from matplotlib import pyplot as pb, numpy as np
[docs]def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
"""
Plot latent space X in 1D:
- if fig is given, create input_dim subplots in fig and plot in these
- if ax is given plot input_dim 1D latent space plots of X into each `axis`
- if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions input_dim
"""
if ax is None:
fig = pb.figure(num=fignum, figsize=figsize)
if colors is None:
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)
lines = []
fills = []
bg_lines = []
means, variances = parameterized.mean.values, parameterized.variance.values
x = np.arange(means.shape[0])
for i in range(means.shape[1]):
if ax is None:
a = fig.add_subplot(means.shape[1], 1, i + 1)
elif isinstance(ax, (tuple, list)):
a = ax[i]
else:
raise ValueError("Need one ax per latent dimension input_dim")
bg_lines.append(a.plot(means, c='k', alpha=.3))
lines.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
fills.append(a.fill_between(x,
means.T[i] - 2 * np.sqrt(variances.T[i]),
means.T[i] + 2 * np.sqrt(variances.T[i]),
facecolor=lines[-1].get_color(),
alpha=.3))
a.legend(borderaxespad=0.)
a.set_xlim(x.min(), x.max())
if i < means.shape[1] - 1:
a.set_xticklabels('')
pb.draw()
a.figure.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return dict(lines=lines, fills=fills, bg_lines=bg_lines)
[docs]def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_side=True):
"""
Plot latent space X in 1D:
- if fig is given, create input_dim subplots in fig and plot in these
- if ax is given plot input_dim 1D latent space plots of X into each `axis`
- if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions input_dim
"""
if ax is None:
if side_by_side:
fig = pb.figure(num=fignum, figsize=(16, min(12, (2 * parameterized.mean.shape[1]))))
else:
fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
if colors is None:
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)
plots = []
means, variances, gamma = parameterized.mean, parameterized.variance, parameterized.binary_prob
x = np.arange(means.shape[0])
for i in range(means.shape[1]):
if side_by_side:
sub1 = (means.shape[1],2,2*i+1)
sub2 = (means.shape[1],2,2*i+2)
else:
sub1 = (means.shape[1]*2,1,2*i+1)
sub2 = (means.shape[1]*2,1,2*i+2)
# mean and variance plot
a = fig.add_subplot(*sub1)
a.plot(means, c='k', alpha=.3)
plots.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
a.fill_between(x,
means.T[i] - 2 * np.sqrt(variances.T[i]),
means.T[i] + 2 * np.sqrt(variances.T[i]),
facecolor=plots[-1].get_color(),
alpha=.3)
a.legend(borderaxespad=0.)
a.set_xlim(x.min(), x.max())
if i < means.shape[1] - 1:
a.set_xticklabels('')
# binary prob plot
a = fig.add_subplot(*sub2)
a.bar(x,gamma[:,i],bottom=0.,linewidth=1.,width=1.0,align='center')
a.set_xlim(x.min(), x.max())
a.set_ylim([0.,1.])
pb.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig