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119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
import re
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from fuzzywuzzy import fuzz
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from documents.models import MatchingModel, Correspondent, DocumentType, Tag
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def match_correspondents(document_content, classifier):
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if classifier:
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pred_id = classifier.predict_correspondent(document_content)
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else:
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pred_id = None
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correspondents = Correspondent.objects.all()
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return list(filter(
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lambda o: matches(o, document_content) or o.pk == pred_id,
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correspondents))
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def match_document_types(document_content, classifier):
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if classifier:
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pred_id = classifier.predict_document_type(document_content)
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else:
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pred_id = None
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document_types = DocumentType.objects.all()
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return list(filter(
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lambda o: matches(o, document_content) or o.pk == pred_id,
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document_types))
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def match_tags(document_content, classifier):
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if classifier:
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predicted_tag_ids = classifier.predict_tags(document_content)
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else:
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predicted_tag_ids = []
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tags = Tag.objects.all()
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return list(filter(
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lambda o: matches(o, document_content) or o.pk in predicted_tag_ids,
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tags))
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def matches(matching_model, document_content):
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search_kwargs = {}
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document_content = document_content.lower()
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# Check that match is not empty
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if matching_model.match.strip() == "":
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return False
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if matching_model.is_insensitive:
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search_kwargs = {"flags": re.IGNORECASE}
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if matching_model.matching_algorithm == MatchingModel.MATCH_ALL:
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for word in _split_match(matching_model):
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search_result = re.search(
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rf"\b{word}\b", document_content, **search_kwargs)
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if not search_result:
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return False
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return True
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elif matching_model.matching_algorithm == MatchingModel.MATCH_ANY:
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for word in _split_match(matching_model):
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if re.search(rf"\b{word}\b", document_content, **search_kwargs):
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return True
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return False
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elif matching_model.matching_algorithm == MatchingModel.MATCH_LITERAL:
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return bool(re.search(
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rf"\b{matching_model.match}\b",
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document_content,
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**search_kwargs
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))
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elif matching_model.matching_algorithm == MatchingModel.MATCH_REGEX:
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return bool(re.search(
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re.compile(matching_model.match, **search_kwargs),
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document_content
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))
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elif matching_model.matching_algorithm == MatchingModel.MATCH_FUZZY:
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match = re.sub(r'[^\w\s]', '', matching_model.match)
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text = re.sub(r'[^\w\s]', '', document_content)
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if matching_model.is_insensitive:
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match = match.lower()
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text = text.lower()
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return fuzz.partial_ratio(match, text) >= 90
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elif matching_model.matching_algorithm == MatchingModel.MATCH_AUTO:
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# this is done elsewhere.
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return False
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else:
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raise NotImplementedError("Unsupported matching algorithm")
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def _split_match(matching_model):
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"""
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Splits the match to individual keywords, getting rid of unnecessary
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spaces and grouping quoted words together.
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Example:
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' some random words "with quotes " and spaces'
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==>
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["some", "random", "words", "with+quotes", "and", "spaces"]
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"""
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findterms = re.compile(r'"([^"]+)"|(\S+)').findall
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normspace = re.compile(r"\s+").sub
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return [
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normspace(" ", (t[0] or t[1]).strip()).replace(" ", r"\s+")
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for t in findterms(matching_model.match)
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]
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