def __full_embedding_update(self, resource, args):
n = resource.get("n")
d = resource.get("d")
S = resource.get_list("S")
verbose = False
X_old = numpy.array(resource.get("X"))
X2_old = numpy.array(resource.get("X2"))
# set maximum time allowed to update embedding
t_max = 5.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
mu = 0.05
emp_loss_old, hinge_loss_old, log_loss_old = utilsCrowdKernel.getLoss(X_old, S)
X, tmp = utilsCrowdKernel.computeEmbeddingWithEpochSGD(
n, d, S, mu, max_num_passes=16, epsilon=0, verbose=verbose
)
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=1, epsilon=epsilon, verbose=verbose
)
k = 1
while (time.time() - t_start < 0.5 * t_max) and (acc > epsilon):
# take a single gradient step
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=2 ** k, epsilon=epsilon, verbose=verbose
)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsCrowdKernel.getLoss(X, S)
if emp_loss_old < emp_loss_new:
X = X_old
if d == 2:
X2 = X
else:
emp_loss_old, hinge_loss_old, log_loss_old = utilsCrowdKernel.getLoss(X2_old, S)
X2, tmp = utilsCrowdKernel.computeEmbeddingWithEpochSGD(
n, 2, S, mu, max_num_passes=16, epsilon=0, verbose=verbose
)
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, max_iters=1, epsilon=epsilon, verbose=verbose
)
k = 1
while (time.time() - t_start < 0.5 * t_max) and (acc > epsilon):
# take a single gradient step
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, max_iters=2 ** k, epsilon=epsilon, verbose=verbose
)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsCrowdKernel.getLoss(X2, S)
if emp_loss_old < emp_loss_new:
X2 = X2_old
_ts = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
_te = time.time()
resource.set("X", X.tolist())
resource.set("X2", X2.tolist())
resource.set("tau", tau.tolist())
def __full_embedding_update(self, resource, args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose = False
X_old = numpy.array(resource.get('X'))
X2_old = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 5.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
mu = .05
emp_loss_old, hinge_loss_old, log_loss_old = utilsCrowdKernel.getLoss(
X_old, S)
X, tmp = utilsCrowdKernel.computeEmbeddingWithEpochSGD(
n, d, S, mu, max_num_passes=16, epsilon=0, verbose=verbose)
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=1, epsilon=epsilon, verbose=verbose)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=2**k, epsilon=epsilon, verbose=verbose)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsCrowdKernel.getLoss(
X, S)
if emp_loss_old < emp_loss_new:
X = X_old
if d == 2:
X2 = X
else:
emp_loss_old, hinge_loss_old, log_loss_old = utilsCrowdKernel.getLoss(
X2_old, S)
X2, tmp = utilsCrowdKernel.computeEmbeddingWithEpochSGD(
n, 2, S, mu, max_num_passes=16, epsilon=0, verbose=verbose)
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, max_iters=1, epsilon=epsilon, verbose=verbose)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2,
S,
mu,
max_iters=2**k,
epsilon=epsilon,
verbose=verbose)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsCrowdKernel.getLoss(
X2, S)
if emp_loss_old < emp_loss_new:
X2 = X2_old
_ts = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
_te = time.time()
resource.set('X', X.tolist())
resource.set('X2', X2.tolist())
resource.set('tau', tau.tolist())
def __incremental_embedding_update(self, resource, args):
n = resource.get("n")
d = resource.get("d")
S = resource.get_list("S")
verbose = False
X = numpy.array(resource.get("X"))
X2 = numpy.array(resource.get("X2"))
# set maximum time allowed to update embedding
t_max = 1.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
mu = 0.05
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, epsilon=epsilon, max_iters=1
)
_te = time.time()
k = 1
while (time.time() - t_start < 0.5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=2 ** k, epsilon=epsilon, verbose=verbose
)
k += 1
if verbose == True:
print "Incremental embedding time of X gradient step at iteration %s is %s" % (
str(k),
str(time.time() - ts),
)
if d == 2:
X2 = X
else:
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, epsilon=epsilon, max_iters=1
)
k = 1
while (time.time() - t_start < 0.5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, max_iters=2 ** k, epsilon=epsilon, verbose=verbose
)
k += 1
if verbose:
print "Incremental embedding time of X2 gradient step at itration %s is %s" % (
str(k),
str(time.time() - ts),
)
t_s = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
if verbose:
print "Time to compute tau %s" % str(time.time() - t_s)
resource.set("X", X.tolist())
resource.set("X2", X2.tolist())
_ts = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
_te = time.time()
resource.set("tau", tau.tolist())
def __incremental_embedding_update(self, resource, args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose = False
X = numpy.array(resource.get('X'))
X2 = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 1.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
mu = .05
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, epsilon=epsilon, max_iters=1)
_te = time.time()
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X, S, mu, max_iters=2**k, epsilon=epsilon, verbose=verbose)
k += 1
if verbose == True:
print "Incremental embedding time of X gradient step at iteration %s is %s" % (
str(k), str(time.time() - ts))
if d == 2:
X2 = X
else:
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2, S, mu, epsilon=epsilon, max_iters=1)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsCrowdKernel.computeEmbeddingWithGD(
X2,
S,
mu,
max_iters=2**k,
epsilon=epsilon,
verbose=verbose)
k += 1
if verbose:
print "Incremental embedding time of X2 gradient step at itration %s is %s" % (
str(k), str(time.time() - ts))
t_s = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
if verbose:
print "Time to compute tau %s" % str(time.time() - t_s)
resource.set('X', X.tolist())
resource.set('X2', X2.tolist())
_ts = time.time()
tau = utilsCrowdKernel.getCrowdKernelTauDistribution(X, S, mu)
_te = time.time()
resource.set('tau', tau.tolist())