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
