def test_build_to_other_relative_path(self):
filepath = './some_doc_path/'
doc_name = 'Doc name'
doc = Document(doc_name, filepath=filepath)
doc += 'Some text'
try:
doc.build(show_pdf=False)
assert os.path.exists(filepath + doc_name + '.tex')
assert os.path.exists(filepath + doc_name + '.pdf')
finally:
shutil.rmtree('./some_doc_path/')
def test_build_with_relative_path_from_source(self):
filepath = './some_doc_path/'
doc_name = 'Doc name'
doc = Document(doc_name, filepath=filepath)
doc += 'Some text'
try:
doc.build(show_pdf=False, build_from_dir='source')
assert os.path.exists(filepath + doc_name + '.tex')
assert os.path.exists(filepath + doc_name + '.pdf')
finally:
if os.path.exists(filepath): shutil.rmtree(filepath)
def test_deletes_files_all(self):
filepath = './some_doc_path/'
doc_name = 'doc_name'
doc = Document(doc_name, filepath=filepath)
doc += 'Some text'
try:
doc.build(show_pdf=False, delete_files='all')
assert not os.path.exists(filepath + doc_name + '.tex')
assert os.path.exists(filepath + doc_name + '.pdf')
finally:
shutil.rmtree('./some_doc_path/')
def test_deletes_files_aux_and_log(self):
filepath = './some_doc_path/'
doc_name = 'doc_name'
doc = Document(doc_name, filepath=filepath)
doc += 'Some text'
try:
doc.build(show_pdf=False, delete_files=['aux', 'log'])
assert not os.path.exists(filepath + doc_name + '.aux')
assert not os.path.exists(filepath + doc_name + '.log')
assert os.path.exists(filepath + doc_name + '.pdf')
assert os.path.exists(filepath + doc_name + '.tex')
finally:
if os.path.exists(filepath): shutil.rmtree(filepath)
def test_build_with_options(self):
doc = Document('With options', doc_type='standalone', options=['12pt', 'Spam'], egg=42)
assert doc.build(False, False, False) == cleandoc(r'''\documentclass[12pt, Spam, egg=42]{standalone}
\usepackage[utf8]{inputenc}
\usepackage[top=2.5cm, bottom=2.5cm, left=2.5cm, right=2.5cm]{geometry}
\begin{document}
\end{document}''')
def write(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
doc = Document(filename='table4',
filepath=output_path,
doc_type='article',
options=('12pt', ))
doc.add_to_preamble(r"\usepgfplotslibrary{fillbetween}")
doc.add_to_preamble(r'\usepgfplotslibrary{colorbrewer}')
doc.add_to_preamble(r'\pgfplotsset{compat=1.15, colormap/Blues}')
sec = doc.new_section('All graphs')
self.write_CSLS(sec, output_path)
self.write_vocabulary_cutoff(sec, output_path)
self.write_stochastic(sec, output_path)
doc.build(save_to_disk=True, compile_to_pdf=False, show_pdf=False)
def write(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
doc = Document(filename='grid_search_experiments',
filepath=output_path,
doc_type='article',
options=('12pt', ))
doc.add_to_preamble(r"\usepgfplotslibrary{fillbetween}")
doc.add_to_preamble(r'\usepgfplotslibrary{colorbrewer}')
doc.add_to_preamble(
r'\pgfplotsset{compat=1.15, colormap/Blues, every axis/.append style={label style={font=\footnotesize}, tick label style={font=\footnotesize}}}'
)
sec = doc.new_section('All graphs')
self.write_CSLS(sec, output_path)
self.write_vocabulary_cutoff(sec, output_path)
self.write_stochastic(sec, output_path)
doc.build(save_to_disk=True, compile_to_pdf=False, show_pdf=False)
def test_preamble_appears_in_document(self):
doc = Document('test')
colored_text = textcolor(Color(1, 0, 0, color_name='my_color'), 'hello')
