def build_detail_site(data, label_func, j2_env):
for (name, runs) in data.items():
print("Building '%s'" % name)
all_runs = runs.keys()
linestyles = convert_linestyle(create_linestyles(all_runs))
label = label_func(name)
data = {"normal" : [], "scatter" : []}
for plottype in args.plottype:
xn, yn = plot_variants[plottype]
data["normal"].append(create_plot(runs, xn, yn, linestyles, j2_env))
if args.scatter:
data["scatter"].append(create_plot(runs, xn, yn,
linestyles, j2_env, "Scatterplot ", "bubble"))
# create png plot for summary page
data_for_plot = {}
for k in runs.keys():
data_for_plot[k] = prepare_data(runs[k], 'k-nn', 'qps')
plot.create_plot(data_for_plot, False,
False, True, 'k-nn', 'qps', args.outputdir + name + ".png",
create_linestyles(all_runs))
with open(args.outputdir + name + ".html", "w") as text_file:
text_file.write(j2_env.get_template("detail_page.html").
render(title = label, plot_data = data, args = args))
'--y-log',
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')
args = parser.parse_args()
if not args.output:
args.output = 'results/%s.png' % args.dataset
print('writing output to %s' % args.output)
dataset = get_dataset(args.dataset)
dimension = len(dataset['train'][0]) # TODO(erikbern): ugly
point_type = 'float' # TODO(erikbern): should look at the type of X_train
distance = dataset.attrs['distance']
count = int(args.count)
definitions = get_definitions(args.definitions, dimension, point_type,
distance, count)
unique_algorithms = get_unique_algorithms(args.definitions)
linestyles = create_linestyles(unique_algorithms)
results = load_results(args.dataset, count, definitions)
runs = compute_metrics(list(dataset["distances"]), results, args.x_axis,
args.y_axis)
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)
[]).append(ms)
all_runs_by_dataset[idx].setdefault(sdn, {}).setdefault(algo,
[]).append(ms)
return (all_runs_by_dataset, all_runs_by_algorithm)
j2_env = Environment(loader=FileSystemLoader("./templates/"), trim_blocks=True)
j2_env.globals.update(zip=zip, len=len)
runs_by_ds, runs_by_algo = load_all_results()
dataset_names = [
get_dataset_label(x) for x in list(runs_by_ds['batch'].keys()) +
list(runs_by_ds['non-batch'].keys())
]
algorithm_names = list(runs_by_algo['batch'].keys()) + list(
runs_by_algo['non-batch'].keys())
linestyles = {
**create_linestyles(dataset_names),
**create_linestyles(algorithm_names)
}
build_detail_site(runs_by_ds['non-batch'],
lambda label: get_dataset_label(label), j2_env, linestyles,
False)
build_detail_site(runs_by_ds['batch'], lambda label: get_dataset_label(label),
j2_env, linestyles, True)
build_detail_site(runs_by_algo['non-batch'], lambda x: x, j2_env, linestyles,
False)
build_detail_site(runs_by_algo['batch'], lambda x: x, j2_env, linestyles, True)
build_index_site(runs_by_ds, runs_by_algo, j2_env, "index.html")
if not algo_ds in all_runs_by_algorithm[algo]:
all_runs_by_algorithm[algo][algo_ds] = []
all_runs_by_algorithm[algo][algo_ds].extend(ms)
if not sdn in all_runs_by_dataset:
all_runs_by_dataset[sdn] = {}
dataset_information[sdn] = d
if not algo in all_runs_by_dataset[sdn]:
all_runs_by_dataset[sdn][algo] = []
all_runs_by_dataset[sdn][algo].extend(ms)
del query_cache
# Build a website for each dataset
for (ds, runs) in all_runs_by_dataset.items():
all_algos = runs.keys()
linestyles = convert_linestyle(create_linestyles(all_algos))
output_str = get_html_header(ds)
ds_name = dataset_information[ds]["dataset"] + " (k = " + str(dataset_information[ds]["count"]) + ")"
output_str += """
<div class="container">
<h2>Plots for %(id)s</h2>""" % { "id" : ds_name }
for plottype in args.plottype:
xn, yn = plot_variants[plottype]
print "Processing '%s' with %s" % (ds, plottype)
output_str += create_plot(ds_name, runs, xn, yn, linestyles)
if args.scatter:
output_str += """
<hr />
<h2>Scatterplots for %(id)s""" % { "id" : ds_name }
for plottype in args.plottype:
xn, yn = plot_variants[plottype]
