def model(y1, s0, s1):
if is_theano(y1, s0, s1):
math = tt
else:
math = np
# Compute the background component
# TODO: This step can be sped up...
A = math.dot(
math.reshape(s0, (-1, 1)),
math.reshape(
math.concatenate(([1.0], math.zeros_like(y1)), axis=0), (1, -1)
),
)
a = math.reshape(math.transpose(A), (-1,))
if math == tt:
M0 = math.reshape(ts.dot(D, a), (M, -1))
else:
M0 = math.reshape(D.dot(a), (M, -1))
# Compute the spot component
A = math.dot(
math.reshape(s1, (-1, 1)),
math.reshape(math.concatenate(([1.0], y1), axis=0), (1, -1)),
)
a = math.reshape(math.transpose(A), (-1,))
if math == tt:
M1 = math.reshape(ts.dot(D, a), (M, -1))
else:
M1 = math.reshape(D.dot(a), (M, -1))
# Remove the baseline
b = math.reshape(2.0 + math.dot(B1, y1), (M, -1))
return (M0 + M1) / b
def __init__(self, rng, P_input, L2_input, **kwargs):
#symbol declaration, initialization and definition
x_1_tm1, x_t = (\
sparse.csr_matrix("x_1_tm1", dtype=theano.config.floatX),\
sparse.csr_matrix("x_t",dtype=theano.config.floatX)\
)\
if P_input is None else P_input[:2]
#elements of history
shape = kwargs.get("shape")
if shape is not None:
dict_size = shape[0]
if len(shape) <= 1:
del shape["shape"]
else:
shape["shape"] = shape["shape"][1:]
else:
dict_size = (16,1,32,32)
D_1_tm1 = theano.shared(rng.normal(size=dict_size).astype(theano.config.floatX))
Dx_1_tm1 = sparse.dot(x_1_tm1, D_1_tm1)#array access=dot operation
super(SequenceCNN, self).__init__(rng=rng, inputsymbol=Dx_1_tm1, **kwargs)#attaches new elements into the fgraph
self.L2_output_1_tm1 = self.L2_output
#elements of current time
D_t = theano.shared(rng.normal(size=dict_size).astype(theano.config.floatX))
Dx_t = sparse.dot(x_t, D_t)#array access=dot operation
self.L2_output_t = theano.clone(self.L2_output_1_tm1, replace={Dx_1_tm1:Dx_t})
#element prepartion for model building
self.P_input = (x_1_tm1,x_t)
self.params += [D_1_tm1, D_t]
self.L2_output = self.L2_output_1_tm1*self.L2_output_t
def u(self, value):
value = np.atleast_1d(value)
assert (len(value.shape) == 1
), "Wavelength-dependent limb darkening not yet supported."
self._u = value
# Did the degree of limb darkening change?
if len(self._u) != self._udeg:
self._udeg = len(self._u)
# Force the re-instantiation of the internal map
self.ydeg = self._ydeg
if self._udeg > 0:
# Set the coeffs
self._map[1:] = self._u
# Compute the limb darkening operator
F = self._map.ops.F(
tt.as_tensor_variable(np.append([-1.0], self._u)),
tt.as_tensor_variable([np.pi]),
)
self._L = ts.dot(ts.dot(self._map.ops.A1Inv, F),
self._map.ops.A1).eval()
self._L = csr_matrix(self._L)
def create_TrainFunc_tranPES(simfn, embeddings, marge=0.5, alpha=1., beta=1.):
# parse the embedding data
embedding = embeddings[0] # D x N matrix
lembedding = embeddings[1]
# declare the symbolic variables for training triples
hp = S.csr_matrix('head positive') # N x batchsize matrix
rp = S.csr_matrix('relation')
tp = S.csr_matrix('tail positive')
hn = S.csr_matrix('head negative')
tn = S.csr_matrix('tail negative')
lemb = T.scalar('embedding learning rate')
lremb = T.scalar('relation learning rate')
subtensorE = T.ivector('batch entities set')
subtensorR = T.ivector('batch link set')
# Generate the training positive and negative triples
hpmat = S.dot(embedding.E, hp).T # batchsize x D dense matrix
rpmat = S.dot(lembedding.E, rp).T
tpmat = S.dot(embedding.E, tp).T
hnmat = S.dot(embedding.E, hn).T
tnmat = S.dot(embedding.E, tn).T
# calculate the score
pos = tranPES3(simfn, T.concatenate([hpmat, tpmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tpmat)
negh = tranPES3(simfn, T.concatenate([hnmat, tpmat], axis=1).reshape((hnmat.shape[0], 2, hnmat.shape[1])).dimshuffle(0, 2, 1), hnmat, rpmat, tpmat)
negt = tranPES3(simfn, T.concatenate([hpmat, tnmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tnmat)
costh, outh = margeCost(pos, negh, marge)
costt, outt = margeCost(pos, negt, marge)
embreg = regEmb(embedding, subtensorE, alpha)
lembreg = regLink(lembedding, subtensorR, beta)
cost = costh + costt + embreg[0] + lembreg
out = T.concatenate([outh, outt])
outc = embreg[1]
# list of inputs to the function
list_in = [lemb, lremb, hp, rp, tp, hn, tn, subtensorE, subtensorR]
# updating the embeddings using gradient descend
emb_grad = T.grad(cost, embedding.E)
New_embedding = embedding.E - lemb*emb_grad
remb_grad = T.grad(cost, lembedding.E)
New_rembedding = lembedding.E - lremb * remb_grad
updates = OrderedDict({embedding.E: New_embedding, lembedding.E: New_rembedding})
return theano.function(list_in, [cost, T.mean(out), T.mean(outc), embreg[0], lembreg],
updates=updates, on_unused_input='ignore')
def create_TrainFunc_tranPES(simfn, embeddings, marge=0.5, alpha=1., beta=1.):
# parse the embedding data
embedding = embeddings[0] # D x N matrix
lembedding = embeddings[1]
# declare the symbolic variables for training triples
hp = S.csr_matrix('head positive') # N x batchsize matrix
rp = S.csr_matrix('relation')
tp = S.csr_matrix('tail positive')
hn = S.csr_matrix('head negative')
tn = S.csr_matrix('tail negative')
lemb = T.scalar('embedding learning rate')
lremb = T.scalar('relation learning rate')
subtensorE = T.ivector('batch entities set')
subtensorR = T.ivector('batch link set')
# Generate the training positive and negative triples
hpmat = S.dot(embedding.E, hp).T # batchsize x D dense matrix
rpmat = S.dot(lembedding.E, rp).T
tpmat = S.dot(embedding.E, tp).T
hnmat = S.dot(embedding.E, hn).T
tnmat = S.dot(embedding.E, tn).T
# calculate the score
pos = tranPES3(simfn, T.concatenate([hpmat, tpmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tpmat)
negh = tranPES3(simfn, T.concatenate([hnmat, tpmat], axis=1).reshape((hnmat.shape[0], 2, hnmat.shape[1])).dimshuffle(0, 2, 1), hnmat, rpmat, tpmat)
negt = tranPES3(simfn, T.concatenate([hpmat, tnmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tnmat)
costh, outh = margeCost(pos, negh, marge)
costt, outt = margeCost(pos, negt, marge)
embreg = regEmb(embedding, subtensorE, alpha)
lembreg = regLink(lembedding, subtensorR, beta)
cost = costh + costt + embreg[0] + lembreg
out = T.concatenate([outh, outt])
outc = embreg[1]
# list of inputs to the function
list_in = [lemb, lremb, hp, rp, tp, hn, tn, subtensorE, subtensorR]
# updating the embeddings using gradient descend
emb_grad = T.grad(cost, embedding.E)
New_embedding = embedding.E - lemb*emb_grad
remb_grad = T.grad(cost, lembedding.E)
New_rembedding = lembedding.E - lremb * remb_grad
updates = OrderedDict({embedding.E: New_embedding, lembedding.E: New_rembedding})
return theano.function(list_in, [cost, T.mean(out), T.mean(outc), embreg[0], lembreg],
updates=updates, on_unused_input='ignore')
def fprop(self, state_below, add_noise=True):
self.input_space.validate(state_below)
if self.requires_reformat:
if not isinstance(state_below, tuple):
for sb in get_debug_values(state_below):
if sb.shape[0] != self.dbm.batch_size:
raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0]))
assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim
state_below = self.input_space.format_as(state_below, self.desired_space)
self.x = state_below
# linear part
if isinstance(self.x, S.SparseVariable):
z = S.dot(self.x,self.W[0]) + self.b[0]
else:
z = T.dot(self.x,self.W[0]) + self.b[0]
self.z = self.activate(z, self.expert_activation)
# first layer non-linear part
if isinstance(self.x, S.SparseVariable):
h = S.dot(self.x,self.W[1]) + self.b[1]
else:
h = T.dot(self.x,self.W[1]) + self.b[1]
# activate hidden units of non-linear part
self.h = self.activate(h, self.hidden_activation)
noise = 0.
if add_noise:
rng = MRG_RandomStreams(self.mlp.rng.randint(2**15))
noise = rng.normal(size = self.z.shape,
std=self.noise_stdev ,
dtype=self.z.type.dtype)
# second layer non-linear part
self.a = T.dot(self.h,self.W[2]) + self.b[2] + noise
# activate non-linear part
self.m_mean = self.activate(self.a, self.gater_activation)
# how many are over 0:
self.effective_sparsity = T.cast(T.gt(self.m_mean, 0),
theano.config.floatX).mean()
# mix output of linear part with output of non-linear part
self.p = self.m_mean * self.z
if self.layer_name is not None:
self.z.name = self.layer_name + '_z'
self.h.name = self.layer_name + '_h'
self.a.name = self.layer_name + '_a'
self.m_mean.name = self.layer_name + '_m_mean'
self.p.name = self.layer_name + '_p'
return self.p
def get_output_for(self, input, **kwargs):
if not isinstance(input, (S.SparseVariable, S.SparseConstant,
S.sharedvar.SparseTensorSharedVariable)):
raise ValueError("Input for this layer must be sparse")
activation = S.dot(input, self.W)
#do the convolution
activation = S.dot(self.H, activation)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def ForwardFn(fnsim, embeddings, leftop, rightop, marge=1.0):
"""
This function returns a theano function to perform a forward step,
contrasting couples of positive and negative triplets. members are given
as sparse matrices. For one positive triplet there is one negative
triplet.
: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 marge: marge for the cost function.
:note: this is useful for W_SABIE [Weston et al., IJCAI 2011]
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# inputs
inpr = S.csr_matrix()
inpl = S.csr_matrix()
inpo = S.csr_matrix()
inpln = S.csr_matrix()
inprn = S.csr_matrix()
inpon = S.csr_matrix()
# graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
simin = fnsim(leftop(lhsn, relln), rightop(rhsn, relrn))
cost, out = margincost(simi, simin, marge)
"""
Theano function inputs.
:input inpl: sparse csr matrix representing the indexes of the positive
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the positive
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpo: sparse csr matrix representing the indexes of the positive
triplet relation member, shape=(#examples,N [Embeddings]).
:input inpln: sparse csr matrix representing the indexes of the negative
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inprn: sparse csr matrix representing the indexes of the negative
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpon: sparse csr matrix representing the indexes of the negative
triplet relation member, shape=(#examples,N [Embeddings]).
Theano function output.
:output out: binary vector representing when the margin is violated, i.e.
when an update occurs.
"""
return theano.function([inpl, inpr, inpo,
inpln, inprn, inpon], [out],
on_unused_input='ignore')
def SimFn(fnsim, embeddings, leftop, rightop):
"""
This function returns a Theano function to measure the similarity score
for sparse matrices inputs.
: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.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr = S.csr_matrix('inpr')
inpl = S.csr_matrix('inpl')
inpo = S.csr_matrix('inpo')
# Graph
#what is T? Are they tensor? lhs, rhs,rell,relr
# we just created inpl and inplr inpo . what does it mean to calculate dot product?
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
# what is this?
#ref:
#leftop = LayerMat('lin', state.ndim, state.nhid)
#rightop = LayerMat('lin', state.ndim, state.nhid)
# on call
#ry = y.reshape((y.shape[0], self.n_inp, self.n_out))
#rx = x.reshape((x.shape[0], x.shape[1], 1))
#return self.act((rx * ry).sum(1))
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
"""
Theano function inputs.
:input inpl: sparse csr matrix (representing the indexes of the 'left'
entities), shape=(#examples, N [Embeddings]).
:input inpr: sparse csr matrix (representing the indexes of the 'right'
entities), shape=(#examples, N [Embeddings]).
:input inpo: sparse csr matrix (representing the indexes of the
relation member), shape=(#examples, N [Embeddings]).
Theano function output
:output simi: matrix of score values.
"""
return theano.function([inpl, inpr, inpo], [simi],
on_unused_input='ignore')
def log_first_stage_indep_normal_priors(theta_tilde,
num_variances,
num_betas,
filtered_rows_to_alts):
"""
Calculates the log of the first stage joint density of error terms
conditional on the alternative specific variances. Note that the
error terms are assumed to be INDEPENDENTLY normally distributed
with mean zero, conditional on the alternative specific variances.