doc += colored_text
assert doc.build(False, False, False) == cleandoc(r'''
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage[top=2.5cm, bottom=2.5cm, left=2.5cm, right=2.5cm]{geometry}
\usepackage[dvipsnames]{xcolor}
\definecolor{my_color}{rgb}{1,0,0}
\begin{document}
\textcolor{my_color}{hello}
\end{document}''')
def test_preamble_appears_in_document(self):
doc = Document('test')
color = Color(1, 2, 3)
command = TexCommand('somecommand', 'param', options=[color])
doc += command
assert doc.build(False, False, False) == cleandoc(r'''
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage[top=2.5cm, bottom=2.5cm, left=2.5cm, right=2.5cm]{geometry}
\definecolor{color1}{rgb}{1,2,3}
\begin{document}
\somecommand{param}[color1]
\end{document}''')
def plot_palette(
doc_name,
palette,
n_colors=5,
width=5,
height=1,
):
palette.n_colors = n_colors
palette.tex_colors = []
palette._init_colors()
color_width = width / n_colors
tikzpic = TexEnvironment('tikzpicture')
tikzpic.add_package('tikz')
for i, color in zip(range(n_colors), palette):
pos = i * color_width
tikzpic += f'\\fill[draw, {build(color, tikzpic)}] ({pos},0) rectangle ({pos+color_width},{height});'
doc = Document(doc_name, filepath='./palettes/', doc_type='standalone')
doc += tikzpic
doc.build(delete_files=['log', 'aux'])
def test_build_with_body_and_packages(self):
doc = Document('With options', doc_type='standalone', options=['12pt', 'Spam'], egg=42)
doc.add_package('tikz')
sec = doc.new_section('Section', label='Section')
sec.add_text('Hey')
assert doc.build(False, False, False) == cleandoc(r'''\documentclass[12pt, Spam, egg=42]{standalone}
\usepackage[utf8]{inputenc}
\usepackage[top=2.5cm, bottom=2.5cm, left=2.5cm, right=2.5cm]{geometry}
\usepackage{tikz}
\begin{document}
\begin{section}{Section}
\label{section:Section}
Hey
\end{section}
\end{document}''')
xticklabel_style='{font=\\footnotesize}',
xtick_style='{draw=none}',
ytick_style='{draw=none}',
)
plot.axis.options += (
'nodes near coords',
'every node near coord/.append style={font=\\scriptsize}',
)
plot.add_plot(x, y1, 'fill', draw='none', legend='Modèle 1')
plot.add_plot(x, y2, 'fill', draw='none', legend='Modèle 2')
plot.legend_position = 'north west'
plot.y_min = 0
plot.y_max = 79
plot.x_min = 0.5
plot.x_ticks = x
plot.x_ticks_labels = labels
plot.x_label = 'Datasets'
plot.y_label = "Précision (\%)"
doc = Document('bar_chart_example',
filepath='./examples/',
doc_type='standalone')
doc += plot
doc.build(delete_files=['log', 'aux'])
from python2latex import Document, Plot import numpy as np # Document type 'standalone' will only show the plot, but does not support all tex environments. filepath = './examples/simple plot example/' filename = 'simple_plot_example' doc = Document(filename, doc_type='standalone', filepath=filepath) # Create the data X = np.linspace(0,2*np.pi,100) Y1 = np.sin(X) Y2 = np.cos(X) # Create a plot plot = doc.new(Plot(X, Y1, X, Y2, plot_path=filepath, as_float_env=False)) tex = doc.build()
from python2latex import Document
doc = Document(filename='simple_document_example', filepath='./examples/simple document example', doc_type='article', options=('12pt',))
doc.set_margins(top='3cm', bottom='3cm', margins='2cm')
sec = doc.new_section('Spam and Egg', label='spam_egg')
sec.add_text('The Monty Python slays the Spam and eats the Egg.')
