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import os
import shutil
import numpy as np
import torch
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from examples.sentence_level.wmt_2018.common.util.download import download_from_google_drive
from examples.sentence_level.wmt_2018.common.util.draw import draw_scatterplot, print_stat
from examples.sentence_level.wmt_2018.common.util.normalizer import fit, un_fit
from examples.sentence_level.wmt_2018.common.util.postprocess import format_submission
from examples.sentence_level.wmt_2018.common.util.reader import read_annotated_file, read_test_file
from examples.sentence_level.wmt_2018.en_de.smt.monotransquest_config import TEMP_DIRECTORY, GOOGLE_DRIVE, DRIVE_FILE_ID, MODEL_NAME, \
monotransquest_config, MODEL_TYPE, SEED, RESULT_FILE, SUBMISSION_FILE, RESULT_IMAGE
from transquest.algo.sentence_level.monotransquest.evaluation import pearson_corr, spearman_corr
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
if not os.path.exists(TEMP_DIRECTORY):
os.makedirs(TEMP_DIRECTORY)
if GOOGLE_DRIVE:
download_from_google_drive(DRIVE_FILE_ID, MODEL_NAME)
TRAIN_FOLDER = "examples/sentence_level/wmt_2018/en_de/data/en_de/"
DEV_FOLDER = "examples/sentence_level/wmt_2018/en_de/data/en_de/"
TEST_FOLDER = "examples/sentence_level/wmt_2018/en_de/data/en_de/"
train = read_annotated_file(path=TRAIN_FOLDER, original_file="train.smt.src", translation_file="train.smt.mt", hter_file="train.smt.hter")
dev = read_annotated_file(path=DEV_FOLDER, original_file="dev.smt.src", translation_file="dev.smt.mt", hter_file="dev.smt.hter")
test = read_test_file(path=TEST_FOLDER, original_file="test.smt.src", translation_file="test.smt.mt")
train = train[['original', 'translation', 'hter']]
dev = dev[['original', 'translation', 'hter']]
test = test[['index', 'original', 'translation']]
index = test['index'].to_list()
train = train.rename(columns={'original': 'text_a', 'translation': 'text_b', 'hter': 'labels'}).dropna()
dev = dev.rename(columns={'original': 'text_a', 'translation': 'text_b', 'hter': 'labels'}).dropna()
test = test.rename(columns={'original': 'text_a', 'translation': 'text_b'}).dropna()
test_sentence_pairs = list(map(list, zip(test['text_a'].to_list(), test['text_b'].to_list())))
train = fit(train, 'labels')
dev = fit(dev, 'labels')
if monotransquest_config["evaluate_during_training"]:
if monotransquest_config["n_fold"] > 1:
dev_preds = np.zeros((len(dev), monotransquest_config["n_fold"]))
test_preds = np.zeros((len(test), monotransquest_config["n_fold"]))
for i in range(monotransquest_config["n_fold"]):
if os.path.exists(monotransquest_config['output_dir']) and os.path.isdir(monotransquest_config['output_dir']):
shutil.rmtree(monotransquest_config['output_dir'])
model = MonoTransQuestModel(MODEL_TYPE, MODEL_NAME, num_labels=1, use_cuda=torch.cuda.is_available(),
args=monotransquest_config)
train_df, eval_df = train_test_split(train, test_size=0.1, random_state=SEED*i)
model.train_model(train_df, eval_df=eval_df, pearson_corr=pearson_corr, spearman_corr=spearman_corr,
mae=mean_absolute_error)
model = MonoTransQuestModel(MODEL_TYPE, monotransquest_config["best_model_dir"], num_labels=1, use_cuda=torch.cuda.is_available(), args=monotransquest_config)
result, model_outputs, wrong_predictions = model.eval_model(dev, pearson_corr=pearson_corr,
spearman_corr=spearman_corr,
mae=mean_absolute_error)
predictions, raw_outputs = model.predict(test_sentence_pairs)
dev_preds[:, i] = model_outputs
test_preds[:, i] = predictions
dev['predictions'] = dev_preds.mean(axis=1)
test['predictions'] = test_preds.mean(axis=1)
else:
model = MonoTransQuestModel(MODEL_TYPE, MODEL_NAME, num_labels=1, use_cuda=torch.cuda.is_available(),
args=monotransquest_config)
train, eval_df = train_test_split(train, test_size=0.1, random_state=SEED)
model.train_model(train, eval_df=eval_df, pearson_corr=pearson_corr, spearman_corr=spearman_corr,
mae=mean_absolute_error)
model = MonoTransQuestModel(MODEL_TYPE, monotransquest_config["best_model_dir"], num_labels=1,
use_cuda=torch.cuda.is_available(), args=monotransquest_config)
result, model_outputs, wrong_predictions = model.eval_model(dev, pearson_corr=pearson_corr,
spearman_corr=spearman_corr,
mae=mean_absolute_error)
predictions, raw_outputs = model.predict(test_sentence_pairs)
dev['predictions'] = model_outputs
test['predictions'] = predictions
else:
model = MonoTransQuestModel(MODEL_TYPE, MODEL_NAME, num_labels=1, use_cuda=torch.cuda.is_available(),
args=monotransquest_config)
model.train_model(train, pearson_corr=pearson_corr, spearman_corr=spearman_corr, mae=mean_absolute_error)
result, model_outputs, wrong_predictions = model.eval_model(dev, pearson_corr=pearson_corr,
spearman_corr=spearman_corr, mae=mean_absolute_error)
predictions, raw_outputs = model.predict(test_sentence_pairs)
dev['predictions'] = model_outputs
test['predictions'] = predictions
dev = un_fit(dev, 'labels')
dev = un_fit(dev, 'predictions')
test = un_fit(test, 'predictions')
dev.to_csv(os.path.join(TEMP_DIRECTORY, RESULT_FILE), header=True, sep='\t', index=False, encoding='utf-8')
draw_scatterplot(dev, 'labels', 'predictions', os.path.join(TEMP_DIRECTORY, RESULT_IMAGE), "English-German-SMT")
print_stat(dev, 'labels', 'predictions')
format_submission(df=test, index=index, method="TransQuest", path=os.path.join(TEMP_DIRECTORY, SUBMISSION_FILE))
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