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utils.py
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utils.py
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import gzip
import logging
import sys
# from gensim.models.word2vec import Word2Vec
import numpy as np
import torch
asap_ranges = {
0: (0, 60),
1: (2, 12),
2: (1, 6),
3: (0, 3),
4: (0, 3),
5: (0, 4),
6: (0, 4),
7: (0, 30),
8: (0, 60),
11:(1,3),
12:(1,3),
13:(1,3),
14:(1,3),
15:(1,3),
16:(1,3),
17:(1,3),
18:(1,3)
}
def convert_to_dataset_friendly_score(score,prompt_id):
low, high = asap_ranges[prompt_id]
score = (score)*(high - low)+low
return score
def get_logger(name, level=logging.INFO, handler=sys.stdout,
formatter='%(name)s - %(levelname)s - %(message)s'):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
formatter = logging.Formatter(formatter)
stream_handler = logging.StreamHandler(handler)
stream_handler.setLevel(level)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return logger
def padding_sentence_sequences(index_sequences, scores, max_sentnum, max_sentlen, post_padding=True):
X = np.empty([len(index_sequences), max_sentnum, max_sentlen], dtype=np.int32)
Y = np.empty([len(index_sequences), 1], dtype=np.float32)
mask = np.zeros([len(index_sequences), max_sentnum, max_sentlen])
for i in range(len(index_sequences)):
sequence_ids = index_sequences[i]
num = len(sequence_ids)
for j in range(num):
word_ids = sequence_ids[j]
length = len(word_ids)
# X_len[i] = length
for k in range(length):
wid = word_ids[k]
# print wid
X[i, j, k] = wid
# Zero out X after the end of the sequence
X[i, j, length:] = 0
# Make the mask for this sample 1 within the range of length
mask[i, j, :length] = 1
X[i, num:, :] = 0
Y[i] = scores[i]
return X, Y, mask
def padding_sequences(word_indices, char_indices, scores, max_sentnum, max_sentlen, maxcharlen, post_padding=True):
# support char features
X = np.empty([len(word_indices), max_sentnum, max_sentlen], dtype=np.int32)
Y = np.empty([len(word_indices), 1], dtype=np.float32)
mask = np.zeros([len(word_indices), max_sentnum, max_sentlen], dtype=theano.config.floatX)
char_X = np.empty([len(char_indices), max_sentnum, max_sentlen, maxcharlen], dtype=np.int32)
for i in range(len(word_indices)):
sequence_ids = word_indices[i]
num = len(sequence_ids)
for j in range(num):
word_ids = sequence_ids[j]
length = len(word_ids)
# X_len[i] = length
for k in range(length):
wid = word_ids[k]
# print wid
X[i, j, k] = wid
# Zero out X after the end of the sequence
X[i, j, length:] = 0
# Make the mask for this sample 1 within the range of length
mask[i, j, :length] = 1
X[i, num:, :] = 0
Y[i] = scores[i]
for i in range(len(char_indices)):
sequence_ids = char_indices[i]
num = len(sequence_ids)
for j in range(num):
word_ids = sequence_ids[j]
length = len(word_ids)
for k in range(length):
wid = word_ids[k]
charlen = len(wid)
for l in range(charlen):
cid = wid[l]
char_X[i, j, k, l] = cid
char_X[i, j, k, charlen:] = 0
char_X[i, j, length:, :] = 0
char_X[i, num:, :] = 0
return X, char_X, Y, mask
def load_word_embedding_dict(embedding, embedding_path, word_alphabet, logger, embedd_dim=100):
"""
load word embeddings from file
:param embedding:
:param embedding_path:
:param logger:
:return: embedding dict, embedding dimention, caseless
"""
# if embedding == 'word2vec':
# # loading word2vec
# logger.info("Loading word2vec ...")
# word2vec = Word2Vec.load_word2vec_format(embedding_path, binary=False, unicode_errors='ignore')
# embedd_dim = word2vec.vector_size
# return word2vec, embedd_dim, False
# elif embedding == 'glove':
if embedding == 'glove':
# loading GloVe
logger.info("Loading GloVe ...")
embedd_dim = -1
embedd_dict = dict()
with open(embedding_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split()
if embedd_dim < 0:
embedd_dim = len(tokens) - 1
else:
assert (embedd_dim + 1 == len(tokens))
embedd = np.empty([1, embedd_dim])
embedd[:] = tokens[1:]
embedd_dict[tokens[0]] = embedd
return embedd_dict, embedd_dim, True
elif embedding == 'senna':
# loading Senna
logger.info("Loading Senna ...")
embedd_dim = -1
embedd_dict = dict()
with gzip.open(embedding_path, 'r') as file:
for line in file:
line = line.strip()
if len(line) == 0:
continue
tokens = line.split()
if embedd_dim < 0:
embedd_dim = len(tokens) - 1
else:
assert (embedd_dim + 1 == len(tokens))
embedd = np.empty([1, embedd_dim], dtype=theano.config.floatX)
embedd[:] = tokens[1:]
