def run_inference(options, data_path):
exp, W_test, y_test = set_up_radar_experiment(options, data_path)
t0 = utils.tic()
mu_grid = exp.model.predict(X=None, grid=True, verbose=True, tol=1e-2)[0]
t0f = utils.toc(t0)
inference_time = utils.toc_report(t0f, tag="InfGP", return_val=True)
y_predict = (W_test * mu_grid).squeeze()
mae_mp = compute_mae(np.ones_like(y_test) * np.mean(y_test), y_test)
print("Mae: ", compute_mae(y_predict, y_test))
print("Smae: ", compute_mae(y_predict, y_test) / mae_mp)
print("Mse: ", np.mean((y_predict - y_test)**2))
print("Rmse: ", np.sqrt(np.mean((y_predict - y_test)**2)))
return inference_time
def run_logdet_computation(options, data_path, W_path=None):
ntrials = options.ntrials
nrank = options.maxiter
exp, __, __ = set_up_radar_experiment(options, data_path, W_path=W_path)
sigma = exp.model.noise_covar.noise
if options.method == utils.MethodName.GSGP:
t1 = utils.tic()
K = exp.model.covar_module._inducing_forward()
A_hat = GsGpLinearOperator(exp.model.WT_times_W,
K,
sigma,
dtype=exp.model.WT_times_Y.dtype)
if options.variant == 1:
estimate_logdet = logdet_estimate_using_lz_variants(
A_hat,
WT=exp.WT,
trials=ntrials,
rank=nrank,
verbose=True,
dump=options.dump,
dump_path=options.log_dir)[0]
elif options.variant == 2:
estimate_logdet = logdet_estimate_using_cg_variants(
A_hat,
WT=exp.WT,
trials=ntrials,
tol=options.tol,
rank=nrank,
verbose=True)[0]
else:
raise NotImplementedError
print("Estimated log-det: ", estimate_logdet)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
elif options.method == utils.MethodName.KISSGP:
t1 = utils.tic()
W_train = exp.model.W
K = exp.model.covar_module._inducing_forward()
A = KissGpLinearOperator(W_train, K, sigma, dtype=W_train.dtype)
if options.variant == 1:
estimate_logdet = logdet_estimate_using_lz_variants(
A,
trials=ntrials,
rank=nrank,
verbose=True,
)[0]
elif options.variant == 2:
estimate_logdet = logdet_estimate_using_cg_variants(
A, trials=ntrials, tol=options.tol, rank=nrank,
verbose=True)[0]
else:
raise NotImplementedError
print("Estimated log-det: ", estimate_logdet)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
else:
raise NotImplementedError
return inference_time
def run_ski_cov_inf_experiment(exp, options):
sigma = exp.model.noise_covar.noise
if options.method == configs.MethodName.KISSGP:
W_test = exp.model.covar_module(exp.test_x)[0]
t1 = utils.tic()
W_train, K, __ = exp.model.covar_module(exp.train_x, is_kmm=True)
A = KissGpLinearOperator(W_train, K, sigma, dtype=W_train.dtype)
probes = W_train * A.kmm_matmat(
W_test.T.todense()) # Computing over test data points
cov = bcg(A,
probes,
tol=options.tol,
maxiter=options.maxiter,
verbose=True)
covf = np.dot(probes.T, cov)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
elif options.method == configs.MethodName.GSGP:
W_test = exp.model.covar_module(exp.test_x)[0]
t1 = utils.tic()
K = exp.model.covar_module._inducing_forward()
A_hat = GsGpLinearOperator(exp.model.WT_times_W,
K,
sigma,
dtype=exp.model.WT_times_Y.dtype)
r0_hat = A_hat.kmm_matmat(W_test.T.todense())
x_diff = bfcg(A_hat,
r0_hat,
yty=np.linalg.norm(r0_hat, axis=0)**2,
maxiter=options.maxiter,
verbose=True,
tol=options.tol)
covf = np.dot(r0_hat.T, x_diff)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
else:
raise NotImplementedError
if options.store_ref:
os.makedirs(options.log_dir, exist_ok=True)
pickle.dump(
covf,
open(
options.log_dir + "/" + options.data_type.name.lower() +
"_ski_dump.pkl", "wb"))
return 0.0, 0.0
# Computing l2norm
cov_ref_path = os.environ[
'PRJ'] + '/data/refs/' + options.data_type.name.lower(
) + '_ski_dump.pkl'
assert os.path.exists(
cov_ref_path
), cov_ref_path + "doesn't exists. Follow readme to generate refs."
