def test_loadmat():
"""
Tests :meth:`bet.sampling.basicSampling.loadmat`
"""
np.random.seed(1)
mdat1 = {'num_samples': 5}
mdat2 = {'num_samples': 6}
model = "this is not a model"
my_input1 = sample_set(1)
my_input1.set_values(np.random.random((5, 1)))
my_output = sample_set(1)
my_output.set_values(np.random.random((5, 1)))
my_input2 = sample_set(1)
my_input2.set_values(np.random.random((6, 1)))
sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
bet.sample.save_discretization(disc(my_input1, my_output),
(os.path.join(local_path, 'testfile1')),
globalize=True)
bet.sample.save_discretization(disc(my_input2, None),
os.path.join(local_path, 'testfile2'),
"NAME",
globalize=True)
(loaded_sampler1,
discretization1) = bsam.loadmat(os.path.join(local_path, 'testfile1'))
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output.get_values())
assert loaded_sampler1.num_samples == 5
assert loaded_sampler1.lb_model is None
(loaded_sampler2,
discretization2) = bsam.loadmat(os.path.join(local_path, 'testfile2'),
disc_name="NAME",
model=model)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
assert discretization2._output_sample_set is None
assert loaded_sampler2.num_samples == 6
assert loaded_sampler2.lb_model == model
if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
os.remove(os.path.join(local_path, 'testfile1.mat'))
if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
os.remove(os.path.join(local_path, 'testfile2.mat'))
def test_loadmat():
"""
Tests :meth:`bet.sampling.basicSampling.loadmat`
"""
np.random.seed(1)
mdat1 = {'num_samples':5}
mdat2 = {'num_samples':6}
model = "this is not a model"
my_input1 = sample_set(1)
my_input1.set_values(np.random.random((5,1)))
my_output = sample_set(1)
my_output.set_values(np.random.random((5,1)))
my_input2 = sample_set(1)
my_input2.set_values(np.random.random((6,1)))
sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
bet.sample.save_discretization(disc(my_input1, my_output),
(os.path.join(local_path, 'testfile1')), globalize=True)
bet.sample.save_discretization(disc(my_input2, None),
os.path.join(local_path, 'testfile2'), "NAME", globalize=True)
(loaded_sampler1, discretization1) = bsam.loadmat(os.path.join(local_path,
'testfile1'))
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output.get_values())
assert loaded_sampler1.num_samples == 5
assert loaded_sampler1.lb_model is None
(loaded_sampler2, discretization2) = bsam.loadmat(os.path.join(local_path,
'testfile2'), disc_name="NAME", model=model)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
assert discretization2._output_sample_set is None
assert loaded_sampler2.num_samples == 6
assert loaded_sampler2.lb_model == model
if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
os.remove(os.path.join(local_path, 'testfile1.mat'))
if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
os.remove(os.path.join(local_path, 'testfile2.mat'))
def verify_compute_QoI_and_create_discretization(model, sampler,
input_sample_set, savefile):
"""
Verify that the user samples are correct.
"""
# evalulate the model at the samples directly
output_values = (model(input_sample_set._values))
if len(output_values.shape) == 1:
output_sample_set = sample_set(1)
else:
output_sample_set = sample_set(output_values.shape[1])
output_sample_set.set_values(output_values)
discretization = disc(input_sample_set, output_sample_set)
# evaluate the model at the sample
print savefile, input_sample_set.get_dim()
my_discretization = sampler.compute_QoI_and_create_discretization(\
input_sample_set, savefile, globalize=True)
#comm.barrier()
my_num = my_discretization.check_nums()
# compare the samples
nptest.assert_array_equal(my_discretization._input_sample_set.get_values(),
discretization._input_sample_set.get_values())
# compare the data
nptest.assert_array_equal(
my_discretization._output_sample_set.get_values(),
discretization._output_sample_set.get_values())
# did num_samples get updated?
assert my_num == sampler.num_samples
# did the file get correctly saved?
saved_disc = bet.sample.load_discretization(savefile)
mdat = sio.loadmat(savefile)
print "HERE HERE", mdat, my_num
#comm.barrier()
# compare the samples
nptest.assert_array_equal(my_discretization._input_sample_set.get_values(),
saved_disc._input_sample_set.get_values())
# compare the data
nptest.assert_array_equal(
my_discretization._output_sample_set.get_values(),
saved_disc._output_sample_set.get_values())
def verify_compute_QoI_and_create_discretization(model, sampler,
input_sample_set,
savefile):
"""
Verify that the user samples are correct.
