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GPy/GPy/plotting/gpy_plot/latent_plots.py
mzwiessele 452c662084 [plotting] added plotting for missing data
2016-03-10 13:14:25 +00:00

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#===============================================================================
# Copyright (c) 2015, Max Zwiessele
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of GPy.plotting.gpy_plot.latent_plots nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#===============================================================================
import numpy as np
from . import plotting_library as pl
from .plot_util import get_x_y_var,\
update_not_existing_kwargs, \
helper_for_plot_data, scatter_label_generator, subsample_X,\
find_best_layout_for_subplots
def _wait_for_updates(view, updates):
if view is not None:
try:
if updates:
clear = raw_input('yes or enter to deactivate updates - otherwise still do updates - use plots[imshow].deactivate() to clear')
if clear.lower() in 'yes' or clear == '':
view.deactivate()
else:
view.deactivate()
except AttributeError:
# No updateable view:
pass
except TypeError:
# No updateable view:
pass
def _new_canvas(self, projection, kwargs, which_indices):
input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
if input_3 is None:
zlabel = None
else:
zlabel = 'latent dimension %i' % input_3
canvas, kwargs = pl().new_canvas(projection=projection, xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel=zlabel, **kwargs)
return canvas, projection, kwargs, sig_dims
def _plot_latent_scatter(canvas, X, visible_dims, labels, marker, num_samples, projection='2d', **kwargs):
from .. import Tango
Tango.reset()
X, labels = subsample_X(X, labels, num_samples)
scatters = []
generate_colors = 'color' not in kwargs
for x, y, z, this_label, _, m in scatter_label_generator(labels, X, visible_dims, marker):
update_not_existing_kwargs(kwargs, pl().defaults.latent_scatter)
if generate_colors:
kwargs['color'] = Tango.nextMedium()
if projection == '3d':
scatters.append(pl().scatter(canvas, x, y, Z=z, marker=m, label=this_label, **kwargs))
else: scatters.append(pl().scatter(canvas, x, y, marker=m, label=this_label, **kwargs))
return scatters
def plot_latent_scatter(self, labels=None,
which_indices=None,
legend=True,
plot_limits=None,
marker='<>^vsd',
num_samples=1000,
projection='2d',
**kwargs):
"""
Plot a scatter plot of the latent space.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param str marker: markers to use - cycle if more labels then markers are given
:param kwargs: the kwargs for the scatter plots
"""
canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
X, _, _ = get_x_y_var(self)
if labels is None:
labels = np.ones(self.num_data)
legend = False
else:
legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
scatters = _plot_latent_scatter(canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
def plot_latent_inducing(self,
which_indices=None,
legend=False,
plot_limits=None,
marker='^',
num_samples=1000,
projection='2d',
**kwargs):
"""
Plot a scatter plot of the inducing inputs.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param str marker: markers to use - cycle if more labels then markers are given
:param kwargs: the kwargs for the scatter plots
"""
canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
Z = self.Z.values
labels = np.array(['inducing'] * Z.shape[0])
scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
def _plot_magnification(self, canvas, which_indices, Xgrid,
xmin, xmax, resolution, updates,
mean=True, covariance=True,
kern=None,
**imshow_kwargs):
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], Xgrid.shape[1]))
Xtest_full[:, which_indices] = x
mf = self.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
return mf.reshape(resolution, resolution).T
imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl().defaults.magnification)
try:
if updates:
return pl().imshow_interact(canvas, plot_function, (xmin[0], xmax[0], xmin[1], xmax[1]), resolution=resolution, **imshow_kwargs)
else: raise NotImplementedError
except NotImplementedError:
return pl().imshow(canvas, plot_function(Xgrid[:, which_indices]), (xmin[0], xmax[0], xmin[1], xmax[1]), **imshow_kwargs)
def plot_magnification(self, labels=None, which_indices=None,
resolution=60, marker='<>^vsd', legend=True,
plot_limits=None,
updates=False,
mean=True, covariance=True,
kern=None, num_samples=1000,
scatter_kwargs=None, plot_scatter=True,
**imshow_kwargs):
"""
Plot the magnification factor of the GP on the inputs. This is the
density of the GP as a gray scale.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param int resolution: the resolution at which we predict the magnification factor
:param str marker: markers to use - cycle if more labels then markers are given
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param bool mean: use the mean of the Wishart embedding for the magnification factor
:param bool covariance: use the covariance of the Wishart embedding for the magnification factor
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param kwargs: the kwargs for the scatter plots
"""
input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
X = get_x_y_var(self)[0]
_, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)
canvas, imshow_kwargs = pl().new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **imshow_kwargs)
plots = {}
if legend and plot_scatter:
if (labels is not None):
legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
else:
