def make_prior_plot(model,ip,opts):
import plotting
import pylab
NUM_CLUSTERS = 5
NUM_SAMPLES = 30
NUM_SUBPLOTS = 3
WALK_LENGTH = 1000
data = zeros((model.dims,NUM_CLUSTERS*NUM_SAMPLES))
labels = zeros(NUM_CLUSTERS*NUM_SAMPLES,dtype=int32)
for i in range(NUM_SUBPLOTS):
pylab.subplot(1,3,i+1)
for n in range(NUM_CLUSTERS):
params = model.sample_prior()
for s in range(NUM_SAMPLES):
data[:,n*NUM_SAMPLES + s] = rnorm(params.mu,params.lam)
labels[n*NUM_SAMPLES + s] = n
color = pylab.get_cmap("flag")(n*3)
plotting.plot_diagonal_gaussian(params.mu,params.lam,color)
plotting.plot_scatter_2d(data,labels)
pylab.grid()
pylab.axis([-5,5,-5,5])
F = pylab.gcf()
F.set_size_inches(6,1.8)
pylab.savefig("prior_draw.eps")
pylab.clf()
import explore_kernel
explore_kernel.diagnostic_plots(model,WALK_LENGTH)
pylab.savefig("walk_diagnostics.eps")
pylab.clf()
params = model.sample_prior()
data = zeros((model.dims,WALK_LENGTH))
for t in range(0,1000):
data[:,t] = rnorm(params.mu,params.lam)
params = model.walk(params)
pylab.plot(data[0,:],data[1,:],'x')
pylab.savefig("walk_draw.eps")
def do_plotting(options):
print "Generating Plots ..."
particle_id = options.use_particle
ext = "." + options.output_format
plot_dir = options.output_dir + "/" + options.identifier + "/plots"
if not exists(plot_dir):
mkdir(plot_dir)
data,data_time,true_labels = experimenter.load_data(options)
if options.true_labels:
predicted_labels = true_labels
predicted_labels.shape = (1,predicted_labels.shape[0])
ext = "_true" + ext
else:
predicted_labels = load_labels(options)
s_data,s_data_time,s_predicted_labels,idx = subsample(
data,data_time,predicted_labels,options)
T = data_time.shape[0]
ess = load_ess(options)
# Labeled 2D scatter plot of the first two PCs
clf()
plotting.plot_scatter_2d(s_data[0:2,:],s_predicted_labels[particle_id,:])
grid()
savefig(plot_dir + "/" + "scatter_predicted" + ext)
# 2D scatter plot with entropy heatmap
clf()
#ent = compute_label_entropy(s_predicted_labels)
ent = ess[2,idx]
certain = ent==0
uncertain = logical_not(certain)
if sum(certain)>0:
scatter(s_data[0,certain],s_data[1,certain],10,marker="s",facecolors="none",
linewidth=0.3)
if sum(uncertain)>0:
scatter(s_data[0,uncertain],s_data[1,uncertain],10,ent[uncertain],
linewidth=0.3,cmap=cm.hot)
grid()
title("Label Entropy")
savefig(plot_dir + "/" + "scatter_entropy" + ext)
# Label entropy vs. time
clf()
ent = compute_label_entropy(predicted_labels)
#subplot(2,1,1)
axes([0.25, 0.2, 0.7, 0.7])
plot(data_time,ent,'x',linewidth=1.5)
#title("Label Entropy")
#ylabel("Entropy")
xlabel("Time")
axis([-1,max(data_time)+2,-0.1,1.1])
grid()
#subplot(2,1,2)
#plot(data_time,ess[2,:],'x',linewidth=1.5)
#ylabel("Entropy")
#xlabel("Time (ms)")
#axis([0,60000,0,1])
#title("Average Label Filtering Distribution Entropy (SMC)")
#grid()
F = gcf()
F.set_size_inches(2,2)
savefig(plot_dir + "/" + "entropy" + ext)
clf()
subplot(2,1,1)
#axes([0.25, 0.2, 0.7, 0.7])
plot(data_time,ent,linewidth=0.5)
title("Label Entropy (M-H sampler)")
ylabel("Entropy")
#xlabel("Time")
axis([0,60000,-0.1,1.1])
grid()
subplot(2,1,2)
plot(data_time,ess[2,:],linewidth=0.5)
ylabel("Entropy")
xlabel("Time (ms)")
axis([0,60000,-0.1,1.1])
title("Average Label Filtering Distribution Entropy (SMC)")
grid()
F = gcf()
F.set_size_inches(6,4)
savefig(plot_dir + "/" + "entropy_both" + ext)
# 2D scatter plot of PCs against time with predicted labels (1st particle)
clf()
plotting.plot_pcs_against_time_labeled(s_data,s_data_time,
s_predicted_labels[particle_id,:])
F = gcf()
F.set_size_inches(8.3,4*data.shape[0])
savefig(plot_dir + "/" + "pcs_vs_time_predicted" + ext)
# 2D scatter plot of PCs against time for RPV candidates
clf()
isi = data_time[1:]-data_time[:-1]
rpvs = where(isi < 2)[0] + 1
rpvs = hstack((rpvs,rpvs-1))
if rpvs.shape[0] > 0:
plotting.plot_pcs_against_time_labeled(data[:,rpvs],data_time[rpvs],
predicted_labels[particle_id,rpvs])
F = gcf()
F.set_size_inches(8.3,4*data.shape[0])
savefig(plot_dir + "/" + "pcs_vs_time_rpv" + ext)
# 2D scatter plot with binary labels
if options.binary_label:
clf()
match = find_best_match(predicted_labels[particle_id,:],true_labels)
matches = (predicted_labels[particle_id,:]==match)[idx]
non_matches = (predicted_labels[particle_id,:]!=match)[idx]
subplot(1,2,1)
plot(s_data[0,matches],s_data[1,matches],'x')
plot(s_data[0,non_matches],s_data[1,non_matches],'.')
