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)
weighted_relations.append(rel_triple)
for idx in range(len(q)):
obj = q[idx][2]
subj = q[idx][0]
weight = 4.0
rel_triple = (weight, obj, subj)
obj_list.append(obj)
# weighted_triple = (rel_triple, weight)
weighted_relations.append(rel_triple)
print len(weighted_relations)
#print len(obj_list)
obj_list = set(obj_list)
print len(obj_list)
matrix = divisi2.make_sparse(weighted_relations)
#print matrix
# ConceptNet Matrix
A = divisi2.network.conceptnet_matrix('en')
A_concept_axes, A_axis_weights, A_feature_axes = A.svd(k=100)
blended_matrix = blend([matrix, A])
concept_axes, axis_weights, feature_axes = blended_matrix.svd(k=100)
common_objects = list(set(obj_list).intersection(A.row_labels))
print len(A.row_labels)
# Save embeddings for ConceptNet
cnet_object_embeddings = np.array(
[A_concept_axes.row_named(obj) for obj in common_objects])
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')
def test_set_matrix():
m = make_sparse(data)
mm = OurSparseMatrix()
mm.set(m)
assert_equal(matrix.get(), mm.get())
def test_equal():
m = make_sparse(data)
assert_equal(matrix.get(), m)
