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import numpy as np
from . import plotting_library as pl
#from .. import gpy_plot
from .plot_util import get_x_y_var, get_free_dims, get_which_data_ycols,\
get_which_data_rows, update_not_existing_kwargs, helper_predict_with_model
[docs]def plot_data(self, which_data_rows='all',
which_data_ycols='all', visible_dims=None,
projection='2d', label=None, **plot_kwargs):
"""
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 which_data_rows: which of the training data to plot (default all)
:type which_data_rows: 'all' or a slice object to slice self.X, self.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 {'2d','3d'} projection: whether to plot in 2d or 3d. This only applies when plotting two dimensional inputs!
:param str label: the label for the plot
:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
:returns list: of plots created.
"""
canvas, plot_kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
plots = _plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, label, **plot_kwargs)
return pl().add_to_canvas(canvas, plots)
def _plot_data(self, canvas, which_data_rows='all',
which_data_ycols='all', visible_dims=None,
projection='2d', label=None, **plot_kwargs):
ycols = get_which_data_ycols(self, which_data_ycols)
rows = get_which_data_rows(self, which_data_rows)
X, _, Y = get_x_y_var(self)
free_dims = get_free_dims(self, visible_dims, None)
plots = {}
plots['dataplot'] = []
#one dimensional plotting
if len(free_dims) == 1:
for d in ycols:
update_not_existing_kwargs(plot_kwargs, pl().defaults.data_1d) # @UndefinedVariable
plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims], Y[rows, d], label=label, **plot_kwargs))
#2D plotting
elif len(free_dims) == 2:
if projection=='2d':
for d in ycols:
update_not_existing_kwargs(plot_kwargs, pl().defaults.data_2d) # @UndefinedVariable
plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
color=Y[rows, d], label=label, **plot_kwargs))
else:
for d in ycols:
update_not_existing_kwargs(plot_kwargs, pl().defaults.data_2d) # @UndefinedVariable
plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
Z=Y[rows, d], color=Y[rows, d], label=label, **plot_kwargs))
elif len(free_dims) == 0:
pass #Nothing to plot!
else:
raise NotImplementedError("Cannot plot in more then two dimensions")
return plots
[docs]def plot_data_error(self, which_data_rows='all',
which_data_ycols='all', visible_dims=None,
projection='2d', label=None, **error_kwargs):
"""
Plot the training data input error.
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 self.X, self.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 {'2d','3d'} projection: whether to plot in 2d or 3d. This only applies when plotting two dimensional inputs!
:param dict error_kwargs: kwargs for the error plot for the plotting library you are using
:param str label: the label for the plot
:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
:returns list: of plots created.
"""
canvas, error_kwargs = pl().new_canvas(projection=projection, **error_kwargs)
plots = _plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, label, **error_kwargs)
return pl().add_to_canvas(canvas, plots)
def _plot_data_error(self, canvas, which_data_rows='all',
which_data_ycols='all', visible_dims=None,
projection='2d', label=None, **error_kwargs):
ycols = get_which_data_ycols(self, which_data_ycols)
rows = get_which_data_rows(self, which_data_rows)
X, X_variance, Y = get_x_y_var(self)
free_dims = get_free_dims(self, visible_dims, None)
plots = {}
if X_variance is not None:
plots['input_error'] = []
#one dimensional plotting
if len(free_dims) == 1:
for d in ycols:
update_not_existing_kwargs(error_kwargs, pl().defaults.xerrorbar)
plots['input_error'].append(pl().xerrorbar(canvas, X[rows, free_dims].flatten(), Y[rows, d].flatten(),
2 * np.sqrt(X_variance[rows, free_dims].flatten()), label=label,
**error_kwargs))
#2D plotting
elif len(free_dims) == 2:
update_not_existing_kwargs(error_kwargs, pl().defaults.xerrorbar) # @UndefinedVariable
plots['input_error'].append(pl().xerrorbar(canvas, X[rows, free_dims[0]].flatten(), X[rows, free_dims[1]].flatten(),
2 * np.sqrt(X_variance[rows, free_dims[0]].flatten()), label=label,
**error_kwargs))
plots['input_error'].append(pl().yerrorbar(canvas, X[rows, free_dims[0]].flatten(), X[rows, free_dims[1]].flatten(),
2 * np.sqrt(X_variance[rows, free_dims[1]].flatten()), label=label,
**error_kwargs))
elif len(free_dims) == 0:
pass #Nothing to plot!
