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)
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)
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)