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[plotting&kern] bugfixes in plotting and kernel size

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This commit is contained in:
Max Zwiessele 2016-01-22 11:26:29 +00:00
parent 1bda209469
commit 7b5422b694
5 changed files with 21 additions and 17 deletions
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9
GPy/core/gp.py
View file

@ -365,13 +365,14 @@ class GP(Model):
mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable) mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable)
dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1])) dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1]))
one = np.ones((1,1))
for i in range(self._predictive_variable.shape[0]): for i in range(self._predictive_variable.shape[0]):
dK_dXnew_full[i] = kern.gradients_X([[1.]], Xnew, self._predictive_variable[[i]]) dK_dXnew_full[i] = kern.gradients_X(one, Xnew, self._predictive_variable[[i]])
if full_cov: if full_cov:
dK2_dXdX = kern.gradients_XX([[1.]], Xnew) dK2_dXdX = kern.gradients_XX(one, Xnew)
else: else:
dK2_dXdX = kern.gradients_XX_diag([[1.]], Xnew) dK2_dXdX = kern.gradients_XX_diag(one, Xnew)
def compute_cov_inner(wi): def compute_cov_inner(wi):
if full_cov: if full_cov:
@ -458,7 +459,7 @@ class GP(Model):
m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs) m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs)
if self.normalizer is not None: if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v) m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
def sim_one_dim(m, v): def sim_one_dim(m, v):
if not full_cov: if not full_cov:
return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T

4
GPy/kern/src/kern.py
View file

@ -61,12 +61,12 @@ class Kern(Parameterized):
self.psicomp = PSICOMP_GH() self.psicomp = PSICOMP_GH()
def __setstate__(self, state): def __setstate__(self, state):
self._all_dims_active = range(0, max(state['active_dims'])+1) self._all_dims_active = np.arange(0, max(state['active_dims'])+1)
super(Kern, self).__setstate__(state) super(Kern, self).__setstate__(state)
@property @property
def _effective_input_dim(self): def _effective_input_dim(self):
return self._all_dims_active.size return np.size(self._all_dims_active)
@Cache_this(limit=20) @Cache_this(limit=20)
def _slice_X(self, X): def _slice_X(self, X):

4
GPy/kern/src/stationary.py
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@ -97,7 +97,7 @@ class Stationary(Kern):
r = self._scaled_dist(X, X2) r = self._scaled_dist(X, X2)
return self.K_of_r(r) return self.K_of_r(r)
@Cache_this(limit=20, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def dK_dr_via_X(self, X, X2): def dK_dr_via_X(self, X, X2):
#a convenience function, so we can cache dK_dr #a convenience function, so we can cache dK_dr
return self.dK_dr(self._scaled_dist(X, X2)) return self.dK_dr(self._scaled_dist(X, X2))
@ -127,7 +127,7 @@ class Stationary(Kern):
r2 = np.clip(r2, 0, np.inf) r2 = np.clip(r2, 0, np.inf)
return np.sqrt(r2) return np.sqrt(r2)
@Cache_this(limit=20, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def _scaled_dist(self, X, X2=None): def _scaled_dist(self, X, X2=None):
""" """
Efficiently compute the scaled distance, r. Efficiently compute the scaled distance, r.

16
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -46,7 +46,7 @@ def plot_mean(self, plot_limits=None, fixed_inputs=None,
""" """
Plot the mean of the GP. Plot the mean of the GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -116,7 +116,7 @@ def plot_confidence(self, lower=2.5, upper=97.5, plot_limits=None, fixed_inputs=
E.g. the 95% confidence interval is $2.5, 97.5$. E.g. the 95% confidence interval is $2.5, 97.5$.
Note: Only implemented for one dimension! Note: Only implemented for one dimension!
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -170,7 +170,7 @@ def plot_samples(self, plot_limits=None, fixed_inputs=None,
""" """
Plot the mean of the GP. Plot the mean of the GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -231,7 +231,7 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
E.g. the 95% confidence interval is $2.5, 97.5$. E.g. the 95% confidence interval is $2.5, 97.5$.
Note: Only implemented for one dimension! Note: Only implemented for one dimension!
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -288,7 +288,7 @@ def plot(self, plot_limits=None, fixed_inputs=None,
""" """
Convenience function for plotting the fit of a GP. Convenience function for plotting the fit of a GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -330,6 +330,8 @@ def plot(self, plot_limits=None, fixed_inputs=None,
# It does not make sense to plot the data (which lives not in the latent function space) into latent function space. # It does not make sense to plot the data (which lives not in the latent function space) into latent function space.
plot_data = False plot_data = False
plots = {} plots = {}
if hasattr(self, 'Z') and plot_inducing:
plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
if plot_data: if plot_data:
plots.update(_plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data")) plots.update(_plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data"))
plots.update(_plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data Error")) plots.update(_plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data Error"))
@ -340,8 +342,6 @@ def plot(self, plot_limits=None, fixed_inputs=None,
get_which_data_ycols(self, which_data_ycols), get_which_data_ycols(self, which_data_ycols),
predict_kw, samples_likelihood) predict_kw, samples_likelihood)
plots.update(_plot_samples(canvas, helper_data, helper_prediction, projection, "Lik Samples")) plots.update(_plot_samples(canvas, helper_data, helper_prediction, projection, "Lik Samples"))
if hasattr(self, 'Z') and plot_inducing:
plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
return pl().add_to_canvas(canvas, plots, legend=legend) return pl().add_to_canvas(canvas, plots, legend=legend)
@ -362,7 +362,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
If you want fine graned control use the specific plotting functions supplied in the model. If you want fine graned control use the specific plotting functions supplied in the model.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.

5
GPy/plotting/gpy_plot/plot_util.py
View file

@ -285,7 +285,10 @@ def get_x_y_var(model):
X = model.X.mean.values X = model.X.mean.values
X_variance = model.X.variance.values X_variance = model.X.variance.values
else: else:
X = model.X.values try:
X = model.X.values
except AttributeError:
X = model.X
X_variance = None X_variance = None
try: try:
Y = model.Y.values Y = model.Y.values