def createSparseMatrix(assertions, path, use_left_features = True):
def _get_matrix_cells(assertion):
concept1, relation, concept2 = assertion
value1 = float(1)
row1 = concept1
col1 = ('right', relation, concept2)
yield value1, row1, col1
if use_left_features:
value2 = float(1)
row2 = concept2
col2 = ('left', relation, concept1)
yield value2, row2, col2
values, rows, cols = [], [], []
for assertion in assertions:
for value, row, col in _get_matrix_cells(assertion):
values.append(value)
rows.append(row)
cols.append(col)
row_labels = set(rows)
col_labels = set(cols)
sparseMatrix = SparseMatrix((len(row_labels), len(col_labels)), row_labels=row_labels, col_labels=col_labels)
assert len(values) == len(rows) and len(rows) == len(cols)
for i in xrange(len(values)):
value, row, col = values[i], rows[i], cols[i]
# TODO: more explicit handling of multiple entries for same cell
sparseMatrix.set_entry_named(row, col, value)
divisi2.save(sparseMatrix, path)
def buildMatrix(csv_filename, pickle_name, languages=['en'], cutoff=2):
csv_file = open(csv_filename, 'rb')
asserts_dict = extractAsserts(csv_file, languages)
csv_file.close()
triples = formTriples(asserts_dict)
matrix = divisi2.make_sparse(triples)
matrix = matrix.squish(cutoff)
divisi2.save(matrix, pickle_name)
def conceptnet_assoc(lang):
import divisi2
try:
matrix = divisi2.load('data:matrices/conceptnet_assoc_%s.gz' % lang)
return matrix
except IOError:
graph = divisi2.load('data:graphs/conceptnet_%s.graph.gz' % lang)
matrix = sparse_matrix(graph, 'concepts', 'concepts', 3)
divisi2.save(matrix, 'data:matrices/conceptnet_assoc_%s.gz' % lang)
return matrix
def conceptnet_assoc(lang):
import divisi2
try:
matrix = divisi2.load('data:matrices/conceptnet_assoc_%s.gz' % lang)
return matrix
except IOError:
graph = divisi2.load('data:graphs/conceptnet_%s.graph.gz' % lang)
matrix = sparse_matrix(graph, 'concepts', 'concepts', 3)
divisi2.save(matrix, 'data:matrices/conceptnet_assoc_%s.gz' % lang)
return matrix
def conceptnet_matrix(lang):
# load from the included pickle file
import divisi2
try:
matrix = divisi2.load('data:matrices/conceptnet_%s.gz' % lang)
return matrix
except IOError:
graph = divisi2.load('data:graphs/conceptnet_%s.graph.gz' % lang)
matrix = sparse_matrix(graph, 'concepts', 'features', 3)
divisi2.save(matrix, 'data:matrices/conceptnet_%s.gz' % lang)
return matrix
def conceptnet_matrix(lang):
# load from the included pickle file
import divisi2
try:
matrix = divisi2.load('data:matrices/conceptnet_%s.gz' % lang)
return matrix
except IOError:
graph = divisi2.load('data:graphs/conceptnet_%s.graph.gz' % lang)
matrix = sparse_matrix(graph, 'concepts', 'features', 3)
divisi2.save(matrix, 'data:matrices/conceptnet_%s.gz' % lang)
return matrix
def conceptnet5_assoc():
import divisi2
try:
matrix = divisi2.load('data:matrices/conceptnet_assoc_5.1.1.gz')
return matrix
except IOError:
filename = divisi2.fileIO.data_filename('data:graphs/conceptnet-5.1.1-sparse-links.csv')
triples = divisi2.dataset.conceptnet5_links(filename)
matrix = divisi2.SparseMatrix.from_named_entries(triples)
divisi2.save(matrix, 'data:matrices/conceptnet_assoc_5.1.1.gz')
return matrix
def update_doc_matrix(self, study_name='all'):
"""
Collect the documents in a particular study, and make a dense matrix
from them representing their positions in this semantic space.
FIXME: this filename may conflict with other things like 'tags'.
"""
docs = self.docs_in_study(study_name)
npmat = np.zeros((len(docs), self.config['num_axes']))
dmat = divisi2.DenseMatrix(npmat, row_labels=docs)
for docid in docs:
row = dmat.row_index(docid)
dmat[row] = self.vector_from_document(docid)
divisi2.save(dmat, self.filename_in_dir(study_name+'.dmat'))
def update_tag_matrix(self):
"""
Collect the tags in a particular study, and make a dense matrix
from them representing their average positions in this semantic space.
"""
all_tags = self.database.all_tags()
npmat = np.zeros((len(all_tags), self.config['num_axes']))
dmat = divisi2.DenseMatrix(npmat, row_labels=all_tags)
for key, value in all_tags:
row = dmat.row_index((key, value))
ndocs = 0
for docid in self.database.documents_with_tag_value(key, value):
dmat[row] += self.vector_from_document(docid)
ndocs += 1
if ndocs > 0:
dmat[row] /= ndocs
divisi2.save(dmat, self.filename_in_dir('tags.dmat'))
self._tag_matrix = dmat
return dmat
from conceptnet.models import *
import divisi2
import os
import codecs
sparse_pieces = []
for filename in os.listdir('.'):
if filename.startswith('conceptnet_zh_'):
for line in codecs.open(filename, encoding='utf-8', errors='replace'):
line = line.strip()
if line:
parts = line.split(', ')
user, frame_id, concept1, concept2 = parts
relation = Frame.objects.get(id=int(frame_id)).relation
left_feature = u"%s\\%s" % (concept1, relation)
right_feature = u"%s/%s" % (relation, concept2)
sparse_pieces.append((1, concept1, right_feature))
sparse_pieces.append((1, concept2, left_feature))
matrix = divisi2.make_sparse(sparse_pieces)
divisi2.save(matrix, 'feature_matrix_zh.smat')