else:
sdn = \
"%(dataset)s_%(count)d_%(limit)d_%(query_dataset)s_%(distance)s" % d
cqf = datasets.get_query_cache_path(
d["dataset"], d["count"], d["limit"], d["distance"],
d["query_dataset"])
if not os.path.isfile(cqf):
print """\
warning: query file "%s" is missing, skipping""" % cqf
continue
else:
algo = d["algorithm"]
if not cqf in query_cache:
with open(cqf, "r") as fp:
query_cache[cqf] = pickle.load(fp)
ms, _ = compute_metrics(query_cache[cqf], [r])
ms = ms[algo]
if not algo in all_runs_by_algorithm:
all_runs_by_algorithm[algo] = {}
algo_ds = d["dataset"] + " (k = " + str(d["count"]) + ")"
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)
'--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)
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, 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)
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(
'-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(
'--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']
definitions = get_definitions(args.definitions, dimension, point_type, distance, args.count)
unique_algorithms = get_unique_algorithms(args.definitions)
linestyles = create_linestyles(unique_algorithms)
results = load_results(args.dataset, args.count, definitions)
runs = compute_metrics(dataset, results, args.x_axis, args.y_axis)
create_plot(runs, args.raw, args.x_log,
args.y_log, args.x_axis, args.y_axis, args.output, linestyles)
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)
parser.add_argument('-y',
'--y-axis',
help='Which metric to use on the Y-axis',
choices=metrics.keys(),
default="qps")
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)
distance = dataset.attrs['distance']
runs, all_algos = compute_metrics(
dataset, get_results(args.dataset, args.count, distance))
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)
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)
