def out_to_mulan_format(training_data_list, feature_map_numeric,
feature_map_character, tfidf, tfidf_score_map,
feature_space, motif_vector, tfidf_idea, args):
"""
mulan用にデータフォーマットを作成する．
RETURN void
"""
exno=args.experiment_no;
training_data_list_feature_space=feature_function.convert_to_feature_space(training_data_list,
feature_map_character,
feature_map_numeric,
tfidf_score_map, tfidf,
tfidf_idea, args);
#------------------------------------------------------------
#arffファイルのheader部分を作成
#xmlファイルも同時に作成
file_contents_stack=[];
xml_contents_stack=[];
file_contents_stack.append(u'@relation hoge\n\n');
xml_contents_stack.append(u'<?xml version="1.0" encoding="utf-8"?>\n<labels xmlns="http://mulan.sourceforge.net/labels">\n')
for feature_tuple in sorted(feature_map_numeric.items(), key=lambda x:x[1]):
file_contents_stack.append(u'@attribute {} numeric\n'.format(feature_tuple[1]));
for motif_name in motif_vector:
file_contents_stack.append(u'@attribute {} {{0,1}}\n'.format(motif_name));
xml_contents_stack.append(u'<label name="{}"></label>\n'.format(motif_name));
xml_contents_stack.append(u'</labels>');
file_contents_stack.append(u'\n\n');
#------------------------------------------------------------
#時間がかかるため，argsの引数でトレーニングデータの量を管理．ただし，デフォルトの時は，何もしない
num_training_instances=len(training_data_list_feature_space);
print 'All training instances is {}'.format(num_training_instances);
if args.training_amount=='0.95':
training_amount_limit=num_training_instances;
else:
training_amount_limit=int(num_training_instances*args.training_amount);
#------------------------------------------------------------
#arffファイルのデータ部分を作成
debug_l=[];
file_contents_stack.append(u'@data\n');
for instance_index, one_instance in enumerate(training_data_list_feature_space):
feature_space_for_one_instance=[0]*feature_space;
motif_vector_numeric=[0]*len(motif_vector);
for motif in one_instance[0]:
motif_vector_numeric[motif_vector.index(motif)]=1;
debug_l.append(motif_vector_numeric);
for feature_number_tuple in one_instance[1]:
#素性番号は１始まりに設定しているので，インデックス調整のために-1する必要がある
feature_space_for_one_instance[feature_number_tuple[0]-1]=feature_number_tuple[1];
feature_space_for_one_instance=[str(item) for item in feature_space_for_one_instance];
motif_vector_str=[str(item) for item in motif_vector_numeric];
file_contents_stack.append(u','.join(feature_space_for_one_instance)\
+u','+u','.join(motif_vector_str)\
+u'\n');
#limitの上限に達したら打ち切り
if instance_index==training_amount_limit:
break;
file_contents_stack.append(u'\n');
#------------------------------------------------------------
output_filepath=u'../classifier/mulan/';
output_filestem=u'exno{}.arff'.format(exno);
with codecs.open(output_filepath+output_filestem, 'w', 'utf-8') as f:
f.writelines(file_contents_stack);
#------------------------------------------------------------
output_filestem=u'exno{}.xml'.format(exno);
with codecs.open(output_filepath+output_filestem, 'w', 'utf-8') as f:
f.writelines(xml_contents_stack);
#============================================================
call_mulan(args);
