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[Merge] merge devel

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Max Zwiessele 2016-03-30 09:52:40 +01:00
commit c793ca77a9
116 changed files with 1134 additions and 636 deletions
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2
GPy/plotting/Tango.py
View file

@ -104,4 +104,4 @@ cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1
(2./5,colorsRGB['Aluminium3'][2]/256.,colorsRGB['Aluminium3'][2]/256.),
(3./5,colorsRGB['Aluminium4'][2]/256.,colorsRGB['Aluminium4'][2]/256.),
(4./5,colorsRGB['Aluminium5'][2]/256.,colorsRGB['Aluminium5'][2]/256.),
(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}
(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}

116
GPy/plotting/__init__.py
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@ -25,18 +25,66 @@ def change_plotting_library(lib):
current_lib[0] = PlotlyPlots()
if lib == 'none':
current_lib[0] = None
inject_plotting()
#===========================================================================
except (ImportError, NameError):
config.set('plotting', 'library', 'none')
raise
import warnings
warnings.warn(ImportWarning("You spevified {} in your configuration, but is not available. Install newest version of {} for plotting".format(lib, lib)))
from ..util.config import config, NoOptionError
try:
lib = config.get('plotting', 'library')
change_plotting_library(lib)
except NoOptionError:
print("No plotting library was specified in config file. \n{}".format(error_suggestion))
def inject_plotting():
if current_lib[0] is not None:
# Inject the plots into classes here:
# Already converted to new style:
from . import gpy_plot
from ..core import GP
GP.plot_data = gpy_plot.data_plots.plot_data
GP.plot_data_error = gpy_plot.data_plots.plot_data_error
GP.plot_errorbars_trainset = gpy_plot.data_plots.plot_errorbars_trainset
GP.plot_mean = gpy_plot.gp_plots.plot_mean
GP.plot_confidence = gpy_plot.gp_plots.plot_confidence
GP.plot_density = gpy_plot.gp_plots.plot_density
GP.plot_samples = gpy_plot.gp_plots.plot_samples
GP.plot = gpy_plot.gp_plots.plot
GP.plot_f = gpy_plot.gp_plots.plot_f
GP.plot_magnification = gpy_plot.latent_plots.plot_magnification
from ..core import SparseGP
SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
from ..kern import Kern
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
def deprecate_plot(self, *args, **kwargs):
import warnings
warnings.warn(DeprecationWarning('Kern.plot is being deprecated and will not be available in the 1.0 release. Use Kern.plot_covariance instead'))
return self.plot_covariance(*args, **kwargs)
Kern.plot = deprecate_plot
Kern.plot_ARD = gpy_plot.kernel_plots.plot_ARD
from ..inference.optimization import Optimizer
Optimizer.plot = gpy_plot.inference_plots.plot_optimizer
# Variational plot!
def plotting_library():
if current_lib[0] is None:
@ -53,54 +101,10 @@ def show(figure, **kwargs):
"""
return plotting_library().show_canvas(figure, **kwargs)
if config.get('plotting', 'library') is not 'none':
# Inject the plots into classes here:
# Already converted to new style:
from . import gpy_plot
from ..core import GP
GP.plot_data = gpy_plot.data_plots.plot_data
GP.plot_data_error = gpy_plot.data_plots.plot_data_error
GP.plot_errorbars_trainset = gpy_plot.data_plots.plot_errorbars_trainset
GP.plot_mean = gpy_plot.gp_plots.plot_mean
GP.plot_confidence = gpy_plot.gp_plots.plot_confidence
GP.plot_density = gpy_plot.gp_plots.plot_density
GP.plot_samples = gpy_plot.gp_plots.plot_samples
GP.plot = gpy_plot.gp_plots.plot
GP.plot_f = gpy_plot.gp_plots.plot_f
GP.plot_magnification = gpy_plot.latent_plots.plot_magnification
from ..core import SparseGP
SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
from ..kern import Kern
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
def deprecate_plot(self, *args, **kwargs):
import warnings
warnings.warn(DeprecationWarning('Kern.plot is being deprecated and will not be available in the 1.0 release. Use Kern.plot_covariance instead'))
return self.plot_covariance(*args, **kwargs)
Kern.plot = deprecate_plot
Kern.plot_ARD = gpy_plot.kernel_plots.plot_ARD
from ..inference.optimization import Optimizer
Optimizer.plot = gpy_plot.inference_plots.plot_optimizer
# Variational plot!
from ..util.config import config, NoOptionError
try:
lib = config.get('plotting', 'library')
change_plotting_library(lib)
except NoOptionError:
print("No plotting library was specified in config file. \n{}".format(error_suggestion))

