mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-08 15:05:15 +02:00
[mrd] plot_scales and plot_latent added
This commit is contained in:
Max Zwiessele
parent
c4020cd2eb
commit
885d3722cc
8 changed files with 112 additions and 148 deletions
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@ -459,7 +459,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim)
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k = kern.Linear(Q) + kern.White(Q, variance=1e-4)
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k = kern.Linear(Q, ARD=True) + kern.White(Q, variance=1e-4)
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m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="PCA_concat", initz='permute', **kw)
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m['.*noise'] = [Y.var() / 40. for Y in Ylist]
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@ -479,7 +479,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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k = kern.Linear(Q) + kern.White(Q, variance=1e-4)
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k = kern.Linear(Q, ARD=True) + kern.White(Q, variance=1e-4)
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inanlist = []
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for Y in Ylist:
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@ -15,4 +15,4 @@
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# [plotting]
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# library = matplotlib # plotly
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# library = matplotlib # plotly, none
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@ -5,14 +5,14 @@ import numpy as np
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import itertools, logging
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from ..kern import Kern
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from GPy.core.parameterization.variational import NormalPrior
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from ..core.parameterization.variational import NormalPrior
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from ..core.parameterization import Param
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from paramz import ObsAr
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from ..inference.latent_function_inference.var_dtc import VarDTC
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from ..inference.latent_function_inference import InferenceMethodList
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from ..likelihoods import Gaussian
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from ..util.initialization import initialize_latent
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from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
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from ..models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
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class MRD(BayesianGPLVMMiniBatch):
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"""
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@ -215,40 +215,6 @@ class MRD(BayesianGPLVMMiniBatch):
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Z = np.random.randn(self.num_inducing, self.input_dim) * X.var()
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return Z
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def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
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import matplotlib.pyplot as plt
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if axes is None:
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fig = plt.figure(num=fignum)
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sharex_ax = None
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sharey_ax = None
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plots = []
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for i, g in enumerate(self.bgplvms):
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try:
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if sharex:
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sharex_ax = ax # @UndefinedVariable
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sharex = False # dont set twice
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if sharey:
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sharey_ax = ax # @UndefinedVariable
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sharey = False # dont set twice
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except:
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pass
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if axes is None:
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ax = fig.add_subplot(1, len(self.bgplvms), i + 1, sharex=sharex_ax, sharey=sharey_ax)
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elif isinstance(axes, (tuple, list, np.ndarray)):
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ax = axes[i]
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else:
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raise ValueError("Need one axes per latent dimension input_dim")
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plots.append(plotf(i, g, ax))
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if sharey_ax is not None:
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plt.setp(ax.get_yticklabels(), visible=False)
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plt.draw()
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if axes is None:
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try:
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fig.tight_layout()
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except:
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pass
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return plots
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def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, Yindex=0):
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"""
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Prediction for data set Yindex[default=0].
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@ -270,59 +236,53 @@ class MRD(BayesianGPLVMMiniBatch):
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# sharex=sharex, sharey=sharey)
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# return fig
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def plot_scales(self, fignum=None, ax=None, titles=None, sharex=False, sharey=True, *args, **kwargs):
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def plot_scales(self, titles=None, fig_kwargs=dict(figsize=None, tight_layout=True), **kwargs):
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"""
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TODO: Explain other parameters
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Plot input sensitivity for all datasets, to see which input dimensions are
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significant for which dataset.
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:param titles: titles for axes of datasets
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kwargs go into plot_ARD for each kernel.
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"""
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from ..plotting import plotting_library as pl
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if titles is None:
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titles = [r'${}$'.format(name) for name in self.names]
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ymax = reduce(max, [np.ceil(max(g.kern.input_sensitivity())) for g in self.bgplvms])
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def plotf(i, g, ax):
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#ax.set_ylim([0,ymax])
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return g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
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fig = self._handle_plotting(fignum, ax, plotf, sharex=sharex, sharey=sharey)
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return fig
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M = len(self.bgplvms)
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fig = pl().figure(rows=1, cols=M, **fig_kwargs)
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plots = {}
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for c in range(M):
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canvas = self.bgplvms[c].kern.plot_ARD(title=titles[c], figure=fig, col=c+1, **kwargs)
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plots[titles[c]] = canvas
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pl().show_canvas(canvas)
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return plots
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def plot_latent(self, 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=True, legend=True,
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resolution=60, legend=True,
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plot_limits=None,
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aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
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updates=False,
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kern=None, marker='<>^vsd',
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num_samples=1000, projection='2d',
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predict_kwargs={},
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scatter_kwargs=None, **imshow_kwargs):
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"""
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see plotting.matplot_dep.dim_reduction_plots.plot_latent
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if predict_kwargs is None, will plot latent spaces for 0th dataset (and kernel), otherwise give
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predict_kwargs=dict(Yindex='index') for plotting only the latent space of dataset with 'index'.
