def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
dgen, args, model, _outdir, plotdir = standard_eval_prologue(cmdargs)
truth_array, preds_array = get_truth_preds_arrays(dgen, model)
sgn_bkg_cumsums = calc_sgn_bkg_cumsums(
truth_array, preds_array, dgen.weights, cmdargs.bins
)
fom_dict = {
k : calc_foms(sgn_bkg_cumsums, func) \
for k, func in FOM_SPEC_DICT.items()
}
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
for fom_label, rhist_fom_list in fom_dict.items():
plot_separate_foms(
rhist_fom_list, labels, fom_label, plotdir, cmdargs.ext
)
plot_overlayed_foms(
fom_dict, [ 'efficiency', 'purity', 'selection' ],
labels, plotdir, cmdargs.ext
)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
dgen, args, model, _outdir, plotdir = standard_eval_prologue(cmdargs)
truth_array, preds_array = get_truth_preds_arrays(dgen, model)
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
truth = truth_array[:cmdargs.limit]
preds = preds_array[:cmdargs.limit, :]
embedded_preds = calc_embedding(preds, cmdargs.perplexity, cmdargs.dims)
fname_base = 'tsne_%d_perp(%e)_lim(%d)' % (
cmdargs.dims, cmdargs.perplexity, cmdargs.limit)
fname_base = os.path.join(plotdir, fname_base)
if cmdargs.dims == 2:
plot_2d_embedding_scatter(truth, embedded_preds, labels, fname_base,
cmdargs.ext)
plot_2d_embedding_density(truth, embedded_preds, labels, cmdargs.bins,
fname_base, cmdargs.ext)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
dgen, args, reco_preds, outdir, plotdir = reco_eval_prologue(cmdargs)
err_mat = calc_reco_err_matrix(reco_preds, dgen)
save_error_matrix(outdir, err_mat)
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
plot_error_matrix(err_mat, labels, plotdir, cmdargs.ext)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
dgen, args, model, outdir, plotdir = standard_eval_prologue(cmdargs)
err_mat = make_error_matrix(dgen, model, args.target_pdg_iscc_list)
save_error_matrix(outdir, err_mat)
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
plot_error_matrix(err_mat, labels, plotdir, cmdargs.ext)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
dgen, args, model, _outdir, plotdir = standard_eval_prologue(cmdargs)
truth_array, preds_array = get_truth_preds_arrays(dgen, model)
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
plot_distributions(
truth_array, preds_array, dgen.weights, cmdargs.bins, labels,
plotdir, cmdargs.ext
)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
args = Args.load(savedir = cmdargs.outdir)
eval_config = EvalConfig.from_cmdargs(cmdargs)
eval_config.modify_eval_args(args)
_, dgen = load_data(args)
outdir = make_eval_outdir(cmdargs.outdir, eval_config)
plotdir = make_plotdir(outdir)
counts = count_events(dgen)
labels = convert_targets_to_labels(args.target_pdg_iscc_list)
plot_counts(counts, labels, plotdir, cmdargs.ext)
def main():
# pylint: disable=missing-function-docstring
setup_logging()
cmdargs = parse_cmdargs()
args, model, dgen, plotdir, eval_specs = prologue(cmdargs)
var_list = ['none']
stat_list = [eval_model(args, dgen, model, eval_specs['fom'])[0]]
make_perturb_profile(slice_var_generator(dgen, cmdargs.smear), var_list,
stat_list, args, model, eval_specs, plotdir, 'slice',
cmdargs)
make_perturb_profile(png2d_var_generator(dgen, cmdargs.smear), var_list,
stat_list, args, model, eval_specs, plotdir, 'png2d',
cmdargs)
make_perturb_profile(png3d_var_generator(dgen, cmdargs.smear), var_list,
stat_list, args, model, eval_specs, plotdir, 'png3d',
cmdargs)
'seed': 0,
'steps_per_epoch': 500,
'target_pdg_iscc_list': [(12, 1), (14, 1), (16, 1), (0, 0)],
'test_size': 200000,
# Args
'outdir': 'prod4/02_model_selection/02_layers_post',
})
search_space = []
for layers_post in range(10):
search_space.append({
'model': {
'kwargs': {
'layers_post': [
128,
] * layers_post,
}
},
})
parse_concurrency_cmdargs(config)
setup_logging(logging.DEBUG,
os.path.join(ROOT_OUTDIR, config['outdir'], "train.log"))
speval(lambda x: create_and_train_model(**config, extra_kwargs=x),
search_space,
os.path.join(ROOT_OUTDIR, config['outdir'], "trials.db"),
timeout=24 * 60 * 60)
'weights' : {
'name' : 'flat',
'kwargs' : { 'bins' : 50, 'range' : (0, 5) },
},
# Args:
'vars_mod_png2d' : None,
'vars_mod_png3d' : None,
'vars_mod_slice' : None,
'outdir' : \
'numu/prod4/final_model_perturbations/stack_of_3dlstms/',
}
)
parse_concurrency_cmdargs(config)
logger = setup_logging(
log_file=os.path.join(ROOT_OUTDIR, config['outdir'], "train.log"))
search_space = [{}]
for w in (32, 64, 128, 256):
search_space += [{
'model': {
'kwargs': {
'lstm3d_spec': [
(w, 'forward'),
] * N,
}
}
} for N in range(2, 6)]
search_space += [{
'model': {
