def EnergyVecFn(fnsim, embeddings, leftop, rightop):
embedding, relationl, relationr = parse_embeddings(embeddings)
idxl, idxo, idxr = T.ivector('idxl'), T.ivector('idxo'), T.ivector('idxr')
lhs, rhs = embedding.E[:, idxl].T, embedding.E[:, idxr].T
rell, relr = relationl.E[:, idxo].T, relationr.E[:, idxo].T
energy = - fnsim(leftop(lhs, rell), rightop(rhs, relr))
return theano.function([idxl, idxr, idxo], [energy], on_unused_input='ignore')
def RankLeftFnIdx_Schema(fnsim, embeddings, prior, leftop, rightop, subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxr, idxo = T.iscalar('idxr'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
if subtensorspec is not None:
# We compute the score only for a subset of entities
lhs = (embedding.E[:, :subtensorspec]).T
else:
lhs = embedding.E.T
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
tmp = rightop(rhs, relr)
simi = fnsim(leftop(lhs, rell), tmp.reshape((1, tmp.shape[1])))
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxr, idxo, g], [simi], on_unused_input='ignore')
def RankLeftFnIdx_Schema(fnsim,
embeddings,
prior,
leftop,
rightop,
subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxr, idxo = T.iscalar('idxr'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
if subtensorspec is not None:
# We compute the score only for a subset of entities
lhs = (embedding.E[:, :subtensorspec]).T
else:
lhs = embedding.E.T
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
tmp = rightop(rhs, relr)
simi = fnsim(leftop(lhs, rell), tmp.reshape((1, tmp.shape[1])))
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxr, idxo, g], [simi], on_unused_input='ignore')
def RankRightFnIdx_Schema(fnsim, embeddings, prior, leftop, rightop, subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rell = (relationl.E[:, idxo]).reshape((1, relationl.D)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape((1, relationr.D)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
# Negative Energy
simi = fnsim(tmp.reshape((1, tmp.shape[1])), rightop(rhs, relr)) # simi = fnsim(a, b)
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxl, idxo, g], [simi], on_unused_input='ignore')
def RankRightFnIdx_filtered(fnsim, embeddings, leftop, rightop, subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'right' entities given couples of relation and 'left' entities (as
index values).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
rightparts = T.ivector('rightparts')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rhs = rhs[rightparts, :] # select the right parts not appearing
# in the train/valid/test sets
rell = (relationl.E[:, idxo]).reshape((1, relationl.D)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape((1, relationr.D)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
simi = fnsim(tmp.reshape((1, tmp.shape[1])), rightop(rhs, relr)) # simi = fnsim(a, b)
return theano.function([idxl, idxo, rightparts], [simi], on_unused_input='ignore')
def EnergyFn(fnsim, embeddings, leftop, rightop):
embedding, relationl, relationr = parse_embeddings(embeddings)
idxl, idxo, idxr = T.iscalar('idxl'), T.iscalar('idxo'), T.iscalar('idxr')
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D))
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
energy = - fnsim(leftop(lhs, rell), rightop(rhs, relr))
return theano.function([idxl, idxr, idxo], [energy], on_unused_input='ignore')
def RankRightFnIdx(fnsim, embeddings, leftop, rightop, subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'right' entities given couples of relation and 'left' entities (as
index values).
:param fnsim: similarity function (on Theano variables).
:param embeddings: an Embeddings instance.
:param leftop: class for the 'left' operator.
:param rightop: class for the 'right' operator.
:param subtensorspec: only measure the similarity score for the entities
corresponding to the first subtensorspec (int)
entities of the embedding matrix (default None: all
entities).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
# Graph
lhs = (embedding.E[:, idxl]).reshape(
(1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rell = (relationl.E[:, idxo]).reshape(
(1, relationl.D
)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape(
(1, relationr.D
)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
simi = fnsim(tmp.reshape((1, tmp.shape[1])),
rightop(rhs, relr)) # simi = fnsim(a, b)
"""
Theano function inputs.
:input idxl: index value of the 'left' member.
:input idxo: index value of the relation member.
Theano function output.
:output simi: vector of score values.
