def load_train_classifier(params, features, labels, feature_names, sizes, nb_holdout): logging.info('train classifier...') seg_clf.feature_scoring_selection(features, labels, feature_names, path_out=params['path_exp']) cv = seg_clf.CrossValidatePSetsOut(sizes, nb_hold_out=nb_holdout) # feature norm & train classification fname_classif = seg_clf.TEMPLATE_NAME_CLF.format(params['classif']) path_classif = os.path.join(params['path_exp'], fname_classif) if os.path.isfile(path_classif) and not FORCE_RETRAIN_CLASSIF: logging.info('loading classifier: %s', path_classif) params_local = params.copy() dict_classif = seg_clf.load_classifier(path_classif) classif = dict_classif['clf_pipeline'] params = dict_classif['params'] params.update({k: params_local[k] for k in params_local if k.startswith('path_') or k.startswith('gc_')}) logging.debug('loaded PARAMETERS: %s', repr(params)) else: classif, path_classif = seg_clf.create_classif_train_export( params['classif'], features, labels, cross_val=cv, params=params, feature_names=feature_names, nb_search_iter=params['nb_classif_search'], nb_jobs=params['nb_jobs'], pca_coef=params['pca_coef'], path_out=params['path_exp']) params['path_classif'] = path_classif cv = seg_clf.CrossValidatePSetsOut(sizes, nb_hold_out=nb_holdout) seg_clf.eval_classif_cross_val_scores(params['classif'], classif, features, labels, cross_val=cv, path_out=params['path_exp']) seg_clf.eval_classif_cross_val_roc(params['classif'], classif, features, labels, cross_val=cv, path_out=params['path_exp']) return params, classif, path_classif
def main_train(params): """ PIPELINE for training 0) load triplets or create triplets from path to images, annotations 1) load precomputed data or compute them now 2) train classifier with hyper-parameters 3) perform Leave-One-Out experiment :param {str: any} params: """ params = prepare_experiment_folder(params, FOLDER_EXPERIMENT) tl_expt.set_experiment_logger(params['path_expt']) logging.info(tl_expt.string_dict(params, desc='PARAMETERS')) tl_expt.save_config_yaml( os.path.join(params['path_expt'], NAME_YAML_PARAMS), params) tl_expt.create_subfolders(params['path_expt'], LIST_SUBDIRS) df_paths, _ = load_df_paths(params) path_dump_data = os.path.join(params['path_expt'], NAME_DUMP_TRAIN_DATA) if not os.path.isfile(path_dump_data) or FORCE_RECOMP_DATA: (dict_imgs, dict_segms, dict_slics, dict_points, dict_centers, dict_features, dict_labels, feature_names) = \ dataset_load_images_segms_compute_features(params, df_paths, params['nb_workers']) assert len(dict_imgs) > 0, 'missing images' save_dump_data( path_dump_data, dict_imgs, dict_segms, dict_slics, dict_points, dict_centers, dict_features, dict_labels, feature_names, ) else: (dict_imgs, dict_segms, dict_slics, dict_points, dict_centers, dict_features, dict_labels, feature_names) = load_dump_data(path_dump_data) if is_drawing(params['path_expt']) and EXPORT_TRAINING_DATA: export_dataset_visual(params['path_expt'], dict_imgs, dict_segms, dict_slics, dict_points, dict_labels, params['nb_workers']) # concentrate features, labels features, labels, sizes = seg_clf.convert_set_features_labels_2_dataset( dict_features, dict_labels, drop_labels=[-1], balance_type=params['balance']) # remove all bad values from features space features[np.isnan(features)] = 0 features[np.isinf(features)] = -1 assert np.sum(sizes) == len(labels), \ 'not equal sizes (%d) and labels (%i)' \ % (int(np.sum(sizes)), len(labels)) # feature norm & train classification nb_holdout = int(np.ceil(len(sizes) * CROSS_VAL_LEAVE_OUT_SEARCH)) cv = seg_clf.CrossValidateGroups(sizes, nb_holdout) classif, params[ 'path_classif'] = seg_clf.create_classif_search_train_export( params['classif'], features, labels, cross_val=cv, params=params, feature_names=feature_names, nb_search_iter=params['nb_classif_search'], pca_coef=params.get('pca_coef', None), nb_workers=params['nb_workers'], path_out=params['path_expt'], ) nb_holdout = int(np.ceil(len(sizes) * CROSS_VAL_LEAVE_OUT_EVAL)) cv = seg_clf.CrossValidateGroups(sizes, nb_holdout) seg_clf.eval_classif_cross_val_scores(params['classif'], classif, features, labels, cross_val=cv, path_out=params['path_expt']) seg_clf.eval_classif_cross_val_roc(params['classif'], classif, features, labels, cross_val=cv, path_out=params['path_expt']) if RUN_LEAVE_ONE_OUT: experiment_loo(classif, dict_imgs, dict_segms, dict_centers, dict_slics, dict_points, dict_features, feature_names, params)