def plot_field(dyn_field, coords, normfield=False, color=''):
x = coords[0]
y = coords[1]
z = coords[2]
#This plots the dynamics field first
row_sums = dyn_field.sum(axis=0)
if normfield:
norm_dyn_field = 100 * dyn_field / row_sums
else:
norm_dyn_field = dyn_field
dyn_field[np.isinf(dyn_field)] = np.nan
norm_dyn_field[np.isinf(norm_dyn_field)] = np.nan
if color == '':
obj = quiver3d(x,
y,
z,
norm_dyn_field[0, :, :, :],
norm_dyn_field[1, :, :, :],
norm_dyn_field[2, :, :, :],
opacity=0.9)
else:
obj = quiver3d(x,
y,
z,
norm_dyn_field[0, :, :, :],
norm_dyn_field[1, :, :, :],
norm_dyn_field[2, :, :, :],
opacity=0.9,
color=color,
mode='2dhooked_arrow')
def _parameter_initialiser(self, x, c=None, n=None, t=None, offset=False):
log_x = np.log(x)
log_x[np.isnan(log_x)] = -np.inf
if (2 in c) or (-1 in c):
heuristic = "Turnbull"
else:
heuristic = "Fleming-Harrington"
data = {'x' : x, 'c' : c, 'n' : n, 't' : t}
model = para.Parametric(self, 'MPP', data, offset, False, False)
fitting_info = {}
fitting_info['rr'] = 'x'
fitting_info['heuristic'] = heuristic
fitting_info['on_d_is_0'] = True
fitting_info['turnbull_estimator'] = 'Fleming-Harrington'
fitting_info['init'] = None
model.fitting_info = fitting_info
if offset:
results = mpp(model)
return (results['gamma'], *results['params'])
else:
gumb = para.Gumbel.fit(log_x, c, n, t, how='MLE')
if not gumb.res.success:
gumb = para,Gumbel.fit(log_x, c, n, t, how='MPP', heuristic=heuristic)
mu, sigma = gumb.params
alpha, beta = np.exp(mu), 1. / sigma
if (np.isinf(alpha) | np.isnan(alpha)):
alpha = np.median(x)
if (np.isinf(beta) | np.isnan(beta)):
beta = 1.
return alpha, beta
def check_bad_values(self):
# Check if x or f(x) is Nan or inf - symptoms that the algorithm reached
# the constraint barrier
if np.isnan(self.x[self.k]).any() or np.isinf(self.x[self.k]).any() or \
np.isnan(self.costFunc(self.x[self.k])).any() or np.isinf(self.costFunc(self.x[self.k])).any():
self.alpha[self.k] *= self.betaParam
return self.alpha[self.k]
def is_real_num(x):
"""return true if x is a real number"""
try:
float(x)
return not (np.isnan(x) or np.isinf(x))
except ValueError:
return False
def train(self, scale=1.0):
theta = self.rand_theta(scale)
self.loss = np.inf
theta0 = np.copy(theta)
self.theta = np.copy(theta)
def loss(theta):
nlz = self.neg_likelihood(theta)
return nlz
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss, theta0, gloss, maxiter=self.bfgs_iter, m=100, iprint=self.debug)
except np.linalg.LinAlgError:
print('Increase noise term and re-optimization')
theta0 = np.copy(self.theta)
theta0[1] += np.log(10)
theta0[2] += np.log(10)
try:
fmin_l_bfgs_b(loss, theta0, gloss, maxiter=self.bfgs_iter, m=10, iprint=self.debug)
except:
print('Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
except:
print('Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
if(np.isnan(self.loss) or np.isinf(self.loss)):
print('Fail to build GP model')
sys.exit(1)
self.alpha = chol_inv(self.L, self.y.T)
def is_real_num(x):
"""return true if x is a real number"""
try:
float(x)
return not (np.isnan(x) or np.isinf(x))
except ValueError:
return False
def graph_from_smiles(smiles):
graph = MolGraph()
mol = MolFromSmiles(smiles)
if not mol:
raise ValueError("Could not parse SMILES string:", smiles)
atoms_by_rd_idx = {}
rdPartialCharges.ComputeGasteigerCharges(mol)
for atom in mol.GetAtoms():
add_Gasteiger = float(atom.GetProp('_GasteigerCharge'))
if np.isnan(add_Gasteiger) or np.isinf(add_Gasteiger):
add_Gasteiger = 0.0
new_atom_node = graph.new_node('atom',
features=atom_features(
atom, add_Gasteiger),
rdkit_ix=atom.GetIdx())
atoms_by_rd_idx[atom.GetIdx()] = new_atom_node