embedd_dict[tokens[0]] = embedd
return embedd_dict, embedd_dim, True
# elif embedding == 'random':
# # loading random embedding table
# logger.info("Loading Random ...")
# embedd_dict = dict()
# words = word_alphabet.get_content()
# scale = np.sqrt(3.0 / embedd_dim)
# # print words, len(words)
# for word in words:
# embedd_dict[word] = np.random.uniform(-scale, scale, [1, embedd_dim])
# return embedd_dict, embedd_dim, False
else:
raise ValueError("embedding should choose from [word2vec, senna]")
def build_embedd_table(word_alphabet, embedd_dict, embedd_dim, logger, caseless):
scale = np.sqrt(3.0 / embedd_dim)
embedd_table = np.empty([len(word_alphabet), embedd_dim])
embedd_table[0, :] = np.zeros([1, embedd_dim])
oov_num = 0
for word, index in word_alphabet.items():
ww = word.lower() if caseless else word
# show oov ratio
if ww in embedd_dict:
embedd = embedd_dict[ww]
else:
embedd = np.random.uniform(-scale, scale, [1, embedd_dim])
oov_num += 1
embedd_table[index, :] = embedd
oov_ratio = float(oov_num)/(len(word_alphabet)-1)
logger.info("OOV number =%s, OOV ratio = %f" % (oov_num, oov_ratio))
return embedd_table
def rescale_tointscore(scaled_scores, set_ids):
'''
rescale scaled scores range[0,1] to original integer scores based on their set_ids
:param scaled_scores: list of scaled scores range [0,1] of essays
:param set_ids: list of corresponding set IDs of essays, integer from 1 to 8
'''
# print type(scaled_scores)
# print scaled_scores[0:100]
if isinstance(set_ids, int):
prompt_id = set_ids
set_ids = np.ones(scaled_scores.shape[0],) * prompt_id
assert scaled_scores.shape[0] == len(set_ids)
int_scores = np.zeros((scaled_scores.shape[0], 1))
for k, i in enumerate(set_ids):
assert i in range(1, 9)
# TODO
if i == 1:
minscore = 2
maxscore = 12
elif i == 2:
minscore = 1
maxscore = 6
elif i in [3, 4]:
minscore = 0
maxscore = 3
elif i in [5, 6]:
minscore = 0
maxscore = 4
elif i == 7:
minscore = 0
maxscore = 30
elif i == 8:
minscore = 0
maxscore = 60
else:
print("Set ID error")
# minscore = 0
# maxscore = 60
int_scores[k] = scaled_scores[k]*(maxscore-minscore) + minscore
return np.around(int_scores).astype(int)
def domain_specific_rescale(y_true, y_pred, set_ids):
'''
rescaled scores to original integer scores based on their set ids
and partition the score list based on its specific prompot
return 8-prompt int score list for y_true and y_pred respectively
:param y_true: true score list, contains all 8 prompts
:param y_pred: pred score list, also contains 8 prompts
:param set_ids: list that indicates the set/prompt id for each essay
'''
# prompts_truescores = []
# prompts_predscores = []
y_true = y_true.flatten()
y_pred = y_pred.flatten()
y1_true, y1_pred = [], []
y2_true, y2_pred = [], []
y3_true, y3_pred = [], []
y4_true, y4_pred = [], []
y5_true, y5_pred = [], []
y6_true, y6_pred = [], []
y7_true, y7_pred = [], []
y8_true, y8_pred = [], []
for k, i in enumerate(set_ids):
assert i in range(1, 9)
if i == 1:
minscore = 2
maxscore = 12
y1_true.append(y_true[k]*(maxscore-minscore) + minscore)
y1_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 2:
minscore = 1
maxscore = 6
y2_true.append(y_true[k]*(maxscore-minscore) + minscore)
y2_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 3:
minscore = 0
maxscore = 3
y3_true.append(y_true[k]*(maxscore-minscore) + minscore)
y3_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 4:
minscore = 0
maxscore = 3
y4_true.append(y_true[k]*(maxscore-minscore) + minscore)
y4_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 5:
minscore = 0
maxscore = 4
y5_true.append(y_true[k]*(maxscore-minscore) + minscore)
y5_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 6:
minscore = 0
maxscore = 4
y6_true.append(y_true[k]*(maxscore-minscore) + minscore)
y6_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 7:
minscore = 0
maxscore = 30
y7_true.append(y_true[k]*(maxscore-minscore) + minscore)
y7_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
elif i == 8:
minscore = 0
maxscore = 60
y8_true.append(y_true[k]*(maxscore-minscore) + minscore)
y8_pred.append(y_pred[k]*(maxscore-minscore) + minscore)
else:
print("Set ID error")
prompts_truescores = [np.around(y1_true), np.around(y2_true), np.around(y3_true), np.around(y4_true), \
np.around(y5_true), np.around(y6_true), np.around(y7_true), np.around(y8_true)]
prompts_predscores = [np.around(y1_pred), np.around(y2_pred), np.around(y3_pred), np.around(y4_pred), \
np.around(y5_pred), np.around(y6_pred), np.around(y7_pred), np.around(y8_pred)]
return prompts_truescores, prompts_predscores
# def plot_convergence(train_stats, dev_stats, test_stats, metric_type='mse'):
# '''
# Plot convergence curve of training process
# :param train_stats: list of train metrics at each epoch
# :param dev_stats: list of dev metrics at each epoch
# :param test_stas: list of test metrics at each epoch
# '''
# num_epochs = len(train_stats)
# x = range(1, num_epochs+1)
# plt.plot(x, train_stats)
# plt.plot(x, dev_stats)
# plt.plot(x, test_stats)
# plt.legend(['train', 'dev', 'test'], loc='upper right')
# plt.xlabel('num of epochs')
# if metric_type == 'kappa':
# y_label = 'Kappa value'
# else:
# y_label = 'Mean square error'
# plt.ylabel('%s' % y_label)
# plt.show()