def pairs_lines(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
ylabels = {},
legend=True,
**kwargs):
'''
Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
lines multiplot. It shows the behavior of two outcomes over time against
each other. The origin is denoted with a circle and the end is denoted
with a '+'.
:param results: return from perform_experiments.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictionary where the
key denotes the name of the group.
:param ylabels: ylabels is a dictionary with the outcome names as keys, the
specified values will be used as labels for the y axis.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend.
:param point_in_time: the point in time at which the scatter is to be made.
If None is provided, the end states are used.
point_in_time should be a valid value on time
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
and a dict with the individual axes.
'''
#unravel return from run_experiments
debug("making a pars lines plot")
prepared_data = prepare_pairs_data(results, outcomes_to_show, group_by,
grouping_specifiers, None)
outcomes, outcomes_to_show, grouping_labels = prepared_data
grid = gridspec.GridSpec(len(outcomes_to_show), len(outcomes_to_show))
grid.update(wspace = 0.1,
hspace = 0.1)
#the plotting
figure = plt.figure()
axes_dict = {}
if group_by and legend:
make_legend(grouping_labels, figure)
combis = [(field1, field2) for field1 in outcomes_to_show\
for field2 in outcomes_to_show]
for field1, field2 in combis:
i = outcomes_to_show.index(field1)
j = outcomes_to_show.index(field2)
ax = figure.add_subplot(grid[i,j])
axes_dict[(field1, field2)] = ax
if group_by:
for x, entry in enumerate(grouping_labels):
data1 = outcomes[entry][field1]
data2 = outcomes[entry][field2]
color = COLOR_LIST[x]
if i==j:
color = 'white'
simple_pairs_lines(ax, data1, data2, color)
else:
data1 = outcomes[field1]
data2 = outcomes[field2]
color = 'b'
if i==j:
color = 'white'
simple_pairs_lines(ax, data1, data2, color)
do_text_ticks_labels(ax, i, j, field1, field2, ylabels,
outcomes_to_show)
return figure, axes_dict
def pairs_scatter(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
ylabels = {},
legend=True,
point_in_time=-1,
filter_scalar=True,
**kwargs):
'''
Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
scatter multiplot. In case of time-series data, the end states are used.
:param results: return from perform_experiments.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictionary where the
key denotes the name of the group.
:param ylabels: ylabels is a dictionary with the outcome names as keys, the
specified values will be used as labels for the y axis.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend.
:param point_in_time: the point in time at which the scatter is to be made.
If None is provided, the end states are used.
point_in_time should be a valid value on time
:param filter_scalar: boolean, remove the non-time-series outcomes.
Defaults to True.
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
and a dict with the individual axes.
.. note:: the current implementation is limited to seven different
categories in case of column, categories, and/or discretesize.
This limit is due to the colors specified in COLOR_LIST.
'''
debug("generating pairwise scatter plot")
prepared_data = prepare_pairs_data(results, outcomes_to_show, group_by,
grouping_specifiers, point_in_time,
filter_scalar)
outcomes, outcomes_to_show, grouping_labels = prepared_data
grid = gridspec.GridSpec(len(outcomes_to_show), len(outcomes_to_show))
grid.update(wspace = 0.1,
hspace = 0.1)
#the plotting
figure = plt.figure()
axes_dict = {}
if group_by and legend:
make_legend(grouping_labels, figure, legend_type='scatter')
combis = [(field1, field2) for field1 in outcomes_to_show\
for field2 in outcomes_to_show]
for field1, field2 in combis:
i = outcomes_to_show.index(field1)
j = outcomes_to_show.index(field2)
ax = figure.add_subplot(grid[i,j])
axes_dict[(field1, field2)] = ax
if group_by:
for x, group in enumerate(grouping_labels):
y_data = outcomes[group][field1]
x_data = outcomes[group][field2]
facecolor = COLOR_LIST[x]
edgecolor = 'k'
if i==j:
facecolor = 'white'
edgecolor = 'white'
ax.scatter(x_data, y_data,
facecolor=facecolor, edgecolor=edgecolor)
else:
y_data = outcomes[field1]
x_data = outcomes[field2]
facecolor = 'b'
edgecolor = 'k'
if i==j:
facecolor = 'white'
edgecolor = 'white'
ax.scatter(x_data, y_data,
facecolor=facecolor, edgecolor=edgecolor)
do_text_ticks_labels(ax, i, j, field1, field2, ylabels,
outcomes_to_show)
return figure, axes_dict
def pairs_density(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
ylabels = {},
point_in_time=-1,
log=True,
gridsize=50,
colormap='jet',
filter_scalar=True):
'''
Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
hexbin density multiplot. In case of time-series data, the end states are
used.
hexbin makes hexagonal binning plot of x versus y, where x, y are 1-D
sequences of the same length, N. If C is None (the default), this is a
histogram of the number of occurences of the observations at (x[i],y[i]).
For further detail see `matplotlib on hexbin <http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.hexbin>`_
:param results: return from perform_experiments.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictionary where the
key denotes the name of the group.
:param ylabels: ylabels is a dictionary with the outcome names as keys, the
specified values will be used as labels for the y axis.
:param point_in_time: the point in time at which the scatter is to be made.
If None is provided, the end states are used.
point_in_time should be a valid value on time
:param log: boolean, indicating whether density should be log scaled.
Defaults to True.
:param gridsize: controls the gridsize for the hexagonal binning
:param cmap: color map that is to be used in generating the hexbin. For
details on the available maps,
see `pylab <http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html#pylab-examples-show-colormaps>`_.
(Defaults = jet)
:param filter_scalar: boolean, remove the non-time-series outcomes.
Defaults to True.
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
and a dict with the individual axes.
'''
debug("generating pairwise density plot")
prepared_data = prepare_pairs_data(results, outcomes_to_show, group_by,
grouping_specifiers, point_in_time,
filter_scalar)
outcomes, outcomes_to_show, grouping_specifiers = prepared_data
if group_by:
#figure out the extents for each combination
extents = determine_extents(outcomes, outcomes_to_show)
axes_dicts = {}
figures = []
for key, value in outcomes.items():
figure, axes_dict = simple_pairs_density(value, outcomes_to_show,
log, colormap, gridsize, ylabels,
extents=extents, title=key)
axes_dicts[key] = axes_dict
figures.append(figure)
# harmonize the color scaling across figures
combis = [(field1, field2) for field1 in outcomes_to_show\
for field2 in outcomes_to_show]
for combi in combis:
vmax = -1
for entry in axes_dicts.values():
vmax = max(entry[combi].collections[0].norm.vmax, vmax)
for entry in axes_dicts.values():
ax = entry[combi]
ax.collections[0].set_clim(vmin=0, vmax=vmax)
del vmax
return figures, axes_dicts
else:
return simple_pairs_density(outcomes, outcomes_to_show, log, colormap,
gridsize, ylabels)