2
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -420,4 +420,4 @@ def _plot(self, canvas, plots, helper_data, helper_prediction, levels, plot_indu
if helper_prediction[2] is not None:
plots.update(_plot_samples(self, canvas, helper_data, helper_prediction, projection, "Samples"))
return plots
return plots

14
GPy/plotting/gpy_plot/kernel_plots.py
View file

@ -33,7 +33,7 @@ from .. import Tango
from .plot_util import update_not_existing_kwargs, helper_for_plot_data
from ...kern.src.kern import Kern, CombinationKernel
def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
def plot_ARD(kernel, filtering=None, legend=False, canvas=None, **kwargs):
"""
If an ARD kernel is present, plot a bar representation using matplotlib
@ -62,7 +62,11 @@ def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
bars = []
kwargs = update_not_existing_kwargs(kwargs, pl().defaults.ard)
canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
if canvas is None:
canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
for i in range(ard_params.shape[0]):
if parts[i].name in filtering:
c = Tango.nextMedium()
@ -96,7 +100,7 @@ def plot_covariance(kernel, x=None, label=None,
"""
X = np.ones((2, kernel._effective_input_dim)) * [[-3], [3]]
_, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
from numbers import Number
if x is None:
from ...kern.src.stationary import Stationary
@ -104,7 +108,7 @@ def plot_covariance(kernel, x=None, label=None,
elif isinstance(x, Number):
x = np.ones((1, kernel._effective_input_dim))*x
K = kernel.K(Xgrid, x)
if projection == '3d':
xlabel = 'X[:,0]'
ylabel = 'X[:,1]'
@ -136,4 +140,4 @@ def plot_covariance(kernel, x=None, label=None,
return pl().add_to_canvas(canvas, plots)
else:
raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")
raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")

8
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -147,6 +147,7 @@ def _plot_magnification(self, canvas, which_indices, Xgrid,
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)
@ -215,7 +216,12 @@ def _plot_latent(self, canvas, which_indices, Xgrid,
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], Xgrid.shape[1]))
Xtest_full[:, which_indices] = x
mf = np.log(self.predict(Xtest_full, kern=kern)[1])
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)

2
GPy/plotting/matplot_dep/__init__.py
View file

@ -18,4 +18,4 @@
from .util import align_subplot_array, align_subplots, fewerXticks, removeRightTicks, removeUpperTicks
from . import controllers, base_plots
from . import controllers, base_plots

2
GPy/plotting/matplot_dep/controllers/__init__.py
View file

@ -1 +1 @@
from .imshow_controller import ImshowController, ImAnnotateController
from .imshow_controller import ImshowController, ImAnnotateController

2
GPy/plotting/matplot_dep/controllers/imshow_controller.py
View file

@ -72,4 +72,4 @@ class ImAnnotateController(ImshowController):
text.set_x(x+xoffset)
text.set_y(y+yoffset)
text.set_text("{}".format(X[1][j, i]))
return view
return view

2
GPy/plotting/matplot_dep/defaults.py
View file

@ -72,4 +72,4 @@ latent = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
gradient = dict(aspect='auto', cmap='RdBu', interpolation='nearest', alpha=.7)
magnification = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
latent_scatter = dict(s=40, linewidth=.2, edgecolor='k', alpha=.9)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