tex = doc.build() # Builds to tex and compile to pdf
print(tex) # Prints the tex string that generated the pdf
def write(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
experiment = self.experiments['Reproduced Results']
metrics = experiment.aggregate_runs()
doc = Document(filename='table1',
filepath=output_path,
doc_type='article',
options=('12pt', ))
sec = doc.new_section('Table 1')
col, row = 17, 4
table = sec.new(
LatexTable(shape=(row, col),
alignment=['l'] + ['c'] * 16,
float_format='.1f',
label='original_results'))
table.caption = self.CAPTION
table.label_pos = 'bottom'
# Main header
table[0, 1:5].multicell(bold('EN-DE'), h_align='c')
table[0, 5:9].multicell(bold('EN-ES'), h_align='c')
table[0, 9:13].multicell(bold('EN-FI'), h_align='c')
table[0, 13:17].multicell(bold('EN-IT'), h_align='c')
table[0, 1:5].add_rule(trim_left=True, trim_right='.3em')
table[0, 5:9].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 9:13].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 13:17].add_rule(trim_left='.3em', trim_right=True)
# Sub header
table[1, 1:17] = (['best', 'avg', 's', 't'] * 4)
table[1, 0:17].add_rule(trim_left=True, trim_right=True)
table[2, 0] = 'Original'
table[2, 1] = self.ORIGINAL_RESULTS['de']['best']
table[2, 2] = self.ORIGINAL_RESULTS['de']['avg']
table[2, 3] = self.ORIGINAL_RESULTS['de']['successful']
table[2, 4] = self.ORIGINAL_RESULTS['de']['time']
table[2, 5] = self.ORIGINAL_RESULTS['es']['best']
table[2, 6] = self.ORIGINAL_RESULTS['es']['avg']
table[2, 7] = self.ORIGINAL_RESULTS['es']['successful']
table[2, 8] = self.ORIGINAL_RESULTS['es']['time']
table[2, 9] = self.ORIGINAL_RESULTS['fi']['best']
table[2, 10] = self.ORIGINAL_RESULTS['fi']['avg']
table[2, 11] = self.ORIGINAL_RESULTS['fi']['successful']
table[2, 12] = self.ORIGINAL_RESULTS['fi']['time']
table[2, 13] = self.ORIGINAL_RESULTS['it']['best']
table[2, 14] = self.ORIGINAL_RESULTS['it']['avg']
table[2, 15] = self.ORIGINAL_RESULTS['it']['successful']
table[2, 16] = self.ORIGINAL_RESULTS['it']['time']
table[3, 0] = bold('Reproduced')
table[3, 1] = np.max(metrics['accuracies']['de'])
table[3, 2] = np.average(metrics['accuracies']['de'])
table[3,
3] = np.sum(np.array(metrics['accuracies']['de']) > 1.0) / len(
metrics['accuracies']['de'])
table[3, 4] = np.average(metrics['times']['de'])
table[3, 5] = np.max(metrics['accuracies']['es'])
table[3, 6] = np.average(metrics['accuracies']['es'])
table[3,
7] = np.sum(np.array(metrics['accuracies']['es']) > 1.0) / len(
metrics['accuracies']['es'])
table[3, 8] = np.average(metrics['times']['es'])
table[3, 9] = np.max(metrics['accuracies']['fi'])
table[3, 10] = np.average(metrics['accuracies']['fi'])
table[3,
11] = np.sum(np.array(metrics['accuracies']['fi']) > 1.0) / len(
metrics['accuracies']['fi'])
table[3, 12] = np.average(metrics['times']['fi'])
table[3, 13] = np.max(metrics['accuracies']['it'])
table[3, 14] = np.average(metrics['accuracies']['it'])
table[3,
15] = np.sum(np.array(metrics['accuracies']['it']) > 1.0) / len(
metrics['accuracies']['it'])
table[3, 16] = np.average(metrics['times']['it'])
tex = doc.build(save_to_disk=True,
compile_to_pdf=False,
show_pdf=False)
def write(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
experiment = self.experiments['Other Languages']
metrics = experiment.aggregate_runs()
random_experiment = self.experiments[
'Other Languages Unsup. Init (Random)']
random_metrics = random_experiment.aggregate_runs()
random_cutoff_experiment = self.experiments[
'Other Languages Unsup. Init (Random Cutoff)']
random_cutoff_metrics = random_cutoff_experiment.aggregate_runs()
stochastic_experiment = self.experiments['Other Languages Stochastic']
stochastic_metrics = stochastic_experiment.aggregate_runs()
csls_experiment = self.experiments['Other Languages CSLS']
csls_metrics = csls_experiment.aggregate_runs()
bidirectional_experiment = self.experiments[
'Other Languages Bidrectional']
bidirectional_metrics = bidirectional_experiment.aggregate_runs()
reweighting_experiment = self.experiments[
'Other Languages Re-weighting']
reweighting_metrics = reweighting_experiment.aggregate_runs()
doc = Document(filename='table3',
filepath=output_path,
