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imikolov.py
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imikolov.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import tarfile
import numpy as np
from paddle.dataset.common import _check_exists_and_download
from paddle.io import Dataset
__all__ = []
URL = 'https://dataset.bj.bcebos.com/imikolov%2Fsimple-examples.tgz'
MD5 = '30177ea32e27c525793142b6bf2c8e2d'
class Imikolov(Dataset):
"""
Implementation of imikolov dataset.
Args:
data_file(str): path to data tar file, can be set None if
:attr:`download` is True. Default None
data_type(str): 'NGRAM' or 'SEQ'. Default 'NGRAM'.
window_size(int): sliding window size for 'NGRAM' data. Default -1.
mode(str): 'train' 'test' mode. Default 'train'.
min_word_freq(int): minimal word frequence for building word dictionary. Default 50.
download(bool): whether to download dataset automatically if
:attr:`data_file` is not set. Default True
Returns:
Dataset: instance of imikolov dataset
Examples:
.. code-block:: python
import paddle
from paddle.text.datasets import Imikolov
class SimpleNet(paddle.nn.Layer):
def __init__(self):
super().__init__()
def forward(self, src, trg):
return paddle.sum(src), paddle.sum(trg)
imikolov = Imikolov(mode='train', data_type='SEQ', window_size=2)
for i in range(10):
src, trg = imikolov[i]
src = paddle.to_tensor(src)
trg = paddle.to_tensor(trg)
model = SimpleNet()
src, trg = model(src, trg)
print(src.shape, trg.shape)
"""
def __init__(
self,
data_file=None,
data_type='NGRAM',
window_size=-1,
mode='train',
min_word_freq=50,
download=True,
):
assert data_type.upper() in [
'NGRAM',
'SEQ',
], f"data type should be 'NGRAM', 'SEQ', but got {data_type}"
self.data_type = data_type.upper()
assert mode.lower() in [
'train',
'test',
], f"mode should be 'train', 'test', but got {mode}"
self.mode = mode.lower()
self.window_size = window_size
self.min_word_freq = min_word_freq
self.data_file = data_file
if self.data_file is None:
assert (
download
), "data_file is not set and downloading automatically disabled"
self.data_file = _check_exists_and_download(
data_file, URL, MD5, 'imikolov', download
)
# Build a word dictionary from the corpus
self.word_idx = self._build_work_dict(min_word_freq)
# read dataset into memory
self._load_anno()
def word_count(self, f, word_freq=None):
if word_freq is None:
word_freq = collections.defaultdict(int)
for l in f:
for w in l.strip().split():
word_freq[w] += 1
word_freq['<s>'] += 1
word_freq['<e>'] += 1
return word_freq
def _build_work_dict(self, cutoff):
train_filename = './simple-examples/data/ptb.train.txt'
test_filename = './simple-examples/data/ptb.valid.txt'
with tarfile.open(self.data_file) as tf:
trainf = tf.extractfile(train_filename)
testf = tf.extractfile(test_filename)
word_freq = self.word_count(testf, self.word_count(trainf))
if '<unk>' in word_freq:
# remove <unk> for now, since we will set it as last index
del word_freq['<unk>']
word_freq = [
x for x in word_freq.items() if x[1] > self.min_word_freq
]
word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*word_freq_sorted))
word_idx = dict(list(zip(words, range(len(words)))))
word_idx['<unk>'] = len(words)
return word_idx
def _load_anno(self):
self.data = []
with tarfile.open(self.data_file) as tf:
filename = f'./simple-examples/data/ptb.{self.mode}.txt'
f = tf.extractfile(filename)
UNK = self.word_idx['<unk>']
for l in f:
if self.data_type == 'NGRAM':
assert self.window_size > -1, 'Invalid gram length'
l = ['<s>'] + l.strip().split() + ['<e>']
if len(l) >= self.window_size:
l = [self.word_idx.get(w, UNK) for w in l]
for i in range(self.window_size, len(l) + 1):
self.data.append(tuple(l[i - self.window_size : i]))
elif self.data_type == 'SEQ':
l = l.strip().split()
l = [self.word_idx.get(w, UNK) for w in l]
src_seq = [self.word_idx['<s>']] + l
trg_seq = l + [self.word_idx['<e>']]
if self.window_size > 0 and len(src_seq) > self.window_size:
continue
self.data.append((src_seq, trg_seq))
else:
raise AssertionError('Unknow data type')
def __getitem__(self, idx):
return tuple([np.array(d) for d in self.data[idx]])
def __len__(self):
return len(self.data)