model_pp_attachment.py 文件源码

def print_attention_values(self, input_file, test_inputs, output_file):
        sent_attention_outputs = self.get_attention(test_inputs)
        tagged_sentences = [x.strip().split("\t")[1] for x in codecs.open(input_file).readlines()]
        outfile = codecs.open(output_file, "w", "utf-8")
        full_json_struct = []
        for sent_attention, tagged_sentence in zip(sent_attention_outputs, tagged_sentences):
            sent_json = {}
            sent_json["input"] = tagged_sentence
            sent_json["tokens"] = []
            tagged_words = tagged_sentence.split()
            for tagged_word, word_attention in zip(tagged_words, sent_attention):
                token_json = {}
                token_json["surface_form"] = tagged_word
                token_json["senses"] = []
                for sense_num, sense_attention in enumerate(word_attention):
                    if len(sense_attention) == 0:
                        continue
                    sense_json = {}
                    sense_json["id"] = sense_num
                    sense_json["hypernyms"] = []
                    for hyp_name, hyp_att in sense_attention:
                        if isinstance(hyp_att, tuple):
                            # Averaging forward and backward attention
                            sense_json["hypernyms"].append({hyp_name: {"forward": float(hyp_att[0]),
                                                                       "backward": float(hyp_att[1])}})
                        else:
                            sense_json["hypernyms"].append({hyp_name: float(hyp_att)})
                    token_json["senses"].append(sense_json)
                sent_json["tokens"].append(token_json)
            full_json_struct.append(sent_json)
        print >>outfile, json.dumps(full_json_struct, indent=2)
        outfile.close()