Source code for condor.models.term_document_matrix

Just a normal text document matrix representation, the actual matrix is
stored off site on a numpy file.
import hashlib
import os

import numpy
from nltk import collections

from sqlalchemy import Column, ForeignKey, Unicode
from sqlalchemy.orm import relationship

from condor.config import MATRIX_PATH, TERM_LIST_PATH
from condor.models.base import AuditableMixing, DeclarativeBase
from condor.normalize import CompleteNormalizer

[docs]class TermDocumentMatrix(AuditableMixing, DeclarativeBase): """ Represents a term document matrix. """ __tablename__ = 'term_document_matrix' bibliography_eid = Column( Unicode(40), ForeignKey('bibliography.eid') ) bibliography_options = Column(Unicode(512), nullable=False) processing_options = Column(Unicode(512), nullable=False) term_list_path = Column(Unicode(512), nullable=False) matrix_path = Column(Unicode(512), nullable=False) bibliography = relationship( 'Bibliography', back_populates='term_document_matrices', ) ranking_matrices = relationship( 'RankingMatrix', back_populates='term_document_matrix', cascade='all, delete-orphan', )
[docs] @classmethod def from_bibliography_set(cls, bibliography, regularise=True, fields=None, normalizer_class=None): """ Build a matrix from a document set. This has the side effect of creating the matrix a and terms files. :param bibliography: a document set :param regularise: apply TF IDF regularization. :param fields: fields of interest :param normalizer_class: normalizer class to use :return: a term document matrix. """ fields = fields or ['title', 'description', 'keywords'] normalizer_class = normalizer_class or CompleteNormalizer words = bibliography.words(fields=fields, normalizer_class=CompleteNormalizer) frequency = numpy.zeros( (len(bibliography.documents), len(words)), dtype=int ) for row, col, freq in cls._matrix(words, bibliography, fields, normalizer_class): frequency[row][col] = freq if regularise: tf = (frequency.T / numpy.sum(frequency, axis=1)).T df = numpy.sum(frequency > 0, axis=0) idf = numpy.log(len(bibliography.documents) / df) + 1 frequency = tf * idf unique_hash = hashlib.sha1( '{}{}{}{}'.format( ''.join(words), ''.join(fields), normalizer_class.__mro__, regularise ).encode() ).hexdigest() matrix_filename = os.path.join(MATRIX_PATH, unique_hash + '.npy') words_filename = os.path.join(TERM_LIST_PATH, unique_hash + '.txt'), frequency) with open(words_filename, 'w') as file: file.write('\n'.join(words)) return cls( bibliography_options='', processing_options=str(normalizer_class.__mro__), term_list_path=words_filename, matrix_path=matrix_filename, bibliography_eid=bibliography.eid )
@staticmethod def _matrix(words, bibliography, fields, normalizer_class): word_dict = {word: pos for pos, word in enumerate(words)} for ind, bib in enumerate(bibliography.documents): raw = bib.raw_data(fields, normalizer_class) frequency = collections.Counter(raw) for word, freq in frequency.items(): yield ind, word_dict[word], freq @property def words(self): """ Load the words stored off site. """ with open(self.term_list_path) as term_file: terms ='\n') return terms @property def matrix(self): """ Load the matrix stored off site. """ return numpy.load(self.matrix_path)