def plot_z_histogram(cfg, var, title, fname):
"""For a variable, plot correlation in the last dimension."""
size = var.shape[-1] + 1
var = var.reshape((-1, var.shape[-1]))
x = var.cpu().numpy()
fig, ax = plt.subplots(figsize=(12, 10))
cmap = sns.cubehelix_palette(8,
start=2,
rot=0,
dark=0,
light=.95,
reverse=False)
cmap = ListedColormap(cmap.as_hex())
linspace = np.linspace(0, var.shape[-1], var.shape[-1], dtype=int)
ax.hist2d(x[:, 0], x[:, 1], bins=(linspace, linspace), cmap=cmap)
ticks = list(range(1, size + 1))
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set(title=title)
plt.savefig(cfg.train_dir / fname, bbox_inches="tight")
plt.close()
def plot_q_nu_hist(cfg,
device,
q_nu,
title,
fname,
linspace,
axes_transform=lambda x: x):
with torch.no_grad():
nu, _ = q_nu(num_samples=cfg.num_eps_samples_plot)
np_nu = nu.cpu().numpy()
fig, ax = plt.subplots(figsize=(12, 10))
cmap = sns.cubehelix_palette(8,
start=2,
rot=0,
dark=0,
light=.95,
reverse=False)
cmap = ListedColormap(cmap.as_hex())
x = axes_transform(np_nu)
linspace = axes_transform(linspace)
ax.hist2d(x[:, 0], x[:, 1], bins=(linspace, linspace), cmap=cmap)
ax.set(title=title, xlabel='dimension 1', ylabel='dimension 2')
plt.savefig(cfg.train_dir / fname, bbox_inches="tight")
plt.close()
def seaborn_palette(feat_type='', fig_type='diff'):
""" Get colors for difference cases.
Parameters
----------
feat_type
fig_type
Returns
-------
"""
# 1 bright used for basic
# Set the palette to the "pastel" default palette:
sns.set_palette("bright")
# sns.palplot(sns.color_palette());
# plt.show()
colors_bright = sns.color_palette()
# muted for FFT
sns.set_palette("muted")
colors_muted = sns.color_palette()
# dark for feature + size
sns.set_palette("dark")
colors_dark = sns.color_palette()
# deep for feature + header
sns.set_palette("deep") # for feature+header
colors_deep = sns.color_palette()
# colorblind for diff
sns.set_palette("colorblind") # for feature+header
colors_colorblind = sns.color_palette()
colors_bright = ListedColormap(colors_bright.as_hex()).colors
colors_dark = ListedColormap(colors_dark.as_hex()).colors
colors_muted = ListedColormap(colors_muted.as_hex()).colors
colors_deep = ListedColormap(colors_deep.as_hex()).colors
colors_colorblind = ListedColormap(colors_colorblind.as_hex()).colors
feat_type = feat_type.upper()
fig_type = fig_type.upper()
C_STATS = 4 # purple
C_IAT = 2 # green
C_SIZE = 0 # blue
C_SAMP_NUM = 3 # red
C_SAMP_SIZE = 5 # brown
raw_feat = {
'STATS': colors_bright[C_STATS],
'IAT': colors_bright[C_IAT],
'SIZE': colors_bright[C_SIZE],
'SAMP-NUM': colors_bright[C_SAMP_NUM],
'SAMP-SIZE': colors_bright[C_SAMP_SIZE]
}
if feat_type == "basic_representation".upper():
if fig_type == 'raw'.upper():
colors = {
'STATS': raw_feat['STATS'],
'IAT': raw_feat['IAT'],
'IAT-FFT': colors_dark[C_IAT],
'SAMP-NUM': raw_feat['SAMP-NUM'],
'SAMP-NUM-FFT': colors_dark[C_SAMP_NUM]
} # red
elif fig_type == 'diff'.upper():
# 'IAT' vs. IAT-FFT
colors = {
'IAT vs. IAT-FFT': raw_feat['IAT'], # green
'SAMP-NUM vs. SAMP-NUM-FFT': raw_feat['SAMP-NUM'],
'SAMP-SIZE vs. SAMP-SIZE-FFT': raw_feat['SAMP-SIZE']
}
else:
msg = f'{feat_type} is not implemented yet.'
raise ValueError(msg)
elif feat_type == "effect_size".upper():
if fig_type == 'raw'.upper():
colors = {
'STATS': raw_feat['STATS'],
'SIZE': raw_feat['SIZE'],
'IAT': raw_feat['IAT'],
'IAT+SIZE': colors_dark[C_SIZE],
'SAMP-NUM': raw_feat['SAMP-NUM'],
'SAMP-SIZE': raw_feat['SAMP-SIZE']
} # red
elif fig_type == 'diff'.upper():
colors = {
'IAT vs. IAT+SIZE': raw_feat['IAT'], # green
'SAMP-NUM vs. SAMP-SIZE': raw_feat['SAMP-SIZE']
} # red
else:
msg = f'{feat_type} is not implemented yet.'
raise ValueError(msg)
elif feat_type == "effect_header".upper():
if fig_type == 'raw'.upper():
colors = {
'STATS (wo. header)': raw_feat['STATS'],
'STATS (w. header)': colors_dark[C_STATS],
'IAT+SIZE (wo. header)': colors_dark[C_SIZE],
'IAT+SIZE (w. header)': colors_deep[C_SIZE],
# green
'SAMP-SIZE (wo. header)': raw_feat['SAMP-SIZE'],
'SAMP-SIZE (w. header)': colors_deep[C_SAMP_SIZE]
} # red
elif fig_type == 'diff'.upper():
colors = {
'STATS (wo. header) vs. STATS (w. header)':
raw_feat['STATS'],
'IAT+SIZE (wo. header) vs. IAT+SIZE (w. header)':
raw_feat['IAT'], # green
'SAMP-SIZE (wo. header) vs. SAMP-SIZE (w. header)':
raw_feat['SAMP-SIZE']
} # red
else:
msg = f'{feat_type} is not implemented yet.'
raise ValueError(msg)
else:
msg = f'{feat_type} is not implemented yet.'
raise ValueError(msg)
return colors
