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beckdaniel 2016-04-11 11:26:46 +01:00
commit 45f692340a
170 changed files with 39094 additions and 4107 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.))}

13
GPy/plotting/__init__.py
View file

@ -52,6 +52,17 @@ def inject_plotting():
GP.plot_f = gpy_plot.gp_plots.plot_f
GP.plot_magnification = gpy_plot.latent_plots.plot_magnification
from ..models import StateSpace
StateSpace.plot_data = gpy_plot.data_plots.plot_data
StateSpace.plot_data_error = gpy_plot.data_plots.plot_data_error
StateSpace.plot_errorbars_trainset = gpy_plot.data_plots.plot_errorbars_trainset
StateSpace.plot_mean = gpy_plot.gp_plots.plot_mean
StateSpace.plot_confidence = gpy_plot.gp_plots.plot_confidence
StateSpace.plot_density = gpy_plot.gp_plots.plot_density
StateSpace.plot_samples = gpy_plot.gp_plots.plot_samples
StateSpace.plot = gpy_plot.gp_plots.plot
StateSpace.plot_f = gpy_plot.gp_plots.plot_f
from ..core import SparseGP
SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
@ -107,4 +118,4 @@ 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))
print("No plotting library was specified in config file. \n{}".format(error_suggestion))

4
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -235,8 +235,6 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
Give the Y_metadata in the predict_kw if you need it.
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
:type plot_limits: np.array
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
@ -420,4 +418,4 @@ 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

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

@ -140,4 +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")

12
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -131,7 +131,9 @@ def plot_latent_inducing(self,
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)
kwargs['marker'] = marker
update_not_existing_kwargs(kwargs, pl().defaults.inducing_2d) # @UndefinedVariable
scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, num_samples=num_samples, projection=projection, **kwargs)
return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
@ -147,6 +149,7 @@ 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 +218,12 @@ 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)

3
GPy/plotting/gpy_plot/plot_util.py
View file

@ -194,6 +194,7 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
x = X[index, input_1]
y = X[index, input_2]
z = X[index, input_3]
yield x, y, z, this_label, index, m
def subsample_X(X, labels, num_samples=1000):
@ -385,5 +386,5 @@ def x_frame2D(X,plot_limits=None,resolution=None):
resolution = resolution or 50
xx, yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
Xnew = np.vstack((xx.flatten(),yy.flatten())).T
Xnew = np.c_[xx.flat, yy.flat]
return Xnew, xx, yy, xmin, xmax

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

26
GPy/plotting/matplot_dep/defaults.py
View file

@ -1,21 +1,21 @@
#===============================================================================
# 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 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
@ -34,20 +34,20 @@ from .. import Tango
'''
This file is for defaults for the gpy plot, specific to the plotting library.
Create a kwargs dictionary with the right name for the plotting function
Create a kwargs dictionary with the right name for the plotting function
you are implementing. If you do not provide defaults, the default behaviour of
the plotting library will be used.
the plotting library will be used.
In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
it gives back an empty default, when defaults are not defined.
'''
# Data plots:
data_1d = dict(lw=1.5, marker='x', edgecolor='k')
data_1d = dict(lw=1.5, marker='x', color='k')
data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5)
inducing_1d = dict(lw=0, s=500, facecolors=Tango.colorsHex['darkRed'])
inducing_2d = dict(s=14, edgecolors='k', linewidth=.4, facecolors='white', alpha=.5, marker='^')
inducing_3d = dict(lw=.3, s=500, facecolors='white', edgecolors='k')
inducing_2d = dict(s=17, edgecolor='k', linewidth=.4, color='white', alpha=.5, marker='^')
inducing_3d = dict(lw=.3, s=500, color=Tango.colorsHex['darkRed'], edgecolor='k')
xerrorbar = dict(color='k', fmt='none', elinewidth=.5, alpha=.5)
yerrorbar = dict(color=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, alpha=.5)
@ -71,5 +71,5 @@ ard = dict(edgecolor='k', linewidth=1.2)
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)
latent_scatter = dict(s=20, linewidth=.2, edgecolor='k', alpha=.9)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

43
GPy/plotting/matplot_dep/util.py
View file

@ -1,5 +1,5 @@
#===============================================================================
# Copyright (c) 2015, Max Zwiessele
# Copyright (c) 2016, Max Zwiessele, Alan saul
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
@ -116,4 +116,43 @@ def align_subplot_array(axes,xlim=None, ylim=None):
if i<(M*(N-1)):
ax.set_xticks([])
else:
removeUpperTicks(ax)
removeUpperTicks(ax)
def fixed_inputs(model, non_fixed_inputs, fix_routine='median', as_list=True, X_all=False):
"""
Convenience function for returning back fixed_inputs where the other inputs
are fixed using fix_routine
:param model: model
:type model: Model
:param non_fixed_inputs: dimensions of non fixed inputs
:type non_fixed_inputs: list
:param fix_routine: fixing routine to use, 'mean', 'median', 'zero'
:type fix_routine: string
:param as_list: if true, will return a list of tuples with (dimension, fixed_val) otherwise it will create the corresponding X matrix
:type as_list: boolean
"""
from ...inference.latent_function_inference.posterior import VariationalPosterior
f_inputs = []
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
X = model.X.mean.values.copy()
elif isinstance(model.X, VariationalPosterior):
X = model.X.values.copy()
else:
if X_all:
X = model.X_all.copy()
else:
X = model.X.copy()
for i in range(X.shape[1]):
if i not in non_fixed_inputs:
if fix_routine == 'mean':
f_inputs.append( (i, np.mean(X[:,i])) )
if fix_routine == 'median':
f_inputs.append( (i, np.median(X[:,i])) )
else: # set to zero zero
f_inputs.append( (i, 0) )
if not as_list:
X[:,i] = f_inputs[-1][1]
if as_list:
return f_inputs
else:
return X

8
GPy/plotting/matplot_dep/variational_plots.py
View file

@ -15,7 +15,9 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
if ax is None:
fig = pb.figure(num=fignum, figsize=figsize)
if colors is None:
colors = pb.gca()._get_lines.color_cycle
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)
@ -64,7 +66,9 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid
else:
fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
if colors is None:
colors = pb.gca()._get_lines.color_cycle
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)

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)

7
GPy/plotting/plotly_dep/plot_definitions.py
View file

@ -131,14 +131,15 @@ class PlotlyPlots(AbstractPlottingLibrary):
#not matplotlib marker
pass
marker_kwargs = marker_kwargs or {}
marker_kwargs.setdefault('symbol', marker)
if 'symbol' not in marker_kwargs:
marker_kwargs['symbol'] = marker
if Z is not None:
return Scatter3d(x=X, y=Y, z=Z, mode='markers',
showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs),
name=label, **kwargs)
return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}),
marker=Marker(color=color, colorscale=cmap, **marker_kwargs),
name=label, **kwargs)
def plot(self, ax, X, Y, Z=None, color=None, label=None, line_kwargs=None, **kwargs):
@ -254,7 +255,7 @@ 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):