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Ricardo
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248
GPy/plotting/matplot_dep/dim_reduction_plots.py
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248
GPy/plotting/matplot_dep/dim_reduction_plots.py
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import pylab as pb
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import numpy as np
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from ... import util
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from latent_space_visualizations.controllers.imshow_controller import ImshowController,ImAnnotateController
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from GPy.util.misc import param_to_array
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import itertools
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import Tango
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from matplotlib.cm import get_cmap
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def most_significant_input_dimensions(model, which_indices):
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"""
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Determine which dimensions should be plotted
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"""
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if which_indices is None:
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if model.input_dim == 1:
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input_1 = 0
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input_2 = None
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if model.input_dim == 2:
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input_1, input_2 = 0, 1
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else:
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try:
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input_1, input_2 = np.argsort(model.input_sensitivity())[::-1][:2]
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except:
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raise ValueError, "cannot automatically determine which dimensions to plot, please pass 'which_indices'"
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else:
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input_1, input_2 = which_indices
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return input_1, input_2
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def plot_latent(model, labels=None, which_indices=None,
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resolution=50, ax=None, marker='o', s=40,
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fignum=None, plot_inducing=False, legend=True,
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aspect='auto', updates=False):
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"""
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:param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
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:param resolution: the resolution of the grid on which to evaluate the predictive variance
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"""
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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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util.plot.Tango.reset()
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if labels is None:
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labels = np.ones(model.num_data)
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input_1, input_2 = most_significant_input_dimensions(model, which_indices)
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X = param_to_array(model.X)
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# first, plot the output variance as a function of the latent space
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Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(X[:, [input_1, input_2]], resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
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def plot_function(x):
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Xtest_full[:, [input_1, input_2]] = x
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mu, var, low, up = model.predict(Xtest_full)
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var = var[:, :1]
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return np.log(var)
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view = ImshowController(ax, plot_function,
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tuple(X[:, [input_1, input_2]].min(0)) + tuple(X[:, [input_1, input_2]].max(0)),
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resolution, aspect=aspect, interpolation='bilinear',
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cmap=pb.cm.binary)
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# ax.imshow(var.reshape(resolution, resolution).T,
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# extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary, interpolation='bilinear', origin='lower')
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# make sure labels are in order of input:
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ulabels = []
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for lab in labels:
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if not lab in ulabels:
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ulabels.append(lab)
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marker = itertools.cycle(list(marker))
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for i, ul in enumerate(ulabels):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i' % ul
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else:
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this_label = 'class %i' % i
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m = marker.next()
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index = np.nonzero(labels == ul)[0]
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if model.input_dim == 1:
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x = X[index, input_1]
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y = np.zeros(index.size)
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else:
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x = X[index, input_1]
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y = X[index, input_2]
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ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
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ax.set_xlabel('latent dimension %i' % input_1)
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ax.set_ylabel('latent dimension %i' % input_2)
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if not np.all(labels == 1.) and legend:
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ax.legend(loc=0, numpoints=1)
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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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ax.grid(b=False) # remove the grid if present, it doesn't look good
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ax.set_aspect('auto') # set a nice aspect ratio
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if plot_inducing:
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Z = param_to_array(model.Z)
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ax.plot(Z[:, input_1], Z[:, input_2], '^w')
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if updates:
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ax.figure.canvas.show()
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raw_input('Enter to continue')
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return ax
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def plot_magnification(model, labels=None, which_indices=None,
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resolution=60, ax=None, marker='o', s=40,
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fignum=None, plot_inducing=False, legend=True,
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aspect='auto', updates=False):
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"""
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:param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
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:param resolution: the resolution of the grid on which to evaluate the predictive variance
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"""
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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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util.plot.Tango.reset()
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if labels is None:
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labels = np.ones(model.num_data)
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input_1, input_2 = most_significant_input_dimensions(model, which_indices)
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# first, plot the output variance as a function of the latent space
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Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
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def plot_function(x):
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Xtest_full[:, [input_1, input_2]] = x
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mf=model.magnification(Xtest_full)
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return mf
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view = ImshowController(ax, plot_function,
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tuple(model.X.min(0)[:, [input_1, input_2]]) + tuple(model.X.max(0)[:, [input_1, input_2]]),
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resolution, aspect=aspect, interpolation='bilinear',
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cmap=pb.cm.gray)
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# make sure labels are in order of input:
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ulabels = []
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for lab in labels:
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if not lab in ulabels:
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ulabels.append(lab)
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marker = itertools.cycle(list(marker))
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for i, ul in enumerate(ulabels):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i' % ul
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else:
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this_label = 'class %i' % i
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m = marker.next()
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index = np.nonzero(labels == ul)[0]
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if model.input_dim == 1:
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x = model.X[index, input_1]
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y = np.zeros(index.size)
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else:
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x = model.X[index, input_1]
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y = model.X[index, input_2]
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ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
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ax.set_xlabel('latent dimension %i' % input_1)
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ax.set_ylabel('latent dimension %i' % input_2)
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if not np.all(labels == 1.) and legend:
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ax.legend(loc=0, numpoints=1)
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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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ax.grid(b=False) # remove the grid if present, it doesn't look good
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ax.set_aspect('auto') # set a nice aspect ratio
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if plot_inducing:
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ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
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if updates:
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ax.figure.canvas.show()
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raw_input('Enter to continue')
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pb.title('Magnification Factor')
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return ax
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def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None, labels=None, data_labels=None, data_marker='o', data_s=40, resolution=20, aspect='auto', updates=False, ** kwargs):
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input_1, input_2 = significant_dims = most_significant_input_dimensions(model, which_indices)
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X = np.zeros((resolution ** 2, model.input_dim))
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indices = np.r_[:X.shape[0]]
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if labels is None:
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labels = range(model.output_dim)
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def plot_function(x):
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X[:, significant_dims] = x
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dmu_dX = model.dmu_dXnew(X)
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argmax = np.argmax(dmu_dX, 1)
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return dmu_dX[indices, argmax], np.array(labels)[argmax]
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if ax is None:
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fig = pyplot.figure(num=fignum)
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ax = fig.add_subplot(111)
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if data_labels is None:
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data_labels = np.ones(model.num_data)
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ulabels = []
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for lab in data_labels:
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if not lab in ulabels:
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ulabels.append(lab)
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marker = itertools.cycle(list(data_marker))
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for i, ul in enumerate(ulabels):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i' % ul
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else:
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this_label = 'class %i' % i
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m = marker.next()
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index = np.nonzero(data_labels == ul)[0]
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x = model.X[index, input_1]
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y = model.X[index, input_2]
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ax.scatter(x, y, marker=m, s=data_s, color=Tango.nextMedium(), label=this_label)
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ax.set_xlabel('latent dimension %i' % input_1)
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ax.set_ylabel('latent dimension %i' % input_2)
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controller = ImAnnotateController(ax,
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plot_function,
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tuple(model.X.min(0)[:, significant_dims]) + tuple(model.X.max(0)[:, significant_dims]),
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resolution=resolution,
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aspect=aspect,
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cmap=get_cmap('jet'),
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**kwargs)
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ax.legend()
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ax.figure.tight_layout()
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if updates:
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pyplot.show()
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clear = raw_input('Enter to continue')
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if clear.lower() in 'yes' or clear == '':
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controller.deactivate()
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return controller.view
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