()
table.end_table_footer()
# table.add_hline()
# table.add_row((MultiColumn(n_cols, align='r', data=''),))
# table.add_hline()
table.end_table_last_footer()
for i in range(len(args[0])):
table.add_row([NoEscape(j[i]) for j in args])
table.add_hline()
return table
dataset = [
'vehicle', 'ionosphere', 'ecoli', 'german', 'breast_cancer', 'cmc',
'hepatitis', 'haberman', 'transfusion', 'car', 'glass', 'abalone16_29',
'heart_cleveland', 'balance_scale', 'postoperative'
]
sections = [
"Accuracy", "Sensitivity", "Specificity", "F-1 klasa mniejszosciowa",
'G-mean'
]
random_state = 5
tables = []
for tab in range(5):
table = LongTable('c|c|cccccc')
table.add_hline()
table.add_row(('Zbior danych', 'Glebokosc drzewa.', "-", "5", "10", "20",
"50", "100"))
table.add_hline()
tables.append(table)
# liczba powtorzen klasyfikacji
iterations = 10
# liczba fold w sprawdzianie krzyzowym
folds = 10
#glebokosc drzewa i liczba estymatorow
depths = [None, 1, 3, 5]
estimators = [5, 10, 20, 50, 100]
depths_name = ['-']
depths_name.extend(depths[1:])
clfs = []
samp = 0.8
feat = 0.9
# liczbya fold w sprawdzianie krzyzowym
folds = 10
# glebokosc drzew
depths = [None, 3, 5, 7, 10, 15, 20]
estimators = [5, 10, 20, 50]
estimators_name = ['-']
estimators_name.extend(estimators)
for tab in range(5):
table = LongTable('c|c|ccccccc')
table.add_hline()
table.add_row(('Glebokosc drzewa', 'Liczba est.', "-", "3", "5", "7", "10",
"15", "20"))
table.add_hline()
tables.append(table)
clfs = []
temp_clf = []
for depth in depths:
temp_clf.append(tree.DecisionTreeClassifier(max_depth=depth))
clfs.append(temp_clf)
# tworzenie klasyfikatorow
for estimator in estimators:
temp2_clf = []
for depth in depths:
temp2_clf.append(
BaggingClassifier(tree.DecisionTreeClassifier(max_depth=depth),
n_estimators=estimator,
def write_file(m_l=3, m_u=70, s_l=3, s_u=60, findProc=False):
doc = Document('BIGRUN_ALL')
doc_one = Document('BIGRUN_ONE')
doc_open = Document('Open')
doc_non_FC = Document('Non_FC')
doc_V3 = Document('V3')
doc_gaps = Document('GAPS')
table = LongTable('|c|c|c|c|c|c|c|c|')
table.add_hline()
table.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table.add_hline()
table.add_hline()
table_one = LongTable('|c|c|c|c|c|c|c|c|')
table_one.add_hline()
table_one.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table_one.add_hline()
table_one.add_hline()
table_open = LongTable('|c|c|c|c|c|c|c|c|')
table_open.add_hline()
table_open.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table_open.add_hline()
table_open.add_hline()
table_non_FC = LongTable('|c|c|c|c|c|c|c|c|')
table_non_FC.add_hline()
table_non_FC.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table_non_FC.add_hline()
table_non_FC.add_hline()
table_V3 = LongTable('|c|c|c|c|c|c|c|c|')
table_V3.add_hline()
table_V3.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table_V3.add_hline()
table_V3.add_hline()
table_gaps = LongTable('|c|c|c|c|c|c|c|c|')
table_gaps.add_hline()
table_gaps.add_row((bold('M'), bold('S'), bold('Method'), bold('Open?'),
bold('LB'), bold('UB'), bold('LB-CD'), bold('UB-CD')))
table_gaps.add_hline()
table_gaps.add_hline()
