def dimension_reduce(
self,
algorithm: Literal['pca', 'umap', 'tsne', 'knn', 'kmean'] = 'tsne',
seed: int = 1,
) -> np.ndarray:
"""Applying dimension reduction on latents space, this method will cache the
returns to lower computational cost."""
key = f'{id(self)}_{id(self.dist_to_tensor)}_{algorithm}_{int(seed)}'
if key in _CACHE_LATENTS:
return _CACHE_LATENTS[key]
x = self.dist_to_tensor(self.latents).numpy()
x = dimension_reduce(x, algo=algorithm, random_state=seed)
_CACHE_LATENTS[key] = x
return x
def plot_uncertainty_scatter(self, factors=None, n_samples=2, algo='tsne'):
r""" Plotting the scatter points of the mean and sampled latent codes,
colored by the factors.
Arguments:
factors : list of Integer or String. The index or name of factors taken
into account for analyzing.
"""
factors = self._check_factors(factors)
# this all include tarin and test data separatedly
z_mean = np.concatenate(self.representations_mean)
z_var = np.concatenate(
[np.mean(var, axis=1) for var in self.representations_variance])
z_samples = [
z for z in np.concatenate(self.representations_sample(int(n_samples)),
axis=1)
]
F = np.concatenate(self.original_factors, axis=0)[:, factors]
labels = self.factors_name[factors]
# preprocessing
inputs = tuple([z_mean] + z_samples)
Z = dimension_reduce(*inputs,
algo=algo,
n_components=2,
return_model=False,
combined=True,
random_state=self.randint)
V = utils.discretizing(z_var[:, np.newaxis], n_bins=10).ravel()
# the figure
nrow = 3
ncol = int(np.ceil(len(labels) / nrow))
fig = vs.plot_figure(nrow=nrow * 4, ncol=ncol * 4, dpi=80)
for idx, (name, y) in enumerate(zip(labels, F.T)):
ax = vs.plot_subplot(nrow, ncol, idx + 1)
for i, x in enumerate(Z):
kw = dict(val=y,
color="coolwarm",
ax=ax,
x=x,
grid=False,
legend_enable=False,
centroids=True,
fontsize=12)
if i == 0: # the mean value
vs.plot_scatter(size=V,
size_range=(8, 80),
alpha=0.3,
linewidths=0,
cbar=True,
cbar_horizontal=True,
title=name,
**kw)
else: # the samples
vs.plot_scatter_text(size=8,
marker='x',
alpha=0.8,
weight='light',
**kw)
# fig.tight_layout()
self.add_figure("uncertainty_scatter_%s" % algo, fig)
return self