mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-18 13:55:14 +02:00
[plotting] cleanup first commit, this cleans the plotting library and adds plotting tests
This commit is contained in:
mzwiessele
parent
fee2f3f727
commit
b9bfd0fc6d
10 changed files with 200 additions and 690 deletions
254
GPy/core/gp.py
254
GPy/core/gp.py
|
|
@ -477,260 +477,6 @@ class GP(Model):
|
|||
Ysim = self.likelihood.samples(fsim, Y_metadata=Y_metadata)
|
||||
return Ysim
|
||||
|
||||
def plot_f(self, plot_limits=None, which_data_rows='all',
|
||||
which_data_ycols='all', fixed_inputs=[],
|
||||
levels=20, samples=0, fignum=None, ax=None, resolution=None,
|
||||
plot_raw=True,
|
||||
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx',
|
||||
apply_link=False):
|
||||
"""
|
||||
Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
|
||||
This is a call to plot with plot_raw=True.
|
||||
Data will not be plotted in this, as the GP's view of the world
|
||||
may live in another space, or units then the data.
|
||||
|
||||
Can plot only part of the data and part of the posterior functions
|
||||
using which_data_rowsm which_data_ycols.
|
||||
|
||||
: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 which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_ycols: 'all' or a list of integers
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param levels: number of levels to plot in a contour plot.
|
||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
||||
:type levels: int
|
||||
:param samples: the number of a posteriori samples to plot
|
||||
:type samples: int
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
|
||||
:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
|
||||
:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param Y_metadata: additional data associated with Y which may be needed
|
||||
:type Y_metadata: dict
|
||||
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
|
||||
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
|
||||
:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*
|
||||
:type apply_link: boolean
|
||||
"""
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import models_plots
|
||||
kw = {}
|
||||
if linecol is not None:
|
||||
kw['linecol'] = linecol
|
||||
if fillcol is not None:
|
||||
kw['fillcol'] = fillcol
|
||||
return models_plots.plot_fit(self, plot_limits, which_data_rows,
|
||||
which_data_ycols, fixed_inputs,
|
||||
levels, samples, fignum, ax, resolution,
|
||||
plot_raw=plot_raw, Y_metadata=Y_metadata,
|
||||
data_symbol=data_symbol, apply_link=apply_link, **kw)
|
||||
|
||||
def plot(self, plot_limits=None, which_data_rows='all',
|
||||
which_data_ycols='all', fixed_inputs=[],
|
||||
levels=20, samples=0, fignum=None, ax=None, resolution=None,
|
||||
plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
|
||||
data_symbol='kx', predict_kw=None, plot_training_data=True, samples_y=0, apply_link=False):
|
||||
"""
|
||||
Plot the posterior of the GP.
|
||||
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
|
||||
- In two dimsensions, a contour-plot shows the mean predicted function
|
||||
- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
|
||||
|
||||
Can plot only part of the data and part of the posterior functions
|
||||
using which_data_rowsm which_data_ycols.
|
||||
|
||||
: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 which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_ycols: 'all' or a list of integers
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param levels: number of levels to plot in a contour plot.
|
||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
||||
:type levels: int
|
||||
:param samples: the number of a posteriori samples to plot, p(f*|y)
|
||||
:type samples: int
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
|
||||
:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
|
||||
:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param Y_metadata: additional data associated with Y which may be needed
|
||||
:type Y_metadata: dict
|
||||
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
|
||||
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
|
||||
:param plot_training_data: whether or not to plot the training points
|
||||
:type plot_training_data: boolean
|
||||
:param samples_y: the number of a posteriori samples to plot, p(y*|y)
|
||||
:type samples_y: int
|
||||
:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
|
||||
:type apply_link: boolean
|
||||
"""
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import models_plots
|
||||
kw = {}
|
||||
if linecol is not None:
|
||||
kw['linecol'] = linecol
|
||||
if fillcol is not None:
|
||||
kw['fillcol'] = fillcol
|
||||
return models_plots.plot_fit(self, plot_limits, which_data_rows,
|
||||
which_data_ycols, fixed_inputs,
|
||||
levels, samples, fignum, ax, resolution,
|
||||
plot_raw=plot_raw, Y_metadata=Y_metadata,
|
||||
data_symbol=data_symbol, predict_kw=predict_kw,
|
||||
plot_training_data=plot_training_data, samples_y=samples_y, apply_link=apply_link, **kw)
|
||||
|
||||
def plot_density(self, levels=20, plot_limits=None, fignum=None, ax=None,
|
||||
fixed_inputs=[], plot_raw=False, edgecolor='none', facecolor='#3465a4',
|
||||
predict_kw=None,Y_metadata=None,
|
||||
apply_link=False, resolution=200, **patch_kw):
|
||||
"""
|
||||
Plot the posterior density of the GP.
