genienlp/utils/post_process_decoded_result...

202 lines
6.9 KiB
Python

#!/usr/bin/python3
#
# Copyright 2017 The Board of Trustees of the Leland Stanford Junior University
#
# Author: Mehrad Moradshahi <mehrad@cs.stanford.edu>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
Created on Aug 27, 2018
@author: mehrad
'''
import sys
import os
import re
import argparse
from collections import defaultdict
parser = argparse.ArgumentParser()
parser.add_argument('--reference_gold', default='./test/test.en-tt.tt', type=str)
parser.add_argument('--input_sentences', default='./test/test.en-tt.en', type=str)
parser.add_argument('--gold_program', default='./test/almond.gold.txt', type=str)
parser.add_argument('--predicted_program', default='./test/almond.txt', type=str)
parser.add_argument('--output_file', default='./test/out_file', type=str)
args = parser.parse_args()
def compute_accuracy(pred, gold):
return pred == gold
def compute_accuracy_without_params(pred, gold):
pred_list, gold_list = get_quotes(pred, gold)
pred_cleaned = [pred.replace(val, '') for val in pred_list]
gold_cleaned = [gold.replace(val, '') for val in gold_list]
return pred_cleaned == gold_cleaned
def compute_grammar_accuracy(pred):
return len(pred.split(' ')) != 0
def compute_device_correctness(pred, gold):
return get_devices(pred) == get_devices(gold)
def get_devices(program):
return [x.rsplit('.', 1)[0] for x in program.split(' ') if x.startswith('@')]
def compute_funtion_correctness(pred, gold):
return get_functions(pred) == get_functions(gold)
def get_functions(program):
return [x for x in program.split(' ') if x.startswith('@')]
def flatten(list):
return [item for l in list for item in l]
def compute_correct_tokens(pred, gold):
pred_list, gold_list = get_quotes(pred, gold)
if len(gold_list) == 0:
return False
pred_list = flatten(map(lambda x: x.split(' '), pred_list))
gold_list = flatten(map(lambda x: x.split(' '), gold_list))
common = [token for token in gold_list if token in pred_list]
return len(common) / len(gold_list) * 100.0
def compute_correct_quotes(pred, gold):
pred_list, gold_list = get_quotes(pred, gold)
if len(gold_list) == 0:
return False
common = [quote for quote in gold_list if quote in pred_list]
return len(common) / len(gold_list) * 100.0
def get_quotes(pred, gold):
quotes_list_pred = []
quotes_list_gold = []
quoted = re.compile('"[^"]*"')
for value in quoted.findall(pred):
quotes_list_pred.append(value)
for value in quoted.findall(gold):
quotes_list_gold.append(value)
return quotes_list_pred, quotes_list_gold
def find_indices(ref, shuf):
# models preprocess datasets before training and testing
# during this procedure the dataset gets shuffled
# this function finds a mapping between original ordering of data and shuffled data in the dataset
ref_list = []
shuf_list = []
with open(ref, 'r') as f_ref:
for line in f_ref:
line = line[:-1].lower()
ref_list.append(line)
with open(shuf, 'r') as f_shuf:
for line in f_shuf:
line = line[1:-2].replace(r'\"', '"').lower()
shuf_list.append(line)
indices = []
for i, val in enumerate(shuf_list):
indices.append(ref_list.index(val))
return indices
def run():
indices = find_indices(args.reference_gold, args.gold_program)
inputs = []
with open(args.input_sentences, 'r') as input_file:
for line in input_file:
inputs.append(line)
res = [inputs[i] for i in indices]
errors_dev = defaultdict(int)
errors_func = defaultdict(lambda: defaultdict(int))
cnt_dev = 0
cnt_func = 0
with open(args.gold_program, 'r') as gold_file,\
open(args.predicted_program, 'r') as pred_file,\
open(args.output_file, 'w') as out:
for line in zip(res, gold_file, pred_file):
input, gold, pred = line
input = input.replace(r'<s>', '').strip()
gold = gold.strip()
pred = pred.strip()
accuracy = compute_accuracy(pred, gold)
accuracy_without_params = compute_accuracy_without_params(pred, gold)
grammar_accuracy = compute_grammar_accuracy(pred)
function_correctness = compute_funtion_correctness(pred, gold)
device_correctness = compute_device_correctness(pred, gold)
correct_tokens = compute_correct_tokens(pred, gold)
correct_quotes = compute_correct_quotes(pred, gold)
##########
# error analysis
if not device_correctness:
gold_devs = get_devices(gold)
pred_devs = get_devices(pred)
cnt_dev += 1
if len(gold_devs) == len(pred_devs):
for i, gold in enumerate(gold_devs):
if gold != pred_devs[i]:
errors_dev[(gold, pred_devs[i])] += 1
elif not function_correctness:
gold_funcs = get_functions(gold)
pred_funcs = get_functions(pred)
cnt_func += 1
if len(gold_funcs) == len(pred_funcs):
devices = get_devices(gold)
for i, device in enumerate(devices):
if gold_funcs[i] != pred_funcs[i]:
errors_func[device][(gold_funcs[i].rsplit('.', 1)[1], pred_funcs[i].rsplit('.', 1)[1])] += 1
##########
out.write(input + ' || ' + gold + ' || ' + pred + ' || '
+ str(accuracy) + ' || '
+ str(accuracy_without_params) + '_w/o_params' + ' || '
+ str(grammar_accuracy) + '_grammar' + ' || '
+ str(function_correctness) + '_function' + ' || '
+ str(device_correctness) + '_device')
if correct_quotes != False:
out.write(' || ' + str("{0:.2f}".format(correct_quotes)) + '%_correct_quotes')
if correct_tokens != False:
out.write(' || ' + str("{0:.2f}".format(correct_tokens)) + '%_correct_tokens')
out.write('\n')
out.write('\n')
out.write('\n')
print('cnt_dev: ', cnt_dev)
print('cnt_func: ', cnt_func)
print('errors_dev: ', errors_dev.items())
print('errors_func: ', errors_func.items())
if __name__ == '__main__':
run()