# Copyright 2017 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 numpy as np
from typing import List, Callable
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.component import Component
[docs]@register('bow')
class BoWEmbedder(Component):
"""
Performs one-hot encoding of tokens based on a pre-built vocabulary of tokens.
Example:
.. code:: python
>>> bow = BoWEmbedder()
>>> bow(['a', 'b', 'c'], vocab={'a': 0, 'b': 1})
[array([1, 0], dtype=int32),
array([0, 1], dtype=int32),
array([0, 0], dtype=int32)]
"""
def __init__(self, **kwargs) -> None:
pass
def _encode(self, tokens, vocab):
bow = np.zeros([len(vocab)], dtype=np.int32)
for token in tokens:
if token in vocab:
idx = vocab[token]
bow[idx] += 1
return bow
def __call__(self, batch: List[List[str]], vocab: Component) -> List[List[int]]:
return [self._encode(sample, vocab) for sample in batch]