COV_REF = pickle.load(open(cov_ref_path, "rb"))
l2_norm = np.linalg.norm(covf - COV_REF)
print("L2norm: ", l2_norm)
return inference_time, l2_norm
def run_approx_cov_inf_experiment(exp, options):
if exp.config.data_type not in [configs.DatasetType.SOUND, configs.DatasetType.PRECIPITATION]:
nrank = min(configs.DEFAULT_LANCZOS_RANK, options.maxiter)
print("\n\n\nLanczos rank is reduced to ", nrank, " from ", options.maxiter)
print("\n\n")
else:
nrank = options.maxiter
sigma = exp.model.noise_covar.noise
if exp.config.data_type == configs.DatasetType.SOUND:
max_num_test_vectors = 1000
else:
max_num_test_vectors = 50
if exp.test_x.shape[0] > max_num_test_vectors:
W_test = exp.model.covar_module(exp.test_x[:min(max_num_test_vectors, exp.test_x.shape[0]), :])[0]
else:
W_test = exp.model.covar_module(exp.test_x)[0]
if options.method == configs.MethodName.KISSGP:
t1 = utils.tic()
W_train, K, __ = exp.model.covar_module(exp.train_x, is_kmm=True)
A = KissGpLinearOperator(W_train, K, sigma, dtype=W_train.dtype)
probes = A.kmm_matmat(W_train.T.todense()).T
v = np.mean(probes, axis=1)
Q, T = bsla(A, v, k=nrank)
T_diag = T.diagonal()
T_subdiag = T.diagonal(1)
L_diag, L_subdiag = chol_trid(T_diag, T_subdiag)
R = A.kmm_matmat(A.WT * Q)
Rprime = chol_trid_solve(L_diag, L_subdiag, R.T).T
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
elif options.method == configs.MethodName.GSGP:
t1 = utils.tic()
K = exp.model.covar_module._inducing_forward()
probes = fm.Kron(*K).getArray().real if len(K) > 1 else K[0].getArray().real
v = np.mean(probes, axis=1)
A_hat = GsGpLinearOperator(exp.model.WT_times_W, K, sigma, dtype=exp.model.WT_times_Y.dtype)
Q, T = bfsla1(A_hat, v, k=nrank)
T_diag = T.diagonal()
T_subdiag = T.diagonal(1)
L_diag, L_subdiag = chol_trid(T_diag, T_subdiag)
R = A_hat.kmm_matmat(A_hat.WTW * Q)
Rprime = chol_trid_solve(L_diag, L_subdiag, R.T).T
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", )
else:
raise NotImplementedError
# Computing l2norm
cov_ref_path = os.environ['PRJ'] + '/data/refs/' + options.data_type.name.lower() + '_ski_dump.pkl'
assert os.path.exists(cov_ref_path), cov_ref_path + "doesn't exists. Follow readme to generate refs."
