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[density] plotting of likelihoods permitted

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mzwiessele 2015-10-02 07:47:57 +01:00
parent d8243383b4
commit fee2f3f727
2 changed files with 13 additions and 17 deletions
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6
GPy/core/gp.py
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@ -272,7 +272,7 @@ class GP(Model):
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata)
return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, kern=None):
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, kern=None, likelihood=None):
"""
Get the predictive quantiles around the prediction at X
@ -288,7 +288,9 @@ class GP(Model):
m, v = self._raw_predict(X, full_cov=False, kern=kern)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
if likelihood is None:
likelihood = self.likelihood
return likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
def predictive_gradients(self, Xnew):
"""

24
GPy/plotting/matplot_dep/models_plots.py
View file

@ -9,7 +9,7 @@ from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalized
from scipy import sparse
from ...core.parameterization.variational import VariationalPosterior
from matplotlib import pyplot as plt
from GPy.plotting.matplot_dep.base_plots import plot_gradient_fill
from .base_plots import plot_gradient_fill
def plot_data(model, which_data_rows='all',
@ -336,19 +336,9 @@ def plot_density(model, levels=20, plot_limits=None, fignum=None, ax=None,
#make a prediction on the frame and plot it
if plot_raw:
raise NotImplementedError('What to do? What to do?')
#===================================================================
# m, v = model._raw_predict(Xgrid, **predict_kw)
# percentiles = model.predict_quantiles(Xgrid, )
# if apply_link:
# lower = model.likelihood.gp_link.transf(m - 2*np.sqrt(v))
# upper = model.likelihood.gp_link.transf(m + 2*np.sqrt(v))
# #Once transformed this is now the median of the function
# m = model.likelihood.gp_link.transf(m)
# else:
# lower = m - 2*np.sqrt(v)
# upper = m + 2*np.sqrt(v)
#===================================================================
from scipy import stats
from ...likelihoods import Gaussian
lik = Gaussian(variance=0)
else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
extra_data = Xgrid[:,-1:].astype(np.int)
@ -356,7 +346,11 @@ def plot_density(model, levels=20, plot_limits=None, fignum=None, ax=None,
Y_metadata = {'output_index': extra_data}
else:
Y_metadata['output_index'] = extra_data
percentiles = [i[:, 0] for i in model.predict_quantiles(Xgrid, percs, Y_metadata=Y_metadata, **predict_kw)]
lik = None
percentiles = [i[:, 0] for i in model.predict_quantiles(Xgrid, percs, Y_metadata=Y_metadata, likelihood=lik, **predict_kw)]
if apply_link:
percentiles = model.likelihood.gp_link.transf(percentiles)
patch_kwargs['facecolor'] = facecolor
patch_kwargs['edgecolor'] = edgecolor
plots['density'] = plot_gradient_fill(ax, Xgrid[:, 0], percentiles, **patch_kwargs)