for bond in mol.GetBonds():
atom1_node = atoms_by_rd_idx[bond.GetBeginAtom().GetIdx()]
atom2_node = atoms_by_rd_idx[bond.GetEndAtom().GetIdx()]
new_bond_node = graph.new_node('bond', features=bond_features(bond))
new_bond_node.add_neighbors((atom1_node, atom2_node))
atom1_node.add_neighbors((atom2_node, ))
mol_node = graph.new_node('molecule')
mol_node.add_neighbors(graph.nodes['atom'])
return graph
def _lop_p(self, theta):
log_p = 0.0
for property_type in self._property_types:
reference_data = self._reference_data[property_type]
precisions = self._reference_precisions[property_type]
temperatures = reference_data[:, 0]
reference_values = reference_data[:, 1]
surrogate_values = self._surrogate_model.evaluate(
property_type, theta, temperatures)
precisions = precisions**-2.0
if (any(numpy.isnan(surrogate_values))
or any(numpy.isinf(surrogate_values))
or any(surrogate_values > 1e10)):
return -numpy.inf
# Compute likelihood based on gaussian penalty function
log_p += autograd.numpy.sum(
distributions.Normal(surrogate_values,
precisions).log_pdf(reference_values))
return log_p
def _parameter_initialiser(self, x, c=None, n=None, offset=False):
log_x = np.log(x)
log_x[np.isnan(log_x)] = 0
gumb = para.Gumbel.fit(log_x, c, n, how='MLE')
if not gumb.res.success:
gumb = para.Gumbel.fit(log_x, c, n, how='MPP')
mu, sigma = gumb.params
alpha, beta = np.exp(mu), 1. / sigma
if (np.isinf(alpha) | np.isnan(alpha)):
alpha = np.median(x)
if (np.isinf(beta) | np.isnan(beta)):
beta = 1.
if offset:
gamma = np.min(x) - (np.max(x) - np.min(x)) / 10.
return gamma, alpha, beta, 1.
else:
return alpha, beta, 1.
def stop(x, fx, dfdx, cost, it, step, dcost, dx, scales, updated):
if dcost is None or dx is None or np.isnan(dcost) or np.isinf(dcost):
done = False
else:
done = it >= maxIts
if not done and updated:
done = np.abs(dcost) < dcostTol or np.linalg.norm(dx) < dxTol
return done
def conditional_expected_number_of_purchases_up_to_time(
self,
t,
frequency,
recency,
T
):
"""
Conditional expected number of purchases up to time.
Calculate the expected number of repeat purchases up to time t for a
randomly chosen individual from the population, given they have
purchase history (frequency, recency, T).
This function uses equation (10) from [2]_.
Parameters
----------
t: array_like
times to calculate the expectation for.
frequency: array_like
historical frequency of customer.
recency: array_like
historical recency of customer.
T: array_like
age of the customer.
Returns
-------
array_like
References
----------
.. [2] Fader, Peter S., Bruce G.S. Hardie, and Ka Lok Lee (2005a),
"Counting Your Customers the Easy Way: An Alternative to the
Pareto/NBD Model," Marketing Science, 24 (2), 275-84.
"""
x = frequency
r, alpha, a, b = self._unload_params("r", "alpha", "a", "b")
_a = r + x
_b = b + x
_c = a + b + x - 1
_z = t / (alpha + T + t)
ln_hyp_term = np.log(hyp2f1(_a, _b, _c, _z))
# if the value is inf, we are using a different but equivalent
# formula to compute the function evaluation.
ln_hyp_term_alt = np.log(hyp2f1(_c - _a, _c - _b, _c, _z)) + (_c - _a - _b) * np.log(1 - _z)
ln_hyp_term = np.where(np.isinf(ln_hyp_term), ln_hyp_term_alt, ln_hyp_term)
first_term = (a + b + x - 1) / (a - 1)
second_term = 1 - np.exp(ln_hyp_term + (r + x) * np.log((alpha + T) / (alpha + t + T)))
numerator = first_term * second_term
denominator = 1 + (x > 0) * (a / (b + x - 1)) * ((alpha + T) / (alpha + recency)) ** (r + x)
return numerator / denominator
def _poisson_obj_single(structures,
counts,
alpha,
lengths,
bias=None,
multiscale_factor=1,
multiscale_variances=None,
mixture_coefs=None):
"""Computes the poisson objective function for each counts matrix.