else:
raise NotImplementedError("Cannot plot in more then two dimensions")
return plots
[docs]def plot_inducing(self, visible_dims=None, projection='2d', label='inducing', legend=True, **plot_kwargs):
"""
Plot the inducing inputs of a sparse gp model
:param array-like visible_dims: an array specifying the input dimensions to plot (maximum two)
:param kwargs plot_kwargs: keyword arguments for the plotting library
"""
canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
plots = _plot_inducing(self, canvas, visible_dims, projection, label, **kwargs)
return pl().add_to_canvas(canvas, plots, legend=legend)
def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs):
if visible_dims is None:
sig_dims = self.get_most_significant_input_dimensions()
visible_dims = [i for i in sig_dims if i is not None]
free_dims = get_free_dims(self, visible_dims, None)
Z = self.Z.values
plots = {}
#one dimensional plotting
if len(free_dims) == 1:
update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_1d) # @UndefinedVariable
plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], label=label, **plot_kwargs)
#2D plotting
elif len(free_dims) == 2 and projection == '3d':
update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_3d) # @UndefinedVariable
plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], label=label, **plot_kwargs)
elif len(free_dims) == 2:
update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_2d) # @UndefinedVariable
plots['inducing'] = pl().scatter(canvas, Z[:, free_dims[0]], Z[:, free_dims[1]],
label=label, **plot_kwargs)
elif len(free_dims) == 0:
pass #Nothing to plot!
else:
raise NotImplementedError("Cannot plot in more then two dimensions")
return plots
[docs]def plot_errorbars_trainset(self, which_data_rows='all',
which_data_ycols='all', fixed_inputs=None,
plot_raw=False, apply_link=False, label=None, projection='2d',
predict_kw=None, **plot_kwargs):
"""
Plot the errorbars of the GP likelihood on the training data.
These are the errorbars after the appropriate
approximations according to the likelihood are done.
This also works for heteroscedastic likelihoods.
Give the Y_metadata in the predict_kw if you need it.
:param which_data_rows: which of the training data to plot (default all)
:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
: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 dict predict_kwargs: kwargs for the prediction used to predict the right quantiles.
:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
"""
canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
plots = _plot_errorbars_trainset(self, canvas, which_data_rows, which_data_ycols,
fixed_inputs, plot_raw, apply_link, label, projection, predict_kw, **kwargs)
return pl().add_to_canvas(canvas, plots)
def _plot_errorbars_trainset(self, canvas,
which_data_rows='all', which_data_ycols='all',
fixed_inputs=None,
plot_raw=False, apply_link=False,
label=None, projection='2d', predict_kw=None, **plot_kwargs):
ycols = get_which_data_ycols(self, which_data_ycols)
rows = get_which_data_rows(self, which_data_rows)
X, _, Y = get_x_y_var(self)
if fixed_inputs is None:
fixed_inputs = []
free_dims = get_free_dims(self, None, fixed_inputs)
Xgrid = X.copy()
for i, v in fixed_inputs:
Xgrid[:, i] = v
plots = []
if len(free_dims)<=2 and projection=='2d':
update_not_existing_kwargs(plot_kwargs, pl().defaults.yerrorbar)
if predict_kw is None:
predict_kw = {}
if 'Y_metadata' not in predict_kw:
predict_kw['Y_metadata'] = self.Y_metadata or {}
mu, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw,
apply_link, (2.5, 97.5),
ycols, predict_kw)
if len(free_dims)==1:
for d in ycols:
plots.append(pl().yerrorbar(canvas, X[rows,free_dims[0]], mu[rows,d],
np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
label=label,
**plot_kwargs))
# elif len(free_dims) == 2:
# for d in ycols:
# plots.append(pl().yerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
# np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
# #color=Y[rows,d],
# label=label,
# **plot_kwargs))
# plots.append(pl().xerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
# np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
# #color=Y[rows,d],
# label=label,
# **plot_kwargs))
else:
raise NotImplementedError("Cannot plot in more then one dimensions, or 3d")
return dict(yerrorbars=plots)