# 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.
from typing import List
import numpy as np
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.
Parameters:
depth: size of output numpy vector.
with_counts: flag denotes whether to use binary encoding (with zeros and ones),
or to use counts as token representation.
Example:
.. code:: python
>>> bow = BoWEmbedder(depth=3)
>>> bow([[0, 1], [1], [])
[array([1, 1, 0], dtype=int32),
array([0, 1, 0], dtype=int32),
array([0, 0, 0], dtype=int32)]
"""
def __init__(self, depth: int, with_counts: bool = False, **kwargs) -> None:
self.depth = depth
self.with_counts = with_counts
def _encode(self, token_indices: List[int]) -> np.ndarray:
bow = np.zeros([self.depth], dtype=np.int32)
for idx in token_indices:
if self.with_counts:
bow[idx] += 1
else:
bow[idx] = 1
return bow
def __call__(self, batch: List[List[int]]) -> List[np.ndarray]:
return [self._encode(sample) for sample in batch]