"""
if (bias is not None
and bias.sum() == 0) or counts.nnz == 0 or counts.null:
return 0.
if mixture_coefs is not None and len(structures) != len(mixture_coefs):
raise ValueError("The number of structures (%d) and of mixture"
" coefficents (%d) should be identical." %
(len(structures), len(mixture_coefs)))
elif mixture_coefs is None:
mixture_coefs = [1.]
lengths_lowres = decrease_lengths_res(lengths, multiscale_factor)
ploidy = int(structures[0].shape[0] / lengths_lowres.sum())
if multiscale_variances is not None:
if isinstance(multiscale_variances, np.ndarray):
var_per_dis = multiscale_variances[
counts.row3d] + multiscale_variances[counts.col3d]
else:
var_per_dis = multiscale_variances * 2
else:
var_per_dis = 0
num_highres_per_lowres_bins = counts.count_fullres_per_lowres_bins(
multiscale_factor)
lambda_intensity = ag_np.zeros(counts.nnz)
for struct, gamma in zip(structures, mixture_coefs):
dis = ag_np.sqrt((ag_np.square(struct[counts.row3d] -
struct[counts.col3d])).sum(axis=1))
if multiscale_variances is None:
tmp1 = ag_np.power(dis, alpha)
else:
tmp1 = ag_np.power(ag_np.square(dis) + var_per_dis, alpha / 2)
tmp = tmp1.reshape(-1, counts.nnz).sum(axis=0)
lambda_intensity = lambda_intensity + gamma * counts.bias_per_bin(
bias, ploidy) * counts.beta * num_highres_per_lowres_bins * tmp
# Sum main objective function
obj = lambda_intensity.sum() - (counts.data *
ag_np.log(lambda_intensity)).sum()
if ag_np.isnan(obj):
raise ValueError("Poisson component of objective function is nan")
elif ag_np.isinf(obj):
raise ValueError("Poisson component of objective function is infinite")
return counts.weight * obj
def test_optimize_locs_width(self):
"""
Test the function optimize_locs_width(..). Make sure it does not return
unusual results.
"""
# sample source
n = 600
dim = 2
seed = 17
ss = data.SSGaussMeanDiff(dim, my=1.0)
#ss = data.SSGaussVarDiff(dim)
#ss = data.SSSameGauss(dim)
# ss = data.SSBlobs()
dim = ss.dim()
dat = ss.sample(n, seed=seed)
tr, te = dat.split_tr_te(tr_proportion=0.5, seed=10)
xy_tr = tr.stack_xy()
# initialize test_locs by drawing the a Gaussian fitted to the data
# number of test locations
J = 3
V0 = util.fit_gaussian_draw(xy_tr, J, seed=seed + 1)
med = util.meddistance(xy_tr, subsample=1000)
gwidth0 = med**2
assert gwidth0 > 0
# optimize
V_opt, gw2_opt, opt_info = tst.GaussUMETest.optimize_locs_width(
tr,
V0,
gwidth0,
reg=1e-2,
max_iter=100,
tol_fun=1e-5,
disp=False,
locs_bounds_frac=100,
gwidth_lb=None,
gwidth_ub=None)
# perform the test using the optimized parameters on the test set
alpha = 0.01
ume_opt = tst.GaussUMETest(V_opt,
gw2_opt,
n_simulate=2000,
alpha=alpha)
test_result = ume_opt.perform_test(te)
assert test_result['h0_rejected']
assert util.is_real_num(gw2_opt)
assert gw2_opt > 0
assert np.all(np.logical_not((np.isnan(V_opt))))
assert np.all(np.logical_not((np.isinf(V_opt))))
def Newton(f, x, y, n=1000, eps=np.sqrt(eps)):
while True:
fxy = f(x, y)
if np.isinf(fxy).any() or np.isnan(fxy).any() or n == 0:
return np.nan, np.nan
else:
xn, yn = Newton_step(f, x, y)
# print("x, y / xn, yn:", x, y, "/", xn, yn)
if (x - xn) * (x - xn) + (y - yn) * (y - yn) <= eps * eps:
return xn, yn
x, y = xn, yn
n = n - 1
def fit_new_py(x, model):
x0 = np.copy(x).reshape(-1)
best_x = np.copy(x)
best_loss = np.inf
def loss(x0):
nonlocal best_x
nonlocal best_loss
x0 = x0.reshape(model.dim, -1)
py, ps2 = model.models[0].predict(x0)
tmp_loss = py.sum()
for i in range(1, model.outdim):
py, ps2 = model.models[0].predict(x0)
tmp_loss += np.maximum(0, py).sum()
if tmp_loss < best_loss:
best_loss = tmp_loss
best_x = np.copy(x0)
return tmp_loss
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
bounds=[[-0.5, 0.5]] * x.size,
maxiter=2000,
m=100,
iprint=model.debug)
except np.linalg.LinAlgError:
print('Fit_new_py. Increase noise term and re-optimization')
x0 = np.copy(best_x).reshape(-1)
x0[0] += 0.01
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
bounds=[[-0.5, 0.5]] * x.size,
maxiter=2000,
m=10,
iprint=model.debug)
except:
print('Fit_new_py. Exception caught, L-BFGS early stopping...')
