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https://github.com/explosion/spaCy.git
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Merging multiple docs into one (#5032)
* Add static method to Doc to allow merging of multiple docs. * Add error description for the error that occurs if docs with different vocabs (from different languages) are merged in Doc.from_docs(). * Add test for Doc.from_docs() implementation. * Fix using numpy's concatenate in Doc.from_docs. * Replace typing's type annotations in from_docs. * Simply remove type annotations in from_docs. * Add documentation for Doc.from_docs to api. * Simplify from_docs, its test and the api doc for codebase consistency. * Fix merging of Doc objects that end with whitespaces (Achieved by simply not setting the SPACY attribute on whitespace tokens). Remove two unnecessary imports of attributes. * Add merging of user data from Doc objects in from_docs. Add user data test case to corresponding test. Add applicable warning messages. * Fix incorrect setting of tokens idx by using concatenated spaces (again). Add test case to corresponding test. * Add MORPH to attrs * Update warnings calls * Remove out-dated error from merge * Rename space_delimiter to ensure_whitespace Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
This commit is contained in:
Jan Jessewitsch
GitHub
parent
41b65fd0f8
commit
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@ -159,6 +159,8 @@ class Warnings(object):
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W100 = ("Skipping unsupported morphological feature(s): '{feature}'. "
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"Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or "
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"string \"Field1=Value1,Value2|Field2=Value3\".")
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W101 = ("Skipping `Doc` custom extension '{name}' while merging docs.")
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W102 = ("Skipping unsupported user data '{key}: {value}' while merging docs.")
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@add_codes
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@ -593,7 +595,9 @@ class Errors(object):
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E997 = ("Tokenizer special cases are not allowed to modify the text. "
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"This would map '{chunk}' to '{orth}' given token attributes "
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"'{token_attrs}'.")
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E999 = ("Unable to merge the `Doc` objects because they do not all share "
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"the same `Vocab`.")
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@add_codes
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class TempErrors(object):
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@ -303,6 +303,60 @@ def test_doc_from_array_sent_starts(en_vocab):
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assert new_doc.is_parsed
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def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
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en_texts = ["Merging the docs is fun.", "They don't think alike."]
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de_text = "Wie war die Frage?"
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en_docs = [en_tokenizer(text) for text in en_texts]
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docs_idx = en_texts[0].index('docs')
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de_doc = de_tokenizer(de_text)
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en_docs[0].user_data[("._.", "is_ambiguous", docs_idx, None)] = (True, None, None, None)
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assert Doc.from_docs([]) is None
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assert de_doc is not Doc.from_docs([de_doc])
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assert str(de_doc) == str(Doc.from_docs([de_doc]))
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with pytest.raises(ValueError):
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Doc.from_docs(en_docs + [de_doc])
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m_doc = Doc.from_docs(en_docs)
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assert len(en_docs) == len(list(m_doc.sents))
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assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
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assert str(m_doc) == " ".join(en_texts)
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p_token = m_doc[len(en_docs[0])-1]
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assert p_token.text == "." and bool(p_token.whitespace_)
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en_docs_tokens = [t for doc in en_docs for t in doc]
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assert len(m_doc) == len(en_docs_tokens)
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think_idx = len(en_texts[0]) + 1 + en_texts[1].index('think')
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assert m_doc[9].idx == think_idx
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with pytest.raises(AttributeError):
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not_available = m_doc[2]._.is_ambiguous # not callable, because it was not set via set_extension
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assert len(m_doc.user_data) == len(en_docs[0].user_data) # but it's there
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m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
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assert len(en_docs) == len(list(m_doc.sents))
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assert len(str(m_doc)) == len(en_texts[0]) + len(en_texts[1])
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assert str(m_doc) == "".join(en_texts)
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p_token = m_doc[len(en_docs[0]) - 1]
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assert p_token.text == "." and not bool(p_token.whitespace_)
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en_docs_tokens = [t for doc in en_docs for t in doc]
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assert len(m_doc) == len(en_docs_tokens)
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think_idx = len(en_texts[0]) + 0 + en_texts[1].index('think')
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assert m_doc[9].idx == think_idx
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m_doc = Doc.from_docs(en_docs, attrs=['lemma', 'length', 'pos'])
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with pytest.raises(ValueError): # important attributes from sentenziser or parser are missing
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assert list(m_doc.sents)
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assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
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assert str(m_doc) == " ".join(en_texts) # space delimiter considered, although spacy attribute was missing
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p_token = m_doc[len(en_docs[0]) - 1]
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assert p_token.text == "." and bool(p_token.whitespace_)
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en_docs_tokens = [t for doc in en_docs for t in doc]
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assert len(m_doc) == len(en_docs_tokens)
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think_idx = len(en_texts[0]) + 1 + en_texts[1].index('think')
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assert m_doc[9].idx == think_idx
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def test_doc_lang(en_vocab):
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doc = Doc(en_vocab, words=["Hello", "world"])
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assert doc.lang_ == "en"
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@ -5,6 +5,7 @@ from libc.string cimport memcpy, memset
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from libc.math cimport sqrt
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from libc.stdint cimport int32_t, uint64_t
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import copy
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from collections import Counter
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import numpy
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import numpy.linalg
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@ -24,7 +25,7 @@ from ..attrs cimport LENGTH, POS, LEMMA, TAG, MORPH, DEP, HEAD, SPACY, ENT_IOB
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from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, attr_id_t
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from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
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from ..attrs import intify_attrs, IDS
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from ..attrs import intify_attr, intify_attrs, IDS
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from ..util import normalize_slice
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from ..compat import copy_reg, pickle
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from ..errors import Errors, Warnings
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@ -806,7 +807,7 @@ cdef class Doc:
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attrs = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
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for id_ in attrs]
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if array.dtype != numpy.uint64:
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warnings.warn(Warnings.W028.format(type=array.dtype))
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warnings.warn(Warnings.W101.format(type=array.dtype))
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if SENT_START in attrs and HEAD in attrs:
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raise ValueError(Errors.E032)
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@ -882,6 +883,87 @@ cdef class Doc:
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set_children_from_heads(self.c, length)
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return self
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@staticmethod
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def from_docs(docs, ensure_whitespace=True, attrs=None):
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"""Concatenate multiple Doc objects to form a new one. Raises an error if the `Doc` objects do not all share
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the same `Vocab`.