bigrun_csv = open('data/bigrun_all.csv', 'w')
csv_writer = csv.writer(bigrun_csv)
csv_writer.writerow(['M', 'S', 'FC', 'HALF', 'INT', 'MID', 'EBM', 'HBM', 'VHBM'])
for s in range(s_l, s_u + 1):
m_start = s + 1 if s + 1 > m_l else m_l
for m in range(m_start, m_u + 1):
if relatively_prime(m, s):
ub, ans_types, all_results = f(m, s, bigrun=True)
csv_writer.writerow([m, s] + all_results)
open_prob = ''
lb = ''
lb_cd = ''
ub_cd = ''
used_gaps = False
try:
if checkProc(findProc, m, s, ub):
open_prob = ''
else:
used_gaps = True
lb = Fraction(ub.numerator - 2, ub.denominator)
lb = lb if lb > Fraction(1, 3) else Fraction(1, 3)
lb, lb_type = closer_bounds(m, s, lb, ub, max_denom=750)
if lb_type == 'Open':
open_prob = 'Open'
lb_cd, ub_cd = convert_den(lb, ub)
else:
ans_types = [lb_type]
ub = lb
lb = ''
except procedures.TimeoutError:
open_prob = 'Timeout'
except KeyError:
open_prob = 'Timeout'
ans_types_str = functools.reduce(lambda a, b: a + ',' + str(b), ans_types)
row = (m, s, ans_types_str, open_prob, str(lb), str(ub), lb_cd, ub_cd)
table.add_row(row)
row_one = (m, s, ans_types[0], open_prob, str(lb), str(ub), lb_cd, ub_cd)
table_one.add_row(row_one)
print(row_one)
table.add_hline()
table_one.add_hline()
if len(open_prob) > 0:
table_open.add_row(row)
table_open.add_hline()
if len(open_prob) > 0 or 'FC' not in ans_types_str:
table_non_FC.add_row(row)
table_non_FC.add_hline()
V, _, _ = calcSv(m, s)
if V == 3:
if not used_gaps:
_, ans_types_all = f(m, s, bigrun=False)
ans_types_all_str = functools.reduce(lambda a, b: a + ',' + str(b), ans_types)
row = (m, s, ans_types_all_str, open_prob, str(lb), str(ub), lb_cd, ub_cd)
table_V3.add_row(row)
table_V3.add_hline()
if len(open_prob) == 0 and used_gaps:
table_gaps.add_row(row)
table_gaps.add_hline()
doc.append(table)
doc.generate_pdf('bigrun/BIGRUN_all', clean_tex=False)
doc_one.append(table_one)
doc_one.generate_pdf('bigrun/BIGRUN_one', clean_tex=False)
doc_open.append(table_open)
doc_open.generate_pdf('bigrun/BIGRUN_opens', clean_tex=False)
doc_non_FC.append(table_non_FC)
doc_non_FC.generate_pdf('bigrun/BIGRUN_non_FC', clean_tex=False)
doc_V3.append(table_V3)
doc_V3.generate_pdf('bigrun/BIGRUN_V3', clean_tex=False)
doc_gaps.append(table_gaps)
doc_gaps.generate_pdf('bigrun/BIGRUN_GAPS', clean_tex=False)
temp_clf.append(KNeighborsClassifier(n_neighbors=neighbors))
clfs.append(temp_clf)
# dodawanie klasyfikatorow
for estimator in estimators:
temp2_clf = []
for neighbors in n_neighbors:
temp2_clf.append(
BaggingClassifier(KNeighborsClassifier(n_neighbors=neighbors), n_estimators=estimator, max_features=1.0,
max_samples=0.8))
clfs.append(temp2_clf)
for tab in range(5):
table = LongTable('c|c|ccccc')
table.add_hline()
table.add_row(('Glebokosc drzewa', 'Liczba est.', "1", "2", "3", "5", "7"))
table.add_hline()
tables.append(table)
for data in dataset:
db = getattr(importdata, 'load_' + data)()
print("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
print('Zbior danych: %s' % data)
importdata.print_info(db.target)
# klasyfikacja
for id, (clfs_, name) in enumerate(zip(clfs, estimators_name)):
rows = []
if id == 0:
