def plot(args):
# Set SIGCONF settings.
sigconf_settings()
# Load data.
with open(args.data, 'rb') as f:
(mun_votes, mun_info) = pickle.load(f)
print(f'Loaded {args.data}')
# Filter data.
lang_avail = (~mun_info.language.isna())
cant_avail = (~mun_info.canton.isna())
mun_votes = mun_votes[lang_avail & cant_avail]
mun_info = mun_info[lang_avail & cant_avail]
mun_info = mun_info.drop([3805, 3810])
mun_votes = mun_votes.drop([3805, 3810])
# Compute SVD embedding.
embedding = svd(mun_votes.values)
# Define colors.
colors = {
'fr': 'C0',
'de': 'C1',
'ro': 'C2',
'it': 'C3',
'unknown': 'black'
}
# Define labels.
labels = {
'fr': 'French',
'de': 'German',
'ro': 'Romansh',
'it': 'Italian'
}
# Plot.
fig, ax = plt.subplots(ncols=1, figsize=(3.6, 1.8))
plot_svd_with_lang(
embedding=embedding,
languages=list(mun_info.language),
colors=colors,
labels=labels,
fig=fig,
ax=ax
)
fig.tight_layout()
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')
def main(args):
# Set SIGCONF settings.
sigconf_settings()
# Load data.
ds = GermanParliamentRegion(args.data)
data = ds.M.reshape(ds.shape[0], -1)
print(f'Loaded {args.data}')
parties = ['SPD', 'CDU/CSU', 'Greens', 'FDP', 'Left']
colors = ['C3', 'C0', 'C2', 'C1', 'C6']
colors = [f'C{i}' for i in range(len(parties))]
colorixs = np.argsort(ds.M.mean(axis=1), axis=1)
# Get SVD embedding.
if args.embedding is not None:
with open(args.embedding, 'rb') as f:
(embedding, colorixs) = pickle.load(f)
print(f'Loaded embedding from {args.embedding}')
else:
embedding = svd(data)
path = f'{args.data}/de-embedding.pkl'
with open(path, 'wb') as f:
pickle.dump((embedding, colorixs), f)
print(f'Saved embedding in {path}')
# Plot.
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(3.6, 2))
plot_nth_scatter(embedding=embedding,
n=1,
ax=ax1,
colorixs=colorixs,
colors=colors,
parties=parties,
title='Coloring by First Party',
print_names=True)
plot_nth_scatter(embedding=embedding,
n=3,
ax=ax2,
colorixs=colorixs,
colors=colors,
parties=parties,
title='Coloring by Third Party',
print_names=True)
fig.tight_layout()
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')
def main(args):
# Set SIGCONF settings.
sigconf_settings()
# Load predictions.
data = load(args.data)
# Define labels.
labels = [
'Averaging',
r'\textsc{SubSVD-Bernoulli}',
]
# Define colors.
colors = [
'black',
'C3',
]
# Define line styles.
linestyles = [
'-',
# '--',
'-'
]
# Set plot positions.
positions = ['top', 'bottom']
# Set titles.
titles = ['Affordable Houses', 'Ban on Homophobia']
fig, axes = plt.subplots(nrows=2, figsize=(3.6, 2.8))
for vote, preds in data.items():
if vote not in DATE2VOTES['2020-02-09']:
continue
i = VOTE2IDX[vote]
plot(preds, axes[i], labels, colors, linestyles, titles[i],
positions[i])
fig.tight_layout()
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')
def plot(args):
# Set SIGCONF settings.
sigconf_settings()
# Setup figure layout.
gridspec = dict(wspace=0.1, bottom=0.2, right=0.95)
fig, axes = plt.subplots(
nrows=2,
ncols=2,
figsize=(3.6, 2.66),
sharex=False,
sharey='row',
gridspec_kw=gridspec
)
# Plot district and state results.
plot_state(args, fig, axes[0, 0], axes[1, 0])
plot_district(args, fig, axes[0, 1], axes[1, 1])
# Plot and save figure.
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')
def plot(args):
# Set SIGCONF settings.
sigconf_settings()
# Define type of plot.
mae = 'mae-outcome'
acc = 'nat_correct'
# Set keys to models.
models = [
'Weighted Averaging',
'Matrix Factorization (dim=25,lam_V=0.03,lam_U=31.0)',
# 'Weighted SubSVD (dim=25,l2=0.1)',
'Weighted Logistic SubSVD (dim=25,l2=0.1)',
]
# Define labels.
labels = [
'Averaging',
'MF',
# r'\textsc{SubSVD-Gaussian}',
r'\textsc{SubSVD-Bernoulli}',
]
# Define line styles.
lines = [
'-',
':',
'--',
]
# Define colors.
colors = ['black', 'black', 'C3']
# Define plot title.
title = 'Swiss Referenda'
# Load data.
results = load(args.data)
# Plot.
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(3.5, 2.66))
_plot( # MAE.
results=results,
metric=mae,
title=title,
models=models,
labels=labels,
lines=lines,
colors=colors,
regions='municipalities',
position='top',
ax=ax1,
fig=fig,
x_logscale=True)
_plot( # Accuracy.
results=results,
metric=acc,
title=title,
models=models,
labels=labels,
lines=lines,
colors=colors,
position='bottom',
regions='municipalities',
ax=ax2,
fig=fig,
x_logscale=True)
fig.tight_layout()
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')