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made BGPLVM oil flow demo work, added ARD weights plot

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This commit is contained in:
Nicolo Fusi 2013-03-22 15:58:02 +00:00
parent eeb965d136
commit 55ad96f38b
3 changed files with 44 additions and 27 deletions
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5
GPy/examples/dimensionality_reduction.py
View file

@ -61,20 +61,21 @@ def BGPLVM_oil():
data = GPy.util.datasets.oil()
Y, X = data['Y'], data['X']
X -= X.mean(axis=0)
# X /= X.std(axis=0)
X /= X.std(axis=0)
Q = 10
M = 30
kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
m.scale_factor = 10000.0
# m.scale_factor = 100.0
m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
from sklearn import cluster
km = cluster.KMeans(M, verbose=10)
Z = km.fit(m.X).cluster_centers_
# Z = GPy.util.misc.kmm_init(m.X, M)
m.set('iip', Z)
m.set('bias', 1e-4)
# optimize
# m.ensure_default_constraints()

45
GPy/inference/SGD.py
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@ -33,12 +33,12 @@ class opt_SGD(Optimizer):
self.self_paced = self_paced
self.center = center
self.param_traces = [('noise',[])]
if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
self.param_traces.append(('bias',[]))
if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
self.param_traces.append(('linear',[]))
if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
self.param_traces.append(('rbf_var',[]))
# if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
# self.param_traces.append(('bias',[]))
# if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
# self.param_traces.append(('linear',[]))
# if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
# self.param_traces.append(('rbf_var',[]))
self.param_traces = dict(self.param_traces)
self.fopt_trace = []
@ -156,29 +156,23 @@ class opt_SGD(Optimizer):
Y = self.model.likelihood.Y
samples = self.non_null_samples(self.model.likelihood.Y)
self.model.N = samples.sum()
if self.center:
self.model.likelihood._mean = Y[samples].mean()
self.model.likelihood._std = Y[samples].std()
self.model.likelihood.set_data(Y[samples])
Y = Y[samples]
else:
samples = self.model.likelihood.Y.nonzero()[0]
self.model.N = len(samples)
Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
if self.center:
self.model.likelihood._mean = Y.mean()
self.model.likelihood._std = Y.std()
self.model.likelihood.set_data(Y)
if self.model.N == 0 or Y.std() == 0.0:
return 0, step, self.model.N
# FIXME: get rid of self.center, everything should be centered by default
self.model.likelihood._mean = Y.mean()
self.model.likelihood._std = Y.std()
self.model.likelihood.set_data(Y)
# self.model.likelihood.N = self.model.N
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
self.model.X = X[samples]
if self.model.N == 0 or self.model.likelihood.Y.std() == 0.0:
return 0, step, self.model.N
# if self.center:
# self.model.likelihood.Y -= self.model.likelihood.Y.mean()
# self.model.likelihood.Y /= self.model.likelihood.Y.std()
@ -186,7 +180,8 @@ class opt_SGD(Optimizer):
model_name = self.model.__class__.__name__
if model_name == 'Bayesian_GPLVM':
self.model.likelihood.trYYT = np.sum(np.square(self.model.likelihood.Y))
self.model.likelihood.YYT = np.dot(self.model.likelihood.Y, self.model.likelihood.Y.T)
self.model.likelihood.trYYT = np.trace(self.model.likelihood.YYT)
b, p = self.shift_constraints(j)
f, fp = f_fp(self.x_opt[j])
@ -196,6 +191,7 @@ class opt_SGD(Optimizer):
step[j] = self.momentum * step[j] + self.learning_rate[j] * fp
self.x_opt[j] -= step[j]
self.restore_constraints(b, p)
return f, step, self.model.N
@ -256,14 +252,13 @@ class opt_SGD(Optimizer):
sys.stdout.write(status)
sys.stdout.flush()
last_printed_count = count
self.param_traces['noise'].append(noise)
NLL.append(f)
self.fopt_trace.append(f)
for k in self.param_traces.keys():
self.param_traces[k].append(self.model.get(k)[0])
# for k in self.param_traces.keys():
# self.param_traces[k].append(self.model.get(k)[0])
# should really be a sum(), but earlier samples in the iteration will have a very crappy ll
self.f_opt = np.mean(NLL)

21
GPy/kern/kern.py
View file

@ -51,6 +51,27 @@ class kern(parameterised):
parameterised.__init__(self)
def plot_ARD(self):
"""
If an ARD kernel is present, it bar-plots the ARD parameters
"""
for p in self.parts:
if hasattr(p, 'ARD') and p.ARD:
pb.figure()
pb.title('ARD parameters, %s kernel' % p.name)
if p.name == 'linear':
ard_params = p.variances
else:
ard_params = 1./p.lengthscale
pb.bar(np.arange(len(ard_params))-0.4, ard_params)
def _transform_gradients(self,g):
x = self._get_params()
g[self.constrained_positive_indices] = g[self.constrained_positive_indices]*x[self.constrained_positive_indices]