print(traceback.format_exc())
except:
print('Fit_new_py. Exception caught, L-BFGS early stopping...')
print(traceback.format_exc())
if (np.isnan(best_loss) or np.isinf(best_loss)):
print('Fit_new_py. Fail to build GP model')
sys.exit(1)
return best_x
def kl_tril(L, m, Lzz,u):
"""KL divergence of q(u) and p(u)"""
M = L.shape[0]
traceterm = np.sum(np.linalg.solve(Lzz, L)**2)
mkmterm = np.sum(np.linalg.solve(Lzz,u-m)**2)
logdetk = 2 * np.sum(np.log(np.abs(np.diag(Lzz))))
logdets = 2 * np.sum(np.log(np.abs(np.diag(L))))
kl = 0.5 * (traceterm + logdetk - logdets - M + mkmterm)
if __verify:
S = L @ L.T
Kzz = Lzz @ Lzz.T
traceterm2 = np.trace(np.linalg.solve(Kzz, S))
mkmterm2 = np.dot((u-m).T,np.linalg.solve(Kzz, u-m))[0,0]
logdetk2 = np.log(np.linalg.det(Kzz))
logdets2 = np.log(np.linalg.det(S))
wh.assert_close(traceterm, traceterm2)
wh.assert_close(mkmterm, mkmterm2)
if not np.isinf(logdetk2) and not np.isnan(logdetk2):
wh.assert_close(logdetk, logdetk2, rtol=5e-2)
if not np.isinf(logdets2) and not np.isnan(logdets2):
wh.assert_close(logdets, logdets2, rtol=5e-2)
return kl
def optimize(self, x, bounds):
x0 = np.copy(x)
self.x = np.copy(x)
self.loss = np.inf
def loss(x):
x = x.reshape(self.dim, x.size / self.dim)
py, ps2 = self.predict(x)
py = py.sum()
if py < self.loss:
self.loss = py
self.x = x.copy()
return py
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
bounds=bounds,
maxiter=200,
m=100,
iprint=1)
except np.linalg.LinAlgError:
print('Increase noise term and re-optimization')
x0 = np.copy(self.x)
x0[0] += 1.0
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
bounds=bounds,
maxiter=200,
m=10,
iprint=1)
except:
print('Exception caught, L-BFGS early stopping...')
print(traceback.format.exc())
except:
print('Exception caught, L-BFGS early stopping...')
print(traceback.format_exc())
print('Optimized loss is %g' % self.loss)
if (np.isinf(self.loss) or np.isnan(self.loss)):
print('Fail to build GP model')
sys.exit(1)
print('best_x', self.x)
print('predict', self.predict(self.x))
print('loss', self.loss)
def train(self, scale=1.0):
theta = self.rand_theta(scale)
self.loss = np.inf
theta0 = np.copy(theta)
self.theta = theta0.copy()
def loss(theta):
nlz = self.neg_likelihood(theta)
return nlz
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss,
theta0,
gloss,
maxiter=self.bfgs_iter,
m=100,
iprint=self.debug)
except np.linalg.LinAlgError:
print('GP. Increase noise term and re-optimization')
theta0 = np.copy(self.theta)
theta0[0] += np.log(10)
try:
fmin_l_bfgs_b(loss,
theta0,
gloss,
maxiter=self.bfgs_iter,
m=10,
iprint=self.debug)
except:
print('GP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
except:
print('GP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
if (np.isinf(self.loss) or np.isnan(self.loss)):
print('GP. Fail to build GP model')
sys.exit(1)
self.alpha = chol_inv(self.L, self.train_y.T)
if self.k:
self.for_diag = np.exp(self.theta[1]) * np.exp(
self.theta[3]) + np.exp(self.theta[3 + self.dim])
else:
self.for_diag = np.exp(self.theta[1])
print('GP. Finished training process')
def conditional_expected_number_of_purchases_up_to_time(
self, t, frequency, recency, T):
"""
Conditional expected number of purchases up to time.
