# size = 5
pt.MPI_MESSAGE_SIZE = 2**20
if rank > 0:
chain = pt.PTSlave(comm=comm, statmodel=DPMPG_Chain)
chain.watch()
if rank == 0:
raw = read_csv('./datasets/ivt_nov_mar.csv')
data = Data_From_Raw(raw, True)
data.write_empirical('./output/dppgln/empirical.csv')
model = pt.PTMaster(
comm,
# statmodel = DPMPG_Chain,
temperature_ladder=1.05**np.array(range(size - 1)),
data=data,
prior_eta=GammaPrior(2., 1.))
model.sample(50000)
model.write_to_disk('./output/dppgln/results_2_1e0.db', 25000, 5)
model.complete()
res = DPMPG_Result('./output/dppgln/results_2_1e0.db')
res.write_posterior_predictive('./output/dppgln/postpred_2_1e0.csv')
#
# if __name__ == '__main__':
# raw = read_csv('./datasets/ivt_nov_mar.csv')
# data = Data_From_Raw(raw, True)
# data.write_empirical('./output/dpmpg_empirical_decluster.csv')
#
args.model,
'empirical.csv',
)
out_path = os.path.join(
args.in_path,
args.model,
'results_{}_{}.db'.format(args.eta_shape, args.eta_rate),
)
pp_path = os.path.join(
args.in_path,
args.model,
'postpred_{}_{}.csv'.format(args.eta_shape, args.eta_rate),
)
model = pt.PTMaster(comm,
temperature_ladder=1.05**np.array(range(size - 1)),
data=data,
prior_eta=GammaPrior(float(args.eta_shape),
float(args.eta_rate)))
elif args.model.startswith('m'):
emp_path = os.path.join(
args.in_path,
args.model,
'empirical.csv',
)
out_path = os.path.join(
args.in_path,
args.model,
'results_{}.db'.format(args.nMix),
)
pp_path = os.path.join(
args.in_path,
rank = comm.Get_rank()
size = comm.Get_size()
pt.MPI_MESSAGE_SIZE = 2**20
if rank > 0:
chain = pt.PTSlave(comm=comm, statmodel=DPMPG_Chain)
chain.watch()
if rank == 0:
raw = read_csv('./datasets/ivt_nov_mar.csv')
raw2 = np.hstack((raw.T[-2:].T, raw.T[:-2].T))
data = Data_From_Raw(raw2, True)
data.write_empirical('./output/dppgln2/empirical.csv')
model = pt.PTMaster(comm,
temperature_ladder=1.05**np.array(range(size - 1)),
data=data,
prior_eta=GammaPrior(2., .1))
model.sample(10000)
model.write_to_disk('./output/dppgln2/results_2_1e-1.db', 5000, 1)
model.complete()
# res = DPMPG_Result('./output/dppgln/results_2_1e-1.db') # not working yet.
# res.write_posterior_predictive('./output/dppgln/postpred_2_1e-1.csv')
## relevant to non-MPI version
# if __name__ == '__main__':
# raw = read_csv('./datasets/ivt_nov_mar.csv')
# data = Data_From_Raw(raw, True)
# data.write_empirical('./output/dppgln2/empirical.csv')
#
# model = pt.PTMaster(
rank = comm.Get_rank()
size = comm.Get_size()
# rank = 0
# size = 5
pt.MPI_MESSAGE_SIZE = 2**20
if rank > 0:
chain = pt.PTSlave(comm=comm, statmodel=DPMPG_Chain)
chain.watch()
if rank == 0:
raw = read_csv('./datasets/ivt_nov_mar.csv')
data = Data_From_Raw(raw, True)
data.write_empirical('./output/dppgln/empirical.csv')
model = pt.PTMaster(
comm,
# statmodel = DPMPG_Chain,
temperature_ladder=1.05**np.array(range(size - 1)),
data=data,
fixed_eta=200)
model.sample(20000)
model.write_to_disk('./output/dppgln/results_2_1e1.db', 10000, 1)
model.complete()
res = DPMPG_Result('./output/dppgln/results_2_1e1.db')
res.write_posterior_predictive('./output/dppgln/postpred_2_1e1.csv')
# EOF