def __init__(self,
N,
D,
m,
sigma_p,
num_steps,
step_size,
max_steps=None,
sigma0=0.5,
lmbda0=0.0001,
learn_parameters=False):
IndependentJob.__init__(self, TrajectoryJobResultAggregator())
# job ressources
if N <= 2000 or D < 10:
self.walltime = 1 * 60 * 60
self.memory = 2
elif N <= 5000 or D < 50:
self.walltime = 24 * 60 * 60
self.memory = 3
else:
self.walltime = 72 * 60 * 60
self.memory = 8
self.N = N
self.D = D
self.m = m
self.sigma0 = sigma0
self.lmbda0 = lmbda0
self.sigma_p = sigma_p
self.num_steps = num_steps
self.step_size = step_size
self.max_steps = max_steps
self.learn_parameters = learn_parameters
def __init__(self, aggregator, ozone_posterior, tau, kappa):
IndependentJob.__init__(self, aggregator)
self.ozone_posterior = OzonePosterior(ozone_posterior.prior,
ozone_posterior.logdet_method,
ozone_posterior.solve_method)
self.tau = tau
self.kappa = kappa
def __init__(self, aggregator, ozone_posterior, tau, kappa):
IndependentJob.__init__(self, aggregator)
self.ozone_posterior = OzonePosterior(ozone_posterior.prior,
ozone_posterior.logdet_method,
ozone_posterior.solve_method)
self.tau = tau
self.kappa = kappa
def __init__(self, aggregator,
mod_prm=None,
data=None,
name=None,
save_dir=None):
IndependentJob.__init__(self, aggregator)
self.mod_prm = mod_prm
self.data = data
self.name = name
self.save_dir=save_dir
def __init__(self, aggregator, prob_label, rep, n, job_func):
walltime = 60*59*24
memory = int(tr_proportion*n*1e-1) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
self.prob_label = prob_label
self.rep = rep
self.n = n
self.job_func = job_func
def __init__(self, aggregator, prob_label, rep, n, job_func):
walltime = 60*59*24
memory = int(tr_proportion*n*1e-1) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
self.prob_label = prob_label
self.rep = rep
self.n = n
self.job_func = job_func
def __init__(self, db_fname, result_name="result", seed=None, **param_dict):
IndependentJob.__init__(self, ScalarResultAggregator())
self.db_fname = db_fname
self.param_dict = param_dict
self.result_name = result_name
if seed is None:
# if no seed is set unsigned 32bit int, and store
seed = np.random.randint(2 ** 32 - 1)
self.seed = seed
def __init__(self, aggregator, sample_source, prob_label, rep, job_func):
d = sample_source.dim()
walltime = 60*59*24 if d*sample_size*tr_proportion/15 >= 8000 else 60*59
memory = int(tr_proportion*sample_size*1e-2) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
self.sample_source = sample_source
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
def __init__(self, aggregator, sample_source, prob_label, rep, job_func):
d = sample_source.dim()
#walltime = 60*59*24 if d*sample_size*tr_proportion/15 >= 8000 else 60*59
walltime = 60*59*24
memory = int(tr_proportion*sample_size*1e-2) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
self.sample_source = sample_source
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
def __init__(self, aggregator, prob_label, rep, met_func, n):
#walltime = 60*59*24
walltime = 60 * 59
memory = 54272 #int(n*1e-2) + 50
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.prob_label = prob_label
self.rep = rep
self.met_func = met_func
self.n = n
def __init__(self, aggregator, p, data_source, prob_label, rep, job_func, n):
#walltime = 60*59*24
walltime = 60*59
memory = int(tr_proportion*n*1e-2) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
# p: an UnnormalizedDensity
self.p = p
self.data_source = data_source
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
self.n = n
def __init__(self, aggregator, folder_path, prob_label, rep, job_func, n):
#walltime = 60*59*24
walltime = 60 * 59
memory = int(tr_proportion * n * 1e-2) + 100
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.folder_path = folder_path
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
self.n = n
def __init__(self, aggregator, folder_path, prob_label, rep, job_func):
walltime = 60 * 59 * 24
