def plot_isocontours_expected(ax, model, data, xlimits=[-6, 6], ylimits=[-6, 6],
numticks=101, cmap=None, alpha=1., legend=False):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
n_samps = 10
if len(data) < n_samps:
n_samps = len(data)
for samp_i in range(n_samps):
if samp_i % 1000 == 0:
print samp_i
mean, logvar = model.encode(Variable(torch.unsqueeze(data[samp_i],0)))
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# print aaa.size()
bbb = func(aaa)
# print 'sum:1', torch.sum(bbb)
ddd = torch.exp(bbb)
# print 'sum:', torch.sum(ddd)
# print ddd.size()
# fdsa
if samp_i ==0:
sum_of_all = ddd
else:
sum_of_all = sum_of_all + ddd
avg_of_all = sum_of_all / n_samps
Z = avg_of_all.view(X.shape)
Z=Z.numpy()
# print 'sum:', np.sum(Z)
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
if legend:
nm, lbl = cs.legend_elements()
plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
return Z
def plot_isocontours_expected_norm_ind(ax, model, data, xlimits=[-6, 6], ylimits=[-6, 6],
numticks=101, cmap=None, alpha=1., legend=False, n_samps=10, cs_to_use=None):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
# n_samps = 10
if len(data) < n_samps:
n_samps = len(data)
for samp_i in range(n_samps):
if samp_i % 1000 == 0:
print samp_i
mean, logvar = model.encode(Variable(torch.unsqueeze(data[samp_i],0)))
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
# print aaa.size()
bbb = func(aaa)
zs = bbb.numpy()
max_ = np.max(zs)
zs_sum = np.log(np.sum(np.exp(zs-max_))) + max_
zs = zs - zs_sum
ddd = np.exp(zs)
Z = ddd
Z = Z.reshape(X.shape)
if cs_to_use != None:
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha, levels=cs_to_use.levels)
else:
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
# if samp_i ==0:
# sum_of_all = ddd
# else:
# sum_of_all = sum_of_all + ddd
# avg_of_all = sum_of_all / n_samps
# Z = avg_of_all.reshape(X.shape)
# print 'sum:', np.sum(Z)
# if legend:
# nm, lbl = cs.legend_elements()
# plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
return Z, cs
# plot_isocontours2_exp_norm(ax, func, cmap='Greys', legend=legend,xlimits=xlimits,ylimits=ylimits,alpha=.2)
plot_isocontours2_exp_norm(ax,
func,
cmap='Blues',
legend=legend,
xlimits=xlimits,
ylimits=ylimits,
alpha=1.)
# plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
# plot_kde(ax,samps=z,xlimits=xlimits,ylimits=ylimits,cmap='Blues')
# plot_kde(ax,samps=z,xlimits=xlimits,ylimits=ylimits,cmap='Greens')
mean, logvar = model.q_dist.get_mean_logvar(samp_torch)
func = lambda zs: lognormal4(torch.Tensor(zs),
torch.squeeze(mean.data.cpu()),
torch.squeeze(logvar.data.cpu()))
plot_isocontours(ax,
func,
cmap='Greens',
xlimits=xlimits,
ylimits=ylimits)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# #Plot prob
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# # func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
#Plot prob
row +=1
ax = plt.subplot2grid((rows,cols), (row, samp_i+1), frameon=False)
func = lambda zs: model.logposterior_func(samp_torch,zs)
# plot_isocontours2_exp_norm(ax, func, cmap='Greys', legend=legend,xlimits=xlimits,ylimits=ylimits,alpha=.2)
plot_isocontours2_exp_norm(ax, func, cmap='Blues', legend=legend,xlimits=xlimits,ylimits=ylimits,alpha=1.)
# plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
# plot_kde(ax,samps=z,xlimits=xlimits,ylimits=ylimits,cmap='Blues')
# plot_kde(ax,samps=z,xlimits=xlimits,ylimits=ylimits,cmap='Greens')
mean, logvar = model.q_dist.get_mean_logvar(samp_torch)
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data.cpu()), torch.squeeze(logvar.data.cpu()))
plot_isocontours(ax, func, cmap='Greens',xlimits=xlimits,ylimits=ylimits)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# #Plot prob
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
# # func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
model.load_state_dict(
torch.load(path_to_save_variables, lambda storage, loc: storage))
print 'loaded variables ' + path_to_save_variables
rows = 1
cols = 2
legend = False
# legend=True
n_samps = 10000
alpha = .3
fig = plt.figure(figsize=(4 + cols, 4 + rows), facecolor='white')
prior_func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2),
torch.zeros(2))
for samp_i in range(rows):
#Get a sample
samp = train_x[samp_i]
# print samp.shape
col = 0
# #Plot sample
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# ax.imshow(samp.numpy().reshape(28, 28), vmin=0, vmax=1, cmap="gray")
# ax.set_yticks([])
# ax.set_xticks([])
# if samp_i==0: ax.annotate('Sample', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
if samp_i==0: ax.annotate('Sample', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
# #Plot prior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues')
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Plot q
col +=1
val = 3
ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
plot_isocontours(ax, func, cmap='Reds', xlimits=[-val, val], ylimits=[-val, val])
if samp_i==0: ax.annotate('p(z)\nq(z|x)\np(z|x)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
plot_isocontours(ax, func, cmap='Blues', alpha=.3, xlimits=[-val, val], ylimits=[-val, val])
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
plot_isocontours2_exp_norm(ax, func, cmap='Greens', legend=legend, xlimits=[-val, val], ylimits=[-val, val])
# #Plot logprior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontoursNoExp(ax, func, cmap='Blues', legend=legend)
# if samp_i==0: ax.annotate('Prior\nlogp(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
def plot_isocontours_expected_true_posterior_ind(ax,
model,
data,
xlimits=[-6, 6],
ylimits=[-6, 6],
numticks=101,
cmap=None,
alpha=1.,
legend=False,
n_samps=10,
cs_to_use=None):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(
np.concatenate([np.atleast_2d(X.ravel()),
np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
# n_samps = n_samps
if len(data) < n_samps:
n_samps = len(data)
for samp_i in range(n_samps):
# if samp_i % 100 == 0:
# print samp_i
samp = data[samp_i]
n_Ws = 1
for i in range(n_Ws):
# if i % 10 ==0: print i
# print i
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(
model.decode(Ws, Variable(torch.unsqueeze(zs, 1))),
Variable(torch.unsqueeze(samp, 0))) + Variable(
torch.unsqueeze(
lognormal4(torch.Tensor(zs), torch.zeros(2),
torch.zeros(2)), 1))
bbb = func(aaa)
zs = bbb.data.numpy()
max_ = np.max(zs)
zs_sum = np.log(np.sum(np.exp(zs - max_))) + max_
zs = zs - zs_sum
zs = np.exp(zs)
Z = zs.reshape(X.shape)
if cs_to_use != None:
cs = plt.contour(X,
Y,
Z,
cmap=cmap,
alpha=alpha,
levels=cs_to_use.levels)
else:
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
# if i ==0:
# sum_of_all_i = zs
# else:
# sum_of_all_i = sum_of_all_i + zs
# if samp_i ==0:
# sum_of_all = sum_of_all_i
# else:
# sum_of_all = sum_of_all + sum_of_all_i
# avg_of_all = sum_of_all / n_samps
# print 'sum:', np.sum(avg_of_all)
# if legend:
# nm, lbl = cs.legend_elements()
# plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
return Z
def plot_isocontours_expected_W(ax,
model,
samp,
xlimits=[-6, 6],
ylimits=[-6, 6],
numticks=101,
cmap=None,
alpha=1.,
legend=False):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(
np.concatenate([np.atleast_2d(X.ravel()),
np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
n_Ws = 10
for i in range(n_Ws):
# if i % 10 ==0: print i
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(
model.decode(Ws, Variable(torch.unsqueeze(zs, 1))),
Variable(torch.unsqueeze(samp, 0))) + Variable(
torch.unsqueeze(
lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)
), 1))
bbb = func(aaa)
zs = bbb.data.numpy()
# zs = np.exp(zs/784)
# print zs.shape
max_ = np.max(zs)
# print max_
zs_sum = np.log(np.sum(np.exp(zs - max_))) + max_
zs = zs - zs_sum
zs = np.exp(zs)
if i == 0:
sum_of_all = zs
else:
sum_of_all = sum_of_all + zs
avg_of_all = sum_of_all / n_Ws
Z = avg_of_all.reshape(X.shape)
# Z = zs.view(X.shape)
# Z=Z.numpy()
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
if legend:
nm, lbl = cs.legend_elements()
plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
def plot_isocontours_expected_true_posterior_ind(ax, model, data, xlimits=[-6, 6], ylimits=[-6, 6],
