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data_loader.py
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data_loader.py
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import csv
import math
import os
import numpy as np
from tqdm import tqdm
from defs import SENT_COUNTS_FILENAME, UNK_TOKEN
from util import to_one_hot
DATA_KEYS = [
"sentences",
"scores",
"aspects",
"aspects_w",
"aspects_pos",
"aspect1hot",
"aspect_senti",
"uias_vec",
"random_aspect_senti",
"opinions",
"opinions_pos",
"opinions_w",
"ratings",
]
class DataLoader:
sent_counts_filename = SENT_COUNTS_FILENAME
def __init__(
self,
path,
user2index,
item2index,
word2count,
word2index,
aspect2index,
opinion2index,
aspect_opinions={},
src_aspect_score="aspect_score_efm",
):
self.path = path
self.user2index = user2index
self.item2index = item2index
self.word2count = word2count
self.total_words = sum(word2count.values())
self.word2index = word2index
self.index2word = {v: k for k, v in self.word2index.items()}
self.aspect2index = aspect2index
self.opinion2index = aspect2index
self.n_user = max(self.user2index.values()) + 1
self.n_item = max(self.item2index.values()) + 1
self.n_vocab = max(self.word2index.values()) + 1
self.n_aspect = max(self.aspect2index.values()) + 1
self.n_opinion = max(self.opinion2index.values()) + 1
self.aspect_opinions = aspect_opinions
self.src_aspect_score = src_aspect_score
self.fds = []
def data_group_by_sentence_len(self, data_file_path):
data_by_sentence_len = {}
with open(data_file_path, "r") as f:
reader = csv.DictReader(f)
for row in tqdm(
reader, desc="Read data from {} into memory".format(data_file_path)
):
data = data_by_sentence_len.setdefault(
row["sentence_len"], {key: [] for key in DATA_KEYS}
)
sent_words = row["sentence"].split()
assert len(sent_words) > 1
sent_inds = [
self.word2index.get(word, self.word2index[UNK_TOKEN])
for word in sent_words
]
score = (
float(row[self.src_aspect_score])
if len(row[self.src_aspect_score].strip()) > 0
else 0.0
)
aspect_index = self.aspect2index.get(
row["aspect"], self.aspect2index[UNK_TOKEN]
)
aspect1hot = to_one_hot(aspect_index, self.n_aspect)
user1hot = to_one_hot(self.user2index[row["reviewerID"]], self.n_user)
item1hot = to_one_hot(self.item2index[row["asin"]], self.n_item)
data["sentences"].append(sent_inds)
data["scores"].append([score])
data["aspects"].append(row["aspect"])
data["aspects_pos"].append(row["aspect_pos"])
aspect_w = self.word2index.get(
row["aspect"], self.word2index[UNK_TOKEN]
)
data["aspects_w"].append(aspect_w)
data["aspect1hot"].append(aspect1hot)
data["aspect_senti"].append(aspect1hot * score)
data["uias_vec"].append(
np.concatenate((user1hot, item1hot, aspect1hot * score))
)
data["random_aspect_senti"].append(
to_one_hot(
np.random.choice(self.n_aspect),
self.n_aspect,
np.random.random(1).astype(np.float32) * 4 + 1,
)
) # score range from 1 to 5)
data["opinions"].append(row["opinion"])
data["opinions_pos"].append(row["opinion_pos"])
opinion_w = self.word2index.get(
row["opinion"], self.word2index[UNK_TOKEN]
)
data["opinions_w"].append(opinion_w)
data["ratings"].append(row["overall"])
return data_by_sentence_len
def get_data(self, data_file_path):
return self.data_group_by_sentence_len(data_file_path)
def open(self, batchsize=100):
self.fds = []
with open(os.path.join(self.path, self.sent_counts_filename)) as f:
for line in f:
[filename, count] = line.strip().split()
batches = int(math.ceil(float(count) / batchsize))
fd = open(os.path.join(self.path, filename), "r")
reader = csv.DictReader(fd)
self.fds = self.fds + [reader] * batches
np.random.shuffle(self.fds)
def read_batch(self, reader, batchsize, word2index):
batch = {key: [] for key in DATA_KEYS}
for row in reader:
if len(batch["sentences"]) >= batchsize:
break
sent_words = row["sentence"].split()
assert len(sent_words) > 1
sent_inds = [
word2index.get(word, word2index[UNK_TOKEN]) for word in sent_words
]
score = (
float(row[self.src_aspect_score])
if len(row[self.src_aspect_score].strip()) > 0
else 0.0
)
aspect_index = self.aspect2index.get(
row["aspect"], self.aspect2index[UNK_TOKEN]
)
aspect1hot = to_one_hot(aspect_index, self.n_aspect)
user1hot = to_one_hot(self.user2index[row["reviewerID"]], self.n_user)
item1hot = to_one_hot(self.item2index[row["asin"]], self.n_item)
batch["sentences"].append(sent_inds)
batch["scores"].append([score])
batch["aspects"].append([row["aspect"]])
batch["aspects_pos"].append([row["aspect_pos"]])
aspect_w = word2index.get(row["aspect"], word2index[UNK_TOKEN])
batch["aspects_w"].append(aspect_w)
batch["aspect1hot"].append(aspect1hot)
batch["aspect_senti"].append(aspect1hot * score)
batch["uias_vec"].append(
np.concatenate((user1hot, item1hot, aspect1hot * score))
)
batch["random_aspect_senti"].append(
to_one_hot(
np.random.choice(self.n_aspect),
self.n_aspect,
np.random.random(1).astype(np.float32) * 4 + 1,
)
) # score range from 1 to 5)
batch["opinions"].append(row["opinion"])
batch["opinions_pos"].append(row["opinion_pos"])
opinion_w = word2index.get(row["opinion"], word2index[UNK_TOKEN])
batch["opinions_w"].append(opinion_w)
batch["ratings"].append(row["overall"])
return batch
def batch_iter(self, batchsize=100):
for fd in self.fds:
batch = self.read_batch(fd, batchsize, self.word2index)
if len(batch["sentences"]) > 0:
yield batch
yield {}