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[plotting] added samples plot

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mzwiessele 2015-10-03 21:14:32 +01:00
parent 57c4306d92
commit d7b5f45b71
7 changed files with 118 additions and 31 deletions
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8
GPy/plotting/gpy_plot/data_plots.py
View file

@ -179,8 +179,12 @@ def _plot_errorbars_trainset(self, canvas,
if len(free_dims)<2:
if len(free_dims)==1:
update_not_existing_kwargs(plot_kwargs, pl.defaults.yerrorbar)
_, percs = helper_predict_with_model(self, Xgrid, plot_raw,
apply_link, (2.5, 97.5),
if predict_kw is None:
predict_kw = {}
if 'Y_metadata' not in predict_kw:
predict_kw['Y_metadata'] = self.Y_metadata or {}
_, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw,
apply_link, (0, 100),
ycols, predict_kw)
for d in ycols:
plots.append(pl.yerrorbar(canvas, X[rows,free_dims[0]], Y[rows,d],

71
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -37,7 +37,7 @@ from .plot_util import helper_for_plot_data, update_not_existing_kwargs, \
def plot_mean(self, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=False,
Y_metadata=None, apply_link=False,
apply_link=False,
which_data_ycols='all',
levels=20,
predict_kw=None,
@ -62,22 +62,21 @@ def plot_mean(self, plot_limits=None, fixed_inputs=None,
"""
canvas, kwargs = pl.get_new_canvas(kwargs)
plots = _plot_mean(self, canvas, plot_limits, fixed_inputs,
resolution, plot_raw, Y_metadata,
resolution, plot_raw,
apply_link, which_data_ycols, levels,
predict_kw, **kwargs)
return pl.show_canvas(canvas, plots)
@wraps(plot_mean)
def _plot_mean(self, canvas, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=False,
Y_metadata=None, apply_link=False,
apply_link=False,
which_data_ycols=None,
levels=20,
predict_kw=None, **kwargs):
_, _, _, _, free_dims, Xgrid, x, y, _, _, resolution = helper_for_plot_data(self, plot_limits, fixed_inputs, resolution)
if len(free_dims)<=2:
mu, _ = helper_predict_with_model(self, Xgrid, plot_raw,
mu, _, _ = helper_predict_with_model(self, Xgrid, plot_raw,
apply_link, None,
get_which_data_ycols(self, which_data_ycols),
predict_kw)
@ -141,7 +140,7 @@ def _plot_confidence(self, canvas, lower, upper, plot_limits=None, fixed_inputs=
if len(free_dims)<=1:
if len(free_dims)==1:
_, percs = helper_predict_with_model(self, Xgrid, plot_raw, apply_link,
_, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw, apply_link,
(lower, upper),
ycols, predict_kw)
@ -155,11 +154,63 @@ def _plot_confidence(self, canvas, lower, upper, plot_limits=None, fixed_inputs=
raise RuntimeError('Can only plot confidence interval in one input dimension')
def plot_samples(self, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=True,
apply_link=False,
which_data_ycols='all',
samples=3, predict_kw=None,
**kwargs):
"""
Plot the mean of the GP.
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.
:type fixed_inputs: a list of tuples
:param int resolution: The resolution of the prediction [defaults are 1D:200, 2D:50]
:param bool plot_raw: plot the latent function (usually denoted f) only? This is usually what you want!
:param bool apply_link: whether to apply the link function of the GP to the raw prediction.
:param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
:param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here
:param int levels: for 2D plotting, the number of contour levels to use is
"""
canvas, kwargs = pl.get_new_canvas(kwargs)
plots = _plot_samples(self, canvas, plot_limits, fixed_inputs,
resolution, plot_raw,
apply_link, which_data_ycols, samples,
predict_kw, **kwargs)
return pl.show_canvas(canvas, plots)
def _plot_samples(self, canvas, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=False,
apply_link=False,
which_data_ycols=None,
samples=3,
predict_kw=None, **kwargs):
_, _, _, _, free_dims, Xgrid, x, y, _, _, resolution = helper_for_plot_data(self, plot_limits, fixed_inputs, resolution)
if len(free_dims)<2:
if len(free_dims)==1:
# 1D plotting:
_, _, samples = helper_predict_with_model(self, Xgrid, plot_raw, apply_link,
None, get_which_data_ycols(self, which_data_ycols), predict_kw, samples)
update_not_existing_kwargs(kwargs, pl.defaults.samples_1d)
return dict(gpmean=[pl.plot(canvas, Xgrid[:, free_dims], samples, **kwargs)])
else:
pass # Nothing to plot!
else:
raise RuntimeError('Cannot plot mean in more then 1 input dimensions')
def plot_density(self, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=False,
apply_link=False,
which_data_ycols='all',
levels=20,
levels=35,
predict_kw=None,
**kwargs):
"""
@ -178,7 +229,7 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
:param dict Y_metadata: the Y_metadata (for e.g. heteroscedastic GPs)
:param bool apply_link: whether to apply the link function of the GP to the raw prediction.
:param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
:param int levels: the number of levels in the density (number between 1 and 50, where 50 is very smooth and 1 is the same as plot_confidence)
:param int levels: the number of levels in the density (number bigger then 1, where 35 is smooth and 1 is the same as plot_confidence). You can go higher then 50 if the result is not smooth enough for you.
:param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here
"""
canvas, kwargs = pl.get_new_canvas(kwargs)
@ -193,7 +244,7 @@ def _plot_density(self, canvas, plot_limits=None, fixed_inputs=None,
resolution=None, plot_raw=False,
apply_link=False,
which_data_ycols=None,
levels=20,
levels=35,
predict_kw=None, **kwargs):
_, _, _, _, free_dims, Xgrid, x, y, _, _, resolution = helper_for_plot_data(self, plot_limits, fixed_inputs, resolution)
@ -203,7 +254,7 @@ def _plot_density(self, canvas, plot_limits=None, fixed_inputs=None,
if len(free_dims)<=1:
if len(free_dims)==1:
_, percs = helper_predict_with_model(self, Xgrid, plot_raw,
_, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw,
apply_link, np.linspace(2.5, 97.5, levels*2),
get_which_data_ycols(self, which_data_ycols),
predict_kw)