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docs (list): A list of Doc objects.
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ensure_whitespace (bool): Insert a space between two adjacent docs whenever the first doc does not end in whitespace.
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attrs (list): Optional list of attribute ID ints or attribute name strings.
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RETURNS (Doc): A doc that contains the concatenated docs, or None if no docs were given.
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DOCS: https://spacy.io/api/doc#from_docs
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"""
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if not docs:
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return None
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vocab = {doc.vocab for doc in docs}
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if len(vocab) > 1:
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raise ValueError(Errors.E999)
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(vocab,) = vocab
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if attrs is None:
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attrs = [LEMMA, NORM]
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if all(doc.is_nered for doc in docs):
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attrs.extend([ENT_IOB, ENT_KB_ID, ENT_TYPE])
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# TODO: separate for is_morphed?
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if all(doc.is_tagged for doc in docs):
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attrs.extend([TAG, POS, MORPH])
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if all(doc.is_parsed for doc in docs):
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attrs.extend([HEAD, DEP])
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else:
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attrs.append(SENT_START)
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else:
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if any(isinstance(attr, str) for attr in attrs): # resolve attribute names
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attrs = [intify_attr(attr) for attr in attrs] # intify_attr returns None for invalid attrs
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attrs = list(attr for attr in set(attrs) if attr) # filter duplicates, remove None if present
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if SPACY not in attrs:
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attrs.append(SPACY)
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concat_words = []
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concat_spaces = []
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concat_user_data = {}
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char_offset = 0
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for doc in docs:
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concat_words.extend(t.text for t in doc)
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concat_spaces.extend(bool(t.whitespace_) for t in doc)
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for key, value in doc.user_data.items():
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if isinstance(key, tuple) and len(key) == 4:
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data_type, name, start, end = key
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if start is not None or end is not None:
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start += char_offset
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if end is not None:
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end += char_offset
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concat_user_data[(data_type, name, start, end)] = copy.copy(value)
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else:
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warnings.warn(Warnings.W101.format(name=name))
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else:
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warnings.warn(Warnings.W102.format(key=key, value=value))
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char_offset += len(doc.text) if not ensure_whitespace or doc[-1].is_space else len(doc.text) + 1
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arrays = [doc.to_array(attrs) for doc in docs]
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if ensure_whitespace:
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spacy_index = attrs.index(SPACY)
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for i, array in enumerate(arrays[:-1]):
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if len(array) > 0 and not docs[i][-1].is_space:
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array[-1][spacy_index] = 1
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token_offset = -1
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for doc in docs[:-1]:
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token_offset += len(doc)
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if not doc[-1].is_space:
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concat_spaces[token_offset] = True
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concat_array = numpy.concatenate(arrays)
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concat_doc = Doc(vocab, words=concat_words, spaces=concat_spaces, user_data=concat_user_data)
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concat_doc.from_array(attrs, concat_array)
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return concat_doc
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def get_lca_matrix(self):
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"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
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`Doc`, where LCA[i, j] is the index of the lowest common ancestor among
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@ -349,6 +349,33 @@ array of attributes.
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| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | `Doc` | Itself. |
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## Doc.from_docs {#from_docs tag="staticmethod"}
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Concatenate multiple `Doc` objects to form a new one. Raises an error if the `Doc` objects do not all share the same `Vocab`.
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> #### Example
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>
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> ```python
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> from spacy.tokens import Doc
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> texts = ["London is the capital of the United Kingdom.",
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> "The River Thames flows through London.",
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> "The famous Tower Bridge crosses the River Thames."]
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> docs = list(nlp.pipe(texts))
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> c_doc = Doc.from_docs(docs)
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> assert str(c_doc) == " ".join(texts)
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> assert len(list(c_doc.sents)) == len(docs)
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> assert [str(ent) for ent in c_doc.ents] == \
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> [str(ent) for doc in docs for ent in doc.ents]
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> ```
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| Name | Type | Description |
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| ------------------- | ----- | ----------------------------------------------------------------------------------------------- |
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| `docs` | list | A list of `Doc` objects. |
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| `ensure_whitespace` | bool | Insert a space between two adjacent docs whenever the first doc does not end in whitespace. |
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| `attrs` | list | Optional list of attribute ID ints or attribute name strings. |
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| **RETURNS** | `Doc` | The new `Doc` object that is containing the other docs or `None`, if `docs` is empty or `None`. |
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## Doc.to_disk {#to_disk tag="method" new="2"}
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Save the current state to a directory.
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