def get_analogy_blend(self):
other_matrices = [matrix for name, matrix in
self.other_matrices.items() if name.endswith('.smat')]
other_matrices = self.other_matrices.values()
# find concepts used at least twice
docs = self.get_documents_matrix()
concept_counts = docs.col_op(len)
valid_concepts = set()
for concept, count in concept_counts.to_sparse().named_items():
if count >= 3: valid_concepts.add(concept)
# extract relevant concepts from the doc matrix;
# transpose it so it's concepts vs. documents
orig_doc_matrix = self.get_documents_matrix()
#sdoc_indices = [orig_doc_matrix.row_index(sdoc.name)
# for sdoc in self.study_documents]
concept_indices = [orig_doc_matrix.col_index(c)
for c in valid_concepts]
# NOTE: canonical documents can affect the stats this way.
# Is there a clean way to fix this?
doc_matrix = orig_doc_matrix[:,concept_indices].T.squish()
if doc_matrix is None:
theblend = blend(other_matrices)
study_concepts = set(theblend.row_labels)
else:
theblend = blend([doc_matrix] + other_matrices)
study_concepts = set(doc_matrix.row_labels)
return theblend, study_concepts
def get_analogy_blend(self):
other_matrices = [
matrix for name, matrix in self.other_matrices.items()
if name.endswith('.smat')
]
other_matrices = self.other_matrices.values()
# find concepts used at least twice
docs = self.get_documents_matrix()
concept_counts = docs.col_op(len)
valid_concepts = set()
for concept, count in concept_counts.to_sparse().named_items():
if count >= 3: valid_concepts.add(concept)
# extract relevant concepts from the doc matrix;
# transpose it so it's concepts vs. documents
orig_doc_matrix = self.get_documents_matrix()
#sdoc_indices = [orig_doc_matrix.row_index(sdoc.name)
# for sdoc in self.study_documents]
concept_indices = [
orig_doc_matrix.col_index(c) for c in valid_concepts
]
# NOTE: canonical documents can affect the stats this way.
# Is there a clean way to fix this?
doc_matrix = orig_doc_matrix[:, concept_indices].T.squish()
if doc_matrix is None:
theblend = blend(other_matrices)
study_concepts = set(theblend.row_labels)
else:
theblend = blend([doc_matrix] + other_matrices)
study_concepts = set(doc_matrix.row_labels)
return theblend, study_concepts
def get_assoc_blend(self):
other_matrices = []
doc_matrix = self.get_documents_assoc()
self._step('Blending...')
for name, matrix in self.other_matrices.items():
# use association matrices only
# (unless we figure out how to do both kinds of blending)
if name.endswith('.assoc.smat'):
if matrix.shape[0] != matrix.shape[1]:
raise ValueError("The matrix %s is not square" % name)
other_matrices.append(matrix)
if doc_matrix is None:
theblend = blend(other_matrices)
study_concepts = set(theblend.row_labels)
else:
theblend = blend([doc_matrix] + other_matrices)
study_concepts = set(doc_matrix.row_labels)
return theblend, study_concepts
def get_assoc_blend(self):
other_matrices = []
doc_matrix = self.get_documents_assoc()
self._step('Blending...')
for name, matrix in self.other_matrices.items():
# use association matrices only
# (unless we figure out how to do both kinds of blending)
if name.endswith('.assoc.smat'):
if matrix.shape[0] != matrix.shape[1]:
raise ValueError("The matrix %s is not square" % name)
other_matrices.append(matrix)
if doc_matrix is None:
theblend = blend(other_matrices)
study_concepts = set(theblend.row_labels)
else:
theblend = blend([doc_matrix] + other_matrices)
study_concepts = set(doc_matrix.row_labels)
return theblend, study_concepts
def make_blend(thefile):
conceptnet = divisi2.network.conceptnet_matrix('en').normalize_all()
thegame = divisi2.load(thefile).normalize_all()
blended_matrix = blend([conceptnet, thegame], [0.9, 0.1])
u,s,v = blended_matrix.svd()
similarity = divisi2.reconstruct_similarity(u, s) # offset=1.5)
pd.mkdir(thefile.split('.')[0])
pd[thefile.split('.')[0]]['blend'] = similarity
return similarity
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])
np.save('cnet_object_embeddings.npy', cnet_object_embeddings)
# Save embeddings for Blended Matrix
blended_object_embeddings = np.array(
[concept_axes.row_named(obj) for obj in common_objects])
np.save('blended_object_embeddings.npy', blended_object_embeddings)
v = []
