def main_feature(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
store_matrix(matrix=X, output_dir_path=args.output_dir_path, out_file_name='data_matrix', output_format=args.output_format)
def main_matrix(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
K = metrics.pairwise.pairwise_kernels(X, metric='linear')
store_matrix(matrix=K, output_dir_path=args.output_dir_path, out_file_name='Gram_matrix', output_format=args.output_format)
def main_feature(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
data_matrix = model._data_matrix(iterator)
store_matrix(matrix=data_matrix,
output_dir_path=args.output_dir_path,
out_file_name='data_matrix',
output_format=args.output_format)
def main_matrix(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
K = metrics.pairwise.pairwise_kernels(X, metric='linear')
store_matrix(matrix=K,
output_dir_path=args.output_dir_path,
out_file_name='Gram_matrix',
output_format=args.output_format)