grid()
title("Predicted Labels")
axis([-5,5,-5,5])
subplot(1,2,2)
plot(s_data[0,true_labels[idx]==1],s_data[1,true_labels[idx]==1],'x')
plot(s_data[0,true_labels[idx]!=1],s_data[1,true_labels[idx]!=1],'.')
axis([-5,5,-5,5])
grid()
title("True Labels")
F = gcf()
F.set_size_inches(6,3)
savefig(plot_dir + "/" + "scatter_binary" + ext)
# plot of effective sample size
clf()
subplot(2,1,1)
plot(ess[1,:],linewidth=0.3)
title('Unique Particles')
ylabel("Unique Particles")
axis([0,650,0,1100])
grid()
xlabel("Time Step")
subplot(2,1,2)
plot(ess[0,:],linewidth=0.3)
axis([0,650,0,1100])
title("Effective Sample Size")
xlabel("Time Step")
ylabel("ESS")
grid()
F = gcf()
F.set_size_inches(6,4)
savefig(plot_dir + "/" + "ess" + ext)
# ISI histogram for each neuron
clf()
l = predicted_labels[particle_id,:]
unique_labels = unique(l)
for i in range(unique_labels.shape[0]):
c = unique_labels[i]
points = data[:,l==c]
times = data_time[l==c]
isi = times[1:] - times[0:-1]
subplot(unique_labels.shape[0],2,2*i+1)
label_colors = array(unique_labels,dtype=float64)/max(unique_labels+1)
colors = ones(sum(l==c))*label_colors[i]
scatter(points[0,:],points[1,:],marker=plotting.markers[c],c=colors,
cmap=matplotlib.cm.jet,
norm=no_norm(),
linewidths=(0.3,))
title("Cluster %i (weight=%.2f)" % (c,sum(l==c)/float(l.shape[0])))
grid()
axis([-5,5,-5,5])
subplot(unique_labels.shape[0],2,2*i+2)
hist(isi,bins=100,range=(0,100),normed=True,facecolor='k')
xx = arange(2,100,0.1)
rate = 1/mean(isi-2)
plot(xx,rate*exp(-rate*(xx-2)))
title("ISI (mean = %.2f)" % mean(isi))
F = gcf()
F.set_size_inches(8.3,2*unique_labels.shape[0])
savefig(plot_dir + "/" + "isi" + ext)
particle = experimenter.load_particle(options)
if particle != None:
### plots requiring the information from at least one particle
clf()
# plot of clusters + data at fixed, equally spaced time points
num_plots = 9
timepoints = array(arange(1,num_plots+1)*(T-1)/(float(num_plots)),dtype=int32)
for i in range(num_plots):
subplot(3,3,i+1)
t = timepoints[i]
plotting.plot_state_with_data(particle,data,data_time,t)
title("t = " + str(t))
grid()
F.set_size_inches(6,6)
savefig(plot_dir + "/" + "cluster_evolution" + ext)
# plot of cluster means and variances over time
clf()
plotting.plot_pcs_against_time_labeled_with_particle(
data,data_time,predicted_labels[0,:],particle)
F.set_size_inches(40,8*data.shape[0])
savefig(plot_dir + "/" + "clusters_vs_time" + ext)
# plot of mstore for each clusters
clf()
plotting.plot_mstore_against_time(particle)
savefig(plot_dir + "/" + "mstore" + ext)