doc_type='article',
options=('12pt', ))
sec = doc.new_section('Table 3')
col, row = 17, 9
table = sec.new(
LatexTable(shape=(row, col),
alignment=['l'] + ['c'] * 16,
float_format='.1f',
label='other_languages_results'))
table.caption = self.CAPTION
table.label_pos = 'bottom'
# Main header
table[0, 1:5].multicell(bold('EN-ET'), h_align='c')
table[0, 5:9].multicell(bold('EN-FA'), h_align='c')
table[0, 9:13].multicell(bold('EN-LV'), h_align='c')
table[0, 13:17].multicell(bold('EN-VI'), h_align='c')
table[0, 1:5].add_rule(trim_left=True, trim_right='.3em')
table[0, 5:9].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 9:13].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 13:17].add_rule(trim_left='.3em', trim_right=True)
# Sub header
table[1, 1:17] = (['best', 'avg', 's', 't'] * 4)
table[1, 0:17].add_rule(trim_left=True, trim_right=True)
table[2, 0] = bold('Vecmap')
table[2, 1] = np.max(metrics['accuracies']['et'])
table[2, 2] = np.average(metrics['accuracies']['et'])
table[2,
3] = np.sum(np.array(metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[2, 4] = np.average(metrics['times']['et'])
table[2, 5] = np.max(metrics['accuracies']['fa'])
table[2, 6] = np.average(metrics['accuracies']['fa'])
table[2,
7] = np.sum(np.array(metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[2, 8] = np.average(metrics['times']['fa'])
table[2, 9] = np.max(metrics['accuracies']['lv'])
table[2, 10] = np.average(metrics['accuracies']['lv'])
table[2,
11] = np.sum(np.array(metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[2, 12] = np.average(metrics['times']['lv'])
table[2, 13] = np.max(metrics['accuracies']['vi'])
table[2, 14] = np.average(metrics['accuracies']['vi'])
table[2,
15] = np.sum(np.array(metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[2, 16] = np.average(metrics['times']['vi'])
table[3, 0] = bold('- Unsupervised (Random)')
table[3, 1] = np.max(random_metrics['accuracies']['et'])
table[3, 2] = np.average(random_metrics['accuracies']['et'])
table[3, 3] = np.sum(
np.array(random_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[3, 4] = np.average(random_metrics['times']['et'])
table[3, 5] = np.max(random_metrics['accuracies']['fa'])
table[3, 6] = np.average(random_metrics['accuracies']['fa'])
table[3, 7] = np.sum(
np.array(random_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[3, 8] = np.average(random_metrics['times']['fa'])
table[3, 9] = np.max(random_metrics['accuracies']['lv'])
table[3, 10] = np.average(random_metrics['accuracies']['lv'])
table[3, 11] = np.sum(
np.array(random_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[3, 12] = np.average(random_metrics['times']['lv'])
table[3, 13] = np.max(random_metrics['accuracies']['vi'])
table[3, 14] = np.average(random_metrics['accuracies']['vi'])
table[3, 15] = np.sum(
np.array(random_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[3, 16] = np.average(random_metrics['times']['vi'])
table[4, 0] = bold('- Unsupervised (Random Cutoff)')
table[4, 1] = np.max(random_cutoff_metrics['accuracies']['et'])
table[4, 2] = np.average(random_cutoff_metrics['accuracies']['et'])
table[4, 3] = np.sum(
np.array(random_cutoff_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[4, 4] = np.average(random_cutoff_metrics['times']['et'])
table[4, 5] = np.max(random_cutoff_metrics['accuracies']['fa'])
table[4, 6] = np.average(random_cutoff_metrics['accuracies']['fa'])
table[4, 7] = np.sum(
np.array(random_cutoff_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[4, 8] = np.average(random_cutoff_metrics['times']['fa'])
table[4, 9] = np.max(random_cutoff_metrics['accuracies']['lv'])
table[4, 10] = np.average(random_cutoff_metrics['accuracies']['lv'])
table[4, 11] = np.sum(
np.array(random_cutoff_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[4, 12] = np.average(random_cutoff_metrics['times']['lv'])
table[4, 13] = np.max(random_cutoff_metrics['accuracies']['vi'])
table[4, 14] = np.average(random_cutoff_metrics['accuracies']['vi'])
table[4, 15] = np.sum(
np.array(random_cutoff_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[4, 16] = np.average(random_cutoff_metrics['times']['vi'])
table[5, 0] = bold('- Stochastic')
