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changes in GPLVM plotting
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Nicolò Fusi
parent
c44493077a
commit
8ab1cfaf65
3 changed files with 84 additions and 25 deletions
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@ -256,7 +256,7 @@ class model(parameterised):
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self._set_params_transformed(x)
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LL_gradients = self._transform_gradients(self._log_likelihood_gradients())
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prior_gradients = self._transform_gradients(self._log_prior_gradients())
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return -LL_gradients - prior_gradients
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return - LL_gradients - prior_gradients
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def objective_and_gradients(self, x):
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obj_f = self.objective_function(x)
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@ -3,6 +3,7 @@ import scipy as sp
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import scipy.sparse
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from optimization import Optimizer
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from scipy import linalg, optimize
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import pylab as plt
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import copy
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import sys
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@ -31,6 +32,16 @@ class opt_SGD(Optimizer):
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self.batch_size = batch_size
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self.self_paced = self_paced
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self.center = center
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self.param_traces = [('noise',[])]
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if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
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self.param_traces.append(('bias',[]))
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if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
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self.param_traces.append(('linear',[]))
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if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
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self.param_traces.append(('rbf_var',[]))
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self.param_traces = dict(self.param_traces)
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self.fopt_trace = []
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num_params = len(self.model._get_params())
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if isinstance(self.learning_rate, float):
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@ -48,6 +59,18 @@ class opt_SGD(Optimizer):
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status += "Time elapsed: \t\t\t %s\n" % self.time
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return status
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def plot_traces(self):
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plt.figure()
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plt.subplot(211)
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plt.title('Parameters')
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for k in self.param_traces.keys():
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plt.plot(self.param_traces[k], label=k)
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plt.legend(loc=0)
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plt.subplot(212)
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plt.title('Objective function')
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plt.plot(self.fopt_trace)
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def non_null_samples(self, data):
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return (np.isnan(data).sum(axis=1) == 0)
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@ -128,25 +151,37 @@ class opt_SGD(Optimizer):
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def step_with_missing_data(self, f_fp, X, step, shapes, sparse_matrix):
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N, Q = X.shape
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if not sparse_matrix:
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Y = self.model.likelihood.Y
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samples = self.non_null_samples(self.model.likelihood.Y)
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self.model.N = samples.sum()
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self.model.likelihood.Y = self.model.likelihood.Y[samples]
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if self.center:
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self.model.likelihood._mean = Y[samples].mean()
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self.model.likelihood._std = Y[samples].std()
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self.model.likelihood.set_data(Y[samples])
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else:
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samples = self.model.likelihood.Y.nonzero()[0]
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self.model.N = len(samples)
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self.model.likelihood.Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
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Y = np.asarray(self.model.likelihood.Y[samples].todense(), dtype = np.float64)
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if self.center:
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self.model.likelihood._mean = Y.mean()
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self.model.likelihood._std = Y.std()
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self.model.likelihood.N = self.model.N
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self.model.likelihood.set_data(Y)
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# self.model.likelihood.N = self.model.N
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j = self.subset_parameter_vector(self.x_opt, samples, shapes)
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self.model.X = X[samples]
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if self.model.N == 0 or self.model.likelihood.Y.std() == 0.0:
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return 0, step, self.model.N
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if self.center:
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self.model.likelihood.Y -= self.model.likelihood.Y.mean()
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self.model.likelihood.Y /= self.model.likelihood.Y.std()
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# if self.center:
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# self.model.likelihood.Y -= self.model.likelihood.Y.mean()
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# self.model.likelihood.Y /= self.model.likelihood.Y.std()
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model_name = self.model.__class__.__name__
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@ -154,13 +189,13 @@ class opt_SGD(Optimizer):
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self.model.likelihood.trYYT = np.sum(np.square(self.model.likelihood.Y))
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b, p = self.shift_constraints(j)
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momentum_term = self.momentum * step[j]
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f, fp = f_fp(self.x_opt[j])
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step[j] = self.learning_rate[j] * fp
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self.x_opt[j] -= step[j] + momentum_term