Calculate the expected number of repeat purchases up to time t for a
randomly choose individual from the population, given they have
purchase history (frequency, recency, T)
Parameters
----------
t: array_like
times to calculate the expectation for.
frequency: array_like
historical frequency of customer.
recency: array_like
historical recency of customer.
T: array_like
age of the customer.
Returns
-------
array_like
"""
x = frequency
r, alpha, a, b = self._unload_params("r", "alpha", "a", "b")
_a = r + x
_b = b + x
_c = a + b + x - 1
_z = t / (alpha + T + t)
ln_hyp_term = np.log(hyp2f1(_a, _b, _c, _z))
# if the value is inf, we are using a different but equivalent
# formula to compute the function evaluation.
ln_hyp_term_alt = np.log(hyp2f1(_c - _a, _c - _b, _c,
_z)) + (_c - _a - _b) * np.log(1 - _z)
ln_hyp_term = np.where(np.isinf(ln_hyp_term), ln_hyp_term_alt,
ln_hyp_term)
first_term = (a + b + x - 1) / (a - 1)
second_term = 1 - np.exp(ln_hyp_term +
(r + x) * np.log((alpha + T) /
(alpha + t + T)))
numerator = first_term * second_term
denominator = 1 + (x > 0) * (a / (b + x - 1)) * (
(alpha + T) / (alpha + recency))**(r + x)
return numerator / denominator
def plot_fields(dyn_field, ctrl_field, coords, normfield=False):
x = coords[0]
y = coords[1]
z = coords[2]
#This plots the dynamics field first
row_sums = dyn_field.sum(axis=0)
if normfield:
norm_dyn_field = 100 * dyn_field / row_sums
else:
norm_dyn_field = dyn_field
dyn_field[np.isinf(dyn_field)] = np.nan
norm_dyn_field[np.isinf(norm_dyn_field)] = np.nan
obj = quiver3d(x, y, z, norm_dyn_field[0, :, :, :],
norm_dyn_field[1, :, :, :], norm_dyn_field[2, :, :, :])
obj2 = quiver3d(x,
y,
z,
ctrl_field[0, :, :, :],
ctrl_field[1, :, :],
ctrl_field[2, :, :],
opacity=0.1)
def pixel_likelihood(z, w, m, fluxes, fluxes_ivar, lam0, B):
""" compute the likelihood of 5 bands given
z : (scalar) red-shift of observed source
w : (vector) K positive weights for positive rest-frame basis
x : (vector) 5 pixel values corresponding to UGRIZ
lam0 : basis wavelength values
B : (matrix) K x P basis
"""
if np.isinf(m):
return -np.inf
# at rest frame for lam0
lam_obs = lam0 * (1. + z)
spec = np.dot(w, B)
mu = ru.project_to_bands(spec, lam_obs) * m / (1. + z)
ll = -0.5 * np.sum(fluxes_ivar * (fluxes-mu)*(fluxes-mu))
return ll
def train(self):
theta0 = self.get_default_theta()
self.loss = np.inf
self.theta = np.copy(theta0)
nlz = self.neg_log_likelihood(theta0)
def loss(theta):
nlz = self.neg_log_likelihood(theta)
return nlz
def callback(theta):
if self.nlz < self.loss:
self.loss = self.nlz
self.theta = np.copy(theta)
gloss = value_and_grad(loss)
try:
fmin_l_bfgs_b(gloss, theta0, maxiter=self.bfgs_iter, m = 100, iprint=self.debug, callback=callback)
except np.linalg.LinAlgError:
print('GP. Increase noise term and re-optimization')
theta0 = np.copy(self.theta)
theta0[0] += np.log(10)
try:
fmin_l_bfgs_b(gloss, theta0, maxiter=self.bfgs_iter, m=10, iprint=self.debug, callback=callback)
except:
print('GP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
except:
print('GP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
if(np.isinf(self.loss) or np.isnan(self.loss)):
print('GP. Failed to build GP model')
sys.exit(1)
sn2 = np.exp(self.theta[0])
K = self.kernel(self.train_x, self.train_x, self.theta) + sn2 * np.eye(self.num_train) + self.jitter*np.eye(self.num_train)
self.L = np.linalg.cholesky(K)
self.alpha = chol_inv(self.L, self.train_y.T)
self.for_diag = np.exp(self.theta[1])
print('GP. GP model training process finished')
def fit(self, theta):
self.loss = np.inf
theta0 = np.copy(theta)
self.theta = np.copy(theta)
def loss(theta):
nlz = self.log_likelihood(theta)
return nlz
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss,
theta0,
gloss,
maxiter=self.bfgs_iter,
m=100,
iprint=0)
except np.linalg.LinAlgError:
print('Increase noise term and re-optimization')
theta0 = np.copy(self.theta)
theta0[0] += np.log(10)
try:
fmin_l_bfgs_b(loss,
theta0,
gloss,
maxiter=self.bfgs_iter,
m=10,
iprint=0)
except:
print('Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
