import pt_mpi as pt
# import pt
import numpy as np
from data import Data_From_Raw
from pandas import read_csv
from mpi4py import MPI
comm = MPI.COMM_WORLD
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,
prior_eta=GammaPrior(2., 1.))
model.sample(50000)
model.write_to_disk('./output/dppgln/results_2_1e0.db', 25000, 5)
import mpi4py
mpi4py.rc.recv_mprobe = False
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
pt.MPI_MESSAGE_SIZE = 2**24
args = argparser()
Chain = models.Chains[args.model]
Result = models.Results[args.model]
if rank > 0:
chain = pt.PTSlave(comm=comm, statmodel=Chain)
chain.watch()
if rank == 0:
data = Data(os.path.join(args.in_path, 'data.db'))
if args.model.startswith('dp'):
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.eta_shape, args.eta_rate),