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"""
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from matplotlib import pyplot as plt
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from ..plotting.matplot_dep import dim_reduction_plots
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from ..plotting.gpy_plot.latent_plots import plot_latent
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if "Yindex" not in predict_kwargs:
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predict_kwargs['Yindex'] = 0
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Yindex = predict_kwargs['Yindex']
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if ax is None:
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fig = plt.figure(num=fignum)
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ax = fig.add_subplot(111)
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else:
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fig = ax.figure
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self.kern = self.bgplvms[Yindex].kern
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self.likelihood = self.bgplvms[Yindex].likelihood
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plot = dim_reduction_plots.plot_latent(self, labels, which_indices,
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resolution, ax, marker, s,
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fignum, plot_inducing, legend,
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plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
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ax.set_title(self.bgplvms[Yindex].name)
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try:
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fig.tight_layout()
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except:
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pass
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return plot
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return plot_latent(self, labels, which_indices, resolution, legend, plot_limits, updates, kern, marker, num_samples, projection, scatter_kwargs)
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def __getstate__(self):
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state = super(MRD, self).__getstate__()
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@ -25,18 +25,66 @@ def change_plotting_library(lib):
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current_lib[0] = PlotlyPlots()
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if lib == 'none':
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current_lib[0] = None
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inject_plotting()
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#===========================================================================
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except (ImportError, NameError):
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config.set('plotting', 'library', 'none')
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raise
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import warnings
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warnings.warn(ImportWarning("You spevified {} in your configuration, but is not available. Install newest version of {} for plotting".format(lib, lib)))
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from ..util.config import config, NoOptionError
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try:
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lib = config.get('plotting', 'library')
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change_plotting_library(lib)
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except NoOptionError:
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print("No plotting library was specified in config file. \n{}".format(error_suggestion))
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def inject_plotting():
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if current_lib[0] is not None:
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# Inject the plots into classes here:
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# Already converted to new style:
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from . import gpy_plot
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from ..core import GP
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GP.plot_data = gpy_plot.data_plots.plot_data
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GP.plot_data_error = gpy_plot.data_plots.plot_data_error
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GP.plot_errorbars_trainset = gpy_plot.data_plots.plot_errorbars_trainset
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GP.plot_mean = gpy_plot.gp_plots.plot_mean
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GP.plot_confidence = gpy_plot.gp_plots.plot_confidence
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GP.plot_density = gpy_plot.gp_plots.plot_density
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GP.plot_samples = gpy_plot.gp_plots.plot_samples
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GP.plot = gpy_plot.gp_plots.plot
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GP.plot_f = gpy_plot.gp_plots.plot_f
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GP.plot_magnification = gpy_plot.latent_plots.plot_magnification
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from ..core import SparseGP
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SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
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from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
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GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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from ..kern import Kern
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Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
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def deprecate_plot(self, *args, **kwargs):
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import warnings
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warnings.warn(DeprecationWarning('Kern.plot is being deprecated and will not be available in the 1.0 release. Use Kern.plot_covariance instead'))
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return self.plot_covariance(*args, **kwargs)
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Kern.plot = deprecate_plot
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Kern.plot_ARD = gpy_plot.kernel_plots.plot_ARD
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from ..inference.optimization import Optimizer
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Optimizer.plot = gpy_plot.inference_plots.plot_optimizer
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# Variational plot!