The returned value is correct up to an additive constant (which is
comprised of arbitrary constants as well as the log-marginal evidence).
Parameters
----------
error_terms : 1D ndarray of floats.
The error terms we want to calculate the log of the joint density of.
alt_variances : 1D ndarray of positive floats.
Each value should represent one alternative speciific variance.
filtered_rows_to_alts : 2D sparse array of zeros and ones.
Each element (i, j) should denote whether row i corresponds to
alternative j or not using one's and zero's reespectively.
Returns
-------
log_first_stage : scalar.
The log of the joint density of the error terms, up to an additive
constant that contains the log of the normalization constant and the
log of the other arbitrary contants from the multivariate normal
distribution.
References
----------
Gelman, Andrew, et al. (2014). Bayesian Data Analysis, 3rd Ed. Taylor
& Francis Group. pp. 576-578.
"""
# Get the position in theta_tilde, at which the betas start
beta_neg_idx = -1 * num_betas
# Split theta_tilde into its various components
alt_variances = tt.exp(theta_tilde[:num_variances])
error_terms = theta_tilde[num_variances:beta_neg_idx]
# If the error terms are conditionally independent given the
# alternative specific variances, then the covariance matrix
# for the joint distribution of errors is diagonal and the inverse
# of a diagonal matrix is a diagonal matrix with the inverses on
# the diagonal. The inverses are calculated below
inverse_variances = 1.0 / alt_variances
# Map the inverse variances to their corresponding rows of error terms
long_inverse_variances =\
sparse.dot(filtered_rows_to_alts, inverse_variances)
squared_errors = error_terms**2
# Below, we implement -0.5 * (theta - mu)^T Sigma^{-1} (theta - mu) for
# the specific case of a diagonal Sigma, Mu = 0, and theta = error_terms
log_first_stage =\
-0.5 * tt.sum(tt.mul(long_inverse_variances, squared_errors))
return log_first_stage
def get_train_function(self):
# specify the computational graph
target = T.matrix('target')
weight = theano.shared(np.random.randn(len(self.feature_map), len(self.label_map)), name='weight')
feat_mat = sparse.csr_matrix(name='feat_mat')
mask_mat = sparse.csr_matrix(name='mask_mat')
sum_pred = sparse.dot( mask_mat, T.nnet.softmax( sparse.dot(feat_mat, weight) ) )
pred = sum_pred / sum_pred.sum(axis=1).reshape((sum_pred.shape[0], 1))
objective = T.nnet.categorical_crossentropy(pred, target).sum() + self.param.l2_regularization * (weight ** 2).sum()
grad_weight = T.grad(objective, weight)
# print 'Compiling function ...'
# compile the function
train = theano.function(inputs = [feat_mat, mask_mat, target], outputs = [objective, weight], updates=[(weight, weight - 0.1*grad_weight)] )
return train
def optimize_func(transform_matrix):
t0 = time.time()
M = S.csr_matrix(dtype=theano.config.floatX)
N = S.csr_matrix(dtype=theano.config.floatX)
ON = S.csr_matrix(dtype=theano.config.floatX)
lr = T.scalar('learning rate', dtype=theano.config.floatX)
# print M, N, ON, lr
TN = S.dot(transform_matrix, N)
D = T.sqr(M - TN)
# PD = S.sqr(N-ON)
# PD = T.sqrt(S.sp_sum(PD, 1))
# TPD = T.sqr(TN - ON)
# TPD = T.sqrt(TPD.sum(1))
# D2 = T.sqr(PD-TPD)
cost = T.sum(D) #+ T.sum(D2)
list_in = [lr, M, N, ON]
gradient = T.grad(cost, transform_matrix)
new_transform_matrix = transform_matrix - lr * gradient
t1 = time.time()
print 'opt func cost is ' + str(t1 - t0)
return theano.function(list_in,
cost,
updates=[(transform_matrix, new_transform_matrix)],
on_unused_input='ignore')
def matmul(self, a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None):
if transpose_a:
a = a.T
if transpose_b:
b = b.T
if a_is_sparse or b_is_sparse:
return sparse.dot(a, b)
return T.dot(a, b)
def get_output_for(self, input, **kwargs):
target_indices = kwargs.get('target_indices')
activation = T.dot(input, self.W)
#do the convolution
activation = S.dot(self.H, activation)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
activation = activation[target_indices, :]
return self.nonlinearity(activation)
def _get_diagonal_term(self, X_left, X_right, diag_init):
diag = tn.shared(value=diag_init, name='diag')
if _tn_is_sparse(X_left) or _tn_is_sparse(X_right):
XlXr = tsp.mul(X_left, X_right)
y_pred = tsp.dot(XlXr, diag)
else:
XlXr = T.mul(X_left, X_right)
y_pred = T.dot(XlXr, diag)
return y_pred, [diag]
def _get_diagonal_term(self, X_left, X_right, diag_init):
diag = tn.shared(value=diag_init, name='diag')
if _tn_is_sparse(X_left) or _tn_is_sparse(X_right):
XlXr = tsp.mul(X_left, X_right)
y_pred = tsp.dot(XlXr, diag)
else:
XlXr = T.mul(X_left, X_right)
y_pred = T.dot(XlXr, diag)
return y_pred, [diag]
def labelFunct(self, batchSize, xFeats):
# xFeats [l, h]
# l = batchSize
# self.W = theano.printing.Print("W ") (self.W)
# self.Wb = theano.printing.Print("Wb ") (self.Wb)
scores = sparse.dot(xFeats, self.W) + self.Wb # [l, h] x [h, r] => [l, r]
relationProbs = T.nnet.softmax(scores)
# scores = theano.printing.Print("scores ") (scores)
labels = T.argmax(scores, axis=1) # [l, r] => [l]
# labels = theano.printing.Print("labels ") (labels)
return (labels, relationProbs)
def labelFunct(self, batchSize, xFeats):
# xFeats [l, h]
# l = batchSize
# self.W = theano.printing.Print("W ") (self.W)
# self.Wb = theano.printing.Print("Wb ") (self.Wb)
scores = sparse.dot(xFeats,
self.W) + self.Wb # [l, h] x [h, r] => [l, r]
relationProbs = T.nnet.softmax(scores)
# scores = theano.printing.Print("scores ") (scores)
labels = T.argmax(scores, axis=1) # [l, r] => [l]
# labels = theano.printing.Print("labels ") (labels)
return (labels, relationProbs)
def SimFn(fnsim, embeddings, leftop, rightop, op=''):
"""
This function returns a Theano function to measure the similarity score for sparse matrices inputs.
: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.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr, inpl, inpo = S.csr_matrix('inpr'), S.csr_matrix(
'inpl'), S.csr_matrix('inpo')
# Graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lop, rop = leftop(lhs, rell), rightop(rhs, relr)
simi = fnsim(lop, rop)
"""
Theano function inputs.
:input inpl: sparse csr matrix (representing the indexes of the 'left' entities), shape=(#examples, N [Embeddings]).
:input inpr: sparse csr matrix (representing the indexes of the 'right' entities), shape=(#examples, N [Embeddings]).
:input inpo: sparse csr matrix (representing the indexes of the relation member), shape=(#examples, N [Embeddings]).
Theano function output
:output simi: matrix of score values.
"""
return theano.function([inpl, inpr, inpo], [simi],
on_unused_input='ignore')
def get_train_function(self):
# specify the computational graph
weight = theano.shared(np.random.randn(len(self.feature_map), len(self.label_map)), name='weight')
# weight = theano.shared(np.zeros((len(self.feature_map), len(self.label_map))), name='weight')
feat_mat = sparse.csr_matrix(name='feat_mat')
f_target = T.matrix('f_target')
f_mask_mat = sparse.csr_matrix(name='f_mask_mat')
f_sum_pred = sparse.dot( f_mask_mat, T.nnet.softmax( sparse.dot(feat_mat, weight) ) )
f_pred = f_sum_pred / f_sum_pred.sum(axis=1).reshape((f_sum_pred.shape[0], 1))
i_target = T.matrix('i_target')
i_mask_mat = sparse.csr_matrix(name='l_mask_mat')
i_pred = sparse.dot( i_mask_mat, T.nnet.softmax( sparse.dot(feat_mat, weight) ) )
objective = self.param.feature_lambda * T.nnet.categorical_crossentropy(f_pred, f_target).sum() + T.nnet.categorical_crossentropy(i_pred, i_target).sum() + self.param.l2_lambda * (weight ** 2).sum() / 2
grad_weight = T.grad(objective, weight)
# print 'Compiling function ...'
# compile the function
train = theano.function(inputs = [feat_mat, f_mask_mat, f_target, i_mask_mat, i_target], outputs = [objective, weight], updates=[(weight, weight - 0.1*grad_weight)] )
return train
def __init__(self, rng, P_input, L2_input=None, **kwargs):
#1.symbol declaration, initialization and definition
I = sparse.csr_matrix("I") if P_input is None else P_input
shape = kwargs.get("shape") or [(16,1,32,32), (4,16,16,2,2), (4,4,4,2,2)]
dict_size, kwargs["shape"] = shape[0], shape[1:]
D = theano.shared(\
rng.uniform(low=-1,high=1,size=dict_size).astype(theano.config.floatX)\
)
DI = sparse.dot(I, D)#array access=dot operation
#2.attaches I and D into the fgraph
super(SparseCNN, self).__init__(rng=rng, P_input=DI, **kwargs)
self.params += [D,]
self.P_input = I#take I as input for the sparseCNN
def SimFn(fnsim, embeddings, leftop, rightop, op=''):
"""
This function returns a Theano function to measure the similarity score for sparse matrices inputs.
: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.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr, inpl, inpo = S.csr_matrix('inpr'), S.csr_matrix('inpl'), S.csr_matrix('inpo')
# Graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lop, rop = leftop(lhs, rell), rightop(rhs, relr)
simi = fnsim(lop, rop)
"""
Theano function inputs.
:input inpl: sparse csr matrix (representing the indexes of the 'left' entities), shape=(#examples, N [Embeddings]).
:input inpr: sparse csr matrix (representing the indexes of the 'right' entities), shape=(#examples, N [Embeddings]).
:input inpo: sparse csr matrix (representing the indexes of the relation member), shape=(#examples, N [Embeddings]).
Theano function output
:output simi: matrix of score values.
