def evaluate_srl_predicates(gold_file):
    md = Metadata.load_from_file('srl_predicates')
    nn = taggers.load_network(md)
    reader = taggers.create_reader(md, gold_file=gold_file)
    reader.codify_sentences()
    
    total_tokens = 0
    # true/false positives and negatives
    tp, fp, tn, fn = 0, 0, 0, 0
    
    # for each sentence, tags are 0 at non-predicates and 1 at predicates
    for sent, tags in izip(reader.sentences, reader.tags):
        answer = nn.tag_sentence(sent)
        
        for net_tag, gold_tag in izip(answer, tags):
            if gold_tag == 1:
                if net_tag == gold_tag: tp += 1
                else: fn += 1
            else:
                if net_tag == gold_tag: tn += 1
                else: fp += 1
        
        total_tokens += len(sent)
    
    precision = float(tp) / (tp + fp)
    recall = float(tp) / (tp + fn)
    
    print('True positives: %d, false positives: %d, \
true negatives: %d, false negatives: %d' % (tp, fp, tn, fn))
    print('Accuracy: %f' % (float(tp + tn) / total_tokens))
    print('Precision: %f' % precision)
    print('Recall: %f' % recall)
    print('F-1: %f' % (2 * precision * recall / (precision + recall)))
Evaluates the performance of the network on the SRL task for the
dataset/ETHPy150Open erickrf/nlpnet/bin/nlpnet-test.py/evaluate_srl_predicates