from hepqpr.qallse.dsmaker import create_dataset
if __name__ == '__main__':
#---- Script Constants
test_densities = [0.75] * 3
trials_per_density = 1
input_seed = None
#---- Test Doublet Making
results, indx = [[i] for i in test_densities], 0
for ds in test_densities:
for _ in range(trials_per_density):
print(f'--> Testing doublet_making with {ds} density')
stat_row = create_dataset(density=ds,
min_hits_per_track=5,
high_pt_cut=1.0,
random_seed=input_seed,
double_hits_ok=False,
gen_doublets=True,
test_mode=True)
results[indx].extend(stat_row)
indx += 1
#---- Write Tests to Disk
titles = 'density,runtime,recall,precision,doublets_made,seed'.split(',')
stats = pd.DataFrame(results, columns=titles)
print('--> Stats')
print(stats)
stats.to_csv('75_parallel_results.csv', index=False)
if __name__ == '__main__':
mat = []
for row in ds_info.strip().split('\n'):
e, d, s = row.split(',')
event, ds, seed = int(e), float(d), int(s)
prefix = f'ds{ds*100:.0f}'
print(f'\n>>>> {prefix} <<<<\n')
with time_this() as time_info:
metas, path = create_dataset(density=ds,
input_path=op.join(
trackml_train_path,
f'event00000{event}-hits.csv'),
output_path=output_path,
prefix=prefix,
min_hits_per_track=5,
high_pt_cut=1.0,
random_seed=int(seed),
double_hits_ok=False,
gen_doublets=True)
mat.append([
event,
int(ds * 100),
metas['num_hits'],
metas['num_noise'],
metas['num_tracks'],
metas['num_important_tracks'],
seed,
time_info[0],
prefix += f'_noPhiCut'
ds_options["prefix"] = prefix
# generate the dataset
import os
path = os.path.join(ds_options['output_path'], prefix,
"event000001000")
if os.path.exists(path + "-hits.csv"):
import json
with open(path + "-meta.json") as f:
meta = json.load(f)
with open(path + "-metaHits.pickle", 'rb') as f:
time_info = pickle.load(f)
else:
with time_this() as time_info:
meta, path = create_dataset(**ds_options)
with open(os.path.join(path + "-metaHits.pickle"), 'wb') as f:
pickle.dump(time_info, f)
results[k]['TReadingHits'] = time_info[1]
results[k]['meta'] = meta
from hepqpr.qallse.seeding import generate_doublets, SeedingConfig
if nPhi == 53: seedingConfig = nPhi53SeedingConfig
if nPhi == 100: seedingConfig = nPhi100SeedingConfig
if nPhi == 75: seedingConfig = nPhi75SeedingConfig
if nPhi == 25: seedingConfig = nPhi25SeedingConfig
if nPhi == 10: seedingConfig = nPhi10SeedingConfig
if nPhi == 6: seedingConfig = nPhi6SeedingConfig
if nPhi == 1: seedingConfig = nPhi1SeedingConfig
# generate the doublets: the important part is the config_cls !
model_class = QallseD0 # model class to use
extra_config = dict() # configuration arguments overriding the defaults
#: FIXME experimental pegasus setup
P6 = dnx.pegasus_graph(-100, nice_coordinates=True)
classical_sampler = neal.SimulatedAnnealingSampler()
tabu_sampler = TabuSampler()
#sampler = dimod.StructureComposite(classical_sampler, P6.nodes, P6.edges)
sampler = dimod.StructureComposite(tabu_sampler, P6.nodes, P6.edges)
tempdir = tempfile.TemporaryDirectory()
print(f'using {tempdir.name}')
metas, path = create_dataset(output_path=tempdir.name,
random_seed=240834351,
gen_doublets=True,
**dsmaker_config)
#path = '/tmp/hpt-collapse/ds10/event000001000'
with open(path + '-meta.json') as f:
print(f.read())
# load data
dw = DataWrapper.from_path(path)
doublets = pd.read_csv(path + '-doublets.csv')
if add_missing:
doublets = dw.add_missing_doublets(doublets)
else:
p, r, ms = dw.compute_score(doublets)
print(f'got {len(doublets)}.')