numticks=101, cmap=None, alpha=1., legend=False, n_samps=10, cs_to_use=None):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
# n_samps = n_samps
if len(data) < n_samps:
n_samps = len(data)
for samp_i in range(n_samps):
if samp_i % 100 == 0:
print samp_i
samp = data[samp_i]
n_Ws = 1
for i in range(n_Ws):
# if i % 10 ==0: print i
# print i
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
bbb = func(aaa)
zs = bbb.data.numpy()
max_ = np.max(zs)
zs_sum = np.log(np.sum(np.exp(zs-max_))) + max_
zs = zs - zs_sum
zs = np.exp(zs)
Z = zs.reshape(X.shape)
if cs_to_use != None:
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha, levels=cs_to_use.levels)
else:
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
# if i ==0:
# sum_of_all_i = zs
# else:
# sum_of_all_i = sum_of_all_i + zs
# if samp_i ==0:
# sum_of_all = sum_of_all_i
# else:
# sum_of_all = sum_of_all + sum_of_all_i
# avg_of_all = sum_of_all / n_samps
# print 'sum:', np.sum(avg_of_all)
# if legend:
# nm, lbl = cs.legend_elements()
# plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
return Z
def plot_isocontours_expected_W(ax, model, samp, xlimits=[-6, 6], ylimits=[-6, 6],
numticks=101, cmap=None, alpha=1., legend=False):
x = np.linspace(*xlimits, num=numticks)
y = np.linspace(*ylimits, num=numticks)
X, Y = np.meshgrid(x, y)
# zs = np.exp(func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T))
aaa = torch.from_numpy(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T).type(torch.FloatTensor)
n_Ws = 10
for i in range(n_Ws):
if i % 10 ==0: print i
Ws, logpW, logqW = model.sample_W() #_ , [1], [1]
func = lambda zs: log_bernoulli(model.decode(Ws, Variable(torch.unsqueeze(zs,1))), Variable(torch.unsqueeze(samp,0)))+ Variable(torch.unsqueeze(lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2)), 1))
bbb = func(aaa)
zs = bbb.data.numpy()
# zs = np.exp(zs/784)
# print zs.shape
max_ = np.max(zs)
# print max_
zs_sum = np.log(np.sum(np.exp(zs-max_))) + max_
zs = zs - zs_sum
zs = np.exp(zs)
if i ==0:
sum_of_all = zs
else:
sum_of_all = sum_of_all + zs
avg_of_all = sum_of_all / n_Ws
Z = avg_of_all.reshape(X.shape)
# Z = zs.view(X.shape)
# Z=Z.numpy()
cs = plt.contour(X, Y, Z, cmap=cmap, alpha=alpha)
if legend:
nm, lbl = cs.legend_elements()
plt.legend(nm, lbl, fontsize=4)
ax.set_yticks([])
ax.set_xticks([])
plt.gca().set_aspect('equal', adjustable='box')
ax.set_xticks([])
if samp_i==0: ax.annotate('Sample', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
# #Plot prior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues')
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Plot q
col +=1
ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
func = lambda zs: lognormal4(torch.Tensor(zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
plot_isocontours(ax, func, cmap='Reds',xlimits=xlimits,ylimits=ylimits)
if samp_i==0: ax.annotate('p(z)\nq(z|x)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# #Plot logprior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontoursNoExp(ax, func, cmap='Blues', legend=legend)
# if samp_i==0: ax.annotate('Prior\nlogp(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
# #Plot logq
# col +=1
col +=1
ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
z = model.sample_q(x=samp_torch, k=n_samps)# [P,B,Z]
z = z.view(-1,z_size)
z = z.data.cpu().numpy()
# print (z)
center_val_x = z[0][0]
center_val_y = z[0][1]
xlimits=[center_val_x-lim_val, center_val_x+lim_val]
ylimits=[center_val_y-lim_val, center_val_y+lim_val]
plot_scatter(ax, samps=z ,xlimits=xlimits,ylimits=ylimits)
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
plot_isocontours(ax, func, cmap='Blues', alpha=.3,xlimits=xlimits,ylimits=ylimits)
# #Plot q
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# # # mean, logvar = model.encode(Variable(torch.unsqueeze(samp,0)))
# samp_torch = Variable(torch.from_numpy(np.array([samp]))).type(model.dtype)
# # # [P,B,Z]
xytext=(.3, 1.1),
xy=(0, 1),
textcoords='axes fraction')
# #Plot prior
# col +=1
# ax = plt.subplot2grid((rows,cols), (samp_i,col), frameon=False)
# func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2), torch.zeros(2))
# plot_isocontours(ax, func, cmap='Blues')
# if samp_i==0: ax.annotate('Prior p(z)', xytext=(.3, 1.1), xy=(0, 1), textcoords='axes fraction')
#Plot q
col += 1
ax = plt.subplot2grid((rows, cols), (samp_i, col), frameon=False)
mean, logvar = model.encode(Variable(torch.unsqueeze(samp, 0)))
func = lambda zs: lognormal4(torch.Tensor(
zs), torch.squeeze(mean.data), torch.squeeze(logvar.data))
plot_isocontours(ax,
func,
cmap='Reds',
xlimits=xlimits,
ylimits=ylimits)
if samp_i == 0:
ax.annotate('p(z)\nq(z|x)',
xytext=(.3, 1.1),
xy=(0, 1),
textcoords='axes fraction')
func = lambda zs: lognormal4(torch.Tensor(zs), torch.zeros(2),
torch.zeros(2))
plot_isocontours(ax,
func,
cmap='Blues',