"""
return theano.function([inpl, inpr, inpo], [simi], on_unused_input='ignore')
def _generate_train_model_batch_function(self):
#s = T.matrix('s', dtype=self.floatX)
s = S.csr_matrix('s', dtype=self.floatX)
#u = T.vector('u', dtype=self.intX)
i = T.vector('i', dtype=self.intX)
y = T.vector('y', dtype=self.intX)
#items = T.vector('items', dtype=self.intX)
Sit = self.S
sit = s.T
#Uu = self.U[u]
Iy = self.I[y]
BSy = self.BS[y]
#BUy = self.BU[y]
BIy = self.BI[y]
I1i = self.I1[i]
I2y = self.I2[y]
#predU = T.dot( Iy, Uu.T ).T + BUy.flatten()
se = S.dot( Sit.T, sit )
#se = T.dot( Sit.T, sit )
predS = T.dot( Iy, se ).T + BSy.flatten()
predI = T.dot( I1i, I2y.T ) + BIy.flatten()
pred = predS + predI #+ predU
pred = getattr(self, self.activation )( pred )
cost = getattr(self, self.objective )( pred, y )
param_list = [self.S]
fullparam_list = [self.I,self.I1,self.I2,self.BI,self.BS] #+ [self.U]
subparam_list = [Iy,I1i,I2y,BIy,BSy] #+ [Uu]
subparam_idx = [y,i,y,y,y] #+ [u]
updates = self.descent( cost, param_list, fullparam_list, subparam_list, subparam_idx, self.learning_rate, momentum=self.momentum )
#updates = getattr(self, self.learn)(cost, [self.U,self.S,self.I,self.IC,self.BI,self.BS], self.learning_rate)
#updates = getattr(self, self.learn)(cost, , ,, self.learning_rate, momentum=self.momentum)
#self.train_model_batch = theano.function(inputs=[s, i, u, y, items], outputs=cost, updates=updates )
inp = [s, i, y] #+ [u]
self.train_model_batch = theano.function(inputs=inp, outputs=cost, updates=updates )
def compRelationProbsFunc(self, xFeats):
# xFeats [l, h] matrix
# xFeats = theano.printing.Print("xFeats")(xFeats)
# self.Wb = theano.printing.Print("Wb ") (self.Wb)
# self.W = theano.printing.Print("W ") (self.W)
# scores of each role by a classifier
relationScores = sparse.dot(xFeats, self.W) + self.Wb # [l, h] x [h, r] => [l, r]
#relationScores = theano.printing.Print("relationScores=")(relationScores)
# convert it to probabilities
relationProbs = T.nnet.softmax(relationScores)
#relationProbs = theano.printing.Print("relationProbs = ")(relationProbs)
return relationProbs # [l, r]
def compRelationProbsFunc(self, xFeats):
# xFeats [l, h] matrix
# xFeats = theano.printing.Print("xFeats")(xFeats)
# self.Wb = theano.printing.Print("Wb ") (self.Wb)
# self.W = theano.printing.Print("W ") (self.W)
# scores of each role by a classifier
relationScores = sparse.dot(
xFeats, self.W) + self.Wb # [l, h] x [h, r] => [l, r]
#relationScores = theano.printing.Print("relationScores=")(relationScores)
# convert it to probabilities
relationProbs = T.nnet.softmax(relationScores)
#relationProbs = theano.printing.Print("relationProbs = ")(relationProbs)
return relationProbs # [l, r]
def get_output_for(self, input, **kwargs):
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
# According to pull-request 595 from eduardo4jesus
# Though it might be the case, the activation layer will remain
# dense since Weights represent dense matrix ( Kinda makes sense)
if (type(input) == S.SparseVariable) or (type(input) == S.SparseConstant):
activation = S.dot(input, self.W)
else:
activation = T.dot(input, self.W)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def __init__(self, rng, x, topic_num=100):
#input
L2_input = sparse.csr_matrix("x",dtype=theano.config.floatX)
#params
vocab_size = x.shape[1]
mu, sigma = x.data.mean(), x.data.var()**0.5
rng = numpy.random.RandomState(numpy.random.randint(2**32-1)) if rng is None else rng
self.L2_w = theano.shared(\
numpy.asarray(\
rng.normal(loc=mu,scale=sigma,size=(vocab_size, topic_num)),\
dtype=theano.config.floatX\
),\
borrow=True\
)
self.L2_b = theano.shared(numpy.zeros(topic_num,dtype=theano.config.floatX), borrow=True)
self.params = [self.L2_w, self.L2_b]
#stick-breaking:sticks->orthgonal sticks
L2_stick = sparse.dot(L2_input,self.L2_w)+self.L2_b-\
0.5*(L2_input.size/vocab_size*tensor.sum(self.L2_w**2,0)+self.L2_b**2)
zero_space = tensor.zeros((L2_input.shape[0],1),dtype=theano.config.floatX)
L2_orth_stick = tensor.join(1, L2_stick, zero_space)\
- tensor.join(1, zero_space, tensor.cumsum(L2_stick,1))
Pasterik_orth_stick = tensor.log(1 + tensor.exp(L2_orth_stick))
#training model definition
Likelihood = tensor.mean(Pasterik_orth_stick)
grads = theano.grad(Likelihood, self.params)#gradient w.r.t params
eta = tensor.scalar("eta")
updates = [(param, param+eta*grad) for param, grad in zip(self.params, grads)]
self._fit = theano.function(\
inputs=[L2_input, eta],\
outputs=Likelihood,\
updates=updates\
)
#predict model definition
self._predict = theano.function(\
inputs=[L2_input],\
outputs=tensor.argmax(L2_stick,axis=-1)\
)
self._codec = theano.function(\
inputs=[L2_input],\
outputs=L2_stick>0\
)
def __init__(self, x_in, n_in, n_out, activation=None, rng=None, seed=0):
"""
Initialize the layer.
Inputs:
- x_in: a symbolic theano variable describing the input
- n_in: dimensions the input will have
- n_out: dimensions the output should have
- activation: non-linear activation function applied to the output (if any)
- seed: used to initialize the random number generator
"""
if rng is None:
rng = np.random.RandomState(seed)
# initialize the weights - optimal values depend on the activation function
if activation is None:
W_values = rng.randn(n_in, n_out) * 0.01
else:
W_values = np.asarray(rng.uniform(
low=-np.sqrt(6. / (n_in + n_out)),
high=np.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)),
dtype=theano.config.floatX)
if activation == T.nnet.sigmoid:
W_values *= 4
self.W = theano.shared(value=W_values, name='W', borrow=True)
# initialize the biases b as a vector of n_out 0s
self.b = theano.shared(value=np.zeros((n_out, ),
dtype=theano.config.floatX),
name='b',
borrow=True)
# compute the output
if isinstance(x_in.type, sparse.type.SparseType):
lin_output = sparse.dot(x_in, self.W) + self.b
else:
lin_output = T.dot(x_in, self.W) + self.b
# apply the activation function (if any)
self.output = (lin_output
if not activation else activation(lin_output))
# parameters of the model
self.params = [self.W, self.b]
def __init__(self, rng, x, topic_num=100):
#input
L2_input = sparse.csr_matrix("x",dtype=theano.config.floatX)
#params
vocab_size = x.shape[1]
mu, sigma = x.data.mean(), 2.56*x.data.var()**0.5
rng = numpy.random.RandomState(numpy.random.randint(2**32-1)) if rng is None else rng
self.L2_w = theano.shared(\
numpy.asarray(\
mu + (mu if mu < sigma else sigma)*rng.uniform(low=-1,high=1,size=(vocab_size, topic_num)),\
dtype=theano.config.floatX\
),\
borrow=True\
)
self.L2_b = theano.shared(numpy.zeros(topic_num, dtype=theano.config.floatX), borrow=True)
self.params = [self.L2_w, self.L2_b]
#output
L2_topic = sparse.dot(L2_input,self.L2_w)+self.L2_b
#difference based objective function
Pasterik_topic = tensor.log(tensor.sum(tensor.exp(L2_topic-L2_topic.max(-1, keepdims=True)),-1))#avoiding overflow
d_xw_w2 = tensor.mean(Pasterik_topic) -\
0.5*(L2_input.size*tensor.mean(self.L2_w*self.L2_w)+tensor.dot(self.L2_b,self.L2_b))
grads = theano.grad(d_xw_w2, self.params)#gradient w.r.t params
eta = tensor.scalar("eta")
updates = [(param, param+eta*grad) for param, grad in zip(self.params, grads)]
#training model definition
self._fit = theano.function(\
inputs=[L2_input, eta],\
outputs=d_xw_w2, \
updates=updates\
)
#predict model definition
self._predict = theano.function(\
inputs=[L2_input],\
outputs=tensor.argmax(L2_topic,axis=-1)\
)
def y(self, y):
self._y = np.array(y)
self.ydeg = int(np.sqrt(len(y)) - 1)
self.map_ref = starry.Map(ydeg=self.ydeg, reflected=True)
self.map_emi = starry.Map(ydeg=self.ydeg)
if self.ydeg > 0:
self.map_ref[1:, :] = y[1:]
self.map_emi[1:, :] = y[1:]
self._A1y = ts.dot(self.map_ref.ops.A1, tt.as_tensor_variable(y))
# Reset
self.intensity(0.0,
0.0, [0.0], [0.0], [0.0], [0.0], [0.0],
0.0,
reset=True)
self.flux([0.0], [0.0], [0.0], [0.0], [0.0], 0.0, reset=True)
self.dfluxdro([0.0], [0.0], [0.0], [0.0], [0.0], 0.0, reset=True)
if self.ydeg > 0:
self.flux_emitted([0.0], [0.0], [0.0], [0.0], [0.0],
0.0,
reset=True)
self.dfluxdro_emitted([0.0], [0.0], [0.0], [0.0], [0.0],
0.0,
reset=True)
def theano_safe_sparse_dot(X, Y):
if _tn_is_sparse(X) or _tn_is_sparse(Y):
return tsp.dot(X, Y)
else:
return T.dot(X, Y)
def __init__(self, nodenet):
if nodenet.sparse:
self.propagate = theano.function([], [nodenet.w, nodenet.a], updates={nodenet.a: ST.dot(nodenet.w, nodenet.a)})
else:
self.propagate = theano.function([], [nodenet.w, nodenet.a], updates={nodenet.a: T.dot(nodenet.w, nodenet.a)})
def TrainFn1Member(fnsim, embeddings, leftop, rightop, rel=True,
loss=loss.hinge, loss_margin=1.0, op=None, method='SGD',
decay=0.999, epsilon=1e-6, max_learning_rate=None,
weight_L1_param_regularizer=None, weight_L2_param_regularizer=None,
weight_contractive_regularizer_left=None, weight_contractive_regularizer_right=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr, inpl, inpo = S.csr_matrix('inpr'), S.csr_matrix('inpl'), S.csr_matrix('inpo')
inpln, inprn = S.csr_matrix('inpln'), S.csr_matrix('inprn')
# Learning rates for parameters and embeddings
rate_params = T.scalar('rate_params')
rate_embeddings = T.scalar('rate_embeddings')
# Graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
# Negative 'left' member
similn = fnsim(leftop(lhsn, rell), rightop(rhs, relr))
# Negative 'right' member
simirn = fnsim(leftop(lhs, rell), rightop(rhsn, relr))
costl, outl = loss(simi, similn, margin=loss_margin)
costr, outr = loss(simi, simirn, margin=loss_margin)
cost, out = costl + costr, T.concatenate([outl, outr])
# List of inputs of the function
list_in = [rate_embeddings, rate_params, inpl, inpr, inpo, inpln, inprn]
if rel:
# If rel is True, we also consider a negative relation member
inpon = S.csr_matrix()
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
simion = fnsim(leftop(lhs, relln), rightop(rhs, relrn))
costo, outo = loss(simi, simion, margin=loss_margin)
cost += costo
out = T.concatenate([out, outo])
list_in += [inpon]
# <EXPERIMENTAL_CODE>
# Should I also plug examples from corrupted triples ?
if weight_contractive_regularizer_left is not None:
cost = cost + (weight_contractive_regularizer_left * R.contractive_regularizer(lop, lhs))
if weight_contractive_regularizer_right is not None:
cost = cost + (weight_contractive_regularizer_right * R.contractive_regularizer(rop, rhs))
for rel_param in set([relationl.E, relationr.E]):
if weight_L1_param_regularizer is not None:
cost = cost + (weight_L1_param_regularizer * R.L1_regularizer(rel_param))
if weight_L2_param_regularizer is not None:
cost = cost + (weight_L2_param_regularizer * R.L2_regularizer(rel_param))
# </EXPERIMENTAL_CODE>
params = leftop.params + rightop.params + (fnsim.params if hasattr(fnsim, 'params') else [])
embeds = [embedding.E] + ([relationr.E, relationl.E] if (type(embeddings) == list) else [])
# The function updates the implicit function arguments according to the updates.
updates = collections.OrderedDict()
if (method == 'SGD'):
pass # do nothing
elif (method == 'MOMENTUM'):
param_previous_update_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the previous updates
previous_update_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_previous_update = theano.shared(value=previous_update_value, name='su_' + param.name)
param_previous_update_map[param] = param_previous_update
elif (method == 'ADAGRAD'):
param_squared_gradients_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
elif (method == 'ADADELTA'):
param_squared_gradients_map = collections.OrderedDict()
param_squared_updates_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
# Allocate the sums of squared updates
squared_updates_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_updates = theano.shared(value=squared_updates_value, name='su_' + param.name)
param_squared_updates_map[param] = param_squared_updates
elif (method == 'RMSPROP'):
param_squared_gradients_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
else:
raise ValueError('Unknown method: %s' % (method))
# Parameter Gradients
gradientsparams = T.grad(cost, params)
# Embeddings gradients
gradientsembeds = T.grad(cost, embeds)
# Learning Rates
rates_params = [rate_params for i in range(len(params))]
# In TransE etc. the rate for predicates' embeddings (that do not get normalized) is rate_params, not rate_embeddings
rates_embeddings = [rate_embeddings, rate_params, rate_params] if len(embeds) > 1 else [rate_embeddings] # [rate_embeddings for i in range(len(embeds))]
for param, gradient, rate in zip(params + embeds, gradientsparams + gradientsembeds, rates_params + rates_embeddings):
if (method == 'SGD'): # SGD
U.sgd(param, rate, gradient, updates)
elif (method == 'MOMENTUM'): # SGD+MOMENTUM
param_previous_update = param_previous_update_map[param]
U.momentum(param, rate, decay, gradient, updates, param_previous_update)
elif (method == 'ADAGRAD'): # ADAGRAD
param_squared_gradients = param_squared_gradients_map[param]
U.adagrad(param, rate, epsilon, gradient, updates, param_squared_gradients)
elif (method == 'ADADELTA'): # ADADELTA
param_squared_gradients = param_squared_gradients_map[param]
param_squared_updates = param_squared_updates_map[param]
U.adadelta(param, rate, decay, epsilon, gradient, updates, param_squared_gradients, param_squared_updates)
elif (method == 'RMSPROP'): # RMSPROP
param_squared_gradients = param_squared_gradients_map[param]
U.rmsprop(param, rate, decay, max_learning_rate, epsilon, gradient, updates, param_squared_gradients)
else:
raise ValueError('Unknown method: %s' % (method))
return theano.function(list_in, [T.mean(cost), T.mean(out)], updates=updates, on_unused_input='ignore')
def TrainFn1Member(fnsim, embeddings, leftop, rightop, marge=1.0, rel=True):
"""
This function returns a theano function to perform a training iteration,
contrasting positive and negative triplets. members are given as sparse
matrices. For one positive triplet there are two or three (if rel == True)
negative triplets. To create a negative triplet we replace only one member
at a time.
: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 marge: marge for the cost function.