COV_REF = pickle.load(open(cov_ref_path, "rb"))[-1]
predicted_cov = np.dot(W_test * R, (W_test * Rprime).T)
l2_norm = np.linalg.norm(predicted_cov - COV_REF)
print("L2norm: ", l2_norm)
return inference_time, l2_norm
def run_llk_experiment(exp, options):
sigma = exp.model.noise_covar.noise
ntrials = options.ntrials
nrank = options.maxiter
if type(exp) == KissGpExp:
t1 = utils.tic()
W_train, K, __ = exp.model.covar_module(exp.train_x, is_kmm=True)
A = KissGpLinearOperator(W_train, K, sigma, dtype=W_train.dtype)
if options.variant == 1:
estimate_logdet = logdet_estimate_using_lz_variants(
A,
trials=ntrials,
rank=nrank,
verbose=True,
)[0]
elif options.variant == 2:
estimate_logdet = logdet_estimate_using_cg_variants(
A, trials=ntrials, tol=options.tol, rank=nrank,
verbose=True)[0]
else:
raise NotImplementedError
print("Estimated log-det: ", estimate_logdet)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
elif type(exp) == GsGpExp:
t1 = utils.tic()
K = exp.model.covar_module._inducing_forward()
A_hat = GsGpLinearOperator(exp.model.WT_times_W,
K,
sigma,
dtype=exp.model.WT_times_Y.dtype)
if options.variant == 1:
estimate_logdet = logdet_estimate_using_lz_variants(
A_hat,
WT=exp.WT,
trials=ntrials,
rank=nrank,
verbose=True,
dump=options.dump,
dump_path=options.log_dir)[0]
elif options.variant == 2:
estimate_logdet = logdet_estimate_using_cg_variants(
A_hat,
WT=exp.WT,
trials=ntrials,
tol=options.tol,
rank=nrank,
verbose=True)[0]
else:
raise NotImplementedError
print("Estimated log-det: ", estimate_logdet)
t1f = utils.toc(t1)
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
else:
raise NotImplementedError
return inference_time, estimate_logdet
def pre_process(method, grid_idx, entire_us=False):
zmax = DEFAULT_Z_MAX
grid = configs.get_radar_grid(idx=grid_idx)
num_grid_points = np.product([item[-1] for item in grid])
# Read all of the radars into file
if entire_us:
data_dirpath = RADAR_DATASET_PATH + "/entire_us"
else:
data_dirpath = RADAR_DATASET_PATH + "/ne"
files = os.listdir(data_dirpath)
# Creating directory for processed files
output_data_path = data_dirpath + "_processed/" + method.name.lower(
) + "_grid_" + str(grid_idx)
os.makedirs(output_data_path, exist_ok=True)
if method == utils.MethodName.GSGP:
t0 = utils.tic()
WTW_train, WTy_train, yty_train, n_train, total_nnz = 0, 0, 0, 0, 0
W_test, y_test, n_test = [], [], 0
print("\n\nProcessing data ...\n\n")
print("Reading data ...\n\n")
for scan in files:
filename = '%s' % (scan)
print('Reading %s' % (filename))
try:
print("File name path: ",
data_dirpath + "/" + filename.split("/")[-1])
radar = pyart.io.read_nexrad_archive(data_dirpath + "/" +
filename.split("/")[-1])
except IOError:
pass
print('Processing %s' % (radar.metadata['instrument_name']))
X, y = get_data_poionts(radar, zmax=zmax)
perm = np.random.permutation(len(X))
X = X[perm]
y = y[perm]
ntrain = int(TRAIN_FRAC * len(X))
W_train = get_basis(X[:ntrain], grid)
y_train = y[:ntrain]
WT_train = W_train.T.tocsr()
total_nnz += len(W_train.nonzero()[0])
WTW_train += WT_train * W_train
WTy_train += WT_train * y_train
yty_train += y_train.T @ y_train
n_train += ntrain
W_test += get_basis(X[ntrain:], grid),
y_test += y[ntrain:],
n_test += len(X) - ntrain
t0f = utils.toc(t0)
pre_time = utils.toc_report(t0f, tag="DataGP", return_val=True)
m_logm = num_grid_points * np.log2(num_grid_points)
print("NumPoints:", n_train)
print("NumTestPoints:", n_test)
print("Expected speed up over SKI: ", (2 * total_nnz + m_logm) /
(len(WTW_train.nonzero()[0]) + m_logm))
W_test = scipy.sparse.vstack(W_test)