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)
action='store_true')
parser.add_argument(
'-Y', '--y-log',
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')
args = parser.parse_args()
if not args.output:
args.output = 'results/%s.png' % args.dataset
print('writing output to %s' % args.output)
dataset = get_dataset(args.dataset)
dimension = len(dataset['train'][0]) # TODO(erikbern): ugly
point_type = 'float' # TODO(erikbern): should look at the type of X_train
distance = dataset.attrs['distance']
count = int(args.count)
definitions = get_definitions(args.definitions, dimension, point_type, distance, count)
unique_algorithms = get_unique_algorithms(args.definitions)
linestyles = create_linestyles(unique_algorithms)
results = load_results(args.dataset, count, definitions)
runs = compute_metrics(list(dataset["distances"]), results, args.x_axis, args.y_axis)
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)
parser.add_argument('-X',
'--x-log',
help='Draw the X-axis using a logarithmic scale',
action='store_true')
parser.add_argument('-Y',
'--y-log',
help='Draw the Y-axis using a logarithmic scale',
action='store_true')
parser.add_argument('-G',
'--golden',
help='Use golden ratio as plotsize',
action='store_true')
parser.add_argument('--raw',
help='Also show raw results in faded colours',
action='store_true')
args = parser.parse_args()
dataset = get_dataset(args.dataset)
dimension = len(dataset['train'][0]) # TODO(erikbern): ugly
point_type = 'float' # TODO(erikbern): should look at the type of X_train
distance = dataset.attrs['distance']
definitions = get_definitions(args.definitions, dimension, point_type,
distance, args.count)
results = load_results(args.dataset, args.count, definitions)
runs, all_algos = compute_metrics(dataset, results)
linestyles = create_linestyles(all_algos)
create_plot(runs, args.golden, args.raw, args.x_log, args.y_log,
args.x_axis, args.y_axis, args.output, linestyles)
help='Draw the X-axis using a logarithmic scale',
action='store_true')
parser.add_argument(
'-Y', '--y-log',
help='Draw the Y-axis using a logarithmic scale',
action='store_true')
parser.add_argument(
'-G', '--golden',
help='Use golden ratio as plotsize',
action='store_true')
parser.add_argument(
'--raw',
help='Also show raw results in faded colours',
action='store_true')
args = parser.parse_args()
query_cache_path = get_query_cache_path(
args.dataset, args.count, args.limit, args.distance,
args.query_dataset)
assert os.path.exists(query_cache_path), """\
error: the query cache file \"%s\" does not exist""" % query_cache_path
qs = pickle.load(open(query_cache_path))
runs, all_algos = compute_metrics(qs, get_results(
args.dataset, args.limit, args.count, args.distance,
args.query_dataset))
linestyles = create_linestyles(all_algos)
create_plot(runs, args.golden, args.raw, args.x_log,
args.y_log, args.x_axis, args.y_axis, args.output, linestyles)
algo, {}).setdefault(algo_ds, []).append(ms)
all_runs_by_dataset[idx].setdefault(
sdn, {}).setdefault(algo, []).append(ms)
return (all_runs_by_dataset, all_runs_by_algorithm)
j2_env = Environment(loader=FileSystemLoader("./templates/"), trim_blocks=True)
j2_env.globals.update(zip=zip, len=len)
runs_by_ds, runs_by_algo = load_all_results()
dataset_names = [get_dataset_label(x) for x in list(
runs_by_ds['batch'].keys()) + list(runs_by_ds['non-batch'].keys())]
algorithm_names = list(runs_by_algo['batch'].keys(
)) + list(runs_by_algo['non-batch'].keys())
linestyles = {**create_linestyles(dataset_names),
**create_linestyles(algorithm_names)}
build_detail_site(
runs_by_ds['non-batch'],
lambda label: get_dataset_label(label), j2_env, linestyles, False)
build_detail_site(
runs_by_ds['batch'],
lambda label: get_dataset_label(label), j2_env, linestyles, True)
build_detail_site(
runs_by_algo['non-batch'],
lambda x: x, j2_env, linestyles, False)
build_detail_site(
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)