"""
# evalulate the model at the samples directly
output_values = (model(input_sample_set._values))
if len(output_values.shape) == 1:
output_sample_set = sample_set(1)
else:
output_sample_set = sample_set(output_values.shape[1])
output_sample_set.set_values(output_values)
discretization = disc(input_sample_set, output_sample_set)
# evaluate the model at the sample
print savefile, input_sample_set.get_dim()
my_discretization = sampler.compute_QoI_and_create_discretization(\
input_sample_set, savefile, globalize=True)
#comm.barrier()
my_num = my_discretization.check_nums()
# compare the samples
nptest.assert_array_equal(my_discretization._input_sample_set.get_values(),
discretization._input_sample_set.get_values())
# compare the data
nptest.assert_array_equal(my_discretization._output_sample_set.get_values(),
discretization._output_sample_set.get_values())
# did num_samples get updated?
assert my_num == sampler.num_samples
# did the file get correctly saved?
saved_disc = bet.sample.load_discretization(savefile)
mdat = sio.loadmat(savefile)
print "HERE HERE", mdat, my_num
#comm.barrier()
# compare the samples
nptest.assert_array_equal(my_discretization._input_sample_set.get_values(),
saved_disc._input_sample_set.get_values())
# compare the data
nptest.assert_array_equal(my_discretization._output_sample_set.get_values(),
saved_disc._output_sample_set.get_values())
def test_loadmat_init():
"""
Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
:meth:`bet.sampling.adaptiveSampling.sampler.init`.
"""
np.random.seed(1)
chain_length = 5
mdat1 = {'num_samples': 50, 'chain_length': chain_length}
mdat2 = {'num_samples': 60, 'chain_length': chain_length}
model = "this is not a model"
my_input1 = sample_set(1)
my_input1.set_values(np.random.random((50, 1)))
my_output1 = sample_set(1)
my_output1.set_values(np.random.random((50, 1)))
my_input2 = sample_set(1)
my_input2.set_values(np.random.random((60, 1)))
my_output2 = sample_set(1)
my_output2.set_values(np.random.random((60, 1)))
num_samples = np.array([50, 60])
num_chains_pproc1, num_chains_pproc2 = np.ceil(num_samples/float(
chain_length*comm.size)).astype('int')
num_chains1, num_chains2 = comm.size * np.array([num_chains_pproc1,
num_chains_pproc2])
num_samples1, num_samples2 = chain_length * np.array([num_chains1,
num_chains2])
mdat1['num_chains'] = num_chains1
mdat1['kern_old'] = np.random.random((num_chains1,))
mdat1['step_ratios'] = np.random.random((num_samples[0],))
mdat2['num_chains'] = num_chains2
mdat2['kern_old'] = np.random.random((num_chains2,))
mdat2['step_ratios'] = np.random.random((num_samples[1],))
sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
bet.sample.save_discretization(disc(my_input1, my_output1),
os.path.join(local_path, 'testfile1'), globalize=True)
bet.sample.save_discretization(disc(my_input2, my_output2),
os.path.join(local_path, 'testfile2'), globalize=True)
loaded_sampler1, discretization1, _, _ = asam.loadmat(os.path.join(local_path,
'testfile1'), hot_start=2)
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output1.get_values())
assert loaded_sampler1.num_samples == num_samples1
assert loaded_sampler1.chain_length == chain_length
assert loaded_sampler1.num_chains_pproc == num_chains_pproc1
assert loaded_sampler1.num_chains == num_chains1
nptest.assert_array_equal(np.repeat(np.arange(num_chains1), chain_length, 0),
loaded_sampler1.sample_batch_no)
assert loaded_sampler1.lb_model == None
loaded_sampler2, discretization2, _, _ = asam.loadmat(os.path.join(local_path,
'testfile2'), lb_model=model, hot_start=2)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
assert loaded_sampler2.num_samples == num_samples2
assert loaded_sampler2.chain_length == chain_length
assert loaded_sampler2.num_chains_pproc == num_chains_pproc2
assert loaded_sampler2.num_chains == num_chains2
nptest.assert_array_equal(np.repeat(np.arange(num_chains2), chain_length, 0),
loaded_sampler2.sample_batch_no)
nptest.assert_array_equal(discretization2._output_sample_set.get_values(),
my_output2.get_values())
comm.barrier()
if comm.rank == 0:
if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
os.remove(os.path.join(local_path, 'testfile1.mat'))
if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
os.remove(os.path.join(local_path, 'testfile2.mat'))
def test_loadmat_parallel():
"""
Tests :class:`bet.sampling.basicSampling.sampler.loadmat`.