except:
print('Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
# print('Optimized loss is %g' % self.loss)
if (np.isinf(self.loss) or np.isnan(self.loss)):
print('Fail to build GP model')
sys.exit(1)
sn2, sp2, log_lscale, w = self.split_theta(self.theta)
Phi = self.calc_Phi(w, scale_x(log_lscale, self.train_x))
self.alpha = chol_inv(self.LA, np.dot(Phi, self.train_y.T))
def fit(self, x):
x0 = np.copy(x)
self.x = np.copy(x)
self.loss = np.inf
def loss(x):
loss = -self.wEI(x)
if loss < self.loss:
self.loss = loss
self.x = np.copy(x)
return loss
gloss = grad(x)
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
maxiter=200,
m=100,
iprint=self.debug)
except np.linalg.LinAlgError:
print('Increase noise term and re-optimization')
x0 = np.copy(self.x)
x0[0] += 0.01
try:
fmin_l_bfgs_b(loss,
x0,
gloss,
maxiter=200,
m=10,
iprint=self.debug)
except:
print('Exception caught, L-BFGS early stopping...')
print(traceback.format_exc())
except:
print('Exception caught, L-BFGS early stopping...')
print(traceback.format_exc())
if (np.isnan(self.loss) or np.isinf(self.loss)):
print('Fail to build GP model')
sys.exit(1)
return self.x
def train(self):
theta0 = self.get_default_theta()
self.loss = np.inf
self.theta = np.copy(theta0)
hyp_bounds = [[None, None]] * (self.dim+3)
hyp_bounds.extend([[-1,1]])
nlz = self.neg_log_likelihood(theta0)
def loss(theta):
nlz = self.neg_log_likelihood(theta)
return nlz
def callback(theta):
if self.nlz < self.loss:
self.loss = self.nlz
self.theta = np.copy(theta)
gloss = value_and_grad(loss)
try:
fmin_l_bfgs_b(gloss, theta0, bounds=hyp_bounds, maxiter=self.bfgs_iter, m = 100, iprint=self.debug, callback=callback)
except np.linalg.LinAlgError:
print('TGP. Increase noise term and re-optimization')
theta0 = np.copy(self.theta)
theta0[self.dim+1] += np.log(10)
theta0[self.dim+2] += np.log(10)
try:
fmin_l_bfgs_b(gloss, theta0, bounds=hyp_bounds, maxiter=self.bfgs_iter, m=10, iprint=self.debug, callback=callback)
except:
print('TGP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
except:
print('TGP. Exception caught, L-BFGS early stopping...')
if self.debug:
print(traceback.format_exc())
if(np.isinf(self.loss) or np.isnan(self.loss)):
print('TGP. Failed to build TGP model')
sys.exit(1)
print('TGP. TGP model training process finished')
def test_optimize_locs_width(self):
"""
Test the function optimize_locs_width(..). Make sure it does not return
unusual results.
"""
# sample source
n = 600
dim = 2
seed = 17
ss = data.SSGaussMeanDiff(dim, my=1.0)
#ss = data.SSGaussVarDiff(dim)
#ss = data.SSSameGauss(dim)
# ss = data.SSBlobs()
dim = ss.dim()
dat = ss.sample(n, seed=seed)
tr, te = dat.split_tr_te(tr_proportion=0.5, seed=10)
xy_tr = tr.stack_xy()
# initialize test_locs by drawing the a Gaussian fitted to the data
# number of test locations
J = 3
V0 = util.fit_gaussian_draw(xy_tr, J, seed=seed+1)
med = util.meddistance(xy_tr, subsample=1000)
gwidth0 = med**2
assert gwidth0 > 0
# optimize
V_opt, gw2_opt, opt_info = tst.GaussUMETest.optimize_locs_width(tr, V0, gwidth0, reg=1e-2,
max_iter=100, tol_fun=1e-5, disp=False, locs_bounds_frac=100,
gwidth_lb=None, gwidth_ub=None)
# perform the test using the optimized parameters on the test set
alpha = 0.01
ume_opt = tst.GaussUMETest(V_opt, gw2_opt, n_simulate=2000, alpha=alpha)
test_result = ume_opt.perform_test(te)
assert test_result['h0_rejected']
assert util.is_real_num(gw2_opt)
assert gw2_opt > 0
assert np.all(np.logical_not((np.isnan(V_opt))))
assert np.all(np.logical_not((np.isinf(V_opt))))
def fit(self, theta):
theta0 = theta.copy()
self.loss = np.inf
self.theta = theta0;
def loss(w):
nlz = self.log_likelihood(w);
return nlz
gloss = grad(loss)
try:
fmin_l_bfgs_b(loss, theta0, gloss, maxiter = self.bfgs_iter, m = 100, iprint=1)
except np.linalg.LinAlgError:
print("Increase noise term and re-optimization")
theta0 = np.copy(self.theta);
theta0[0] += np.log(10);
try:
fmin_l_bfgs_b(loss, theta0, gloss, maxiter = self.bfgs_iter, m = 10, iprint=1)
except:
print("Exception caught, L-BFGS early stopping...")
