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Changed quantile computation via sampling and added fallback for predictive mean and variance if conditional mean and variance are not implemented yet

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Alan Saul 2015-07-22 18:33:11 +01:00
parent af20bed747
commit 926b53bfce
2 changed files with 34 additions and 26 deletions
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34
GPy/likelihoods/likelihood.py
View file

@ -298,13 +298,8 @@ class Likelihood(Parameterized):
return self.conditional_mean(f)*p return self.conditional_mean(f)*p
scaled_mean = [quad(int_mean, fmin, fmax,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)] scaled_mean = [quad(int_mean, fmin, fmax,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance)) mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
return mean return mean
def _conditional_mean(self, f):
"""Quadrature calculation of the conditional mean: E(Y_star|f)"""
raise NotImplementedError("implement this function to make predictions")
def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None): def predictive_variance(self, mu,variance, predictive_mean=None, Y_metadata=None):
""" """
Approximation to the predictive variance: V(Y_star) Approximation to the predictive variance: V(Y_star)
@ -608,23 +603,30 @@ class Likelihood(Parameterized):
:param full_cov: whether to use the full covariance or just the diagonal :param full_cov: whether to use the full covariance or just the diagonal
:type full_cov: Boolean :type full_cov: Boolean
""" """
try:
pred_mean = self.predictive_mean(mu, var, Y_metadata) pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata)
pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata) pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata)
except NotImplementedError:
print "Finding predictive mean and variance via sampling rather than quadrature"
Nf_samp = 300
Ny_samp = 1
s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
pred_mean = np.mean(ss_y, axis=1)[:, None]
pred_var = np.var(ss_y, axis=1)[:, None]
return pred_mean, pred_var return pred_mean, pred_var
def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None): def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
#compute the quantiles by sampling!!! #compute the quantiles by sampling!!!
N_samp = 500 Nf_samp = 300
s = np.random.randn(mu.shape[0], N_samp)*np.sqrt(var) + mu Ny_samp = 1
#ss_f = s.flatten() s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu
#ss_y = self.samples(ss_f, Y_metadata) ss_y = self.samples(s, Y_metadata, samples=Ny_samp)
#ss_y = self.samples(s, Y_metadata, samples=100) #ss_y = ss_y.reshape(mu.shape[0], mu.shape[1], Nf_samp*Ny_samp)
ss_y = self.samples(s, Y_metadata)
#ss_y = ss_y.reshape(mu.shape[0], N_samp)
return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles] pred_quantiles = [np.percentile(ss_y, q, axis=1)[:,None] for q in quantiles]
return pred_quantiles
def samples(self, gp, Y_metadata=None, samples=1): def samples(self, gp, Y_metadata=None, samples=1):
""" """

26
GPy/plotting/matplot_dep/models_plots.py
View file

@ -107,11 +107,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
upper = m + 2*np.sqrt(v) upper = m + 2*np.sqrt(v)
else: else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression): if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)} extra_data = Xgrid[:,-1:].astype(np.int)
else: if Y_metadata is None:
meta = None Y_metadata = {'output_index': extra_data}
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw) else:
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=meta) Y_metadata['output_index'] = extra_data
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
for d in which_data_ycols: for d in which_data_ycols:
@ -120,7 +122,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#optionally plot some samples #optionally plot some samples
if samples: #NOTE not tested with fixed_inputs if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples) Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
print Ysim.shape
print Xnew.shape
for yi in Ysim.T: for yi in Ysim.T:
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25) plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs. #ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
@ -185,10 +189,12 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
m, _ = model._raw_predict(Xgrid, **predict_kw) m, _ = model._raw_predict(Xgrid, **predict_kw)
else: else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression): if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)} extra_data = Xgrid[:,-1:].astype(np.int)
else: if Y_metadata is None:
meta = None Y_metadata = {'output_index': extra_data}
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw) else:
Y_metadata['output_index'] = extra_data
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
for d in which_data_ycols: for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T m_d = m[:,d].reshape(resolution, resolution).T
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)