def main(prefix): result = re.load_result(prefix) result.import_dict(ml_glpp_parameters(result)) re.save_result(prefix, result) return result
def load_and_sort(configs): crs = [(c, load_result(c).result) for c in configs] def key(cr): if is_failed(cr[1]): return -float("inf") else: return error_reduction(cr[1]) return sorted(crs, key=key, reverse=True)
def main(prefix): result = re.load_result(prefix) try: an = lnp_result_info(result) except: print " (not a LNP simulation)" an = information_analysis(result.intensity) setattr(result, "info", an) re.save_result(prefix, result) return result
def default(): print("File contents:") with gzip.open(resultpath(config.default_config), mode="rt") as f: for line in f: print(line, end="") print_barline() r = load_result(config.default_config).result print("Error reduction:", error_reduction(r)) print("Prior correctness:", prior_correctness(r)) print("Correctness:", correctness(r))
def complete(): crs = [] for p in Path("data/results").iterdir(): cr = load_result(str(p)) crs.append((cr.config, cr.result)) def key(cr): if is_failed(cr[1]): return -float("inf") else: return error_reduction(cr[1]) crs = sorted(crs, key=key, reverse=True) return crs
def exp10(): # Query visualization. dataset = 'aids10k' model = 'beam80' k = 5 info_dict = { 'draw_node_size': 10, 'draw_node_label_enable': True, 'draw_node_label_font_size': 8, 'draw_node_color_map': { 'C': 'red', 'O': 'blue', 'N': 'green' }, 'draw_edge_label_enable': True, 'draw_edge_label_font_size': 6, 'each_graph_text_list': [], 'each_graph_font_size': 10, 'plot_dpi': 200, 'plot_save_path': '' } r = load_result(dataset, model) ged_mat = r.ged_mat() time_mat = r.time_mat() ids = r.ged_sort_id_mat() m, n = ged_mat.shape train_data = load_data(dataset, train=True) test_data = load_data(dataset, train=False) for i in range(m): q = test_data.graphs[i] gids = ids[i][:k] gs = [train_data.graphs[j] for j in gids] info_dict['each_graph_text_list'] = \ ['query id: {}'.format(q.graph['gid'])] + \ [get_text_label(ged_mat, time_mat, i, j, \ train_data.graphs[j]) for j in gids] info_dict['plot_save_path'] = \ get_root_path() + \ '/files/{}//query_vis/{}/query_vis_{}_{}_{}.png'.format( \ dataset, model, dataset, model, i) vis(q, gs, info_dict)
def test_plot(): p = 'results/single_trial_test_R.pickle' assert(path.exists(p)) res = r.load_result(p) res.plot()