y_test = np.hstack(y_test)
scipy.sparse.save_npz(output_data_path + '/WTW_train.npz', WTW_train)
np.savez(output_data_path + '/WTy_train.npz', WTy_train=WTy_train)
scipy.sparse.save_npz(output_data_path + '/W_test.npz', W_test)
np.savez(output_data_path + '/y_test.npz', y_test=y_test)
pickle.dump((yty_train, n_train, n_test),
open(output_data_path + "/norms.pkl", "wb"))
# Report results in a yaml file
results = {
'n_train': n_train,
'n_test': n_test,
'method': method.value,
'pre_time': float(pre_time),
'grid_size': num_grid_points
}
with open(output_data_path + "/stats.yaml", 'w') as outfile:
yaml.dump(results, outfile, default_flow_style=False)
elif method == utils.MethodName.KISSGP:
print("Reading data ...\n\n")
radars = []
for scan in files:
filename = '%s' % (scan)
print('Reading %s' % (filename))
try:
print("File name path: ",
data_dirpath + "/" + filename.split("/")[-1])
radars.append(
pyart.io.read_nexrad_archive(data_dirpath + "/" +
filename.split("/")[-1]))
except IOError:
pass
t0 = utils.tic()
W_train, y_train, n_train, n_test = [], [], 0, 0
W_test, y_test = [], []
print("\n\nProcessing data ...\n\n")
for radar in radars:
print('Processing %s' % (radar.metadata['instrument_name']))
X, y = get_data_poionts(radar, zmax=zmax)
perm = np.random.permutation(len(X))
X = X[perm]
y = y[perm]
ntrain = int(TRAIN_FRAC * len(X))
W_train += get_basis(X[:ntrain], grid),
y_train += y[:ntrain],
n_train += ntrain
W_test += get_basis(X[ntrain:], grid),
y_test += y[ntrain:],
n_test += len(X) - ntrain
W_train = scipy.sparse.vstack(W_train)
W_test = scipy.sparse.vstack(W_test)
y_train = np.hstack(y_train)
y_test = np.hstack(y_test)
t0f = utils.toc(t0)
pre_time = utils.toc_report(t0f, tag="DataGP", return_val=True)
print("NumPoints:", n_train)
print("NumTestPoints:", n_test)
scipy.sparse.save_npz(output_data_path + '/W_train.npz', W_train)
scipy.sparse.save_npz(output_data_path + '/W_test.npz', W_test)
np.savez(output_data_path + '/y_train.npz', y_train=y_train)
np.savez(output_data_path + '/y_test.npz', y_test=y_test)
# Report results in a yaml file
results = {
'n_train': n_train,
'n_test': n_test,
'method': method.value,
'pre_time': float(pre_time),
'grid_size': num_grid_points
}
with open(output_data_path + "/stats.yaml", 'w') as outfile:
yaml.dump(results, outfile, default_flow_style=False)
else:
raise NotImplementedError
print("Pre-processing time: ", pre_time)
return
def main(options=None):
# Handling experiment configuration
logging.info('Running with args %s', str(sys.argv[1:]))
wandb.init(project="skigp")
options = utils.get_options() if options is None else options
wandb.config.update(options)
# Handling log directory
sweep_name = os.environ.get(wandb.env.SWEEP_ID, 'solo')
output_dir = options.log_dir + '/' + sweep_name
grid_size = -1
if options.grid_size_f != utils.GridSizeFunc.NOT_SUPPLIED:
options.log_dir = output_dir + "/rid_" + str(options.seed) \
+ "_method_" + str(options.method.value) + "_ns_" + str(options.num_samples) + "_gsf_" \
+ str(options.grid_size_f.value)
elif options.data_type == utils.DatasetType.PRECIPITATION:
grid_size = np.prod(configs.get_precip_grid(options.grid_idx))
options.log_dir = output_dir + "/rid_" + str(options.seed) \
+ "_method_" + str(options.method.value) + "_ns_" + str(options.num_samples) + "_gs_" \
+ str(grid_size)
else:
options.log_dir = output_dir + "/rid_" + str(options.seed) \
+ "_method_" + str(options.method.value) + "_gs_" + str(options.grid_size)
logging.info("Logging directory: " + options.log_dir)
os.makedirs(options.log_dir, exist_ok=True)