pl = exglo.parse_prob_label(prob_label)
n = pl['n']
memory = int(tr_proportion * n * 1e-2) + 100
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.folder_path = folder_path
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
def __init__(self, aggregator, paired_source, prob_label, rep, job_func,
prob_param):
#walltime = 60*59*24
walltime = 60 * 59
memory = int(tr_proportion * sample_size * 1e-2) + 50
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.paired_source = paired_source
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
self.prob_param = prob_param
def __init__(self, aggregator, sample_source, prob_label, rep, ni, n, job_func):
d = sample_source.dim()
ntr = int(n*tr_proportion)
#walltime = 60*59*24 if d*ntr/15 >= 8000 else 60*59
walltime = 60*59*24
memory = int(ntr*1e-2) + 50
IndependentJob.__init__(self, aggregator, walltime=walltime,
memory=memory)
self.sample_source = sample_source
self.prob_label = prob_label
self.rep = rep
self.ni = ni
self.n = n
self.job_func = job_func
def __init__(self,
db_fname,
result_name="result",
seed=None,
**param_dict):
IndependentJob.__init__(self, ScalarResultAggregator())
self.db_fname = db_fname
self.param_dict = param_dict
self.result_name = result_name
if seed is None:
# if no seed is set unsigned 32bit int, and store
seed = np.random.randint(2**32 - 1)
self.seed = seed
def __init__(self, aggregator, paired_source, prob_label, rep, job_func,
n):
#walltime = 60*59*24
#walltime = 60*59 if n < 100000 else 60*59*24
walltime = 60 * 59
memory = int(tr_proportion * n * 1e-2) + 100
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.paired_source = paired_source
self.prob_label = prob_label
self.rep = rep
self.job_func = job_func
self.n = n
def __init__(self,
N,
D,
lmbda,
sigma_p,
num_steps,
step_size,
max_steps=None):
IndependentJob.__init__(self, TrajectoryJobResultAggregator())
self.N = N
self.D = D
self.lmbda = lmbda
self.sigma_p = sigma_p
self.num_steps = num_steps
self.step_size = step_size
self.max_steps = max_steps
def __init__(self, aggregator, mix_ratios, data_loader, prob_label, rep,
met_func, n):
walltime = 60 * 59 * 24
# walltime = 60 * 59
memory = int(n * 1e-2) + 50
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
# P, P are kmod.model.Model
self.mix_ratios = mix_ratios
self.data_loader = data_loader
self.prob_label = prob_label
self.rep = rep
self.met_func = met_func
self.n = n
def __init__(self, aggregator, sample_source, prob_label, rep, ni, n,
job_func):
d = sample_source.dim()
ntr = int(n * tr_proportion)
#walltime = 60*59*24 if d*ntr/15 >= 8000 else 60*59
walltime = 60 * 59 * 24
memory = int(ntr * 1e-2) + 50
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
self.sample_source = sample_source
self.prob_label = prob_label
self.rep = rep
self.ni = ni
self.n = n
self.job_func = job_func
def __init__(self, aggregator, P, Q, data_source, prob_label, rep,
met_func, n):
#walltime = 60*59*24
walltime = 60 * 59
memory = int(n * 1e-2) + 50
IndependentJob.__init__(self,
aggregator,
walltime=walltime,
memory=memory)
# P, P are kmod.model.Model
self.P = P
self.Q = Q
self.data_source = data_source
self.prob_label = prob_label
self.rep = rep
self.met_func = met_func
self.n = n
def __init__(self, target,
num_iterations, D, start, statistics={}, num_warmup=500, thin_step=1):
IndependentJob.__init__(self, MCMCJobResultAggregator())
self.target = target
self.num_iterations = num_iterations
self.D = D
self.start = start
self.statistics = statistics
self.num_warmup = num_warmup
self.thin_step = thin_step
assert len(start.shape) == 1
assert len(start) == D
# short queue
self.walltime = 1 * 60 * 60
# running average acceptance prob
self.avg_accept = 0.
self.recompute_log_pdf = False
def __init__(self, aggregator, sleep_time, walltime=1, memory=1, nodes=1):
IndependentJob.__init__(self, aggregator, walltime, memory, nodes)
self.sleep_time = sleep_time
def __init__(self, aggregator):
IndependentJob.__init__(self, aggregator)