"""
np.random.seed(1)
mdat1 = {'num_samples':10}
mdat2 = {'num_samples':20}
model = "this is not a model"
my_input1 = sample_set(1)
my_input1.set_values_local(np.array_split(np.random.random((10,1)),
comm.size)[comm.rank])
my_output1 = sample_set(1)
my_output1.set_values_local(np.array_split(np.random.random((10,1)),
comm.size)[comm.rank])
my_input2 = sample_set(1)
my_input2.set_values_local(np.array_split(np.random.random((20,1)),
comm.size)[comm.rank])
my_output2 = sample_set(1)
my_output2.set_values_local(np.array_split(np.random.random((20,1)),
comm.size)[comm.rank])
file_name1 = 'testfile1.mat'
file_name2 = 'testfile2.mat'
if comm.size > 1:
local_file_name1 = os.path.os.path.join(os.path.dirname(file_name1),
"proc{}_{}".format(comm.rank, os.path.basename(file_name1)))
local_file_name2 = os.path.os.path.join(os.path.dirname(file_name2),
"proc{}_{}".format(comm.rank, os.path.basename(file_name2)))
else:
local_file_name1 = file_name1
local_file_name2 = file_name2
sio.savemat(local_file_name1, mdat1)
sio.savemat(local_file_name2, mdat2)
comm.barrier()
bet.sample.save_discretization(disc(my_input1, my_output1),
file_name1, globalize=False)
bet.sample.save_discretization(disc(my_input2, my_output2),
file_name2, "NAME", globalize=False)
(loaded_sampler1, discretization1) = bsam.loadmat(file_name1)
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output1.get_values())
assert loaded_sampler1.num_samples == 10
assert loaded_sampler1.lb_model is None
(loaded_sampler2, discretization2) = bsam.loadmat(file_name2,
disc_name="NAME", model=model)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
nptest.assert_array_equal(discretization2._output_sample_set.get_values(),
my_output2.get_values())
assert loaded_sampler2.num_samples == 20
assert loaded_sampler2.lb_model == model
if comm.size == 1:
os.remove(file_name1)
os.remove(file_name2)
else:
os.remove(local_file_name1)
os.remove(local_file_name2)
def test_loadmat_init():
"""
Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
:meth:`bet.sampling.adaptiveSampling.sampler.init`.
"""
np.random.seed(1)
chain_length = 5
mdat1 = {'num_samples': 50, 'chain_length': chain_length}
mdat2 = {'num_samples': 60, 'chain_length': chain_length}
model = "this is not a model"
num_samples = np.array([50, 60])
num_chains_pproc1, num_chains_pproc2 = np.ceil(
num_samples / float(chain_length * comm.size)).astype('int')
num_chains1, num_chains2 = comm.size * np.array(
[num_chains_pproc1, num_chains_pproc2])
num_samples1, num_samples2 = chain_length * np.array(
[num_chains1, num_chains2])
my_input1 = sample_set(1)
my_input1.set_values(np.random.random((num_samples1, 1)))
my_output1 = sample_set(1)
my_output1.set_values(np.random.random((num_samples1, 1)))
my_input2 = sample_set(1)
my_input2.set_values(np.random.random((num_samples2, 1)))
my_output2 = sample_set(1)
my_output2.set_values(np.random.random((num_samples2, 1)))
mdat1['num_chains'] = num_chains1
mdat1['kern_old'] = np.random.random((num_chains1, ))
mdat1['step_ratios'] = np.random.random((num_samples1, ))
mdat2['num_chains'] = num_chains2
mdat2['kern_old'] = np.random.random((num_chains2, ))
mdat2['step_ratios'] = np.random.random((num_samples2, ))
sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
bet.sample.save_discretization(disc(my_input1, my_output1),
os.path.join(local_path, 'testfile1'),
globalize=True)
bet.sample.save_discretization(disc(my_input2, my_output2),
os.path.join(local_path, 'testfile2'),
globalize=True)
loaded_sampler1, discretization1, _, _ = asam.loadmat(os.path.join(