:param rel: boolean, if true we also contrast w.r.t. a negative relation
member.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr = S.csr_matrix()
inpl = S.csr_matrix()
inpo = S.csr_matrix()
inpln = S.csr_matrix()
inprn = S.csr_matrix()
lrparams = T.scalar('lrparams')
lrembeddings = T.scalar('lrembeddings')
# Graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
# Negative 'left' member
similn = fnsim(leftop(lhsn, rell), rightop(rhs, relr))
# Negative 'right' member
simirn = fnsim(leftop(lhs, rell), rightop(rhsn, relr))
costl, outl = margincost(simi, similn, marge)
costr, outr = margincost(simi, simirn, marge)
cost = costl + costr
out = T.concatenate([outl, outr])
# List of inputs of the function
list_in = [lrembeddings, lrparams,
inpl, inpr, inpo, inpln, inprn]
if rel:
# If rel is True, we also consider a negative relation member
inpon = S.csr_matrix()
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
simion = fnsim(leftop(lhs, relln), rightop(rhs, relrn))
costo, outo = margincost(simi, simion, marge)
cost += costo
out = T.concatenate([out, outo])
list_in += [inpon]
if hasattr(fnsim, 'params'):
# If the similarity function has some parameters, we update them too.
gradientsparams = T.grad(cost,
leftop.params + rightop.params + fnsim.params)
updates = OrderedDict((i, i - lrparams * j) for i, j in zip(
leftop.params + rightop.params + fnsim.params, gradientsparams))
else:
gradientsparams = T.grad(cost, leftop.params + rightop.params)
updates = OrderedDict((i, i - lrparams * j) for i, j in zip(
leftop.params + rightop.params, gradientsparams))
gradients_embedding = T.grad(cost, embedding.E)
newE = embedding.E - lrembeddings * gradients_embedding
updates.update({embedding.E: newE})
if type(embeddings) == list:
# If there are different embeddings for the relation member.
gradients_embedding = T.grad(cost, relationl.E)
newE = relationl.E - lrparams * gradients_embedding
updates.update({relationl.E: newE})
gradients_embedding = T.grad(cost, relationr.E)
newE = relationr.E - lrparams * gradients_embedding
updates.update({relationr.E: newE})
"""
Theano function inputs.
:input lrembeddings: learning rate for the embeddings.
:input lrparams: learning rate for the parameters.
:input inpl: sparse csr matrix representing the indexes of the positive
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the positive
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpo: sparse csr matrix representing the indexes of the positive
triplet relation member, shape=(#examples,N [Embeddings]).
:input inpln: sparse csr matrix representing the indexes of the negative
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inprn: sparse csr matrix representing the indexes of the negative
triplet 'right' member, shape=(#examples,N [Embeddings]).
:opt input inpon: sparse csr matrix representing the indexes of the
negative triplet relation member, shape=(#examples,N
[Embeddings]).
Theano function output.
:output mean(cost): average cost.
:output mean(out): ratio of examples for which the margin is violated,
i.e. for which an update occurs.
"""
return theano.function(list_in, [T.mean(cost), T.mean(out)],
updates=updates, on_unused_input='ignore')
def TrainFn(fnsim, embeddings, leftop, rightop, marge=1.0):
"""
This function returns a theano function to perform a training iteration,
contrasting couples of positive and negative triplets. members are given
as sparse matrices. for one positive triplet there is one negative
triplet.
: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 marge: marge For the cost function.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr = S.csr_matrix()
inpl = S.csr_matrix()
inpo = S.csr_matrix()
inpln = S.csr_matrix()
inprn = S.csr_matrix()
inpon = S.csr_matrix()
lrparams = T.scalar('lrparams')
lrembeddings = T.scalar('lrembeddings')
# Graph
## Positive triplet
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
## Negative triplet
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
simin = fnsim(leftop(lhsn, relln), rightop(rhsn, relrn))
cost, out = margincost(simi, simin, marge)
# Parameters gradients
if hasattr(fnsim, 'params'):
# If the similarity function has some parameters, we update them too.
gradientsparams = T.grad(cost,
leftop.params + rightop.params + fnsim.params)
updates = OrderedDict((i, i - lrparams * j) for i, j in zip(
leftop.params + rightop.params + fnsim.params, gradientsparams))
else:
gradientsparams = T.grad(cost, leftop.params + rightop.params)
updates = OrderedDict((i, i - lrparams * j) for i, j in zip(
leftop.params + rightop.params, gradientsparams))
# Embeddings gradients
gradients_embedding = T.grad(cost, embedding.E)
newE = embedding.E - lrembeddings * gradients_embedding
updates.update({embedding.E: newE})
if type(embeddings) == list:
# If there are different embeddings for the relation member.
gradients_embedding = T.grad(cost, relationl.E)
newE = relationl.E - lrparams * gradients_embedding
updates.update({relationl.E: newE})
gradients_embedding = T.grad(cost, relationr.E)
newE = relationr.E - lrparams * gradients_embedding
updates.update({relationr.E: newE})
"""
Theano function inputs.
:input lrembeddings: learning rate for the embeddings.
:input lrparams: learning rate for the parameters.
:input inpl: sparse csr matrix representing the indexes of the positive
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the positive
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpo: sparse csr matrix representing the indexes of the positive
triplet relation member, shape=(#examples,N [Embeddings]).
:input inpln: sparse csr matrix representing the indexes of the negative
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inprn: sparse csr matrix representing the indexes of the negative
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpon: sparse csr matrix representing the indexes of the negative
triplet relation member, shape=(#examples,N [Embeddings]).
Theano function output.
:output mean(cost): average cost.
:output mean(out): ratio of examples for which the margin is violated,
i.e. for which an update occurs.
"""
return theano.function([lrembeddings, lrparams, inpl, inpr, inpo,
inpln, inprn, inpon],
[T.mean(cost), T.mean(out)], updates=updates,
on_unused_input='ignore')
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
idx_start = T.lscalar()
idx_stop = T.lscalar()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'))
i2w = dict( (v,k) for k,v in w2i.iteritems() )
i2w[0] = 'UNK'
senna = [ i2w[i] for i in range(len(i2w.keys())) ]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
minsize = hp['minsize']
maxsize = hp['maxsize']
dsize = maxsize - minsize +1
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
path = hp['loadpath']
if path:
load(embedding,path+'/embedding.pkl')
#load(H,path+'/hidden.pkl')
#load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(borrow=True).shape[1]
#hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_embed_left = T.concatenate([posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed_left = T.concatenate([negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
posit_embed_right = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
negat_embed_right = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
posit_embed = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
#posit_embed = ifelse(T.eq(idx_start, 0), posit_embed_left, posit_embed)
#posit_embed = ifelse(T.eq(idx_stop, hp['maxsize']), posit_embed_right, posit_embed)
#negat_embed = ifelse(T.eq(idx_start, 0), negat_embed_left, negat_embed)
#negat_embed = ifelse(T.eq(idx_stop, hp['maxsize']), negat_embed_right, negat_embed)
Hposit = T.tanh(T.dot(posit_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
Hnegat = T.tanh(T.dot(negat_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
posit_score = L.encode(Hposit)
negat_score = L.encode(Hnegat)
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat, idx_start, idx_stop],
(rect(C)).mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'))[:2000]
fname = ''
validsentence = []# cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/valid_debug.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/ints_50000/split'+str(hp['split'])+'.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i,s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['maxsize'] + 2:
continue
rndsize = numpy.random.randint(low=hp['minsize']+1,high=hp['maxsize']-1)
idxsta = numpy.random.randint(low=1, high=hp['maxsize']-rndsize)
idxsto = idxsta+rndsize
print 'r',rndsize,'b',idxsta,'e',idxsto,'shape',H.params['e_bias'].get_value().shape
c =[]
r =[]
if debug:
print ' *** Processing document',i,'with',nword,
sys.stdout.flush()
for j in range(delta,nword-delta):
nd = rndsize/2
rd = rndsize%2
pchunk = s[j-delta:j+delta+rest]
nchunk = []
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for kk in range(hp['nneg']):
tmpchunk = copy.copy(pchunk)
tmpchunk[idxsta+nd] = rndidx[kk]
nchunk += tmpchunk
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
l = opt(p,n, idxsta, idxsto)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0:#(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score']
if not debug:
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def fprop(self, state_below, add_noise=True):
self.input_space.validate(state_below)
if self.requires_reformat:
if not isinstance(state_below, tuple):
for sb in get_debug_values(state_below):
if sb.shape[0] != self.dbm.batch_size:
raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0]))
assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim
state_below = self.input_space.format_as(state_below, self.desired_space)
self.x = state_below
# linear part
if isinstance(self.x, S.SparseVariable):
self.z = S.dot(self.x,self.W[0]) + self.b[0]
else:
self.z = T.dot(self.x,self.W[0]) + self.b[0]
# first layer non-linear part
if isinstance(self.x, S.SparseVariable):
h = S.dot(self.x,self.W[1]) + self.b[1]
else:
h = T.dot(self.x,self.W[1]) + self.b[1]
# activate hidden units of non-linear part
if self.hidden_activation is None:
pass
elif self.hidden_activation == 'tanh':
self.h = T.tanh(h)
elif self.hidden_activation == 'sigmoid':
self.h = T.nnet.sigmoid(h)
elif self.hidden_activation == 'softmax':
self.h = T.nnet.softmax(h)
elif self.hidden_activation == 'rectifiedlinear':
self.h = T.maximum(0, h)
else:
raise NotImplementedError()
noise = 0
if self.noise_beta is not None:
noise = (1.-self.noise_normality) * self.beta_mean
#print self.noise_normality
print (1.-self.noise_normality) * self.noise_scale * (self.sparsity_target - 0.5)
print noise
if add_noise:
rng = MRG_RandomStreams(self.mlp.rng.randint(2**15))
if self.noise_beta is not None:
noise = (1.-self.noise_normality) * self.beta_dist[ \
self.beta_idx:self.beta_idx+self.x.shape[0],:] \
+ (self.noise_normality * self.noise_scale \
* rng.normal(size = self.z.shape,
std=self.noise_stdev ,
dtype=self.z.type.dtype) \
)
else:
noise = self.noise_scale \
* rng.normal(size = self.z.shape,
std=self.noise_stdev ,
dtype=self.z.type.dtype)
#print self.beta_dist.get_value().shape
# second layer non-linear part
self.a = T.dot(self.h,self.W[2]) + self.b[2] + noise
# activate non-linear part to get bernouilli probabilities
self.m_mean = T.nnet.sigmoid(self.a)
# mix output of linear part with output of non-linear part
self.p = self.m_mean * self.z
if self.layer_name is not None:
self.z.name = self.layer_name + '_z'
self.h.name = self.layer_name + '_h'
self.a.name = self.layer_name + '_a'
self.m_mean.name = self.layer_name + '_m_mean'
self.p.name = self.layer_name + '_p'
return self.p
def theano_safe_sparse_dot(X, Y):
if _tn_is_sparse(X) or _tn_is_sparse(Y):
return tsp.dot(X, Y)
else:
return T.dot(X, Y)
def __init__(self,
feature_count,
classifier=False,
k=8,
stdev=0.1,
sparse=False):
self.classifier = classifier
d = feature_count
# *** Symbolic variables ***
if sparse:
X = S.csr_matrix(name='inputs', dtype='float32')
else:
X = T.matrix()
y = T.vector()
beta_w1 = T.scalar()
beta_v = T.scalar()
# *** Model parameters ***
# bias term (intercept)
w0_init = np.zeros(1)
self.w0 = theano.shared(w0_init, allow_downcast=True)
# first order coefficients
w1_init = np.zeros(d)
self.w1 = theano.shared(w1_init, allow_downcast=True)
# interaction factors
v_init = stdev * np.random.randn(k, d)
self.v = theano.shared(v_init, allow_downcast=True)
# *** The Model ***
# The formula for pairwise interactions is from the bottom left
# of page 997 of Rendle 2010, "Factorization Machines."
# This version scales linearly in k and d, as opposed to O(d^2).
if sparse:
interactions = 0.5 * T.sum((S.dot(X, T.transpose(self.v)) ** 2) - \
S.dot(S.mul(X,X), T.transpose(self.v ** 2)), axis=1)
y_hat = T.addbroadcast(self.w0, 0) + S.dot(X,
self.w1) + interactions
else:
interactions = 0.5 * T.sum((T.dot(X, T.transpose(self.v)) ** 2) - \
T.dot(X ** 2, T.transpose(self.v ** 2)), axis=1)
y_hat = T.addbroadcast(self.w0, 0) + T.dot(X,
self.w1) + interactions
if self.classifier:
y_hat = T.nnet.sigmoid(y_hat)
# *** Loss Function ***
if self.classifier:
error = T.mean(T.nnet.binary_crossentropy(y_hat, y))
else:
error = T.mean((y - y_hat)**2)
# regularization
L2 = beta_w1 * T.mean(self.w1**2) + beta_v * T.mean(self.v**2)
loss = error + L2
# *** Learning ***
updates = []
params = [self.w0, self.w1, self.v]
grads = T.grad(cost=loss, wrt=params)
# RMSProp
lr, rho, epsilon = 0.001, 0.9, 1e-6
for p, g in zip(params, grads):
acc = theano.shared(p.get_value() * 0.)
