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plotting in the model behaves better

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This commit is contained in:
James Hensman 2013-06-04 18:25:46 +01:00
parent 5a2722bec6
commit a5e1985d9d
2 changed files with 42 additions and 29 deletions
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51
GPy/core/gp_base.py
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@ -33,7 +33,7 @@ class GPBase(model.model):
# All leaf nodes should call self._set_params(self._get_params()) at # All leaf nodes should call self._set_params(self._get_params()) at
# the end # the end
def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False): def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
""" """
Plot the GP's view of the world, where the data is normalized and the Plot the GP's view of the world, where the data is normalized and the
likelihood is Gaussian. likelihood is Gaussian.
@ -57,46 +57,55 @@ class GPBase(model.model):
if which_data == 'all': if which_data == 'all':
which_data = slice(None) which_data = slice(None)
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
if self.X.shape[1] == 1: if self.X.shape[1] == 1:
Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits) Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
if samples == 0: if samples == 0:
m, v = self._raw_predict(Xnew, which_parts=which_parts) m, v = self._raw_predict(Xnew, which_parts=which_parts)
gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v)) gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
pb.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5) ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
else: else:
m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True) m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
Ysim = np.random.multivariate_normal(m.flatten(), v, samples) Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None]) gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None,], axes=ax)
for i in range(samples): for i in range(samples):
pb.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25) ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
pb.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5) ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
pb.xlim(xmin, xmax) ax.set_xlim(xmin, xmax)
ymin, ymax = min(np.append(self.likelihood.Y, m - 2 * np.sqrt(np.diag(v)[:, None]))), max(np.append(self.likelihood.Y, m + 2 * np.sqrt(np.diag(v)[:, None]))) ymin, ymax = min(np.append(self.likelihood.Y, m - 2 * np.sqrt(np.diag(v)[:, None]))), max(np.append(self.likelihood.Y, m + 2 * np.sqrt(np.diag(v)[:, None])))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin) ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
pb.ylim(ymin, ymax) ax.set_ylim(ymin, ymax)
elif self.X.shape[1] == 2: elif self.X.shape[1] == 2:
resolution = resolution or 50 resolution = resolution or 50
Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution) Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution)
m, v = self._raw_predict(Xnew, which_parts=which_parts) m, v = self._raw_predict(Xnew, which_parts=which_parts)
m = m.reshape(resolution, resolution).T m = m.reshape(resolution, resolution).T
pb.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) ax.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
pb.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max()) ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max())
pb.xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])
pb.ylim(xmin[1], xmax[1]) ax.set_ylim(xmin[1], xmax[1])
else: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20): def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None):
""" """
TODO: Docstrings! TODO: Docstrings!
:param levels: for 2D plotting, the number of contour levels to use :param levels: for 2D plotting, the number of contour levels to use
is ax is None, create a new figure
""" """
# TODO include samples # TODO include samples
if which_data == 'all': if which_data == 'all':
which_data = slice(None) which_data = slice(None)
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
if self.X.shape[1] == 1: if self.X.shape[1] == 1:
Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
@ -104,12 +113,12 @@ class GPBase(model.model):
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits) Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
m, var, lower, upper = self.predict(Xnew, which_parts=which_parts) m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
for d in range(m.shape[1]): for d in range(m.shape[1]):
gpplot(Xnew, m[:,d], lower[:,d], upper[:,d]) gpplot(Xnew, m[:,d], lower[:,d], upper[:,d],axes=ax)
pb.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5) ax.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5)
ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper)) ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin) ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
pb.xlim(xmin, xmax) ax.set_xlim(xmin, xmax)
pb.ylim(ymin, ymax) ax.set_ylim(ymin, ymax)
elif self.X.shape[1] == 2: # FIXME elif self.X.shape[1] == 2: # FIXME
resolution = resolution or 50 resolution = resolution or 50
@ -117,11 +126,11 @@ class GPBase(model.model):
x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution) x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
m, var, lower, upper = self.predict(Xnew, which_parts=which_parts) m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
m = m.reshape(resolution, resolution).T m = m.reshape(resolution, resolution).T
pb.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) ax.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
Yf = self.likelihood.Y.flatten() Yf = self.likelihood.Y.flatten()
pb.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.) ax.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
pb.xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])
pb.ylim(xmin[1], xmax[1]) ax.set_ylim(xmin[1], xmax[1])
else: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" raise NotImplementedError, "Cannot define a frame with more than two input dimensions"

20
GPy/core/sparse_GP.py
View file

@ -281,17 +281,21 @@ class sparse_GP(GPBase):
return mean, var, _025pm, _975pm return mean, var, _025pm, _975pm
def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20): def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20) if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
if self.X.shape[1] == 1: if self.X.shape[1] == 1:
Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
if self.has_uncertain_inputs: if self.has_uncertain_inputs:
pb.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0], Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
xerr=2 * np.sqrt(self.X_variance[which_data, 0]), xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5) ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
Zu = self.Z * self._Xstd + self._Xmean Zu = self.Z * self._Xstd + self._Xmean
pb.plot(Zu, Zu * 0 + pb.ylim()[0], 'r|', mew=1.5, markersize=12) ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
# pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_variance.flatten()))
elif self.X.shape[1] == 2: # FIXME elif self.X.shape[1] == 2:
pb.plot(self.Z[:, 0], self.Z[:, 1], 'wo') Zu = self.Z * self._Xstd + self._Xmean
ax.plot(Zu[:, 0], Zu[:, 1], 'wo')