seed = {seed}
width_i = {width_i}
num_layers = {num_layers}
max_epochs = {max_epochs}
=====================================================================
""".format(split_type = SPLIT_TYPE,
num_train = NUM_TRAIN_EXAMPLE,
num_test = NUM_TEST_EXAMPLE,
seed = SEED,
width_i = WIDTH,
num_layers = NUM_LAYERS,
max_epochs = MAX_EPOCHS)
# DR10 qso dataset and spec files
qso_psf_flux, qso_psf_flux_ivar, qso_psf_flux_mags, qso_z, spec_files, train_idx, test_idx = \
ru.load_DR10QSO_train_test_idx(split_type = SPLIT_TYPE)
spec_ids = [os.path.splitext(os.path.basename(sf))[0] for sf in spec_files]
## randomly subselect NUM_TRAIN
np.random.seed(SEED)
rand_idx = np.random.permutation(len(train_idx))
train_idx_sub = train_idx[rand_idx[0:NUM_TRAIN_EXAMPLE]]
rand_idx = np.random.permutation(len(test_idx))
test_idx_sub = test_idx[rand_idx[0:NUM_TEST_EXAMPLE]]
test_spec_ids = np.array(spec_ids)[test_idx_sub]
# create data matrix and fit model
if FLUX_UNITS == "nanos":
data = np.column_stack((qso_psf_flux, qso_z))
else:
data = np.column_stack((qso_psf_mags, qso_z))
x0=np.log(w),
n=100,
delta=.01)
fig = plt.figure()
plt.plot(ll_rand)
plt.title(" Random direction for fit_weights_given_basis loss")
plt.show()
test_idxs = [0, 10, 95, 139]
plot_fits(qtest, test_idxs, refit_w=True)
if __name__ == "__main__":
# DR10 qso dataset and spec files
qso_psf_flux, qso_z, spec_files, train_idx, test_idx = \
ru.load_DR10QSO_train_test_idx(split_type = "random")
## randomly subselect NUM_TRAIN
np.random.seed(SEED)
rand_idx = np.random.permutation(len(train_idx))
train_idx_sub = train_idx[rand_idx[0:NUM_TRAIN_EXAMPLE]]
#rand_idx = np.random.permutation(len(test_idx))
#test_idx_sub = test_idx[rand_idx[0:NUM_TEST_EXAMPLE]]
## only load in NUM_TRAIN spec files
train_spec_files = np.array(spec_files)[train_idx_sub]
#test_spec_files = np.array(spec_files)[test_idx]
spec_grid, spec_ivar_grid, spec_mod_grid, unique_lams, spec_zs, spec_ids, badids = \
ru.load_specs_from_disk(train_spec_files)
lam0, lam0_delta = ru.get_lam0(lam_subsample=LAM_SUBSAMPLE)
def get_basis_sample(idx, mle = False):
""" Method to return a basis sample to condition on
(or the MLE if specified) """
if mle:
return B_mle
else:
return B_samps[idx]
##########################################################################
## Load in spectroscopically measured quasars + fluxes
##########################################################################
# DR10 qso dataset and spec files
qso_psf_flux, qso_psf_flux_ivar, qso_psf_mags, qso_z, \
spec_files, train_idx, test_idx = \
ru.load_DR10QSO_train_test_idx(split_type = SPLIT_TYPE)
## subselect train/test to match other experiments
if NUM_TRAIN_EXAMPLE == "all":
NUM_TRAIN_EXAMPLE = len(train_idx)
if NUM_TEST_EXAMPLE == "all":
NUM_TEST_EXAMPLE = len(test_idx)
np.random.seed(SEED)
rand_idx = np.random.permutation(len(train_idx))
train_idx_sub = train_idx[rand_idx[0:NUM_TRAIN_EXAMPLE]]
rand_idx = np.random.permutation(len(test_idx))
test_idx_sub = test_idx[rand_idx[0:NUM_TEST_EXAMPLE]]
## grab the we're sampling
n = test_idx_sub[test_n]
spec_id = os.path.basename(spec_files[n])
qtrain['Z'][n],
lam_obs, return_loss=True, sgd_iter=1000)
ll_rand = evaluate_random_direction(fun = lfun, x0=np.log(w), n = 100, delta=.01)
fig = plt.figure()
plt.plot(ll_rand)
plt.title(" Random direction for fit_weights_given_basis loss")
plt.show()
test_idxs = [0, 10, 95, 139]
plot_fits(qtest, test_idxs, refit_w = True)
if __name__=="__main__":
# DR10 qso dataset and spec files
qso_psf_flux, qso_z, spec_files, train_idx, test_idx = \
ru.load_DR10QSO_train_test_idx(split_type = "random")
## randomly subselect NUM_TRAIN
np.random.seed(SEED)
rand_idx = np.random.permutation(len(train_idx))
train_idx_sub = train_idx[rand_idx[0:NUM_TRAIN_EXAMPLE]]
#rand_idx = np.random.permutation(len(test_idx))
#test_idx_sub = test_idx[rand_idx[0:NUM_TEST_EXAMPLE]]
## only load in NUM_TRAIN spec files
train_spec_files = np.array(spec_files)[train_idx_sub]
#test_spec_files = np.array(spec_files)[test_idx]
spec_grid, spec_ivar_grid, spec_mod_grid, unique_lams, spec_zs, spec_ids, badids = \
ru.load_specs_from_disk(train_spec_files)