acc_new = rho * acc + (1 - rho) * g**2
gradient_scaling = T.sqrt(acc_new + epsilon)
g = g / gradient_scaling
updates.append((acc, acc_new))
updates.append((p, p - lr * g))
self.theano_train = theano.function(inputs=[X, y, beta_w1, beta_v],
outputs=loss,
updates=updates,
allow_input_downcast=True)
self.theano_cost = theano.function(inputs=[X, y, beta_w1, beta_v],
outputs=loss,
allow_input_downcast=True)
# *** Prediction ***
self.theano_predict = theano.function(inputs=[X],
outputs=y_hat,
allow_input_downcast=True)
def get_hidden_values(self, inp):
""" Computes the values of the hidden layer """
return T.nnet.sigmoid(sparse.dot(inp, self.W) + self.b)
def fprop(self, state_below, add_noise=True, threshold=None, stochastic=True):
self.input_space.validate(state_below)
if self.requires_reformat:
if not isinstance(state_below, tuple):
for sb in get_debug_values(state_below):
if sb.shape[0] != self.dbm.batch_size:
raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0]))
assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim
state_below = self.input_space.format_as(state_below, self.desired_space)
self.x = state_below
# linear part
if isinstance(self.x, S.SparseVariable):
self.z = S.dot(self.x,self.W[0]) + self.b[0]
else:
self.z = T.dot(self.x,self.W[0]) + self.b[0]
self.stopper = self.x * T.ones_like(self.x)
# first layer non-linear part
if isinstance(self.stopper, S.SparseVariable):
h = S.dot(self.stopper,self.W[1]) + self.b[1]
else:
h = T.dot(self.stopper,self.W[1]) + self.b[1]
# activate hidden units of non-linear part
if self.hidden_activation is None:
pass
elif self.hidden_activation == 'tanh':
self.h = T.tanh(h)
elif self.hidden_activation == 'sigmoid':
self.h = T.nnet.sigmoid(h)
elif self.hidden_activation == 'softmax':
self.h = T.nnet.softmax(h)
elif self.hidden_activation == 'rectifiedlinear':
self.h = T.maximum(0, h)
else:
raise NotImplementedError()
rng = MRG_RandomStreams(self.mlp.rng.randint(2**15))
noise = 0
if self.noise_beta is not None:
noise = (1.-self.noise_normality) * self.beta_mean
print noise
if add_noise:
if self.noise_beta is not None:
noise = (1.-self.noise_normality) * self.beta_dist[ \
self.beta_idx:self.beta_idx+self.x.shape[0],:] \
+ (self.noise_normality * self.noise_scale \
* rng.normal(size = self.z.shape,
std=self.noise_stdev ,
dtype=self.z.type.dtype) \
)
else:
noise = self.noise_scale \
* rng.normal(size = self.z.shape,
std=self.noise_stdev ,
dtype=self.z.type.dtype)
# second layer non-linear part
self.a = T.dot(self.h,self.W[2]) + self.b[2] + noise
# activate non-linear part to get bernouilli probabilities
self.m_mean = T.nnet.sigmoid(self.a)
# Separate stochastic from deterministic part:
self.stoch_m_mean = self.m_mean**self.stochastic_ratio
self.deter_m_mean = self.m_mean**(1.-self.stochastic_ratio)
if threshold is None:
if stochastic:
# sample from bernouili probs to generate a mask
self.m = rng.binomial(size = self.stoch_m_mean.shape, n = 1 , \
p = self.m_mean, dtype=self.stoch_m_mean.type.dtype)
else:
self.m = self.m_mean
else:
# deterministic mask:
self.m = T.cast(T.gt(self.stoch_m_mean, threshold), \
theano.config.floatX)
self.consider_constant = [self.m, self.stopper]
# mix output of linear part with output of non-linear part
self.p = self.m * self.deter_m_mean * self.z
if self.layer_name is not None:
self.z.name = self.layer_name + '_z'
self.h.name = self.layer_name + '_h'
self.a.name = self.layer_name + '_a'
self.m_mean.name = self.layer_name + '_m_mean'
self.stoch_m_mean.name = self.layer_name + '_stoch_m_mean'
self.deter_m_mean.name = self.layer_name + '_deter_m_mean'
self.p.name = self.layer_name + '_p'
return self.p
def fprop(self, state_below, threshold=None, stochastic=True):
self.input_space.validate(state_below)
if self.requires_reformat:
if not isinstance(state_below, tuple):
for sb in get_debug_values(state_below):
if sb.shape[0] != self.dbm.batch_size:
raise ValueError("self.dbm.batch_size is %d but got shape of %d" % (self.dbm.batch_size, sb.shape[0]))
assert reduce(lambda x,y: x * y, sb.shape[1:]) == self.input_dim
state_below = self.input_space.format_as(state_below, self.desired_space)
self.x = state_below
# experts part
if isinstance(self.x, S.SparseVariable):
z = S.dot(self.x,self.W[0]) + self.b[0]
else:
z = T.dot(self.x,self.W[0]) + self.b[0]
# activate hidden units of gater part
if self.expert_activation is None:
self.z = z
elif self.hidden_activation == 'tanh':
self.z = T.tanh(z)
elif self.expert_activation == 'sigmoid':
self.z = T.nnet.sigmoid(z)
elif self.expert_activation == 'softmax':
self.z = T.nnet.softmax(z)
elif self.expert_activation == 'rectifiedlinear':
self.z = T.maximum(0, z)
else:
raise NotImplementedError()
# first layer of gater
if isinstance(self.x, S.SparseVariable):
h = S.dot(self.x,self.W[1]) + self.b[1]
else:
h = T.dot(self.x,self.W[1]) + self.b[1]
# activate hidden units of gater
if self.hidden_activation is None:
self.h = h
elif self.hidden_activation == 'tanh':
self.h = T.tanh(h)
elif self.hidden_activation == 'sigmoid':
self.h = T.nnet.sigmoid(h)
elif self.hidden_activation == 'softmax':
self.h = T.nnet.softmax(h)
elif self.hidden_activation == 'rectifiedlinear':
self.h = T.maximum(0, h)
else:
raise NotImplementedError()
# second layer gater
self.a = T.dot(self.h,self.W[2]) + self.b[2]
# activate gater output to get bernouilli probabilities
self.m_mean = T.nnet.sigmoid(self.a)
if threshold is None:
if stochastic:
# sample from bernouili probs to generate a mask
rng = MRG_RandomStreams(self.mlp.rng.randint(2**15))
self.m = rng.binomial(size = self.m_mean.shape, n = 1,
p = self.m_mean, dtype=self.m_mean.type.dtype)
else:
self.m = self.m_mean
else:
# deterministic mask:
self.m = T.cast(T.gt(self.m_mean, threshold), \
theano.config.floatX)
self.m2 = T.dot(self.m, self.groups)
# mask expert output with samples from gater
self.p = self.m2 * self.z
if self.layer_name is not None:
self.z.name = self.layer_name + '_z'
self.h.name = self.layer_name + '_h'
self.a.name = self.layer_name + '_a'
self.m_mean.name = self.layer_name + '_m_mean'
self.m.name = self.layer_name + '_m'
self.p.name = self.layer_name + '_p'
return self.p
mu_u = np.zeros(N)
mu_u[0] = 1.0
cov_u = 1e-2 * np.eye(N)
cov_u[0, 0] = 1e-10
u = pm.MvNormal("u", mu_u, cov_u, shape=(N, ))
u = tt.reshape(u, (N, 1))
# The spectral basis
mu_vT = np.ones(K)
cov_vT = 1e-2 * np.eye(K)
vT = pm.MvNormal("vT", mu_vT, cov_vT, shape=(K, ))
vT = tt.reshape(vT, (1, K))
# Compute the model
uvT = tt.reshape(tt.dot(u, vT), (N * K, 1))
f_model = tt.reshape(ts.dot(D, uvT), (M * Kobs, ))
# Track some values for plotting later
pm.Deterministic("f_model", f_model)
# Save our initial guess
f_model_guess = xo.eval_in_model(f_model)
# The likelihood function assuming known Gaussian uncertainty
pm.Normal("obs", mu=f_model, sd=ferr, observed=f)
# Maximum likelihood solution
with model:
map_soln = xo.optimize()
# Plot some stuff
def sparse_slice_rows(H, idx):
'''Returns a dense slice H[idx, :]'''
vecs = to_one_hot(idx, H.shape[0], dtype=H.dtype)
return ts.dot(vecs, H)
def init_functions(self):
'''Construct functions for the model'''
# Construct the objective function
# Input variables
u_i, y_s, y_t = T.ivectors(['u_i', 'y_s', 'y_t'])
dropout = T.fscalar(name='p')
# Intermediate variables: n_examples * n_songs
item_scores = T.dot(self._U[u_i], self._V.T) + self._b
# subtract off the row-wise max for numerical stability
item_scores = item_scores - item_scores.max(axis=1, keepdims=True)
e_scores = T.exp(item_scores)
if T.gt(dropout, 0.0):
# Construct a random dropout mask
retain_prob = 1.0 - dropout
M = self._rng.binomial(e_scores.shape,
p=retain_prob,
dtype=theano.config.floatX)
# Importance weight so that E[M[i,j]] = 1
M /= retain_prob
# The positive examples should always be sampled
M = theano.tensor.set_subtensor(M[T.arange(y_t.shape[0]), y_t],
1.0)
e_scores = e_scores * M
# Edge feasibilities: n_examples * n_edges
prev_feas = sparse_slice_rows(self.H, y_s)
# Detect and reset initial-state transitions
prev_feas = theano.tensor.set_subtensor(prev_feas[y_s < 0, :], 1)
# Raw edge probabilities: n_examples * n_edges
edge_given_prev = T.nnet.softmax(prev_feas * self._w)
# Compute edge normalization factors: n_examples * n_edges
# sum of score mass in each edge for each user
edge_norms = ts.dot(e_scores, self.H)
# Slice the edge weights according to incoming feasibilities: n_examples
next_weight = e_scores[T.arange(y_t.shape[0]), y_t]
# Marginalize: n_examples * n_edges
next_feas = sparse_slice_rows(self.H, y_t)
probs = next_weight * T.sum(next_feas * (edge_given_prev / (_EPS + edge_norms)),
axis=1,
keepdims=True)
# Data likelihood term
ll = T.log(probs)
avg_ll = ll.mean()
# Priors
w_prior = -0.5 * self.edge_reg * (self._w**2).sum()
b_prior = -0.5 * self.bias_reg * (self._b**2).sum()
u_prior = -0.5 * self.user_reg * (self._U**2).sum()
v_prior = -0.5 * self.song_reg * (self._V**2).sum()
# negative log-MAP objective
cost = -1.0 * (avg_ll + u_prior + v_prior + b_prior + w_prior)
# Construct the updates
variables = []
if 'e' in self.params:
variables.append(self._w)
if 'b' in self.params:
variables.append(self._b)
if 'u' in self.params:
variables.append(self._U)
if 's' in self.params:
variables.append(self._V)
updates = lasagne.updates.adagrad(cost, variables)
self._train = theano.function(inputs=[u_i, y_s, y_t, dropout],
outputs=[avg_ll, cost],
updates=updates)
self._loglikelihood = theano.function(inputs=[u_i, y_s, y_t,
theano.Param(dropout,
default=0.0,
name='p')],
outputs=[ll])
def solve(self, u=None, vT=None, b=None, u_guess=None,
vT_guess=None, b_guess=None, u_mu=0.0, u_sig=0.01,
vT_mu=1.0, vT_sig=0.3, vT_rho=3.e-5, b_mu=1.0,
b_sig=0.1, niter_adam=100, niter_linear=100,
temp=1.e3, **kwargs):
"""
"""
if not self._loaded:
raise RuntimeError("Please load or generate a dataset first.")