local_path, 'testfile1'),
hot_start=2)
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output1.get_values())
assert loaded_sampler1.num_samples == num_samples1
assert loaded_sampler1.chain_length == chain_length
assert loaded_sampler1.num_chains_pproc == num_chains_pproc1
assert loaded_sampler1.num_chains == num_chains1
nptest.assert_array_equal(
np.repeat(np.arange(num_chains1), chain_length, 0),
loaded_sampler1.sample_batch_no)
assert loaded_sampler1.lb_model is None
loaded_sampler2, discretization2, _, _ = asam.loadmat(os.path.join(
local_path, 'testfile2'),
lb_model=model,
hot_start=2)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
assert loaded_sampler2.num_samples == num_samples2
assert loaded_sampler2.chain_length == chain_length
assert loaded_sampler2.num_chains_pproc == num_chains_pproc2
assert loaded_sampler2.num_chains == num_chains2
nptest.assert_array_equal(
np.repeat(np.arange(num_chains2), chain_length, 0),
loaded_sampler2.sample_batch_no)
nptest.assert_array_equal(discretization2._output_sample_set.get_values(),
my_output2.get_values())
comm.barrier()
if comm.rank == 0:
if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
os.remove(os.path.join(local_path, 'testfile1.mat'))
if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
os.remove(os.path.join(local_path, 'testfile2.mat'))
def test_loadmat_parallel():
"""
Tests :class:`bet.sampling.basicSampling.sampler.loadmat`.
"""
np.random.seed(1)
mdat1 = {'num_samples': 10}
mdat2 = {'num_samples': 20}
model = "this is not a model"
my_input1 = sample_set(1)
my_input1.set_values_local(
np.array_split(np.random.random((10, 1)), comm.size)[comm.rank])
my_output1 = sample_set(1)
my_output1.set_values_local(
np.array_split(np.random.random((10, 1)), comm.size)[comm.rank])
my_input2 = sample_set(1)
my_input2.set_values_local(
np.array_split(np.random.random((20, 1)), comm.size)[comm.rank])
my_output2 = sample_set(1)
my_output2.set_values_local(
np.array_split(np.random.random((20, 1)), comm.size)[comm.rank])
file_name1 = 'testfile1.mat'
file_name2 = 'testfile2.mat'
if comm.size > 1:
local_file_name1 = os.path.os.path.join(
os.path.dirname(file_name1),
"proc{}_{}".format(comm.rank, os.path.basename(file_name1)))
local_file_name2 = os.path.os.path.join(
os.path.dirname(file_name2),
"proc{}_{}".format(comm.rank, os.path.basename(file_name2)))
else:
local_file_name1 = file_name1
local_file_name2 = file_name2
sio.savemat(local_file_name1, mdat1)
sio.savemat(local_file_name2, mdat2)
comm.barrier()
bet.sample.save_discretization(disc(my_input1, my_output1),
file_name1,
globalize=False)
bet.sample.save_discretization(disc(my_input2, my_output2),
file_name2,
"NAME",
globalize=False)
(loaded_sampler1, discretization1) = bsam.loadmat(file_name1)
nptest.assert_array_equal(discretization1._input_sample_set.get_values(),
my_input1.get_values())
nptest.assert_array_equal(discretization1._output_sample_set.get_values(),
my_output1.get_values())
assert loaded_sampler1.num_samples == 10
assert loaded_sampler1.lb_model is None
(loaded_sampler2, discretization2) = bsam.loadmat(file_name2,
disc_name="NAME",
model=model)
nptest.assert_array_equal(discretization2._input_sample_set.get_values(),
my_input2.get_values())
nptest.assert_array_equal(discretization2._output_sample_set.get_values(),
my_output2.get_values())
assert loaded_sampler2.num_samples == 20
assert loaded_sampler2.lb_model == model
if comm.size == 1:
os.remove(file_name1)
os.remove(file_name2)
else:
os.remove(local_file_name1)
os.remove(local_file_name2)