"""
return theano.function([idxl, idxo], [simi], on_unused_input='ignore')
def RankRightFnIdx(fnsim, embeddings, leftop, rightop, subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'right' entities given couples of relation and 'left' entities (as
index values).
:param fnsim: similarity function (on Theano variables).
:param embeddings: an Embeddings instance.
:param leftop: class for the 'left' operator.
:param rightop: class for the 'right' operator.
:param subtensorspec: only measure the similarity score for the entities
corresponding to the first subtensorspec (int)
entities of the embedding matrix (default None: all
entities).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rell = (relationl.E[:, idxo]).reshape((1, relationl.D)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape((1, relationr.D)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
simi = fnsim(tmp.reshape((1, tmp.shape[1])), rightop(rhs, relr)) # simi = fnsim(a, b)
"""
Theano function inputs.
:input idxl: index value of the 'left' member.
:input idxo: index value of the relation member.
Theano function output.
:output simi: vector of score values.
"""
return theano.function([idxl, idxo], [simi], on_unused_input='ignore')
def RankLeftFnIdx_filtered(fnsim,
embeddings,
leftop,
rightop,
subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'left' entities given couples of relation and 'right' entities (as
index values).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxr, idxo = T.iscalar('idxr'), T.iscalar('idxo')
leftparts = T.ivector('leftparts')
# Graph
if subtensorspec is not None:
# We compute the score only for a subset of entities
lhs = (embedding.E[:, :subtensorspec]).T
else:
lhs = embedding.E.T
lhs = lhs[leftparts, :] # select the left parts not appearing
# in the train/valid/test sets
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
tmp = rightop(rhs, relr)
simi = fnsim(leftop(lhs, rell), tmp.reshape((1, tmp.shape[1])))
"""
Theano function inputs.
:input idxr: index value of the 'right' member.
:input idxo: index value of the relation member.
Theano function output.
:output simi: vector of score values.
"""
return theano.function([idxr, idxo, leftparts], [simi],
on_unused_input='ignore')
def RankRightFnIdx_Schema(fnsim,
embeddings,
prior,
leftop,
rightop,
subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
lhs = (embedding.E[:, idxl]).reshape(
(1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rell = (relationl.E[:, idxo]).reshape(
(1, relationl.D
)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape(
(1, relationr.D
)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
# Negative Energy
simi = fnsim(tmp.reshape((1, tmp.shape[1])),
rightop(rhs, relr)) # simi = fnsim(a, b)
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxl, idxo, g], [simi], on_unused_input='ignore')
def RankRightFnIdx_filtered(fnsim,
embeddings,
leftop,
rightop,
subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'right' entities given couples of relation and 'left' entities (as
index values).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
rightparts = T.ivector('rightparts')
# Graph
lhs = (embedding.E[:, idxl]).reshape(
(1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rhs = rhs[rightparts, :] # select the right parts not appearing
# in the train/valid/test sets
rell = (relationl.E[:, idxo]).reshape(
(1, relationl.D
)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape(
(1, relationr.D
)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
simi = fnsim(tmp.reshape((1, tmp.shape[1])),
rightop(rhs, relr)) # simi = fnsim(a, b)
return theano.function([idxl, idxo, rightparts], [simi],
on_unused_input='ignore')
def RankLeftFnIdx_filtered(fnsim, embeddings, leftop, rightop, subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'left' entities given couples of relation and 'right' entities (as
index values).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxr, idxo = T.iscalar('idxr'), T.iscalar('idxo')
leftparts = T.ivector('leftparts')
# Graph
if subtensorspec is not None:
# We compute the score only for a subset of entities
lhs = (embedding.E[:, :subtensorspec]).T
else:
lhs = embedding.E.T
lhs = lhs[leftparts, :] # select the left parts not appearing
# in the train/valid/test sets
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
tmp = rightop(rhs, relr)
simi = fnsim(leftop(lhs, rell), tmp.reshape((1, tmp.shape[1])))
"""
Theano function inputs.
:input idxr: index value of the 'right' member.
:input idxo: index value of the relation member.
Theano function output.
:output simi: vector of score values.
"""
return theano.function([idxr, idxo, leftparts], [simi], on_unused_input='ignore')
