# Copyright 2018 Neural Networks and Deep Learning lab, MIPT
#
# 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 csv
from pathlib import Path
from typing import List, Tuple, Union, Dict
from deeppavlov.core.common.registry import register
from deeppavlov.core.data.dataset_reader import DatasetReader
[docs]@register('ubuntu_v2_mt_reader')
class UbuntuV2MTReader(DatasetReader):
"""The class to read the Ubuntu V2 dataset from csv files taking into account multi-turn dialogue ``context``.
Please, see https://github.com/rkadlec/ubuntu-ranking-dataset-creator.
Args:
data_path: A path to a folder with dataset csv files.
num_context_turns: A maximum number of dialogue ``context`` turns.
padding: "post" or "pre" context sentences padding
"""
[docs] def read(self, data_path: str,
num_context_turns: int = 1,
padding: str = "post",
*args, **kwargs) -> Dict[str, List[Tuple[List[str], int]]]:
"""Read the Ubuntu V2 dataset from csv files taking into account multi-turn dialogue ``context``.
Args:
data_path: A path to a folder with dataset csv files.
num_context_turns: A maximum number of dialogue ``context`` turns.
padding: "post" or "pre" context sentences padding
Returns:
Dictionary with keys "train", "valid", "test" and parts of the dataset as their values
"""
self.num_turns = num_context_turns
self.padding = padding
dataset = {'train': None, 'valid': None, 'test': None}
train_fname = Path(data_path) / 'train.csv'
valid_fname = Path(data_path) / 'valid.csv'
test_fname = Path(data_path) / 'test.csv'
self.sen2int_vocab = {}
self.classes_vocab_train = {}
self.classes_vocab_valid = {}
self.classes_vocab_test = {}
dataset["train"] = self.preprocess_data_train(train_fname)
dataset["valid"] = self.preprocess_data_validation(valid_fname)
dataset["test"] = self.preprocess_data_validation(test_fname)
return dataset
def preprocess_data_train(self, train_fname: Union[Path, str]) -> List[Tuple[List[str], int]]:
contexts = []
responses = []
labels = []
with open(train_fname, 'r') as f:
reader = csv.reader(f)
next(reader)
for el in reader:
contexts.append(self._expand_context(el[0].split('__eot__'), padding=self.padding))
responses.append(el[1])
labels.append(int(el[2]))
data = [el[0] + [el[1]] for el in zip(contexts, responses)]
data = list(zip(data, labels))
return data
def preprocess_data_validation(self, fname: Union[Path, str]) -> List[Tuple[List[str], int]]:
contexts = []
responses = []
with open(fname, 'r') as f:
reader = csv.reader(f)
next(reader)
for el in reader:
contexts.append(self._expand_context(el[0].split('__eot__'), padding=self.padding))
responses.append(el[1:])
data = [el[0] + el[1] for el in zip(contexts, responses)]
data = [(el, 1) for el in data] # NOTE: labels are useless here actually...
return data
def _expand_context(self, context: List[str], padding: str) -> List[str]:
"""
Align context length by using pre/post padding of empty sentences up to ``self.num_turns`` sentences
or by reducing the number of context sentences to ``self.num_turns`` sentences.
Args:
context (List[str]): list of raw context sentences
padding (str): "post" or "pre" context sentences padding
Returns:
List[str]: list of ``self.num_turns`` context sentences
"""
if padding == "post":
sent_list = context
res = sent_list + (self.num_turns - len(sent_list)) * \
[''] if len(sent_list) < self.num_turns else sent_list[:self.num_turns]
return res
elif padding == "pre":
# context[-(self.num_turns + 1):-1] because the last item of `context` is always '' (empty string)
sent_list = context[-(self.num_turns + 1):-1]
if len(sent_list) <= self.num_turns:
tmp = sent_list[:]
sent_list = [''] * (self.num_turns - len(sent_list))
sent_list.extend(tmp)
return sent_list