# Data covariance
self.F_CInv = np.ones_like(self.F) / self.ferr ** 2
self.F_lndet = np.sum(np.log(2 * np.pi * self.F_CInv.reshape(-1)))
# Prior on `u`
self.u_cinv = np.ones(self.N - 1) / u_sig ** 2
self.u_mu = np.ones(self.N - 1) * u_mu
self.u_lndet = np.sum(np.log(2 * np.pi * self.u_cinv))
# Gaussian process prior on `vT`
self.vT_mu = (np.ones(self.Kp) * vT_mu).reshape(1, -1)
if vT_rho > 0.0:
kernel = celerite.terms.Matern32Term(np.log(vT_sig), np.log(vT_rho))
gp = celerite.GP(kernel)
vT_C = gp.get_matrix(self.lam_padded)
cho_C = cho_factor(vT_C)
self.vT_CInv = cho_solve(cho_C, np.eye(self.Kp))
self.vT_CInvmu = cho_solve(cho_C, self.vT_mu.reshape(-1))
self.vT_lndet = -2 * np.sum(np.log(2 * np.pi * np.diag(cho_C[0])))
else:
self.vT_CInv = np.ones(self.Kp) / vT_sig ** 2
self.vT_CInvmu = (self.vT_CInv * self.vT_mu)
self.vT_lndet = np.sum(np.log(2 * np.pi * self.vT_CInv))
self.vT_CInv = np.diag(self.vT_CInv)
# Prior on `b`
self.b_cinv = np.ones(self.M) / b_sig ** 2
self.b_mu = np.ones(self.M) * b_mu
self.b_lndet = self.M * np.log(2 * np.pi / b_sig ** 2)
# Simple linear solves
if (u is not None) and (vT is not None):
self.u = u
self.vT = vT
self._compute_b()
elif (u is not None) and (b is not None):
self.u = u
self.b = b
self._compute_vT()
elif (vT is not None) and (b is not None):
self.b = b
self.vT = vT
self._compute_u()
# Non-linear
else:
# Get our guesses going
if u is not None:
self.u = u
var_names = ["vT", "b"]
if vT_guess is None and b_guess is None:
self.b = np.ones(self.M)
self._compute_vT(T=temp)
elif vT_guess is not None:
self.vT = vT_guess
self._compute_b(T=temp)
elif b_guess is not None:
self.b = b_guess
self._compute_vT(T=temp)
else: raise ValueError("Unexpected branch!")
elif vT is not None:
self.vT = vT
var_names = ["u", "b"]
if u_guess is None and b_guess is None:
self.b = np.ones(self.M)
self._compute_u(T=temp)
elif u_guess is not None:
self.u = u_guess
self._compute_b(T=temp)
elif b_guess is not None:
self.b = b_guess
self._compute_u(T=temp)
else: raise ValueError("Unexpected branch!")
elif b is not None:
self.b = b
var_names = ["u", "vT"]
if u_guess is None and vT_guess is None:
self.u = self.u_mu + u_sig * np.random.randn(self.N - 1)
self._compute_vT(T=temp)
elif u_guess is not None:
self.u = u_guess
self._compute_vT(T=temp)
elif vT_guess is not None:
self.vT = vT_guess
self._compute_u(T=temp)
else: raise ValueError("")
else:
var_names = ["u", "vT", "b"]
if vT_guess is None and b_guess is None and u_guess is None:
self.b = np.ones(self.M)
self.u = self.u_mu + u_sig * np.random.randn(self.N - 1)
self._compute_vT(T=temp)
elif u_guess is not None:
self.u = u_guess
if vT_guess is None and b_guess is None:
self.b = np.ones(self.M)
self._compute_vT(T=temp)
elif vT_guess is not None:
self.vT = vT_guess
self._compute_b(T=temp)
elif b_guess is not None:
self.b = b_guess
self._compute_vT(T=temp)
else: raise ValueError("Unexpected branch!")
elif vT_guess is not None:
self.vT = vT_guess
if b_guess is None:
self.b = np.ones(self.M)
self._compute_u(T=temp)
else:
self.b = b_guess
self._compute_u(T=temp)
elif b_guess is not None:
self.b = b_guess
self.u = self.u_mu + u_sig * np.random.randn(self.N - 1)
self._compute_vT(T=temp)
else: raise ValueError("Unexpected branch!")
# Initialize the variables to the guesses
vars = []
if "u" in var_names:
u = theano.shared(self.u)
vars += [u]
else:
u = tt.as_tensor_variable(self.u)
if "vT" in var_names:
vT = theano.shared(self.vT)
vars += [vT]
else:
vT = tt.as_tensor_variable(self.vT)
# Compute the model
D = ts.as_sparse_variable(self.D)
a = tt.reshape(tt.dot(tt.reshape(
tt.concatenate([[1.0], u]), (-1, 1)),
tt.reshape(vT, (1, -1))), (-1,))
self.map[1:, :] = u
b = self.map.flux(theta=self.theta)
B = tt.reshape(b, (-1, 1))
M = tt.reshape(ts.dot(D, a), (self.M, -1)) / B
# Compute the likelihood
r = tt.reshape(self.F - M, (-1,))
cov = tt.reshape(self.F_CInv, (-1,))
lnlike = -0.5 * (tt.sum(r ** 2 * cov) + self.F_lndet)
# Compute the prior
lnprior = -0.5 * (tt.sum((u - self.u_mu) ** 2 * self.u_cinv) + self.u_lndet)
lnprior += -0.5 * (tt.dot(tt.dot(tt.reshape((vT - self.vT_mu), (1, -1)), self.vT_CInv), tt.reshape((vT - self.vT_mu), (-1, 1)))[0, 0] + self.vT_lndet)
# The full loss
loss = -(lnlike + lnprior)
best_loss = loss.eval()
best_u = u.eval()
best_vT = vT.eval()
best_b = b.eval()
lnlike_val = np.zeros(niter_adam + 1)
lnprior_val = np.zeros(niter_adam + 1)
lnlike_val[0] = lnlike.eval()
lnprior_val[0] = lnprior.eval()
# Optimize
upd = Adam(loss, vars, **kwargs)
train = theano.function([],
[u, vT, b, loss, lnlike, lnprior], updates=upd)
for n in tqdm(1 + np.arange(niter_adam)):
u_val, vT_val, b_val, loss_val, lnlike_val[n], lnprior_val[n] = train()
if (loss_val < best_loss):
best_loss = loss_val
best_u = u_val
best_vT = vT_val
best_b = b_val
# We're done!
self.u = best_u
self.vT = best_vT
self.b = best_b
self.lnlike = lnlike_val
self.lnprior = lnprior_val
self._solved = True
def RankRelFn(fnsim, embeddings, leftop, rightop,
subtensorspec=None, adding=False):
"""
This function returns a Theano function to measure the similarity score of
all relation entities given couples of 'right' and 'left' entities (as
sparse matrices).
: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)
:param adding: if the right member is composed of several entities the
function needs to more inputs: we have to add the embedding
value of the other entities (with the appropriate scaling
factor to perform the mean pooling).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr = S.csr_matrix('inpr')
inpl = S.csr_matrix('inpl')
if adding:
inpoadd = S.csr_matrix('inpoadd')
scal = T.scalar('scal')
# Graph
if subtensorspec is None:
rell = relationl.E
relr = relationr.E
else:
# We compute the score only for a subset of entities
rell = relationl.E[:, :subtensorspec].T
relr = relationr.E[:, :subtensorspec].T
if adding:
# Add the embeddings of the other entities (mean pooling)
rell = rell * scal + (S.dot(relationl.E, inpoadd).T).reshape(
(1, embedding.D))
relr = relr * scal + (S.dot(relationr.E, inpoadd).T).reshape(
(1, embedding.D))
lhs = (S.dot(embedding.E, inpl).T).reshape((1, embedding.D))
rhs = (S.dot(embedding.E, inpr).T).reshape((1, embedding.D))
# hack to prevent a broadcast problem with the Bilinear layer
if hasattr(leftop, 'forwardrankrel'):
tmpleft = leftop.forwardrankrel(lhs, rell)
else:
tmpleft = leftop(lhs, rell)
if hasattr(rightop, 'forwardrankrel'):
tmpright = rightop.forwardrankrel(rhs, relr)
else:
tmpright = rightop(lhs, rell)
simi = fnsim(tmpleft, tmpright)
"""
Theano function inputs.
:input inpl: sparse csr matrix representing the indexes of the 'left'
entities, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the 'right'
entities, shape=(#examples,N [Embeddings]).
:opt input inpoadd: sparse csr matrix representing the indexes of the
other entities of the relation member with the
appropriate scaling factor, shape = (#examples, N
[Embeddings]).
:opt input scal: scaling factor to perform the mean: 1 / [#entities in the
member].
Theano function output.
:output simi: matrix of score values.
"""
if not adding:
return theano.function([inpl, inpr], [simi], on_unused_input='ignore')
else:
return theano.function([inpl, inpr, inpoadd, scal], [simi],
on_unused_input='ignore')
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_bow = T.matrix()
s_idx = T.iscalar()
s_tf = T.scalar()
s_posit = T.matrix() #theano.sparse.csr_matrix()
s_negat = T.matrix() #theano.sparse.csr_matrix()
sentences = cPickle.load(
open('/scratch/rifaisal/data/guten/guten_subset_idx.pkl'))
senna = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
gsubset = cPickle.load(
open('/scratch/rifaisal/data/guten/guten_vocab_subset.pkl')).flatten(
).tolist()
hashtab = dict(zip(gsubset, range(len(gsubset))))
tfidf_data = numpy.load('/scratch/rifaisal/data/guten/guten_tfidf.npy'
).item().tocsr().astype('float32')
#tfidf = cPickle.load(open('/scratch/rifaisal/repos/senna/gutentokenizer.pkl'))
senna = numpy.array(senna)[gsubset].tolist()
s_valid = theano.sparse.csr_matrix()
validsentence = sentences[10000:10010]
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
S = logistic(i_size=hp['embedsize'], h_size=nsenna, act=T.nnet.softmax)
valid_embedding = sparse.supervised.logistic(i_size=nsenna,
h_size=hp['embedsize'],
act=identity)
valid_embedding.params['weights'] = sp.shared(
value=scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(
borrow=True)))
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape(
(1, hp['embedsize'] * hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape(
(hp['nneg'], hp['embedsize'] * hp['wsize']))
valid_embed = sp.dot(s_valid, valid_embedding.params['weights']).reshape(
(nsenna, hp['embedsize'] * hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
s_bow_pred = S.encode(embedding.encode(s_bow))
pred = s_tf * nllsoft(s_bow_pred, s_idx)
CC = (rect(C)).mean() + hp['lambda'] * pred
opt = theano.function(
[s_posit, s_negat, s_bow, s_idx, s_tf], [(rect(C)).mean(), pred],
updates=dict(
S.update(CC, lr) + L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
#validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
freq_idx = cPickle.load(
open('/scratch/rifaisal/data/guten/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [hashtab[idx] for idx in freq_idx]
fname = ''
for e in range(hp['epoch']):
c = []
r = []
count = 1
for i in range(nsent):
rsent = numpy.random.randint(nsent - 1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low=delta, high=nword - delta)
pchunk = sentences[rsent][pidx - delta:pidx + delta + rest]
nchunk = []
st = sentences[rsent][pidx - delta:pidx]
en = sentences[rsent][pidx + 1:pidx + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk) * hp['nneg']
tfidf_chunk = tfidf_data[rsent:rsent + 1].toarray()
#pdb.set_trace()
tfidf_value = tfidf_chunk[0, sentences[rsent][pidx]]
tfidf_chunk[0, sentences[rsent][pidx]] = 0.
tfidx = sentences[rsent][
pidx] # numpy.zeros(tfidf_chunk.shape).astype('float32')
#tfidx[0,sentences[rsent][pidx]] = 1.
p, n, b, iidx, tfval = (idx2mat(pchunk,
nsenna), idx2mat(nchunk, nsenna),
tfidf_chunk, tfidx, tfidf_value)
count += tfval != 0
l, g = opt(p, n, b, iidx, tfval)
c = c
c.append(l)
r.append(g)
"""
if (time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (i+1)%(20*hp['freq']) == 0 and debug==False:
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
"""
if (i + 1) % hp['freq'] == 0 or debug:
hp['score'] = numpy.array(c).sum() / (numpy.array(c) > 0).sum()
hp['pred'] = numpy.array(r).sum() / float(count)
hp['e'] = e
hp['i'] = i
print ''
print e, i, 'NN Score:', hp['score'], 'Reconstruction:', hp[
'pred']
if debug != True:
ne = knn(
freq_idx,
embedding.params['e_weights'].get_value(borrow=True))
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def plot_results(
doppler,
loss=[],
cho_y1=None,
cho_s=None,
name="vogtstar",
nframes=None,
render_movies=False,
open_plots=False,
overlap=2.0,
res=300,
):
"""
Plot the results of the Doppler imaging problem for the SPOT star.