table[5, 1] = np.max(stochastic_metrics['accuracies']['et'])
table[5, 2] = np.average(stochastic_metrics['accuracies']['et'])
table[5, 3] = np.sum(
np.array(stochastic_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[5, 4] = np.average(stochastic_metrics['times']['et'])
table[5, 5] = np.max(stochastic_metrics['accuracies']['fa'])
table[5, 6] = np.average(stochastic_metrics['accuracies']['fa'])
table[5, 7] = np.sum(
np.array(stochastic_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[5, 8] = np.average(stochastic_metrics['times']['fa'])
table[5, 9] = np.max(stochastic_metrics['accuracies']['lv'])
table[5, 10] = np.average(stochastic_metrics['accuracies']['lv'])
table[5, 11] = np.sum(
np.array(stochastic_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[5, 12] = np.average(stochastic_metrics['times']['lv'])
table[5, 13] = np.max(stochastic_metrics['accuracies']['vi'])
table[5, 14] = np.average(stochastic_metrics['accuracies']['vi'])
table[5, 15] = np.sum(
np.array(stochastic_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[5, 16] = np.average(stochastic_metrics['times']['vi'])
table[6, 0] = bold('- CSLS')
table[6, 1] = np.max(csls_metrics['accuracies']['et'])
table[6, 2] = np.average(csls_metrics['accuracies']['et'])
table[6, 3] = np.sum(
np.array(csls_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[6, 4] = np.average(csls_metrics['times']['et'])
table[6, 5] = np.max(csls_metrics['accuracies']['fa'])
table[6, 6] = np.average(csls_metrics['accuracies']['fa'])
table[6, 7] = np.sum(
np.array(csls_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[6, 8] = np.average(csls_metrics['times']['fa'])
table[6, 9] = np.max(csls_metrics['accuracies']['lv'])
table[6, 10] = np.average(csls_metrics['accuracies']['lv'])
table[6, 11] = np.sum(
np.array(csls_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[6, 12] = np.average(csls_metrics['times']['lv'])
table[6, 13] = np.max(csls_metrics['accuracies']['vi'])
table[6, 14] = np.average(csls_metrics['accuracies']['vi'])
table[6, 15] = np.sum(
np.array(csls_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[6, 16] = np.average(csls_metrics['times']['vi'])
table[7, 0] = bold('- Bidirectional')
table[7, 1] = np.max(bidirectional_metrics['accuracies']['et'])
table[7, 2] = np.average(bidirectional_metrics['accuracies']['et'])
table[7, 3] = np.sum(
np.array(bidirectional_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[7, 4] = np.average(bidirectional_metrics['times']['et'])
table[7, 5] = np.max(bidirectional_metrics['accuracies']['fa'])
table[7, 6] = np.average(bidirectional_metrics['accuracies']['fa'])
table[7, 7] = np.sum(
np.array(bidirectional_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[7, 8] = np.average(bidirectional_metrics['times']['fa'])
table[7, 9] = np.max(bidirectional_metrics['accuracies']['lv'])
table[7, 10] = np.average(bidirectional_metrics['accuracies']['lv'])
table[7, 11] = np.sum(
np.array(bidirectional_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[7, 12] = np.average(bidirectional_metrics['times']['lv'])
table[7, 13] = np.max(bidirectional_metrics['accuracies']['vi'])
table[7, 14] = np.average(bidirectional_metrics['accuracies']['vi'])
table[7, 15] = np.sum(
np.array(bidirectional_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[7, 16] = np.average(bidirectional_metrics['times']['vi'])
table[8, 0] = bold('- Reweighting')
table[8, 1] = np.max(reweighting_metrics['accuracies']['et'])
table[8, 2] = np.average(reweighting_metrics['accuracies']['et'])
table[8, 3] = np.sum(
np.array(reweighting_metrics['accuracies']['et']) > 1.0) / len(
metrics['accuracies']['et'])
table[8, 4] = np.average(reweighting_metrics['times']['et'])
table[8, 5] = np.max(reweighting_metrics['accuracies']['fa'])
table[8, 6] = np.average(reweighting_metrics['accuracies']['fa'])
table[8, 7] = np.sum(
np.array(reweighting_metrics['accuracies']['fa']) > 1.0) / len(
metrics['accuracies']['fa'])
table[8, 8] = np.average(reweighting_metrics['times']['fa'])
table[8, 9] = np.max(reweighting_metrics['accuracies']['lv'])
table[8, 10] = np.average(reweighting_metrics['accuracies']['lv'])
table[8, 11] = np.sum(
