survival_fpath = args.survival_fpath duration_col = args.duration_col event_col = args.event_col # setup directories log_dir = make_and_get_dir(results_dir, 'log') fitting_dir = make_and_get_dir(results_dir, 'model_fitting') model_sel_dir = make_and_get_dir(results_dir, 'model_selection') opt_diag_dir = make_and_get_dir(results_dir, 'opt_diagnostics') clust_interpret_dir = make_and_get_dir(results_dir, 'interpret') # load models and data models = load(join(fitting_dir, 'selected_models')) view_data, dataset_names, sample_names, view_feat_names = \ load_data(*fpaths) n_views = len(fpaths) view_data = [pd.DataFrame(view_data[v], index=sample_names, columns=view_feat_names[v]) for v in range(n_views)] # possibly load metadata for comparison if vars2compare_fpath is not None: vars2compare = pd.read_csv(vars2compare_fpath, index_col=0) vars2compare = vars2compare.loc[sample_names, :] else: vars2compare = None # possibly load super data
parser.add_argument('--fpaths', nargs='+', help='Paths to data sets.') args = parser.parse_args() inches = 8 n_top_clust = 10 results_dir = args.results_dir fpaths = args.fpaths fitting_dir = join(results_dir, 'model_fitting') ephys_viz_dir = join(results_dir, 'interpret', 'bd_mvmm', 'ephys_pca_feats') # load models and data models = load(join(fitting_dir, 'selected_models')) view_data, dataset_names, sample_names, view_feat_names = load_data(*fpaths) # load raw ephys data orig_data_dir = join(MouseETPaths().raw_data_dir, 'inh_patchseq_spca_files', 'orig_data_csv') ephys_raw = load_raw_ephys(orig_data_dir, concat=False) for k in ephys_raw.keys(): ephys_raw[k] = ephys_raw[k].loc[sample_names] print(k, ephys_raw[k].shape) n_datasets = len(ephys_raw) # get data for plotting v = 1 cluster_super_means, super_data_means, super_data_stds, y_cnts = \ get_ephys_super_data(model=models['bd_mvmm'].final_.view_models_[v], fit_data=view_data[v],
res_writer = ResultsWriter(join(log_dir, 'single_view_fitting.txt'), delete_if_exists=True) res_writer.write(args) run_start_time = time() n_views = len(args.fpaths) ############# # load data # ############# view_data, dataset_names, sample_names, feat_names = \ load_data(*args.fpaths) for v in range(n_views): res_writer.write('{} (view {}) shape : {}'.format(dataset_names[v], v, view_data[v].shape)) ################ # setup models # ################ cat_gmm_n_comp_seq = np.arange(args.min_ncomp_cat, args.max_ncomp_cat + 1) # view_gmm_n_comp_seq = [np.arange(args.min_ncomp_cat, args.max_ncomp_v0 + 1), # np.arange(args.min_ncomp_cat, args.max_ncomp_v1 + 1)] view_gmm_n_comp_seq = [