if self.debug:
print(traceback.format_exc())
except:
print("Exception caught, L-BFGS early stopping...")
if self.debug:
print(traceback.format_exc())
print("Optimized loss is %g" % self.loss)
if(np.isinf(self.loss) or np.isnan(self.loss)):
print("Fail to build GP model")
sys.exit(1)
# pre-computation
log_sn = self.theta[0]
log_sp = self.theta[1]
log_lscales = self.theta[2:2+self.dim]
w = self.theta[2+self.dim:]
sn2 = np.exp(2 * log_sn)
sp = np.exp(log_sp);
sp2 = np.exp(2*log_sp);
Phi = self.calc_Phi(w, scale_x(self.train_x, log_lscales))
m = self.m
self.alpha = chol_solve(self.LA, np.dot(Phi, self.train_y_zero.T))
def next_t(path, t, dist):
p = path.point(t)
L = path.length()
# t += 1.0 / np.abs(path.derivative(t))
dd = dist/(1 + curv_spacing*np.abs(path.curvature(t)))
if np.isinf(dd) or dd == 0:
dd = dist
itr = 0
while itr < 50:
itr += 1
p1 = path.point(t)
err = np.abs(p1 - p) - dd
d1 = path.derivative(t)
if np.abs(err) < 1e-5:
return t, p1, d1 / np.abs(d1)
derr = np.abs(d1) * L
# do a step in Newton's method (clipped because some of the
# gradients in the curve are really small)
t -= np.clip(err / derr, -1e-2, 1e-2)
t = np.clip(t, 0, 1)
return t, p, d1 / np.abs(d1)
def sgd(learner, numEpochs, mkTrainingData, devData, testData, weights, computeLosses=None, batchSize=1, outputFrequency=1, outputExpDelay=False, eta0=0.01, initial_t=0, power_t=0.5, extraObjective=None, adaptive=False, clipping=False, targetDict=None, senseIsMinimize=True):
global globalEpoch, globalBestWeights
globalEpoch,globalBestWeights = 0, None
printUpdate = makePrintUpdate(learner, mkTrainingData, devData, testData, computeLosses, targetDict=targetDict, senseIsMinimize=senseIsMinimize)
globalEpoch = 0
sum_grad_squared = None
totalExamples = 0
for epoch in range(1, numEpochs+1):
trainingData = mkTrainingData()
for start in range(0, len(trainingData), batchSize):
data = trainingData[start:start+batchSize]
learner.set_weights_copy(weights.copy())
obj_and_grad = value_and_grad(learner, data, weights, extraObjective)
_, gradient = obj_and_grad(weights)
eta = eta0 / (1 if power_t == 0 else ((epoch + initial_t) ** power_t))
gradient[np.isnan(gradient)] = 0
gradient[np.isinf(gradient)] = 0
gradient *= eta
if clipping:
numBig = sum(gradient < -1) + sum(gradient > 1)
if numBig > 0:
print 'clipping %d / %d gradient terms, avg|grad| %g' % (numBig, len(gradient), np.mean(np.abs(gradient)))
gradient[gradient > 1] = 1
gradient[gradient < -1] = -1
if adaptive:
if sum_grad_squared is None:
sum_grad_squared = 1e-4 + gradient * gradient
else:
gradient /= np.sqrt(sum_grad_squared)
sum_grad_squared += gradient * gradient
weights -= gradient
if outputExpDelay and log2(totalExamples) != log2(totalExamples+len(data)):
printUpdate(weights, totalExamples)
totalExamples += len(data)
if epoch % outputFrequency == 0:
printUpdate(weights)
return globalBestWeights
def load_stamps_and_samps(gstamps):
