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A function to delete tokens from a spacy Doc object without losing associated information (PartOfSpeech, Dependance, Lemma, ...)
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| def remove_tokens(doc, index_to_del, list_attr=[LOWER, POS, ENT_TYPE, IS_ALPHA, DEP, LEMMA, LOWER, IS_PUNCT, IS_DIGIT, IS_SPACE, IS_STOP]): | |
| """ | |
| Remove tokens from a Spacy *Doc* object without losing | |
| associated information (PartOfSpeech, Dependance, Lemma, extensions, ...) | |
| Parameters | |
| ---------- | |
| doc : spacy.tokens.doc.Doc | |
| spacy representation of the text | |
| index_to_del : list of integer | |
| positions of each token you want to delete from the document | |
| list_attr : list, optional | |
| Contains the Spacy attributes you want to keep (the default is | |
| [LOWER, POS, ENT_TYPE, IS_ALPHA, DEP, LEMMA, LOWER, IS_PUNCT, IS_DIGIT, IS_SPACE, IS_STOP]) | |
| Returns | |
| ------- | |
| spacy.tokens.doc.Doc | |
| Filtered version of doc | |
| """ | |
| np_array = doc.to_array(list_attr) # Array representation of Doc | |
| # Creating a mask: boolean array of the indexes to delete | |
| mask_to_del = np.ones(len(np_array), np.bool) | |
| mask_to_del[index_to_del] = 0 | |
| np_array_2 = np_array[mask_to_del] | |
| doc2 = Doc(doc.vocab, words=[t.text for t in doc if t.i not in index_to_del]) | |
| doc2.from_array(list_attr, np_array_2) | |
| ### Modification made by @yarongon https://gist.github.com/Jacobe2169/5086c7c4f6c56e9d3c7cfb1eb0010fe8#gistcomment-2941380 | |
| # Handling user extensions | |
| # The `doc.user_data` dictionary is holding the data backing user-defined attributes. | |
| # The data is based on characters offset, so a conversion is needed from the | |
| # old Doc to the new one. | |
| # More info here: https://github.com/explosion/spaCy/issues/2532 | |
| arr = np.arange(len(doc)) | |
| new_index_to_old = arr[mask_to_del] | |
| doc_offset_2_token = {tok.idx : tok.i for tok in doc} # needed for the user data | |
| doc2_token_2_offset = {tok.i : tok.idx for tok in doc2} # needed for the user data | |
| new_user_data = {} | |
| for ((prefix, ext_name, offset, x), val) in doc.user_data.items(): | |
| old_token_index = doc_offset_2_token[offset] | |
| new_token_index = np.where(new_index_to_old == old_token_index)[0] | |
| if new_token_index.size == 0: # Case this index was deleted | |
| continue | |
| new_char_index = doc2_token_2_offset[new_token_index[0]] | |
| new_user_data[(prefix, ext_name, new_char_index, x)] = val | |
| doc2.user_data = new_user_data | |
| return doc2 |
You need to import: from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA, DEP, LEMMA, LOWER, IS_PUNCT, IS_DIGIT, IS_SPACE, IS_STOP
This function was very helpful for me, and really saved me time.
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Hi, I modified your code a little bit both for supporting user extensions and for performance.