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# momentum_term = self.momentum * step[j]
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# step[j] = self.learning_rate[j] * fp
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# self.x_opt[j] -= step[j] + momentum_term
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step[j] = self.momentum * step[j] + self.learning_rate[j] * fp
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self.x_opt[j] -= step[j]
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self.restore_constraints(b, p)
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return f, step, self.model.N
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@ -177,10 +212,14 @@ class opt_SGD(Optimizer):
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missing_data = self.check_for_missing(self.model.likelihood.Y)
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self.model.likelihood.YYT = None
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self.model.likelihood.trYYT = None
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self.model.likelihood._mean = 0.0
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self.model.likelihood._std = 1.0
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num_params = self.model._get_params()
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step = np.zeros_like(num_params)
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step = np.zeros_like(num_params)
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for it in range(self.iterations):
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if it == 0 or self.self_paced is False:
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features = np.random.permutation(Y.shape[1])
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else:
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@ -195,17 +234,21 @@ class opt_SGD(Optimizer):
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for j in features:
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count += 1
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self.model.D = len(j)
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self.model.likelihood.Y = Y[:, j]
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self.model.likelihood.D = len(j)
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self.model.likelihood.set_data(Y[:, j])
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if missing_data or sparse_matrix:
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shapes = self.get_param_shapes(N, Q)
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f, step, Nj = self.step_with_missing_data(f_fp, X, step, shapes, sparse_matrix)
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else:
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Nj = N
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momentum_term = self.momentum * step # compute momentum using update(t-1)
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# momentum_term = self.momentum * step # compute momentum using update(t-1)
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f, fp = f_fp(self.x_opt)
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step = self.learning_rate * fp # compute update(t)
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self.x_opt -= step + momentum_term
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# step = self.learning_rate * fp # compute update(t)
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# self.x_opt -= step + momentum_term
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step = self.momentum * step + self.learning_rate * fp
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self.x_opt -= step
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if self.messages == 2:
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noise = np.exp(self.x_opt)[-1]
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@ -216,12 +259,19 @@ class opt_SGD(Optimizer):
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NLL.append(f)
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self.fopt_trace.append(f)
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for k in self.param_traces.keys():
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self.param_traces[k].append(self.model.get(k)[0])
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# should really be a sum(), but earlier samples in the iteration will have a very crappy ll
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self.f_opt = np.mean(NLL)
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self.model.N = N
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self.model.X = X
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self.model.D = D
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self.model.likelihood.N = N
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self.model.likelihood.D = D
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self.model.likelihood.Y = Y
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# self.model.Youter = np.dot(Y, Y.T)
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@ -67,7 +67,13 @@ class GPLVM(GP):
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"""
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util.plot.Tango.reset()
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# this goes against the current standard in GPy, which currently is to not create
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# figures in the plot() functions. I think the standard should be changed in order
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# to accomodate cases like this
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fig = pb.figure()
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ax = fig.add_subplot(111)
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if labels is None:
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labels = np.ones(self.N)
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if which_indices is None:
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@ -86,15 +92,17 @@ class GPLVM(GP):
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input_1, input_2 = np.argsort(k.lengthscale)[:2]
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elif k.name=='linear':
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input_1, input_2 = np.argsort(k.variances)[::-1][:2]
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else:
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input_1, input_2 = which_indices
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#first, plot the output variance as a function of the latent space
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Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = self.predict(Xtest_full)
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var = var[:, :2]
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')
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Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = self.predict(Xtest_full)
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var = var[:, :1] # FIXME: this was a :2
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],
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extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear')
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for i,ul in enumerate(np.unique(labels)):
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if type(ul) is np.string_:
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@ -121,5 +129,6 @@ class GPLVM(GP):
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pb.xlim(xmin[0],xmax[0])
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pb.ylim(xmin[1],xmax[1])
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pb.grid(b=False) # remove the grid if present, it doesn't look good
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ax.set_aspect('auto') # set a nice aspect ratio
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return input_1, input_2
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