np.array(reweighting_metrics['accuracies']['lv']) > 1.0) / len(
metrics['accuracies']['lv'])
table[8, 12] = np.average(reweighting_metrics['times']['lv'])
table[8, 13] = np.max(reweighting_metrics['accuracies']['vi'])
table[8, 14] = np.average(reweighting_metrics['accuracies']['vi'])
table[8, 15] = np.sum(
np.array(reweighting_metrics['accuracies']['vi']) > 1.0) / len(
metrics['accuracies']['vi'])
table[8, 16] = np.average(reweighting_metrics['times']['vi'])
tex = doc.build(save_to_disk=True,
compile_to_pdf=False,
show_pdf=False)
def write(self, output_path):
if not os.path.exists(output_path):
os.makedirs(output_path)
doc = Document(filename='table2',
filepath=output_path,
doc_type='article',
options=('12pt', ))
sec = doc.new_section('Table 2')
col, row = 17, 17
table = sec.new(
LatexTable(shape=(row, col),
alignment=['l'] + ['c'] * 16,
float_format='.1f',
label='ablation_study'))
table.caption = self.CAPTION
table.label_pos = 'bottom'
# Main header
table[0, 1:5].multicell(bold('EN-DE'), h_align='c')
table[0, 5:9].multicell(bold('EN-ES'), h_align='c')
table[0, 9:13].multicell(bold('EN-FI'), h_align='c')
table[0, 13:17].multicell(bold('EN-IT'), h_align='c')
table[0, 1:5].add_rule(trim_left=True, trim_right='.3em')
table[0, 5:9].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 9:13].add_rule(trim_left='.3em', trim_right='.3em')
table[0, 13:17].add_rule(trim_left='.3em', trim_right=True)
# Sub header
table[1, 1:17] = (['best', 'avg', 's', 't'] * 4)
table[1, 0:17].add_rule(trim_left=True, trim_right=True)
### Full system metrics
table[2, 0] = 'Full System'
table = self.write_original_row(table, 2,
self.ORIGINAL_RESULTS['Full System'])
experiment = self.experiments['Full System']
metrics = experiment.aggregate_runs()
table[3, 0] = bold('Reproduced')
table = self.write_new_row(table, 3, metrics)
table[3, 0:17].add_rule(trim_left=True, trim_right=True)
### Unsup. Init
table[4, 0] = '- Unsup. Init.'
table = self.write_original_row(table, 4,
self.ORIGINAL_RESULTS['Unsup. Init'])
experiment = self.experiments['Unsup. Init (Random)']
metrics = experiment.aggregate_runs()
table[5, 0] = bold('Rand.')
table = self.write_new_row(table, 5, metrics)
experiment = self.experiments['Unsup. Init (Random Cutoff)']
metrics = experiment.aggregate_runs()
table[6, 0] = bold('Rand. Cut.')
table = self.write_new_row(table, 6, metrics)
table[6, 0:17].add_rule(trim_left=True, trim_right=True)
### Stochastic
table[7, 0] = '- Stochastic'
table = self.write_original_row(table, 7,
self.ORIGINAL_RESULTS['Stochastic'])
experiment = self.experiments['Stochastic']
metrics = experiment.aggregate_runs()
table[8, 0] = bold('Reproduced')
table = self.write_new_row(table, 8, metrics)
table[8, 0:17].add_rule(trim_left=True, trim_right=True)
### Cutoff
table[9, 0] = '- Cutoff (k=100k)'
table = self.write_original_row(
table, 9, self.ORIGINAL_RESULTS['Cutoff (k=100k)'])
# experiment = self.experiments['Cutoff (k=100k)']
# metrics = experiment.aggregate_runs()
table[10, 0] = bold('Reproduced')
# table = self.write_new_row(table, 10, metrics)
table[10, 1:] = ['-'] * 16
table[10, 0:17].add_rule(trim_left=True, trim_right=True)
### CSLS
table[11, 0] = '- CSLS'
table = self.write_original_row(table, 11,
self.ORIGINAL_RESULTS['CSLS'])
experiment = self.experiments['CSLS']
metrics = experiment.aggregate_runs()
table[12, 0] = bold('Reproduced')
table = self.write_new_row(table, 12, metrics)
table[12, 0:17].add_rule(trim_left=True, trim_right=True)
### Bidrectional
table[13, 0] = '- Bidrectional'
table = self.write_original_row(table, 13,
self.ORIGINAL_RESULTS['Bidrectional'])
experiment = self.experiments['Bidrectional']
metrics = experiment.aggregate_runs()
table[14, 0] = bold('Reproduced')
table = self.write_new_row(table, 14, metrics)
table[14, 0:17].add_rule(trim_left=True, trim_right=True)
### Re-weighting
table[15, 0] = '- Re-weighting'
table = self.write_original_row(table, 15,
self.ORIGINAL_RESULTS['Re-weighting'])
experiment = self.experiments['Re-weighting']
metrics = experiment.aggregate_runs()
table[16, 0] = bold('Reproduced')
table = self.write_new_row(table, 16, metrics)
tex = doc.build(save_to_disk=True,
compile_to_pdf=False,
show_pdf=False)