labels = np.ones(self.num_data)
legend = False
if plot_scatter:
plots['scatters'] = _plot_latent_scatter(canvas, X, which_indices, labels, marker, num_samples, projection='2d', **scatter_kwargs or {})
plots['view'] = _plot_magnification(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, updates, mean, covariance, kern, **imshow_kwargs)
retval = pl().add_to_canvas(canvas, plots,
legend=legend,
)
_wait_for_updates(plots['view'], updates)
return retval
def _plot_latent(self, canvas, which_indices, Xgrid,
xmin, xmax, resolution, updates,
kern=None,
**imshow_kwargs):
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], Xgrid.shape[1]))
Xtest_full[:, which_indices] = x
mf = self.predict(Xtest_full, kern=kern)[1]
if mf.shape[1]==self.output_dim:
mf = mf.sum(-1)
else:
mf *= self.output_dim
mf = np.log(mf)
return mf.reshape(resolution, resolution).T
imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl().defaults.latent)
try:
if updates:
return pl().imshow_interact(canvas, plot_function, (xmin[0], xmax[0], xmin[1], xmax[1]), resolution=resolution, **imshow_kwargs)
else: raise NotImplementedError
except NotImplementedError:
return pl().imshow(canvas, plot_function(Xgrid[:, which_indices]), (xmin[0], xmax[0], xmin[1], xmax[1]), **imshow_kwargs)
def plot_latent(self, labels=None, which_indices=None,
resolution=60, legend=True,
plot_limits=None,
updates=False,
kern=None, marker='<>^vsd',
num_samples=1000, projection='2d',
scatter_kwargs=None, **imshow_kwargs):
"""
Plot the latent space of the GP on the inputs. This is the
density of the GP posterior as a grey scale and the
scatter plot of the input dimemsions selected by which_indices.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param scatter_kwargs: the kwargs for the scatter plots
"""
if projection != '2d':
raise ValueError('Cannot plot latent in other then 2 dimensions, consider plot_scatter')
input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
X = get_x_y_var(self)[0]
_, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)
canvas, imshow_kwargs = pl().new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **imshow_kwargs)
if legend:
if (labels is not None):
legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
else:
labels = np.ones(self.num_data)
legend = False
scatters = _plot_latent_scatter(canvas, X, which_indices, labels, marker, num_samples, projection='2d', **scatter_kwargs or {})
view = _plot_latent(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, updates, kern, **imshow_kwargs)
retval = pl().add_to_canvas(canvas, dict(scatter=scatters, imshow=view), legend=legend)
_wait_for_updates(view, updates)
return retval
def _plot_steepest_gradient_map(self, canvas, which_indices, Xgrid,
xmin, xmax, resolution, output_labels, updates,
kern=None, annotation_kwargs=None,
**imshow_kwargs):
if output_labels is None:
output_labels = range(self.output_dim)
def plot_function(x):
Xgrid[:, which_indices] = x
dmu_dX = np.sqrt(((self.predictive_gradients(Xgrid, kern=kern)[0])**2).sum(1))
#dmu_dX = self.predictive_gradients(Xgrid, kern=kern)[0].sum(1)
argmax = np.argmax(dmu_dX, 1).astype(int)
return dmu_dX.max(1).reshape(resolution, resolution).T, np.array(output_labels)[argmax].reshape(resolution, resolution).T
annotation_kwargs = update_not_existing_kwargs(annotation_kwargs or {}, pl().defaults.annotation)
imshow_kwargs = update_not_existing_kwargs(imshow_kwargs or {}, pl().defaults.gradient)
try:
if updates:
return dict(annotation=pl().annotation_heatmap_interact(canvas, plot_function, (xmin[0], xmax[0], xmin[1], xmax[1]), resolution=resolution, imshow_kwargs=imshow_kwargs, **annotation_kwargs))
else:
raise NotImplementedError
except NotImplementedError:
imshow, annotation = pl().annotation_heatmap(canvas, *plot_function(Xgrid[:, which_indices]), extent=(xmin[0], xmax[0], xmin[1], xmax[1]), imshow_kwargs=imshow_kwargs, **annotation_kwargs)
return dict(heatmap=imshow, annotation=annotation)
def plot_steepest_gradient_map(self, output_labels=None, data_labels=None, which_indices=None,
resolution=15, legend=True,
plot_limits=None,
updates=False,
kern=None, marker='<>^vsd',
num_samples=1000,
annotation_kwargs=None, scatter_kwargs=None, **imshow_kwargs):
"""
Plot the latent space of the GP on the inputs. This is the
density of the GP posterior as a grey scale and the
scatter plot of the input dimemsions selected by which_indices.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure, if int plot legend columns on legend
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param annotation_kwargs: the kwargs for the annotation plot
:param scatter_kwargs: the kwargs for the scatter plots
"""
input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
X = get_x_y_var(self)[0]
_, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)
canvas, imshow_kwargs = pl().new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **imshow_kwargs)
if (data_labels is not None):
legend = find_best_layout_for_subplots(len(np.unique(data_labels)))[1]
else:
data_labels = np.ones(self.num_data)
legend = False
plots = dict(scatter=_plot_latent_scatter(canvas, X, which_indices, data_labels, marker, num_samples, **scatter_kwargs or {}))
plots.update(_plot_steepest_gradient_map(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, output_labels, updates, kern, annotation_kwargs=annotation_kwargs, **imshow_kwargs))
retval = pl().add_to_canvas(canvas, plots, legend=legend)
_wait_for_updates(plots['annotation'], updates)
return retval
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