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[kernel] plot_ard added (some other fixes as well)
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Max Zwiessele
parent
e3617942d4
commit
d2d8a62d2d
14 changed files with 371 additions and 337 deletions
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@ -1,21 +1,21 @@
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#===============================================================================
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# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
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# All rights reserved.
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#
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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#
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# * Neither the name of GPy nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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@ -28,7 +28,8 @@
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#===============================================================================
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import numpy as np
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from . import pl
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from . import plotting_library as pl
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#from .. import gpy_plot
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from .plot_util import get_x_y_var, get_free_dims, get_which_data_ycols,\
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get_which_data_rows, update_not_existing_kwargs, helper_predict_with_model
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@ -51,12 +52,12 @@ def plot_data(self, which_data_rows='all',
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:param {'2d','3d'} projection: whether to plot in 2d or 3d. This only applies when plotting two dimensional inputs!
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:param str label: the label for the plot
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:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
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:returns list: of plots created.
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"""
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canvas, plot_kwargs = pl.new_canvas(projection=projection, **plot_kwargs)
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canvas, plot_kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
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plots = _plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, label, **plot_kwargs)
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return pl.add_to_canvas(canvas, plots)
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return pl().add_to_canvas(canvas, plots)
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def _plot_data(self, canvas, which_data_rows='all',
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which_data_ycols='all', visible_dims=None,
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@ -66,26 +67,26 @@ def _plot_data(self, canvas, which_data_rows='all',
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X, _, Y = get_x_y_var(self)
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free_dims = get_free_dims(self, visible_dims, None)
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plots = {}
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plots['dataplot'] = []
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#one dimensional plotting
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if len(free_dims) == 1:
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for d in ycols:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.data_1d) # @UndefinedVariable
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plots['dataplot'].append(pl.scatter(canvas, X[rows, free_dims], Y[rows, d], label=label, **plot_kwargs))
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update_not_existing_kwargs(plot_kwargs, pl().defaults.data_1d) # @UndefinedVariable
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plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims], Y[rows, d], label=label, **plot_kwargs))
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#2D plotting
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elif len(free_dims) == 2:
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if projection=='2d':
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for d in ycols:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.data_2d) # @UndefinedVariable
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plots['dataplot'].append(pl.scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
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update_not_existing_kwargs(plot_kwargs, pl().defaults.data_2d) # @UndefinedVariable
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plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
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color=Y[rows, d], label=label, **plot_kwargs))
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else:
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for d in ycols:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.data_2d) # @UndefinedVariable
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plots['dataplot'].append(pl.scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
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update_not_existing_kwargs(plot_kwargs, pl().defaults.data_2d) # @UndefinedVariable
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plots['dataplot'].append(pl().scatter(canvas, X[rows, free_dims[0]], X[rows, free_dims[1]],
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Z=Y[rows, d], color=Y[rows, d], label=label, **plot_kwargs))
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elif len(free_dims) == 0:
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pass #Nothing to plot!
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@ -98,7 +99,7 @@ def plot_data_error(self, which_data_rows='all',
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projection='2d', label=None, **error_kwargs):
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"""
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Plot the training data input error.
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For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
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Can plot only part of the data
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@ -114,12 +115,12 @@ def plot_data_error(self, which_data_rows='all',
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:param dict error_kwargs: kwargs for the error plot for the plotting library you are using
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:param str label: the label for the plot
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:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
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:returns list: of plots created.
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"""
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canvas, error_kwargs = pl.new_canvas(projection=projection, **error_kwargs)
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canvas, error_kwargs = pl().new_canvas(projection=projection, **error_kwargs)
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plots = _plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, label, **error_kwargs)
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return pl.add_to_canvas(canvas, plots)
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return pl().add_to_canvas(canvas, plots)
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def _plot_data_error(self, canvas, which_data_rows='all',
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which_data_ycols='all', visible_dims=None,
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@ -129,27 +130,27 @@ def _plot_data_error(self, canvas, which_data_rows='all',
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X, X_variance, Y = get_x_y_var(self)
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free_dims = get_free_dims(self, visible_dims, None)
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plots = {}
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if X_variance is not None:
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plots['xerrorplot'] = []
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if X_variance is not None:
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plots['xerrorplot'] = []
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#one dimensional plotting
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if len(free_dims) == 1:
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for d in ycols:
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update_not_existing_kwargs(error_kwargs, pl.defaults.xerrorbar)
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plots['xerrorplot'].append(pl.xerrorbar(canvas, X[rows, free_dims].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims].flatten()), label=label,
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update_not_existing_kwargs(error_kwargs, pl().defaults.xerrorbar)
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plots['xerrorplot'].append(pl().xerrorbar(canvas, X[rows, free_dims].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims].flatten()), label=label,
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**error_kwargs))
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#2D plotting
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elif len(free_dims) == 2:
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update_not_existing_kwargs(error_kwargs, pl.defaults.xerrorbar) # @UndefinedVariable
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update_not_existing_kwargs(error_kwargs, pl().defaults.xerrorbar) # @UndefinedVariable
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for d in ycols:
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plots['xerrorplot'].append(pl.xerrorbar(canvas, X[rows, free_dims[0]].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims[0]].flatten()), label=label,
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plots['xerrorplot'].append(pl().xerrorbar(canvas, X[rows, free_dims[0]].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims[0]].flatten()), label=label,
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**error_kwargs))
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plots['yerrorplot'].append(pl.xerrorbar(canvas, X[rows, free_dims[1]].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims[1]].flatten()), label=label,
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plots['yerrorplot'].append(pl().xerrorbar(canvas, X[rows, free_dims[1]].flatten(), Y[rows, d].flatten(),
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2 * np.sqrt(X_variance[rows, free_dims[1]].flatten()), label=label,
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**error_kwargs))
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elif len(free_dims) == 0:
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pass #Nothing to plot!