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def plotting_library():
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if current_lib[0] is None:
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@ -53,54 +101,10 @@ def show(figure, **kwargs):
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"""
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return plotting_library().show_canvas(figure, **kwargs)
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if config.get('plotting', 'library') is not 'none':
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# Inject the plots into classes here:
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# Already converted to new style:
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from . import gpy_plot
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from ..core import GP
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GP.plot_data = gpy_plot.data_plots.plot_data
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GP.plot_data_error = gpy_plot.data_plots.plot_data_error
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GP.plot_errorbars_trainset = gpy_plot.data_plots.plot_errorbars_trainset
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GP.plot_mean = gpy_plot.gp_plots.plot_mean
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GP.plot_confidence = gpy_plot.gp_plots.plot_confidence
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GP.plot_density = gpy_plot.gp_plots.plot_density
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GP.plot_samples = gpy_plot.gp_plots.plot_samples
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GP.plot = gpy_plot.gp_plots.plot
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GP.plot_f = gpy_plot.gp_plots.plot_f
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GP.plot_magnification = gpy_plot.latent_plots.plot_magnification
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from ..core import SparseGP
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SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
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from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
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GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
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SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
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SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
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SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
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from ..kern import Kern
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Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
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def deprecate_plot(self, *args, **kwargs):
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import warnings
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warnings.warn(DeprecationWarning('Kern.plot is being deprecated and will not be available in the 1.0 release. Use Kern.plot_covariance instead'))
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return self.plot_covariance(*args, **kwargs)
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Kern.plot = deprecate_plot
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Kern.plot_ARD = gpy_plot.kernel_plots.plot_ARD
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from ..inference.optimization import Optimizer
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Optimizer.plot = gpy_plot.inference_plots.plot_optimizer
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# Variational plot!
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from ..util.config import config, NoOptionError
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try:
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lib = config.get('plotting', 'library')
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change_plotting_library(lib)
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except NoOptionError:
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print("No plotting library was specified in config file. \n{}".format(error_suggestion))
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@ -33,7 +33,7 @@ from .. import Tango
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from .plot_util import update_not_existing_kwargs, helper_for_plot_data
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from ...kern.src.kern import Kern, CombinationKernel
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def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
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def plot_ARD(kernel, filtering=None, legend=False, canvas=None, **kwargs):
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"""
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If an ARD kernel is present, plot a bar representation using matplotlib
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@ -62,7 +62,11 @@ def plot_ARD(kernel, filtering=None, legend=False, **kwargs):
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bars = []
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kwargs = update_not_existing_kwargs(kwargs, pl().defaults.ard)
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canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
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if canvas is None:
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canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='sensitivity', **kwargs)
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for i in range(ard_params.shape[0]):
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if parts[i].name in filtering:
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c = Tango.nextMedium()
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@ -96,7 +100,7 @@ def plot_covariance(kernel, x=None, label=None,
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"""
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X = np.ones((2, kernel._effective_input_dim)) * [[-3], [3]]
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_, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
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from numbers import Number
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if x is None:
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from ...kern.src.stationary import Stationary
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@ -104,7 +108,7 @@ def plot_covariance(kernel, x=None, label=None,
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elif isinstance(x, Number):
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x = np.ones((1, kernel._effective_input_dim))*x
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K = kernel.K(Xgrid, x)
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if projection == '3d':
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xlabel = 'X[:,0]'
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ylabel = 'X[:,1]'
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@ -42,10 +42,11 @@ class MatplotlibPlots(AbstractPlottingLibrary):
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super(MatplotlibPlots, self).__init__()
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self._defaults = defaults.__dict__
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def figure(self, rows=1, cols=1, **kwargs):
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fig = plt.figure(**kwargs)
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def figure(self, rows=1, cols=1, gridspec_kwargs={}, tight_layout=True, **kwargs):
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fig = plt.figure(tight_layout=tight_layout, **kwargs)
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fig.rows = rows
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fig.cols = cols
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fig.gridspec = plt.GridSpec(rows, cols, **gridspec_kwargs)
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return fig
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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):
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@ -56,7 +57,9 @@ class MatplotlibPlots(AbstractPlottingLibrary):
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if 'ax' in kwargs:
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ax = kwargs.pop('ax')
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else:
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if 'num' in kwargs and 'figsize' in kwargs:
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if figure is not None:
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fig = figure
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||||
elif 'num' in kwargs and 'figsize' in kwargs:
|
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fig = self.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize'))
|
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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
|
||||
|
||||
|
|
|
|||
|
|
@ -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')
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,9 @@
|
|||
# Copyright (c) 2014, Max Zwiessele
|
||||
# Copyright (c) 2014, Max Zwiessele, GPy Authors
|
||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
"""
|
||||
|
||||
MaxZ
|
||||
|
||||
"""
|
||||
import unittest
|
||||
import sys
|
||||
|
||||
def deepTest(reason):
|
||||
if reason:
|
||||
return lambda x:x
|
||||
return unittest.skip("Not deep scanning, enable deepscan by adding 'deep' argument")
|
||||
return unittest.skip("Not deep scanning, enable deepscan by adding 'deep' argument to unittest call")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue