def create_plot(all_data, raw, x_log, y_log, xn, yn, fn_out, linestyles,
batch):
xm, ym = (metrics[xn], metrics[yn])
# Now generate each plot
handles = []
labels = []
plt.figure(figsize=(12, 9))
for algo in sorted(all_data.keys(), key=lambda x: x.lower()):
xs, ys, ls, axs, ays, als = create_pointset(all_data[algo], xn, yn)
color, faded, linestyle, marker = linestyles[algo]
handle, = plt.plot(xs,
ys,
'-',
label=algo,
color=color,
ms=7,
mew=3,
lw=3,
linestyle=linestyle,
marker=marker)
handles.append(handle)
if raw:
handle2, = plt.plot(axs,
ays,
'-',
label=algo,
color=faded,
ms=5,
mew=2,
lw=2,
linestyle=linestyle,
marker=marker)
labels.append(get_algorithm_name(algo, batch))
if x_log:
plt.gca().set_xscale('log')
if y_log:
plt.gca().set_yscale('log')
plt.gca().set_title(get_plot_label(xm, ym))
plt.gca().set_ylabel(ym['description'])
plt.gca().set_xlabel(xm['description'])
box = plt.gca().get_position()
# plt.gca().set_position([box.x0, box.y0, box.width * 0.8, box.height])
plt.gca().legend(handles,
labels,
loc='center left',
bbox_to_anchor=(1, 0.5),
prop={'size': 9})
plt.grid(b=True, which='major', color='0.65', linestyle='-')
if 'lim' in xm:
plt.xlim(xm['lim'])
if 'lim' in ym:
plt.ylim(ym['lim'])
plt.savefig(fn_out, bbox_inches='tight')
plt.close()
def create_plot(all_data, raw, x_log, y_log, xn, yn, fn_out, linestyles,
batch):
xm, ym = (metrics[xn], metrics[yn])
# Now generate each plot
handles = []
labels = []
plt.figure(figsize=(12, 9))
for algo in sorted(all_data.keys(), key=lambda x: x.lower()):
xs, ys, ls, axs, ays, als = create_pointset(all_data[algo], xn, yn)
color, faded, linestyle, marker = linestyles[algo]
handle, = plt.plot(xs, ys, '-', label=algo, color=color,
ms=7, mew=3, lw=3, linestyle=linestyle,
marker=marker)
handles.append(handle)
if raw:
handle2, = plt.plot(axs, ays, '-', label=algo, color=faded,
ms=5, mew=2, lw=2, linestyle=linestyle,
marker=marker)
labels.append(get_algorithm_name(algo, batch))
if x_log:
plt.gca().set_xscale('log')
if y_log:
plt.gca().set_yscale('log')
plt.gca().set_title(get_plot_label(xm, ym))
plt.gca().set_ylabel(ym['description'])
plt.gca().set_xlabel(xm['description'])
box = plt.gca().get_position()
# plt.gca().set_position([box.x0, box.y0, box.width * 0.8, box.height])
plt.gca().legend(handles, labels, loc='center left',
bbox_to_anchor=(1, 0.5), prop={'size': 9})
plt.grid(b=True, which='major', color='0.65', linestyle='-')
if 'lim' in xm:
plt.xlim(xm['lim'])
if 'lim' in ym:
plt.ylim(ym['lim'])
plt.savefig(fn_out, bbox_inches='tight')
plt.close()
help='Draw the Y-axis using a logarithmic scale',
action='store_true')
parser.add_argument(
'--raw',
help='Show raw results (not just Pareto frontier) in faded colours',
action='store_true')
parser.add_argument('--batch',
help='Plot runs in batch mode',
action='store_true')
parser.add_argument('--recompute',
help='Clears the cache and recomputes the metrics',
action='store_true')
args = parser.parse_args()
if not args.output:
args.output = 'results/%s.png' % get_algorithm_name(
args.dataset, args.batch)
print('writing output to %s' % args.output)
dataset = get_dataset(args.dataset)
count = int(args.count)
unique_algorithms = get_unique_algorithms()
results = load_all_results(args.dataset, count, True, args.batch)
linestyles = create_linestyles(sorted(unique_algorithms))
runs = compute_metrics(dataset, results, args.x_axis, args.y_axis,
args.recompute)
if not runs:
raise Exception('Nothing to plot')
create_plot(runs, args.raw, args.x_log, args.y_log, args.x_axis,
args.y_axis, args.output, linestyles, args.batch)
parser.add_argument(
'--raw',
help='Show raw results (not just Pareto frontier) in faded colours',
action='store_true')
parser.add_argument(
'--batch',
help='Plot runs in batch mode',
action='store_true')
parser.add_argument(
'--recompute',
help='Clears the cache and recomputes the metrics',
action='store_true')
args = parser.parse_args()
if not args.output:
args.output = 'results/%s.png' % get_algorithm_name(
args.dataset, args.batch)
print('writing output to %s' % args.output)
dataset = get_dataset(args.dataset)
count = int(args.count)
unique_algorithms = get_unique_algorithms()
results = load_all_results(args.dataset, count, True, args.batch)
linestyles = create_linestyles(sorted(unique_algorithms))
runs = compute_metrics(np.array(dataset["distances"]),
results, args.x_axis, args.y_axis, args.recompute)
if not runs:
raise Exception('Nothing to plot')
create_plot(runs, args.raw, args.x_log,
args.y_log, args.x_axis, args.y_axis, args.output,
linestyles, args.batch)