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plotting in the model behaves better
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James Hensman
parent
5a2722bec6
commit
a5e1985d9d
2 changed files with 42 additions and 29 deletions
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@ -33,7 +33,7 @@ class GPBase(model.model):
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# All leaf nodes should call self._set_params(self._get_params()) at
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# the end
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def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False):
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def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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"""
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Plot the GP's view of the world, where the data is normalized and the
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likelihood is Gaussian.
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@ -57,46 +57,55 @@ class GPBase(model.model):
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if which_data == 'all':
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which_data = slice(None)
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if self.X.shape[1] == 1:
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Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
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if samples == 0:
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v))
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pb.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
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gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
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ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
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else:
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m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
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Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None])
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None,], axes=ax)
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for i in range(samples):
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pb.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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pb.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
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pb.xlim(xmin, xmax)
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ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
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ax.set_xlim(xmin, xmax)
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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])))
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ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
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pb.ylim(ymin, ymax)
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ax.set_ylim(ymin, ymax)
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elif self.X.shape[1] == 2:
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resolution = resolution or 50
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Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution)
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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m = m.reshape(resolution, resolution).T
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pb.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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pb.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max())
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pb.xlim(xmin[0], xmax[0])
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pb.ylim(xmin[1], xmax[1])
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ax.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max())
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ax.set_xlim(xmin[0], xmax[0])
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ax.set_ylim(xmin[1], xmax[1])
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20):
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def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None):
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"""
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TODO: Docstrings!
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:param levels: for 2D plotting, the number of contour levels to use
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is ax is None, create a new figure
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"""
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# TODO include samples
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if which_data == 'all':
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which_data = slice(None)
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if self.X.shape[1] == 1:
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Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
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@ -104,12 +113,12 @@ class GPBase(model.model):
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Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
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m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
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for d in range(m.shape[1]):
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gpplot(Xnew, m[:,d], lower[:,d], upper[:,d])
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pb.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5)
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gpplot(Xnew, m[:,d], lower[:,d], upper[:,d],axes=ax)
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ax.plot(Xu[which_data], self.likelihood.data[which_data,d], 'kx', mew=1.5)
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ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
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ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
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pb.xlim(xmin, xmax)
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pb.ylim(ymin, ymax)
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ax.set_xlim(xmin, xmax)
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ax.set_ylim(ymin, ymax)
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elif self.X.shape[1] == 2: # FIXME
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resolution = resolution or 50
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@ -117,11 +126,11 @@ class GPBase(model.model):
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x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
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m, var, lower, upper = self.predict(Xnew, which_parts=which_parts)
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m = m.reshape(resolution, resolution).T
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pb.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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ax.contour(x, y, m, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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Yf = self.likelihood.Y.flatten()
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pb.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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pb.xlim(xmin[0], xmax[0])
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pb.ylim(xmin[1], xmax[1])
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ax.scatter(self.X[:, 0], self.X[:, 1], 40, Yf, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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ax.set_xlim(xmin[0], xmax[0])
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ax.set_ylim(xmin[1], xmax[1])
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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@ -281,17 +281,21 @@ class sparse_GP(GPBase):
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return mean, var, _025pm, _975pm
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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20):
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GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20)
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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
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if self.X.shape[1] == 1:
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Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
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if self.has_uncertain_inputs:
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pb.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xstd + self._Xmean
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pb.plot(Zu, Zu * 0 + pb.ylim()[0], 'r|', mew=1.5, markersize=12)
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# pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_variance.flatten()))
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 2: # FIXME
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pb.plot(self.Z[:, 0], self.Z[:, 1], 'wo')
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xstd + self._Xmean
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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