"""
# Get the values we'll need for plotting
ydeg = doppler.ydeg
udeg = doppler._udeg
u = doppler.u
theta = doppler.theta
y1_true = doppler.y1_true
s_true = doppler.s_true
s_deconv = doppler.s_deconv
baseline_true = doppler.baseline_true
y1 = np.array(doppler.y1)
s = np.array(doppler.s)
baseline = doppler.baseline().reshape(-1)
model = doppler.model()
F = doppler.F
lnlam = doppler.lnlam
lnlam_padded = doppler.lnlam_padded
M = doppler.M
inc = doppler.inc
# List of figure files we're generating
files = []
# Plot the baseline
# HACK: Append the first measurement to the last to get
# a plot going from -180 to 180 (endpoints included)
theta_ = np.append(theta, [180.0])
baseline_true_ = np.append(baseline_true, [baseline_true[0]])
baseline_ = np.append(baseline, [baseline[0]])
fig, ax = plt.subplots(1, figsize=(8, 5))
ax.plot(theta_, baseline_true_, label="true")
ax.plot(theta_, baseline_, label="inferred")
if cho_y1 is not None:
U = np.triu(cho_y1[0])
B = doppler._map.design_matrix(theta=doppler.theta).eval()[:, 1:]
A = np.linalg.solve(U.T, B.T)
baseline_sig = np.sqrt(np.sum(A**2, axis=0))
baseline_sig_ = np.append(baseline_sig, [baseline_sig[0]])
ax.fill_between(
theta_,
baseline_ - baseline_sig_,
baseline_ + baseline_sig_,
color="C1",
alpha=0.25,
lw=0,
)
ax.legend(loc="lower left", fontsize=14)
ax.set_xlabel(r"$\theta$ (degrees)")
ax.margins(0, None)
ax.set_xticks([-180, -135, -90, -45, 0, 45, 90, 135, 180])
ax.set_ylabel("baseline")
fig.savefig("%s_baseline.pdf" % name, bbox_inches="tight")
files.append("baseline.pdf")
plt.close()
# Plot the loss
if len(np.atleast_1d(loss).flatten()) > 1:
# Compute the loss @ true value
doppler.y1 = y1_true
doppler.s = s_true
loss_true = doppler.loss()
# Print for the record
print("True loss: %.2f" % loss_true)
print("Best loss: %.2f" % np.min(loss))
# Plot
fig, ax = plt.subplots(1, figsize=(12, 5))
ax.plot(loss, label="loss", color="C0")
ax.axhline(loss_true, color="C1", ls="--", label="loss @ true values")
ax.set_yscale("log")
ax.set_ylabel("negative log probability")
ax.set_xlabel("iteration")
ax.legend(loc="upper right")
fig.savefig("%s_prob.pdf" % name, bbox_inches="tight")
files.append("prob.pdf")
plt.close()
# Plot the Ylm coeffs
fig, ax = plt.subplots(1, figsize=(12, 5))
n = np.arange(1, doppler.N)
ax.plot(n, y1_true, "C0-", label="true")
lo = (doppler.y1_mu - doppler.y1_sig) * np.ones_like(y1)
hi = (doppler.y1_mu + doppler.y1_sig) * np.ones_like(y1)
ax.fill_between(n, lo, hi, color="C1", lw=0, alpha=0.25, label="prior")
ax.plot(n, y1, "C1-", label="inferred")
if cho_y1 is not None:
cov_y1 = cho_solve(cho_y1, np.eye(doppler.N - 1))
sig_y1 = np.sqrt(np.diag(cov_y1))
ax.fill_between(n, y1 - sig_y1, y1 + sig_y1, color="C1", alpha=0.5)
ax.set_ylabel("spherical harmonic coefficient")
ax.set_xlabel("coefficient number")
ax.legend(loc="lower right", fontsize=14)
ax.margins(0.01, None)
fig.savefig("%s_coeffs.pdf" % name, bbox_inches="tight")
files.append("coeffs.pdf")
plt.close()
# Render the true map
map = starry.Map(ydeg=ydeg, udeg=udeg)
map.inc = inc
map[1:, :] = y1_true
if udeg > 0:
map[1:] = u
if nframes is None:
nframes = len(theta)
theta_img = np.array(theta)
else:
theta_img = np.linspace(-180, 180, nframes + 1)[:-1]
if render_movies:
map.show(theta=np.linspace(-180, 180, 50), mp4="%s_true.mp4" % name)
files.append("true.mp4")
img_true_rect = (map.render(projection="rect",
res=res).eval().reshape(res, res))
# Render the inferred map
map[1:, :] = y1
img = map.render(theta=theta_img).eval()
if render_movies:
map.show(theta=np.linspace(-180, 180, 50),
mp4="%s_inferred.mp4" % name)
files.append("inferred.mp4")
img_rect = map.render(projection="rect", res=res).eval().reshape(res, res)
# Render the pixelwise uncertainties
if cho_y1 is not None:
# Compute the polynomial transform matrix
xyz = map.ops.compute_rect_grid(tt.as_tensor_variable(res))
P = map.ops.pT(xyz[0], xyz[1], xyz[2])[:, :doppler.N]
# Transform it to Ylm & evaluate it
P = ts.dot(P, map.ops.A1).eval()
# Rotate it so north points up
"""
R = map.ops.R([1, 0, 0], -(90.0 - inc) * np.pi / 180.0)
for l in range(map.ydeg + 1):
idx = slice(l ** 2, (l + 1) ** 2)
P[:, idx] = P[:, idx].dot(R[l])
"""
# Discard Y_{0, 0}, whose variance is zero
P = P[:, 1:]
# NOTE: This is the slow way of computing sigma
# CPT = cho_solve(cho_y1, P.T)
# cov = np.dot(P, CPT)
# sig = np.sqrt(np.diag(cov))
# This is the streamlined version
U = np.triu(cho_y1[0])
A = np.linalg.solve(U.T, P.T)
img_sig_rect = np.sqrt(np.sum(A**2, axis=0)).reshape(res, res)
# This is how I'd compute the *prior* uncertainty on the pixels
nsamp = 1000
prior_std = np.std([
np.dot(
P[0],
doppler.y1_sig * np.random.randn(doppler.N - 1) +
doppler.y1_mu,
) for i in range(nsamp)
])
# Normalize to the maximum for plotting
vmax = np.nanmax(img_true_rect)
img_rect /= vmax
img_true_rect /= vmax
if cho_y1 is not None:
img_sig_rect /= vmax
prior_std /= vmax
# Plot the maps side by side
if cho_y1 is not None:
fig, ax = plt.subplots(3, figsize=(10, 13))
fig.subplots_adjust(hspace=0.3)
else:
fig, ax = plt.subplots(2, figsize=(10, 8))
im = ax[0].imshow(
img_true_rect,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=1,
)
divider = make_axes_locatable(ax[0])
cax = divider.append_axes("right", size="4%", pad=0.25)
plt.colorbar(im, cax=cax, format="%.2f")
im = ax[1].imshow(
img_rect,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=1,
)
divider = make_axes_locatable(ax[1])
cax = divider.append_axes("right", size="4%", pad=0.25)
plt.colorbar(im, cax=cax, format="%.2f")
ax[0].annotate(
"true",
xy=(0, 0),
xytext=(7, 7),
xycoords="axes fraction",
textcoords="offset points",
ha="left",
va="bottom",
fontsize=22,
color="k",
zorder=101,
)
ax[1].annotate(
"inferred",
xy=(0, 0),
xytext=(7, 7),
xycoords="axes fraction",
textcoords="offset points",
ha="left",
va="bottom",
fontsize=22,
color="k",
zorder=101,
)
if cho_y1 is not None:
im = ax[2].imshow(
img_sig_rect,
origin="lower",
extent=(-180, 180, -90, 90),
cmap="plasma",
vmin=0,
vmax=prior_std,
)
ticks = np.linspace(0, prior_std, 5)
ticklabels = ["%.2f" % t for t in ticks]
ticklabels[-1] = r"$\sigma_\mathrm{prior}$"
divider = make_axes_locatable(ax[2])
cax = divider.append_axes("right", size="4%", pad=0.25)
cb = plt.colorbar(im, cax=cax, format="%.2f", ticks=ticks)
cb.ax.set_yticklabels(ticklabels)
ax[2].annotate(
"uncertainty",
xy=(0, 0),
xytext=(7, 7),
xycoords="axes fraction",
textcoords="offset points",
ha="left",
va="bottom",
fontsize=22,
color="k",
zorder=101,
)
for axis in ax:
latlines = np.linspace(-90, 90, 7)[1:-1]
lonlines = np.linspace(-180, 180, 13)
for lat in latlines:
axis.axhline(lat, color="k", lw=0.5, alpha=0.5, zorder=100)
for lon in lonlines:
axis.axvline(lon, color="k", lw=0.5, alpha=0.5, zorder=100)
axis.set_xticks(lonlines)
axis.set_yticks(latlines)
# axis.set_xlabel("Longitude [deg]", fontsize=16)
# axis.set_ylabel("Latitude [deg]", fontsize=16)
for tick in (axis.xaxis.get_major_ticks() +
axis.yaxis.get_major_ticks()):
tick.label.set_fontsize(10)
fig.savefig("%s_rect.pdf" % name, bbox_inches="tight")
files.append("rect.pdf")
plt.close()
# Plot the "Joy Division" graph
fig = plt.figure(figsize=(8, 11.5))
ax_img = [
plt.subplot2grid((nframes, 8), (n, 0), rowspan=1, colspan=1)
for n in range(nframes)
]
ax_f = [plt.subplot2grid((nframes, 8), (0, 1), rowspan=1, colspan=7)]
ax_f += [
plt.subplot2grid(
(nframes, 8),
(n, 1),
rowspan=1,
colspan=7,
sharex=ax_f[0],
sharey=ax_f[0],
) for n in range(1, nframes)
]
for n in range(nframes):
ax_img[n].imshow(img[n],
extent=(-1, 1, -1, 1),
origin="lower",
cmap="plasma")
ax_img[n].axis("off")
m = int(np.round(np.linspace(0, M - 1, nframes)[n]))
ax_f[n].plot(lnlam, F[m], "k.", ms=2, alpha=0.75, clip_on=False)
ax_f[n].plot(lnlam, model[m], "C1-", lw=1, clip_on=False)
ax_f[n].axis("off")
ymed = np.median(F)
ydel = 0.5 * (np.max(F) - np.min(F)) / overlap
ax_f[0].set_ylim(ymed - ydel, ymed + ydel)
fig.savefig("%s_timeseries.pdf" % name, bbox_inches="tight", dpi=400)
files.append("timeseries.pdf")
plt.close()
# Plot the rest frame spectrum
fig, ax = plt.subplots(1)
ax.plot(lnlam_padded, s_true.reshape(-1), "C0-", label="true")
if s_deconv is not None:
ax.plot(
lnlam_padded,
s_deconv.reshape(-1),
"C1--",
lw=1,
alpha=0.5,
label="guess",
)
ax.plot(lnlam_padded, s.reshape(-1), "C1-", label="inferred")
if cho_s is not None:
cov_s = cho_solve(cho_s, np.eye(doppler.Kp))
sig_s = np.sqrt(np.diag(cov_s))
ax.fill_between(lnlam_padded,
s - sig_s,
s + sig_s,
color="C1",
alpha=0.5)
ax.axvspan(lnlam_padded[0], lnlam[0], color="k", alpha=0.3)
ax.axvspan(lnlam[-1], lnlam_padded[-1], color="k", alpha=0.3)
ax.set_xlim(lnlam_padded[0], lnlam_padded[-1])
ax.set_xlabel(r"$\ln\left(\lambda/\lambda_\mathrm{r}\right)$")
ax.set_ylabel(r"Normalized intensity")
ax.legend(loc="lower left", fontsize=12)
fig.savefig("%s_spectrum.pdf" % name, bbox_inches="tight")
files.append("spectrum.pdf")
plt.close()
# Open
if open_plots:
for file in files:
subprocess.run(["open", "%s_%s" % (name, file)])
def TrainFn(fnsim, embeddings, leftop, rightop,
loss=loss.hinge, loss_margin=1.0, op='', method='SGD',
decay=0.999, epsilon=1e-6, max_learning_rate=None,
weight_L1_param_regularizer=None, weight_L2_param_regularizer=None,
weight_contractive_regularizer_left=None, weight_contractive_regularizer_right=None):
"""
This function returns a theano function to perform a training iteration, contrasting couples of positive and negative triplets. members are given
as sparse matrices. for one positive triplet there is one negative triplet.
