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': {