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@ -161,13 +162,13 @@ def _plot_data_error(self, canvas, which_data_rows='all',
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def plot_inducing(self, visible_dims=None, projection='2d', label=None, **plot_kwargs):
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"""
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Plot the inducing inputs of a sparse gp model
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:param array-like visible_dims: an array specifying the input dimensions to plot (maximum two)
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:param kwargs plot_kwargs: keyword arguments for the plotting library
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"""
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canvas, kwargs = pl.new_canvas(projection=projection, **plot_kwargs)
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canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
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plots = _plot_inducing(self, canvas, visible_dims, projection, label, **kwargs)
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return pl.add_to_canvas(canvas, plots)
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return pl().add_to_canvas(canvas, plots)
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def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs):
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if visible_dims is None:
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@ -180,15 +181,15 @@ def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs)
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#one dimensional plotting
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if len(free_dims) == 1:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.inducing_1d) # @UndefinedVariable
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plots['inducing'] = pl.plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
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update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_1d) # @UndefinedVariable
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plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
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#2D plotting
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elif len(free_dims) == 2 and projection == '3d':
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update_not_existing_kwargs(plot_kwargs, pl.defaults.inducing_3d) # @UndefinedVariable
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plots['inducing'] = pl.plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
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update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_3d) # @UndefinedVariable
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plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
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elif len(free_dims) == 2:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.inducing_2d) # @UndefinedVariable
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plots['inducing'] = pl.scatter(canvas, Z[:, free_dims[0]], Z[:, free_dims[1]],
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update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_2d) # @UndefinedVariable
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plots['inducing'] = pl().scatter(canvas, Z[:, free_dims[0]], Z[:, free_dims[1]],
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**plot_kwargs)
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elif len(free_dims) == 0:
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pass #Nothing to plot!
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@ -197,18 +198,18 @@ def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs)
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return plots
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def plot_errorbars_trainset(self, which_data_rows='all',
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which_data_ycols='all', fixed_inputs=None,
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which_data_ycols='all', fixed_inputs=None,
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plot_raw=False, apply_link=False, label=None, projection='2d',
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predict_kw=None, **plot_kwargs):
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"""
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Plot the errorbars of the GP likelihood on the training data.
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These are the errorbars after the appropriate
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These are the errorbars after the appropriate
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approximations according to the likelihood are done.
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This also works for heteroscedastic likelihoods.
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Give the Y_metadata in the predict_kw if you need it.
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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@ -217,13 +218,13 @@ def plot_errorbars_trainset(self, which_data_rows='all',
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:param dict predict_kwargs: kwargs for the prediction used to predict the right quantiles.
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:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
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"""
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canvas, kwargs = pl.new_canvas(projection=projection, **plot_kwargs)
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plots = _plot_errorbars_trainset(self, canvas, which_data_rows, which_data_ycols,
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canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
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plots = _plot_errorbars_trainset(self, canvas, which_data_rows, which_data_ycols,
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fixed_inputs, plot_raw, apply_link, label, projection, predict_kw, **kwargs)
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return pl.add_to_canvas(canvas, plots)
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return pl().add_to_canvas(canvas, plots)
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def _plot_errorbars_trainset(self, canvas,
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which_data_rows='all', which_data_ycols='all',
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def _plot_errorbars_trainset(self, canvas,
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which_data_rows='all', which_data_ycols='all',
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fixed_inputs=None,
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plot_raw=False, apply_link=False,
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label=None, projection='2d', predict_kw=None, **plot_kwargs):
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@ -232,43 +233,43 @@ def _plot_errorbars_trainset(self, canvas,
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rows = get_which_data_rows(self, which_data_rows)
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X, _, Y = get_x_y_var(self)
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if fixed_inputs is None:
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fixed_inputs = []
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free_dims = get_free_dims(self, None, fixed_inputs)
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free_dims = get_free_dims(self, None, fixed_inputs)
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Xgrid = X.copy()
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for i, v in fixed_inputs:
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Xgrid[:, i] = v
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plots = []
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if len(free_dims)<=2:
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update_not_existing_kwargs(plot_kwargs, pl.defaults.yerrorbar)
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update_not_existing_kwargs(plot_kwargs, pl().defaults.yerrorbar)
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if predict_kw is None:
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predict_kw = {}
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if 'Y_metadata' not in predict_kw:
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predict_kw['Y_metadata'] = self.Y_metadata or {}
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mu, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw,
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apply_link, (2.5, 97.5),
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mu, percs, _ = helper_predict_with_model(self, Xgrid, plot_raw,
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apply_link, (2.5, 97.5),
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ycols, predict_kw)
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if len(free_dims)==1:
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for d in ycols:
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plots.append(pl.yerrorbar(canvas, X[rows,free_dims[0]], mu[rows,d],
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plots.append(pl().yerrorbar(canvas, X[rows,free_dims[0]], mu[rows,d],
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np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
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label=label,
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label=label,
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**plot_kwargs))
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elif len(free_dims) == 2:
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for d in ycols:
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plots.append(pl.yerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
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plots.append(pl().yerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
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np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
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color=Y[rows,d],
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label=label,
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label=label,
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**plot_kwargs))
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plots.append(pl.xerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
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plots.append(pl().xerrorbar(canvas, X[rows,free_dims[0]], X[rows,free_dims[1]],
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np.vstack([mu[rows, d] - percs[0][rows, d], percs[1][rows, d] - mu[rows,d]]),
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color=Y[rows,d],
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label=label,
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label=label,
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**plot_kwargs))
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pass #Nothing to plot!
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else:
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