17
GPy/plotting/matplot_dep/plot_definitions.py
View file

@ -42,10 +42,11 @@ class MatplotlibPlots(AbstractPlottingLibrary):
super(MatplotlibPlots, self).__init__()
self._defaults = defaults.__dict__
def figure(self, rows=1, cols=1, **kwargs):
fig = plt.figure(**kwargs)
def figure(self, rows=1, cols=1, gridspec_kwargs={}, tight_layout=True, **kwargs):
fig = plt.figure(tight_layout=tight_layout, **kwargs)
fig.rows = rows
fig.cols = cols
fig.gridspec = plt.GridSpec(rows, cols, **gridspec_kwargs)
return fig
def new_canvas(self, figure=None, row=1, col=1, projection='2d', xlabel=None, ylabel=None, zlabel=None, title=None, xlim=None, ylim=None, zlim=None, **kwargs):
@ -56,7 +57,9 @@ class MatplotlibPlots(AbstractPlottingLibrary):
if 'ax' in kwargs:
ax = kwargs.pop('ax')
else:
if 'num' in kwargs and 'figsize' in kwargs:
if figure is not None:
fig = figure
elif 'num' in kwargs and 'figsize' in kwargs:
fig = self.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize'))
elif 'num' in kwargs:
fig = self.figure(num=kwargs.pop('num'))
@ -66,7 +69,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
fig = self.figure()
#if hasattr(fig, 'rows') and hasattr(fig, 'cols'):
ax = fig.add_subplot(fig.rows, fig.cols, (col,row), projection=projection)
ax = fig.add_subplot(fig.gridspec[row-1, col-1], projection=projection)
if xlim is not None: ax.set_xlim(xlim)
if ylim is not None: ax.set_ylim(ylim)
@ -79,7 +82,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
return ax, kwargs
def add_to_canvas(self, ax, plots, legend=False, title=None, **kwargs):
ax.autoscale_view()
#ax.autoscale_view()
fontdict=dict(family='sans-serif', weight='light', size=9)
if legend is True:
ax.legend(*ax.get_legend_handles_labels())
@ -89,9 +92,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
if title is not None: ax.figure.suptitle(title)
return ax
def show_canvas(self, ax, tight_layout=False, **kwargs):
if tight_layout:
ax.figure.tight_layout()
def show_canvas(self, ax):
ax.figure.canvas.draw()
return ax.figure

6
GPy/plotting/matplot_dep/ssgplvm.py
View file

@ -13,16 +13,16 @@ class SSGPLVM_plot(object):
self.model = model
self.imgsize= imgsize
assert model.Y.shape[1] == imgsize[0]*imgsize[1]
def plot_inducing(self):
fig1 = pylab.figure()
mean = self.model.posterior.mean
arr = mean.reshape(*(mean.shape[0],self.imgsize[1],self.imgsize[0]))
plot_2D_images(fig1, arr)
fig1.gca().set_title('The mean of inducing points')
fig2 = pylab.figure()
covar = self.model.posterior.covariance
plot_2D_images(fig2, covar)
fig2.gca().set_title('The variance of inducing points')

2
GPy/plotting/matplot_dep/util.py
View file

@ -116,4 +116,4 @@ def align_subplot_array(axes,xlim=None, ylim=None):
if i<(M*(N-1)):
ax.set_xticks([])
else:
removeUpperTicks(ax)
removeUpperTicks(ax)

2
GPy/plotting/plotly_dep/defaults.py
View file

@ -73,4 +73,4 @@ latent = dict(colorscale='Greys', reversescale=True, zsmooth='best')
gradient = dict(colorscale='RdBu', opacity=.7)
magnification = dict(colorscale='Greys', zsmooth='best', reversescale=True)
latent_scatter = dict(marker_kwargs=dict(size='5', opacity=.7))
# annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)
# annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

2
GPy/plotting/plotly_dep/plot_definitions.py
View file

@ -254,7 +254,7 @@ class PlotlyPlots(AbstractPlottingLibrary):
font=dict(color='white' if np.abs(var) > 0.8 else 'black', size=10),
opacity=.5,
showarrow=False,
hoverinfo='x'))
))
return imshow, annotations
def annotation_heatmap_interact(self, ax, plot_function, extent, label=None, resolution=15, imshow_kwargs=None, **annotation_kwargs):