: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.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
inpr, inpl, inpo = S.csr_matrix('inpr'), S.csr_matrix('inpl'), S.csr_matrix('inpo')
inpln, inprn, inpon = S.csr_matrix('inpln'), S.csr_matrix('inprn'), S.csr_matrix('inpon')
# Learning rates for parameters and embeddings
rate_params = T.scalar('rate_params')
rate_embeddings = T.scalar('rate_embeddings')
# E: D x N, inp: N x B -> <E, inp>: D x B -> <E, inp>.T: B x D
# Positive triplet functions
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
# Negative triplet functions
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
# Similarity Function, applied to g_lhs and g_rhs
lop, rop = leftop(lhs, rell), rightop(rhs, relr)
lopn, ropn = leftop(lhsn, relln), rightop(rhsn, relrn)
simi = fnsim(lop, rop)
simin = fnsim(lopn, ropn)
supported_loss_args = inspect.getargspec(loss)[0]
loss_args = {} if 'margin' not in supported_loss_args else { 'margin':loss_margin }
cost, out = loss(simi, simin, **loss_args)
# <EXPERIMENTAL_CODE>
# Should I also plug examples from corrupted triples ?
if weight_contractive_regularizer_left is not None:
cost = cost + (weight_contractive_regularizer_left * R.contractive_regularizer(lop, lhs))
if weight_contractive_regularizer_right is not None:
cost = cost + (weight_contractive_regularizer_right * R.contractive_regularizer(rop, rhs))
for rel_param in set([relationl.E, relationr.E]):
if weight_L1_param_regularizer is not None:
cost = cost + (weight_L1_param_regularizer * R.L1_regularizer(rel_param))
if weight_L2_param_regularizer is not None:
cost = cost + (weight_L2_param_regularizer * R.L2_regularizer(rel_param))
# </EXPERIMENTAL_CODE>
params = leftop.params + rightop.params + (fnsim.params if hasattr(fnsim, 'params') else [])
params = list(set(params))
embeds = [embedding.E] + ([relationr.E, relationl.E] if (type(embeddings) == list) else [])
embeds = list(set(embeds))
# The function updates the implicit function arguments according to the updates.
updates = collections.OrderedDict()
if (method == 'SGD'):
pass # do nothing
elif (method == 'MOMENTUM'):
param_previous_update_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the previous updates
previous_update_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_previous_update = theano.shared(value=previous_update_value, name='su_' + param.name)
param_previous_update_map[param] = param_previous_update
elif (method == 'ADAGRAD'):
param_squared_gradients_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
elif (method == 'ADADELTA'):
param_squared_gradients_map = collections.OrderedDict()
param_squared_updates_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
# Allocate the sums of squared updates
squared_updates_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_updates = theano.shared(value=squared_updates_value, name='su_' + param.name)
param_squared_updates_map[param] = param_squared_updates
elif (method == 'RMSPROP'):
param_squared_gradients_map = collections.OrderedDict()
for param in params + embeds:
# Allocate the sums of squared gradients
squared_gradients_value = numpy.zeros(param.get_value().shape, dtype=theano.config.floatX)
param_squared_gradients = theano.shared(value=squared_gradients_value, name='sg_' + param.name)
param_squared_gradients_map[param] = param_squared_gradients
else:
raise ValueError('Unknown method: %s' % (method))
# Parameter Gradients
gradientsparams = T.grad(cost, params)
# Embeddings gradients
gradientsembeds = T.grad(cost, embeds)
# Learning Rates
rates_params = [rate_params for i in range(len(params))]
# In TransE etc. the rate for predicates' embeddings (that do not get normalized) is rate_params, not rate_embeddings
rates_embeddings = [rate_embeddings, rate_params, rate_params] if len(embeds) > 1 else [rate_embeddings] # [rate_embeddings for i in range(len(embeds))]
for param, gradient, rate in zip(params + embeds, gradientsparams + gradientsembeds, rates_params + rates_embeddings):
if (method == 'SGD'): # SGD
U.sgd(param, rate, gradient, updates)
elif (method == 'MOMENTUM'): # SGD+MOMENTUM
param_previous_update = param_previous_update_map[param]
U.momentum(param, rate, decay, gradient, updates, param_previous_update)
elif (method == 'ADAGRAD'): # ADAGRAD
param_squared_gradients = param_squared_gradients_map[param]
U.adagrad(param, rate, epsilon, gradient, updates, param_squared_gradients)
elif (method == 'ADADELTA'): # ADADELTA
param_squared_gradients = param_squared_gradients_map[param]
param_squared_updates = param_squared_updates_map[param]
U.adadelta(param, rate, decay, epsilon, gradient, updates, param_squared_gradients, param_squared_updates)
elif (method == 'RMSPROP'): # RMSPROP
param_squared_gradients = param_squared_gradients_map[param]
U.rmsprop(param, rate, decay, max_learning_rate, epsilon, gradient, updates, param_squared_gradients)
else:
raise ValueError('Unknown method: %s' % (method))
"""
Theano function inputs.
:input rate_embeddings: learning/decay rate for the embeddings.
:input rate_params: learning/decay rate for the parameters.
:input inpl: sparse csr matrix representing the indexes of the positive triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the positive triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpo: sparse csr matrix representing the indexes of the positive triplet relation member, shape=(#examples,N [Embeddings]).
:input inpln: sparse csr matrix representing the indexes of the negative triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inprn: sparse csr matrix representing the indexes of the negative triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpon: sparse csr matrix representing the indexes of the negative triplet relation member, shape=(#examples,N [Embeddings]).
Theano function output.
:output mean(cost): average cost.
:output mean(out): ratio of examples for which the margin is violated,
i.e. for which an update occurs.
"""
return theano.function([rate_embeddings, rate_params, inpl, inpr, inpo, inpln, inprn, inpon],
[T.mean(cost), T.mean(out)], updates=updates, on_unused_input='ignore')
def ForwardFn1Member(fnsim, embeddings, leftop, rightop, marge=1.0, rel=True):
"""
This function returns a theano function to perform a forward step,
contrasting positive and negative triplets. members are given as sparse
matrices. For one positive triplet there are two or three (if rel == True)
negative triplets. To create a negative triplet we replace only one member
at a time.
: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 marge: marge for the cost function.
:param rel: boolean, if true we also contrast w.r.t. a negative relation
member.
:note: this is useful for W_SABIE [Weston et al., IJCAI 2011]
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# inputs
inpr = S.csr_matrix()
inpl = S.csr_matrix()
inpo = S.csr_matrix()
inpln = S.csr_matrix()
inprn = S.csr_matrix()
# graph
lhs = S.dot(embedding.E, inpl).T
rhs = S.dot(embedding.E, inpr).T
rell = S.dot(relationl.E, inpo).T
relr = S.dot(relationr.E, inpo).T
lhsn = S.dot(embedding.E, inpln).T
rhsn = S.dot(embedding.E, inprn).T
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
similn = fnsim(leftop(lhsn, rell), rightop(rhs, relr))
simirn = fnsim(leftop(lhs, rell), rightop(rhsn, relr))
costl, outl = margincost(simi, similn, marge)
costr, outr = margincost(simi, simirn, marge)
list_in = [inpl, inpr, inpo, inpln]
list_out = [outl, outr]
if rel:
inpon = S.csr_matrix()
relln = S.dot(relationl.E, inpon).T
relrn = S.dot(relationr.E, inpon).T
simion = fnsim(leftop(lhs, relln), rightop(rhs, relrn))
costo, outo = margincost(simi, simion, marge)
out = T.concatenate([outl, outr, outo])
list_in += [inpon]
list_out += [outo]
"""
Theano function inputs.
:input inpl: sparse csr matrix representing the indexes of the positive
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inpr: sparse csr matrix representing the indexes of the positive
triplet 'right' member, shape=(#examples,N [Embeddings]).
:input inpo: sparse csr matrix representing the indexes of the positive
triplet relation member, shape=(#examples,N [Embeddings]).
:input inpln: sparse csr matrix representing the indexes of the negative
triplet 'left' member, shape=(#examples,N [Embeddings]).
:input inprn: sparse csr matrix representing the indexes of the negative
triplet 'right' member, shape=(#examples,N [Embeddings]).
:opt input inpon: sparse csr matrix representing the indexes of the
negative triplet relation member, shape=(#examples,N
[Embeddings]).
Theano function output.
:output outl: binary vector representing when the margin is violated, i.e.
when an update occurs, for the 'left' member.
:output outr: binary vector representing when the margin is violated, i.e.
when an update occurs, for the 'right' member.
:opt output outo: binary vector representing when the margin is violated,
i.e. when an update occurs, for the relation member.
"""
return theano.function(list_in, list_out, on_unused_input='ignore')
def solve(self,
y1=None,
s=None,
baseline=None,
y1_guess=None,
s_guess=None,
baseline_guess=None,
niter=100,
T=1.0,
dlogT=-0.25,
optimizer="NAdam",
dcf=10.0,
quiet=False,
**kwargs):
"""Solve the Doppler imaging problem.
Returns:
``(loss, cho_y1, cho_s)``, a tuple containing the array of
loss values during the optimization and the Cholesky factorization
of the covariance matrices of ``y1`` and ``s``, if available
(otherwise the latter two are set to ``None``.)
"""
# Check the optimizer is valid
if optimizer.lower() == "nadam":
optimizer = NAdam
elif optimizer.lower() == "adam":
optimizer = Adam
else:
raise ValueError("Invalid optimizer.")
# Figure out what to solve for
known = []
if s is not None:
known += ["s"]
if y1 is not None:
known += ["y1"]
if ("y1" in known) and ("s" in known):
# Nothing to do here but ingest the values!
self.y1 = y1
self.s = s
return self.loss(), None, None
elif "y1" in known:
# Easy: it's a linear problem
self.y1 = y1
cho_s = self.compute_s()
return self.loss(), None, cho_s
else:
if ("s" in known) and (baseline is not None):
# Still a linear problem!
self.s = s
cho_y1 = self.compute_y1(baseline=baseline)
return self.loss(), cho_y1, None
else:
# Non-linear. Let's use (N)Adam.
if "s" in known:
# We know `s` and need to solve for
# `y1` w/o any baseline knowledge.
s_guess = s
else:
# We know *nothing*!
# Estimate `v^T` from the deconvolved mean spectrum
if s_guess is None:
fmean = np.mean(self.F, axis=0)
fmean -= np.mean(fmean)
diagonals = np.tile(self.kT()[0].reshape(-1, 1),
self.K)
offsets = np.arange(self.W)
A = diags(diagonals,
offsets, (self.K, self.Kp),
format="csr")
LInv = (dcf**2 * self.ferr**2 / self.s_sig**2 *
np.eye(A.shape[1]))
s_guess = 1.0 + np.linalg.solve(
A.T.dot(A).toarray() + LInv, A.T.dot(fmean))
# Save this for later
self.s_deconv = s_guess
# Estimate `y1` w/o baseline knowledge
# If `baseline_guess` is `None`, this is done via
# a Taylor expansion; see ``compute_y1()``.
if y1_guess is None:
self.s = s_guess
self.compute_y1(T=T, baseline=baseline_guess)
y1_guess = self.y1
# Initialize the variables
self.y1 = y1_guess
self.s = s_guess
# Tempering params
if T > 1.0:
T_arr = 10**np.arange(np.log10(T), 0, dlogT)
T_arr = np.append(T_arr, [1.0])
niter_bilin = len(T_arr)
else:
T_arr = [1.0]
niter_bilin = 1
# Loss array
loss_val = np.zeros(niter_bilin + niter + 1)
loss_val[0] = self.loss()
# Iterative bi-linear solve
if niter_bilin > 0:
if not quiet:
print("Running bi-linear solver...")
best_loss = loss_val[0]
best_y1 = self.y1
best_s = self.s
for n in tqdm(range(niter_bilin), disable=quiet):
# Compute `y1` using the previous baseline
self.compute_y1(T=T_arr[n], baseline=self.baseline())
# Compute `s` using the current `y1`
if "s" not in known:
self.compute_s(T=T_arr[n])
loss_val[n + 1] = self.loss()
if loss_val[n + 1] < best_loss:
best_loss = loss_val[n + 1]
best_y1 = self.y1
best_s = self.s
self.y1 = best_y1
self.s = best_s
# Non-linear solve
if niter > 0:
# Theano nonlienar solve. Variables:
y1 = theano.shared(self.y1)
s = theano.shared(self.s)
if "s" in known:
theano_vars = [y1]
else:
theano_vars = [y1, s]
# Compute the model
D = ts.as_sparse_variable(self.D())
a = tt.reshape(
tt.dot(
tt.reshape(tt.concatenate([[1.0], y1]), (-1, 1)),
tt.reshape(s, (1, -1)),
),
(-1, ),
)
b = tt.dot(
self._map.design_matrix(theta=self.theta),
tt.reshape(tt.concatenate([[1.0], y1]), (-1, 1)),
)
B = tt.reshape(b, (-1, 1))
M = tt.reshape(ts.dot(D, a), (self.M, -1)) / B
# Compute the loss
r = tt.reshape(self.F - M, (-1, ))
cov = tt.reshape(self._F_CInv, (-1, ))
lnlike = -0.5 * tt.sum(r**2 * cov)
lnprior = (-0.5 * tt.sum(
(y1 - self.y1_mu)**2 / self.y1_sig**2) + -0.5 * tt.sum(
(b - self.baseline_mu)**2 / self.baseline_sig**2) +
-0.5 * tt.dot(
tt.dot(
tt.reshape((s - self.s_mu), (1, -1)),
self._s_CInv,
),
tt.reshape((s - self.s_mu), (-1, 1)),
)[0, 0])
loss = -(lnlike + lnprior)
best_loss = loss.eval()
best_y1 = y1.eval()
best_s = s.eval()
if not quiet:
print("Running non-linear solver...")
upd = optimizer(loss, theano_vars, **kwargs)
train = theano.function([], [y1, s, loss], updates=upd)
for n in tqdm(1 + niter_bilin + np.arange(niter),
disable=quiet):
y1_val, s_val, loss_val[n] = train()
if loss_val[n] < best_loss:
best_loss = loss_val[n]
best_y1 = y1_val
best_s = s_val
# We are done!
self.y1 = best_y1
self.s = best_s
# Estimate the covariance of `y1` conditioned on `s`
# and the covariance of `s` conditioned on `y1`.
# Note that the covariance of `y1` is computed from
# the linearization that allows us to simultaneously
# solve for the baseline.
y1_curr = np.array(self.y1)
cho_y1 = self.compute_y1()
self.y1 = y1_curr
if "s" not in known:
s_curr = np.array(self.s)
cho_s = self.compute_s()
self.s = s_curr
else:
cho_s = None
return loss_val, cho_y1, cho_s