Source code for deeppavlov.models.embedders.bow_embedder

# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np
from typing import Union, Dict, List

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]