def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
text = []
for margin, graph_info in model.decision_function_info(iterator, key='id'):
if margin > 0:
prediction = 1
else:
prediction = -1
text.append("%d\t%s\t%s\n" % (prediction, margin, graph_info))
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='predictions.txt')
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
text = []
for margin, graph_info in model.decision_function_info(iterator, key='id'):
if margin > 0:
prediction = 1
else:
prediction = -1
text.append("%d\t%s\t%s\n" % (prediction, margin, graph_info))
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='predictions.txt')
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
predictions = model.decision_function(iterator)
text = []
for p in predictions:
text.append(str(p) + "\n")
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='predictions.txt')
text = []
for p in predictions:
if p > 0:
prediction = 1
else:
prediction = -1
text.append(str(prediction) + "\n")
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='classifications.txt')
text = []
from itertools import izip
info_iterator = model.get_info(iterator_)
for p, info in izip(predictions, info_iterator):
text.append("%.4f\t%s\n" % (p, info))
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='info.txt')
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
predictions = model.decision_function(iterator)
text = []
for p in predictions:
text.append(str(p) + "\n")
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='predictions.txt')
text = []
for p in predictions:
if p > 0:
prediction = 1
else:
prediction = -1
text.append(str(prediction) + "\n")
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='classifications.txt')
text = []
from itertools import izip
info_iterator = model.get_info(iterator_)
for p, info in izip(predictions, info_iterator):
text.append("%.4f\t%s\n" % (p, info))
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='info.txt')
