def testall(directory, pred_file=None, label_file=None, out_path=None): folders = os.listdir(directory) networks = [] for folder in folders: if os.path.isfile(directory+folder+"/network.cfg") and os.path.exists(directory+folder+"/results"): networks.append(folder) config_file = directory+networks[0]+"/network.cfg" config = ConfigParser.ConfigParser() config.read(config_file) test_data = LoadData(directory = config.get('Testing Data', 'folders').split(','), data_file_name = config.get('Testing Data', 'data_file'), label_file_name = config.get('Testing Data', 'label_file'), seg_file_name = config.get('Testing Data', 'seg_file')) res = Analyzer(raw = test_data.get_data()[0], target = test_data.get_labels()[0]) for net in networks: config_file = directory+net+"/network.cfg" config = ConfigParser.ConfigParser() config.read(config_file) res.add_results(results_folder = config.get('General','directory'), name = net, prediction_file = config.get('Testing', 'prediction_file')+'_0', learning_curve_file = 'learning_curve') res.analyze(-1, pred_file=pred_file, label_file=label_file, out_path=out_path) return res
def ViewResults(**kwargs): directory = kwargs.get("directory", "") network = kwargs.get("network", None) prediction_file = kwargs.get("predictions_file", None) if network: # Assume that all networks are tested on the same set of data config = ConfigParser.ConfigParser() config.read("networks/" + network + "/network.cfg") data = LoadData( directory=config.get("Testing Data", "folders").split(",")[0], data_file_name=config.get("Testing Data", "data_file"), label_file_name=config.get("Testing Data", "label_file"), ) if not prediction_file: prediction_file = "test_prediction_0" results = Analyzer(target=data.get_labels()[0], raw=data.get_data()[0]) results.add_results(results_folder="networks/" + network + "/", name=network, prediction_file=prediction_file) else: folders = os.listdir(directory) networks = [] for folder in folders: if os.path.isfile(directory + folder + "/network.cfg"): networks.append(folder) # Assume that all networks are tested on the same set of data config = ConfigParser.ConfigParser() config.read(directory + networks[0] + "/network.cfg") data = LoadData( directory=config.get("Testing Data", "folders").split(",")[0], data_file_name=config.get("Testing Data", "data_file"), label_file_name=config.get("Testing Data", "label_file"), ) if not prediction_file: prediction_file = "test_prediction_0" results = Analyzer(target=data.get_labels()[0], raw=data.get_data()[0]) for net in networks: results.add_results(results_folder=directory + net + "/", name=net, prediction_file=prediction_file) return results
def testprediction(config_file, pred_file=None, label_file=None, out_path=None): config = ConfigParser.ConfigParser() config.read(config_file) test_data = LoadData(directory = config.get('Testing Data', 'folders').split(','), data_file_name = config.get('Testing Data', 'data_file'), label_file_name = config.get('Testing Data', 'label_file'), seg_file_name = config.get('Testing Data', 'seg_file')) res = Analyzer(raw = test_data.get_data()[0], target = test_data.get_labels()[0]) res.add_results(results_folder = config.get('General','directory'), name = config_file.split('/')[-3], prediction_file = config.get('Testing', 'prediction_file')+'_0', learning_curve_file = 'learning_curve') res.analyze(-1, pred_file=pred_file, label_file=label_file, out_path=out_path) return res