def eval_on_multi_file(
test_corpus_dir, feature_map_character, feature_map_numeric, feature_show, tfidf_score_map, args
):
exno = args.experiment_no
stop = args.stop
if args.liblinear == True:
multipule_eval_for_liblinear(test_corpus_dir, feature_map_character, feature_map_numeric, feature_show, args)
elif args.arow == True:
multipule_eval_for_arow(test_corpus_dir, feature_map_character, feature_map_numeric, feature_show, args)
elif args.logistic == True:
multipule_eval_for_logistic(test_corpus_dir, feature_map_character, feature_map_numeric, feature_show, args)
elif args.mulan == True:
test_data_list_multi = []
# ------------------------------------------------------------
for test_file in load_files(test_corpus_dir):
# ------------------------------------------------------------
if args.persian_test == True:
tokens_stack, motif_stack = file_loader(test_file, stop)
elif args.dutch_test == True:
tokens_stack, motif_stack = file_loader_dutch(test_file, stop)
# ------------------------------------------------------------
test_data_tuple = unify_stack(tokens_stack, motif_stack)
test_data_list_multi.append(test_data_tuple)
# ------------------------------------------------------------
call_mulan.out_mulan_file(
test_data_list_multi, feature_map_character, feature_map_numeric, tfidf_score_map, args
)
model_type = args.mulan_reduce_method
arff_train = "../get_thompson_motif/classifier/mulan/exno{}.arff".format(exno)
modelsavepath = "../get_thompson_motif/classifier/mulan/exno{}.model".format(exno)
arff_test = "./arff_and_xml/test_{}.arff".format(args.experiment_no)
xml_file = "./arff_and_xml/test_{}.xml".format(args.experiment_no)
call_mulan.mulan_command(model_type, arff_train, xml_file, arff_test, modelsavepath, args.experiment_no)
def eval_on_single_file(
test_corpus_dir, feature_map_numeric, feature_map_character, tfidf_score_map, classifier_path_list, args
):
"""
RETURN void
"""
stop = args.stop
feature_show = args.feature_show
tfidf_flag = args.tfidf
exno = args.experiment_no
# ------------------------------------------------------------
test_filepath = test_corpus_dir
# ------------------------------------------------------------
if args.persian_test == True:
tokens_stack, motif_stack = file_loader(test_filepath, stop)
elif args.dutch_test == True:
tokens_stack, motif_stack = file_loader_dutch(test_filepath, stop)
test_data_list = unify_stack(tokens_stack, motif_stack)
# ------------------------------------------------------------
if args.arow == True:
# この関数はまだ未改修
out_libsvm_format(tokens_stack, feature_map_character, feature_map_numeric)
result_map = eval_with_arow()
elif args.liblinear == True:
out_libsvm_format(tokens_stack, feature_map_character, feature_map_numeric, feature_show, tfidf_flag)
result_map = call_liblinear.eval_with_liblinear(exno)
elif args.mulan == True:
call_mulan.out_mulan_file(test_data_list, feature_map_character, feature_map_numeric, tfidf_score_map, args)
model_type = "RAkEL"
arff_train = "../get_thompson_motif/classifier/mulan/exno{}.arff".format(exno)
modelsavepath = "../get_thompson_motif/classifier/mulan/exno{}.model".format(exno)
arff_test = "./arff_and_xml/test_{}.arff".format(args.experiment_no)
xml_file = "./arff_and_xml/test_{}.xml".format(args.experiment_no)
call_mulan.mulan_command(
model_type, arff_train, xml_file, arff_test, modelsavepath, args.experiment_no, args.reduce_method
)
sys.exit("Stil not implemented")
else:
# この関数はまだ未改修
test_matrix = construct_input_matrix(tokens_stack, feature_map_character, feature_map_numeric)
result_map = predict_labels(test_matrix, classifier_path_list)
# ------------------------------------------------------------
gold_map = {}
list_gold_cap_result = []
for gold_motif in motif_stack:
alphabet_label = gold_motif[0]
gold_map[alphabet_label] = 1
gold_cap_result = 0
for result_label in result_map:
if result_label in gold_map and result_map[result_label] == 1:
gold_cap_result += 1
list_gold_cap_result.append(result_label)
print "-" * 30
print "RESULT\nresult of classifiers:{}\ngold:{}\ncorrect estimation:{}\n".format(
result_map, gold_map, list_gold_cap_result
)
if 1 in result_map.values():
precision = float(gold_cap_result) / len([label for label in result_map.values() if label == 1])
else:
precision = 0
recall = float(gold_cap_result) / len(gold_map)
if not precision == 0 and not recall == 0:
F = float(2 * precision * recall) / (precision + recall)
else:
F = 0
print "Precision:{}\nRecall:{}\nF:{}".format(precision, recall, F)