# Setting up experiment
experiment, data_time = set_up_experiment(options)
if grid_size < 0:
grid_size = options.grid_size
# Performing inference
dump = []
t1 = utils.tic()
if options.data_type == configs.DatasetType.SINE:
error = experiment.compute_mae(maxiter=options.maxiter,
verbose=True,
dump=dump)
t1f = utils.toc(t1)
elif options.data_type == configs.DatasetType.SOUND:
error = experiment.compute_smae(maxiter=options.maxiter,
verbose=True,
dump=dump)
t1f = utils.toc(t1)
elif options.data_type == configs.DatasetType.PRECIPITATION:
predict_y = experiment.model.predict(experiment.test_x,
verbose=True,
tol=options.tol,
maxiter=800,
dump=dump)
t1f = utils.toc(t1)
error = np.mean(
np.abs(predict_y[0].squeeze() -
experiment.test_y.squeeze())) # computing mAE
else:
raise NotImplementedError
iter_count = dump[0]
inference_time = utils.toc_report(t1f, tag="InfGP", return_val=True)
# Report results in a yaml file
results = {
'data_type': options.data_type.name.lower(),
'seed': options.seed,
'method': options.method.value,
'num_samples': options.num_samples,
'inf_time': float(inference_time),
'pre_time': float(data_time),
"error": float(error),
"num_iters": iter_count
}
if options.grid_size_f != utils.GridSizeFunc.NOT_SUPPLIED:
results.update({'gsf': options.grid_size_f.value})
else:
results.update({'grid_size': int(grid_size)})
with open(options.log_dir + "/results.yaml", 'w') as outfile:
yaml.dump(results, outfile, default_flow_style=False)
logging.info("Done with experimentation!")
def set_up_experiment(options):
# Setup random seed
random.seed(options.seed)
np.random.seed(options.seed)
# Setting up the experiment skeleton
if options.data_type == configs.DatasetType.PRECIPITATION:
grid_size = configs.get_precip_grid(idx=options.grid_idx)
config = configs.Structdict()
config['data_type'] = utils.DatasetType.PRECIPITATION
config['num_dims'] = 3
config['grid_size'] = copy.copy(grid_size)
data_reader = DataLoader(config=options)
else:
data_reader = DataLoader(config=options)
if options.method == configs.MethodName.KISSGP:
experiment = KissGpExp(config=options, data_loader=data_reader)
elif options.method == configs.MethodName.GSGP:
experiment = GsGpExp(config=options, data_loader=data_reader)
else:
raise NotImplementedError
# Supply test scenario -- required only for per iteration results
experiment.data_loader.config["one_dim_num_points"] = options.num_samples
if options.sigma > 0:
experiment.data_loader.config['one_dim_noise_level'] = options.sigma
# Setting grid size
num_points = options.num_samples
if options.grid_idx < 0 and experiment.config['grid_size'] < 0:
grid_size = configs.get_grid_size(
num_points=num_points,
grid_size_f=options.grid_size_f,
data_type=experiment.config.data_type)
experiment.config['grid_size'] = grid_size
# Sampling or reading data
experiment.sample_data(
) # this is to cut time used for synthetic data creation
# Processing data
t0 = utils.tic()
experiment.load_data()
t0f = utils.toc(t0)
data_time = utils.toc_report(t0f, tag="DataGP", return_val=True)
# Build experiment
experiment.build()
# Dealing with hyper-parameters
hypers = configs.get_hypers(data_type=options.data_type, options=options)
hypers = {
'noise_covar.noise':
hypers['noise'],
'covar_module.base_kernel.outputscale':
hypers['outputscale'],
'covar_module.base_kernel.base_kernel.lengthscale':
hypers['lengthscale'] if type(hypers['lengthscale']) != list else
np.array(hypers['lengthscale'])
}
experiment.init_params(hypers=hypers)
experiment.print_model_params(raw=False)
return experiment, data_time
