def _search_plot_cmd(
path, y_field, x_field, groupby, spread_measure,
style, do_legend=False, **axes_kwargs):
path = process_path(path)
print("Plotting searches stored at {}.".format(path))
search = HyperSearch(path)
with plt.style.context(style):
ax = plt.axes(xlabel=x_field, ylabel=y_field, **axes_kwargs)
dist = search.objects.load_object('metadata', 'dist')
dist = Config(dist)
df = search.extract_summary_data()
groups = sorted(df.groupby(groupby))
colours = plt.rcParams['axes.prop_cycle'].by_key()['color']
legend = []
for i, (k, _df) in enumerate(groups):
values = list(_df.groupby(x_field))
x = [v[0] for v in values]
ys = [v[1][y_field] for v in values]
y = [_y.mean() for _y in ys]
if spread_measure == 'std_dev':
y_upper = y_lower = [_y.std() for _y in ys]
elif spread_measure == 'conf_int':
conf_int = [confidence_interval(_y.values, 0.95) for _y in ys]
y_lower = y - np.array([ci[0] for ci in conf_int])
y_upper = np.array([ci[1] for ci in conf_int]) - y
elif spread_measure == 'std_err':
y_upper = y_lower = [standard_error(_y.values) for _y in ys]
else:
raise Exception("NotImplemented")
yerr = np.vstack((y_lower, y_upper))
c = colours[i % len(colours)]
ax.semilogx(x, y, c=c, basex=2)
handle = ax.errorbar(x, y, yerr=yerr, c=c)
label = "{} = {}".format(groupby, k)
legend.append((handle, label))
if do_legend:
handles, labels = zip(*legend)
ax.legend(
handles, labels, loc='center left',
bbox_to_anchor=(1.05, 0.5), ncol=1)
# plt.subplots_adjust(
# left=0.09, bottom=0.13, right=0.7, top=0.93, wspace=0.05, hspace=0.18)
filename = "value_plot.pdf"
print("Saving plot as {}".format(filename))
plt.savefig(filename)
def ci95(ys):
conf_int = [confidence_interval(_y, 0.95) for _y in ys]
y = ys.mean(axis=1)
y_lower = y - np.array([ci[0] for ci in conf_int])
y_upper = np.array([ci[1] for ci in conf_int]) - y
return y_upper, y_lower