|
||||
- In one dimension, the function is plotted with a shaded gradient, visualizing the density of the posterior.
|
||||
- Only implemented for one dimension, for higher dimensions use `plot`.
|
||||
|
||||
:param levels: number of levels to plot in the density plot. This is a number between 1 and 100. 1 corresponds to the normal plot_fit.
|
||||
:type levels: int
|
||||
: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 index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param edgecolor: color of line to plot [Tango.colorsHex['darkBlue']]
|
||||
:type edgecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param facecolor: color of fill [Tango.colorsHex['lightBlue']]
|
||||
:type facecolor: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param Y_metadata: additional data associated with Y which may be needed
|
||||
:type Y_metadata: dict
|
||||
:param apply_link: if there is a link function of the likelihood, plot the link(f*) rather than f*, when plotting posterior samples f
|
||||
:type apply_link: boolean
|
||||
:param resolution: resolution of interpolation (how many points to interpolate of the posterior).
|
||||
:type resolution: int
|
||||
:param: patch_kw: the keyword arguments for the patchcollection fill.
|
||||
"""
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import models_plots
|
||||
return models_plots.plot_density(self, levels, plot_limits, fignum, ax,
|
||||
fixed_inputs, plot_raw=plot_raw,
|
||||
Y_metadata=Y_metadata,
|
||||
predict_kw=predict_kw,
|
||||
apply_link=apply_link,
|
||||
edgecolor=edgecolor, facecolor=facecolor,
|
||||
**patch_kw)
|
||||
|
||||
|
||||
def plot_data(self, which_data_rows='all',
|
||||
which_data_ycols='all', visible_dims=None,
|
||||
fignum=None, ax=None, data_symbol='kx'):
|
||||
"""
|
||||
Plot the training data
|
||||
- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
|
||||
|
||||
Can plot only part of the data
|
||||
using which_data_rows and which_data_ycols.
|
||||
|
||||
: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 which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_ycols: 'all' or a list of integers
|
||||
:param visible_dims: an array specifying the input dimensions to plot (maximum two)
|
||||
:type visible_dims: a numpy array
|
||||
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
|
||||
:type resolution: int
|
||||
:param levels: number of levels to plot in a contour plot.
|
||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
||||
:type levels: int
|
||||
:param samples: the number of a posteriori samples to plot, p(f*|y)
|
||||
:type samples: int
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
|
||||
:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
|
||||
:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
|
||||
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
|
||||
"""
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import models_plots
|
||||
kw = {}
|
||||
return models_plots.plot_data(self, which_data_rows,
|
||||
which_data_ycols, visible_dims,
|
||||
fignum, ax, data_symbol, **kw)
|
||||
|
||||
|
||||
def plot_errorbars_trainset(self, which_data_rows='all',
|
||||
which_data_ycols='all', fixed_inputs=[], fignum=None, ax=None,
|
||||
linecol=None, data_symbol='kx', predict_kw=None, plot_training_data=True,lw=None):
|
||||
|
||||
"""
|
||||
Plot the posterior error bars corresponding to the training data
|
||||
- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
|
||||
|
||||
Can plot only part of the data
|
||||
using which_data_rows and which_data_ycols.
|
||||
|
||||
:param which_data_rows: which of the training data to plot (default all)
|
||||
:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
|
||||
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
|
||||
:type which_data_rows: 'all' or a list of integers
|
||||
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
|
||||
:type fixed_inputs: a list of tuples
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
:param plot_training_data: whether or not to plot the training points
|
||||
:type plot_training_data: boolean
|
||||
"""
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import models_plots
|
||||
kw = {}
|
||||
if lw is not None:
|
||||
kw['lw'] = lw
|
||||
return models_plots.plot_errorbars_trainset(self, which_data_rows, which_data_ycols, fixed_inputs,
|
||||
fignum, ax, linecol, data_symbol,
|
||||
predict_kw, plot_training_data, **kw)
|
||||
|
||||
|
||||
def plot_magnification(self, labels=None, which_indices=None,
|
||||
resolution=50, ax=None, marker='o', s=40,
|
||||
fignum=None, legend=True,
|
||||
plot_limits=None,
|
||||
aspect='auto', updates=False, plot_inducing=True, kern=None, **kwargs):
|
||||
|
||||
import sys
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import dim_reduction_plots
|
||||
|
||||
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
|
||||
resolution, ax, marker, s,
|
||||
fignum, plot_inducing, legend,
|
||||
plot_limits, aspect, updates, **kwargs)
|
||||
|
||||
|
||||
def input_sensitivity(self, summarize=True):
|
||||
"""
|
||||
Returns the sensitivity for each dimension of this model
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue