def load_all_results():
"""Read all result files and compute all metrics"""
all_runs_by_dataset = {'batch': {}, 'non-batch': {}}
all_runs_by_algorithm = {'batch': {}, 'non-batch': {}}
cached_true_dist = []
old_sdn = None
for properties, f in results.load_all_results():
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset = get_dataset(properties["dataset"])
cached_true_dist = list(dataset["distances"])
old_sdn = sdn
algo = properties["algo"]
ms = compute_all_metrics(cached_true_dist, f, properties,
args.recompute)
algo_ds = get_dataset_label(sdn)
idx = "non-batch"
if properties["batch_mode"]:
idx = "batch"
all_runs_by_algorithm[idx].setdefault(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)
def load_all_results():
"""Read all result files and compute all metrics"""
all_runs_by_dataset = {'batch': {}, 'non-batch': {}}
all_runs_by_algorithm = {'batch': {}, 'non-batch': {}}
cached_true_dist = []
old_sdn = None
for mode in ["non-batch", "batch"]:
for properties, f in results.load_all_results(
batch_mode=(mode == "batch")):
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset = get_dataset(properties["dataset"])
cached_true_dist = list(dataset["distances"])
old_sdn = sdn
algo_ds = get_dataset_label(sdn)
desc_suffix = ("-batch" if mode == "batch" else "")
algo = properties["algo"] + desc_suffix
sdn += desc_suffix
ms = compute_all_metrics(cached_true_dist, f, properties,
args.recompute)
all_runs_by_algorithm[mode].setdefault(algo, {}).setdefault(
algo_ds, []).append(ms)
all_runs_by_dataset[mode].setdefault(sdn,
{}).setdefault(algo,
[]).append(ms)
return (all_runs_by_dataset, all_runs_by_algorithm)
def load_all_results():
"""Read all result files and compute all metrics"""
all_runs_by_dataset = {}
all_runs_by_algorithm = {}
cached_true_dist = []
old_sdn = None
for f in results.load_all_results():
properties = dict(f.attrs)
# TODO Fix this properly. Sometimes the hdf5 file returns bytes
# This converts these bytes to strings before we work with them
for k in properties.keys():
try:
properties[k]= properties[k].decode()
except:
pass
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset = get_dataset(properties["dataset"])
cached_true_dist = list(dataset["distances"])
old_sdn = sdn
algo = properties["algo"]
ms = compute_all_metrics(cached_true_dist, f, properties["algo"])
algo_ds = get_dataset_label(sdn)
all_runs_by_algorithm.setdefault(algo, {}).setdefault(algo_ds, []).append(ms)
all_runs_by_dataset.setdefault(sdn, {}).setdefault(algo, []).append(ms)
return (all_runs_by_dataset, all_runs_by_algorithm)
def load_all_results():
"""Read all result files and compute all metrics"""
all_runs_by_dataset = {'batch': {}, 'non-batch': {}}
all_runs_by_algorithm = {'batch': {}, 'non-batch': {}}
cached_true_dist = []
old_sdn = None
for properties, f in results.load_all_results():
sdn = get_run_desc(properties)
if sdn != old_sdn:
dataset = get_dataset(properties["dataset"])
cached_true_dist = list(dataset["distances"])
old_sdn = sdn
algo = properties["algo"]
ms = compute_all_metrics(
cached_true_dist, f, properties, args.recompute)
algo_ds = get_dataset_label(sdn)
idx = "non-batch"
if properties["batch_mode"]:
idx = "batch"
all_runs_by_algorithm[idx].setdefault(
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)