def job_nfsicJ3_opt(paired_source, tr, te, r, J=3):
"""NFSIC with test locations optimzied. """
with util.ContextTimer() as t:
nfsic_opt_options = {
'n_test_locs': J,
'max_iter': 200,
'V_step': 1,
'W_step': 1,
'gwidthx_step': 1,
'gwidthy_step': 1,
'batch_proportion': 1.0,
'tol_fun': 1e-4,
'step_pow': 0.5,
'seed': r + 2,
'reg': 1e-6
}
op_V, op_W, op_gwx, op_gwy, info = it.GaussNFSIC.optimize_locs_widths(
tr, alpha, **nfsic_opt_options)
nfsic_opt = it.GaussNFSIC(op_gwx,
op_gwy,
op_V,
op_W,
alpha,
reg='auto',
seed=r + 3)
nfsic_opt_result = nfsic_opt.perform_test(te)
return {
'indtest': nfsic_opt,
'test_result': nfsic_opt_result,
'time_secs': t.secs
}
def job_nfsic_grid(paired_source, tr, te, r):
"""
NFSIC where the test locations are randomized, and the Gaussian widths
are optimized by a grid search.
"""
# randomize the test locations by fitting Gaussians to the data
with util.ContextTimer() as t:
V, W = it.GaussNFSIC.init_locs_2randn(tr, J, seed=r + 2)
xtr, ytr = tr.xy()
n_gwidth_cand = 30
gwidthx_factors = 2.0**np.linspace(-4, 4, n_gwidth_cand)
gwidthy_factors = gwidthx_factors
#gwidthy_factors = 2.0**np.linspace(-3, 4, 40)
medx = util.meddistance(xtr, 1000)
medy = util.meddistance(ytr, 1000)
list_gwidthx = np.hstack(((medx**2) * gwidthx_factors))
list_gwidthy = np.hstack(((medy**2) * gwidthy_factors))
bestij, lambs = it.GaussNFSIC.grid_search_gwidth(
tr, V, W, list_gwidthx, list_gwidthy)
# These are width^2
best_widthx = list_gwidthx[bestij[0]]
best_widthy = list_gwidthy[bestij[1]]
# perform test
nfsic_grid = it.GaussNFSIC(best_widthx, best_widthy, V, W, alpha)
nfsic_grid_result = nfsic_grid.perform_test(te)
return {
'indtest': nfsic_grid,
'test_result': nfsic_grid_result,
'time_secs': t.secs
}
def job_nfsicJ10_stoopt(paired_source, tr, te, r, n_permute=None):
J = 10
k, l = kl_kgauss_median(tr)
medx2 = k.sigma2
medy2 = l.sigma2
fac_min = 5e-2
fac_max = 5e3
with util.ContextTimer() as t:
nfsic_opt_options = {
'n_test_locs': J,
'max_iter': 100,
'V_step': 1,
'W_step': 1,
'gwidthx_step': 1,
'gwidthy_step': 1,
'batch_proportion': 1,
'tol_fun': 1e-4,
'step_pow': 0.5,
'seed': r + 2,
'reg': 1e-6,
'gwidthx_lb': medx2 * 1e-3,
'gwidthx_ub': medx2 * 1e3,
'gwidthy_lb': medy2 * 1e-3,
'gwidthy_ub': medy2 * 1e3
}
op_V, op_W, op_gwx, op_gwy, info = it.GaussNFSIC.optimize_locs_widths(
tr, alpha, **nfsic_opt_options)
# make sure the optimized widths are not too extreme
#last_gwx = info['gwidthxs'][-1]
#last_gwy = info['gwidthys'][-1]
#op_gwx = last_gwx
#op_gwy = last_gwy
op_gwx = max(fac_min * medx2, 1e-5, min(fac_max * medx2, op_gwx))
op_gwy = max(fac_min * medy2, 1e-5, min(fac_max * medy2, op_gwy))
nfsic_opt = it.GaussNFSIC(op_gwx,
op_gwy,
op_V,
op_W,
alpha=alpha,
reg='auto',
n_permute=n_permute,
seed=r + 3)
nfsic_opt_result = nfsic_opt.perform_test(te)
return {
'indtest': nfsic_opt,
'test_result': nfsic_opt_result,
'time_secs': t.secs
}
def setUp(self):
n = 300
dx = 2
pdata_mean = get_pdata_mean(n, dx)
X, Y = pdata_mean.xy()
gwx2 = util.meddistance(X)**2
gwy2 = util.meddistance(Y)**2
J = 2
V = np.random.randn(J, dx)
W = np.random.randn(J, 1)
self.gnfsic = it.GaussNFSIC(gwx2, gwy2, V, W, alpha=0.01)
self.pdata_mean = pdata_mean
def job_nfsicJ3_perm_stoopt(paired_source, tr, te, r):
"""
Use permutations to simulate from the null distribution.
"""
n_permute = 500
J = 3
with util.ContextTimer() as t:
nfsic_opt_options = {
'n_test_locs': J,
'max_iter': 300,
'V_step': 1,
'W_step': 1,
'gwidthx_step': 1,
'gwidthy_step': 1,
'batch_proportion': 0.7,
'tol_fun': 1e-4,
'step_pow': 0.5,
'seed': r + 2,
'reg': 1e-6
}
op_V, op_W, op_gwx, op_gwy, info = it.GaussNFSIC.optimize_locs_widths(
tr, alpha, **nfsic_opt_options)
nfsic_opt = it.GaussNFSIC(op_gwx,
op_gwy,
op_V,
op_W,
alpha,
reg='auto',
n_permute=n_permute,
seed=r + 3)
nfsic_opt_result = nfsic_opt.perform_test(te)
return {
'indtest': nfsic_opt,
'test_result': nfsic_opt_result,
'time_secs': t.secs
}
