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')