def plot_metrics_comparison_lineplot_grid(dataframe,
models_labels,
metrics_labels,
figure_size=(14, 4)):
"""
We define a function to plot the grid.
"""
return (
# Define the plot.
p9.ggplot(
dataframe,
p9.aes(x='threshold',
y='value',
group='variable',
color='variable',
shape='variable'))
# Add the points and lines.
+ p9.geom_point() + p9.geom_line()
# Rename the x axis and give some space to left and right.
+ p9.scale_x_discrete(name='Threshold', expand=(0, 0.2))
# Rename the y axis, give some space on top and bottom, and print the tick labels with 2 decimal digits.
+
p9.scale_y_continuous(name='Value',
expand=(0, 0.05),
labels=lambda l: ['{:.2f}'.format(x) for x in l])
# Replace the names in the legend.
+ p9.scale_shape_discrete(
name='Metric', labels=lambda l: [metrics_labels[x] for x in l])
# Define the colors for the metrics for color-blind people.
+
p9.scale_color_brewer(name='Metric',
labels=lambda l: [metrics_labels[x] for x in l],
type='qual',
palette='Set2')
# Place the plots in a grid, renaming the labels for rows and columns.
+ p9.facet_grid('iterations ~ model',
labeller=p9.labeller(
rows=lambda x: f'iters = {x}',
cols=lambda x: f'{models_labels[x]}'))
# Define the theme for the plot.
+ p9.theme(
# Remove the y axis name.
axis_title_y=p9.element_blank(),
# Set the size of x and y tick labels font.
axis_text_x=p9.element_text(size=7),
axis_text_y=p9.element_text(size=7),
# Place the legend on top, without title, and reduce the margin.
legend_title=p9.element_blank(),
legend_position='top',
legend_box_margin=2,
# Set the size for the figure.
figure_size=figure_size,
))
def plot_fees(fees, title, y_axis, years, filename):
p = pn.ggplot(fees, pn.aes('year', y_axis, color = 'conference', shape = 'conference')) + \
pn.geom_point() + \
pn.geom_line() + \
pn.labs(title = title, x = 'Year', y = 'Fee (€)') + \
pn.ylim(0, 1000) + \
pn.theme_light() + \
pn.scale_x_continuous(breaks = years) + \
pn.scale_colour_discrete(name = 'Conference') + \
pn.scale_shape_discrete(name = 'Conference')
p.save(filename, width=6, height=3, dpi=300)
def test_annotation_stripes_double():
pdf = mtcars.assign(gear=pd.Categorical(mtcars.gear),
am=pd.Categorical(mtcars.am))
p = (
ggplot(pdf) + annotation_stripes(
fills=["#0000FF", "#FF0000"], alpha=0.3, direction='vertical') +
annotation_stripes(
fills=["#AAAAAA", "#FFFFFF"], alpha=0.3, direction='horizontal') +
geom_jitter(aes("gear", "wt", shape="gear", color="am"),
random_state=5) +
scale_shape_discrete(guide=guide_legend(order=1)) # work around #229
)
assert p == "annotation_stripes_double"
def test_annotation_stripes_coord_flip():
pdf = mtcars.assign(gear=pd.Categorical(mtcars.gear),
am=pd.Categorical(mtcars.am))
p = (
ggplot(pdf) + annotation_stripes(
fills=["#AAAAAA", "#FFFFFF", "#7F7FFF"], alpha=0.3) + geom_jitter(
aes("gear", "wt", shape="gear", color="am"), random_state=5) +
geom_vline(xintercept=0.5, color="black") +
geom_vline(xintercept=1.5, color="black") +
geom_vline(xintercept=2.5, color="black") +
geom_vline(xintercept=3.5, color="black") +
scale_shape_discrete(guide=guide_legend(order=1)) # work around #229
+ coord_flip())
assert p == "annotation_stripes_coord_flip"
def baseline_error():
al_baselines_len = len(jsonFile['x'])
df = pd.DataFrame(jsonFile)
method_type = CategoricalDtype(
categories=['Baseline', 'PGD', 'Provable', 'Mix'], ordered=True)
df['method'] = df['method'].astype(method_type)
p = (
ggplot(df) + aes(x='x', y='y', shape='method', color='method') +
geom_point(size=4, stroke=0) + geom_line(size=1) +
scale_shape_discrete(name='Method') +
scale_color_brewer(type='qual', palette=2, name='Method') +
xlab('Training Time (seconds)') + ylab('Adversarial Error') + theme(
# manually position legend, not used anymore
# legend_position=(0.70, 0.65),
# legend_background=element_rect(fill=(0, 0, 0, 0)),
aspect_ratio=0.8, ) + ggtitle("Baseline Error"))
fig_dir = '.'
p.save(os.path.join(fig_dir, 'baselineErr.pdf'))
df = df.append(
pd.DataFrame(np.transpose([
np.repeat("Provable", robust_arr2.shape[0]), robust_arr2[:, 0],
robust_arr2[:, 1]
]),
columns=["method", "x", "y"]))
df = df.append(
pd.DataFrame(np.transpose(
[np.repeat("Mix", mix_arr2.shape[0]), mix_arr2[:, 0], mix_arr2[:, 1]]),
columns=["method", "x", "y"]))
df['x'] = pd.to_numeric((df['x']))
df['y'] = pd.to_numeric((df['y']))
p = (
ggplot(df) + aes(x='x', y='y', shape='method', color='method') +
# + geom_point(size=4, stroke=0)
geom_line(size=1) + scale_shape_discrete(name='Method') +
scale_color_brewer(type='qual', palette=2, name='Method') +
xlab('Training Time (seconds)') + ylab('Error') +
theme(aspect_ratio=0.8, ) + ggtitle("Baseline Error"))
p.save(test + "/baselineErr.png", verbose=False)
df = pd.DataFrame([], columns=["method", "x", "y"])
df = df.append(
pd.DataFrame(np.transpose([
np.repeat("Baseline", baseline_arr2.shape[0]), baseline_arr2[:, 0],
baseline_arr2[:, 2]
]),
columns=["method", "x", "y"]))
df = df.append(
pd.DataFrame(np.transpose([
np.repeat("Madry", madry_arr2.shape[0]), madry_arr2[:, 0],
df = df.append(pd.DataFrame(
np.transpose([np.repeat("Provable", robust_arr2.shape[0]),
robust_arr2[:, 0],
robust_arr2[:, 1]]), columns=["method", "x", "y"]))
df = df.append(pd.DataFrame(
np.transpose([np.repeat("Mix", mix_arr2.shape[0]),
mix_arr2[:, 0],
mix_arr2[:, 1]]), columns=["method", "x", "y"]))
df['x'] = pd.to_numeric((df['x']))
df['y'] = pd.to_numeric((df['y']))
p = (
ggplot(df) +
aes(x='x', y='y', shape='method', color='method') +
# + geom_point(size=4, stroke=0)
geom_line(size=1) +
scale_shape_discrete(name='Method') +
scale_color_brewer(
type='qual',
palette=2,
name='Method') +
xlab('Training Time (seconds)') +
ylab('Error') +
theme(
aspect_ratio=0.8,
) +
ggtitle("Baseline Error")
)
p.save(test + "/baselineErr.png", verbose=False)
df = pd.DataFrame([], columns=["method", "x", "y"])