21
GPy/plotting/gpy_plot/plot_util.py
View file

@ -31,7 +31,7 @@
import numpy as np
from scipy import sparse
def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, which_data_ycols, predict_kw):
def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, which_data_ycols, predict_kw, samples=0):
"""
Make the right decisions for prediction with a model
based on the standard arguments of plotting.
@ -46,12 +46,12 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
if plot_raw:
from ...likelihoods import Gaussian
from ...likelihoods.link_functions import Identity
lik = Gaussian(Identity(), 0) # Make the likelihood not add any noise
lik = Gaussian(Identity(), 1e-9) # Make the likelihood not add any noise
else:
lik = None
predict_kw['likelihood'] = lik
if 'Y_metadata' not in predict_kw:
predict_kw['Y_metadata'] = self.Y_metadata or {}
predict_kw['Y_metadata'] = {}
if 'output_index' not in predict_kw['Y_metadata']:
predict_kw['Y_metadata']['output_index'] = Xgrid[:,-1:].astype(np.int)
@ -61,6 +61,12 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
percentiles = self.predict_quantiles(Xgrid, quantiles=percentiles, **predict_kw)
else: percentiles = []
if samples > 0:
fsamples = self.posterior_samples(Xgrid, full_cov=True, size=samples, **predict_kw)
fsamples = fsamples[which_data_ycols] if fsamples.ndim == 3 else fsamples
else:
fsamples = None
# Filter out the ycolums which we want to plot:
retmu = mu[:, which_data_ycols]
percs = [p[:, which_data_ycols] for p in percentiles]
@ -70,8 +76,13 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
retmu[:, [i]] = self.likelihood.gp_link.transf(mu[:, [i]])
for perc in percs:
perc[:, [i]] = self.likelihood.gp_link.transf(perc[:, [i]])
return retmu, percs
if fsamples is not None and fsamples.ndim == 3:
for s in range(fsamples.shape[-1]):
fsamples[i, :, s] = self.likelihood.gp_link.transf(fsamples[i, :, s])
elif fsamples is not None:
for s in range(fsamples.shape[-1]):
fsamples[:, s] = self.likelihood.gp_link.transf(fsamples[:, s])
return retmu, percs, fsamples
def helper_for_plot_data(self, plot_limits, fixed_inputs, resolution):
"""