# gather all stamp files!
print "loading available stamps"
gstamps.sort()
stamp_ids = extract_stamp_ids(gstamps)
stamps = stamps2array(gstamps)
# gather all samps!
print "loading MCMC sample files"
gal_chain_template = 'samp_cache/run5/gal_samps_stamp_%s_chain_0.bin'
gal_chain_files = [gal_chain_template%sid for sid in stamp_ids]
chain_mask = np.zeros(len(stamp_ids), dtype=np.bool) # keep track of the ones that actually have samples
Nselect = 500
Nskip = 5
samps = []
for i,chain in enumerate(gal_chain_files):
print "Galaxy", os.path.basename(chain)
## 0. load four chains from disk
src_samp_chains, ll_samp_chains, eps_samp_chains = \
io.load_mcmc_chains(chain, num_chains=4)
if len(src_samp_chains) > 0:
th = rec2matrix( np.concatenate(src_samp_chains))
# make sure there are no infinite samples
if np.any(np.isinf(th)) or np.any(np.isnan(th)):
continue
chain_mask[i] = True
samps.append(th[-Nselect*Nskip:-1:Nskip, :])
print "There are %d chains with either missing, zeros, or otherwise unsuitable samples"%((~chain_mask).sum())
# samps and stamps now aligned
stamps = stamps[chain_mask, :, :, :]
samps = np.array(samps)
return stamps, samps
def vi_obj(theta, q, ln_q, ln_1_q, ln_s, mu, sigma, n_u, n_y, raw_sample_w):
c_theta = theta[:8]
u = theta[8:8 + 3 * n_u + 9 * n_u**2]
mu_u = theta[8 + 3 * n_u + 9 * n_u**2:8 + 3 * n_u + 9 * n_u**2 +
n_u].reshape(-1, 1)
sigma_u = theta[8 + 3 * n_u + 9 * n_u**2 + n_u:-6].reshape(-1, 1)
C_u, C_g_u = kernel(c_theta, mu_u, sigma_u)
C_wu, C_g_wu = kernel_test(c_theta, mu, sigma, mu_u, sigma_u)
C_wu = C_wu[:3 * n_u, :].transpose()
for i in range(0, 10):
C_g_wu[i] = C_g_wu[i][:3 * n_u, :].transpose()
C_diag_w, C_g_diag_w = kernel_diag(c_theta, mu, sigma)
sample_w, A_u_g, L_u_g, C_u_g, C_diag_w_g, C_wu_g = \
get_sample_w(u, C_u.ravel(), C_wu.ravel(), C_diag_w.ravel(), raw_sample_w, n_u, n_y)
# mu_shift = match_prior(mu_shift_0=theta[-6:], C_u=C_u, sample_size=32)
mu_shift = theta[-6:]
link_ll = mc_link_lik(sample_w, mu_shift, q, ln_q, ln_1_q, ln_s)
kl = get_noraml_kl(u, C_u.ravel(), n_u)
link_g = -get_mc_link_g(sample_w, mu_shift, q, ln_q, ln_1_q, ln_s)
mu_shift_g = -get_mu_shift_g(sample_w, mu_shift, q, ln_q, ln_1_q, ln_s)
kl_g = get_kl_g(u, C_u.ravel(), n_u).ravel()
kl_g_C_u = get_kl_g_C_u(u, C_u.ravel(), n_u).ravel()
mu_shift_g[numpy.isnan(mu_shift_g)] = 0
mu_shift_g[numpy.isinf(mu_shift_g)] = 0
kl_g[numpy.isnan(kl_g)] = 0
kl_g[numpy.isinf(kl_g)] = 0
A_u_g[numpy.isnan(A_u_g)] = 0
A_u_g[numpy.isinf(A_u_g)] = 0
L_u_g[numpy.isnan(L_u_g)] = 0
L_u_g[numpy.isinf(L_u_g)] = 0
C_u_g[numpy.isnan(C_u_g)] = 0
C_u_g[numpy.isinf(C_u_g)] = 0
C_diag_w_g[numpy.isnan(C_diag_w_g)] = 0
C_diag_w_g[numpy.isinf(C_diag_w_g)] = 0
C_wu_g[numpy.isnan(C_wu_g)] = 0
C_wu_g[numpy.isinf(C_wu_g)] = 0
link_g[numpy.isnan(link_g)] = 0
link_g[numpy.isinf(link_g)] = 0
kl_g_C_u[numpy.isnan(kl_g_C_u)] = 0
kl_g_C_u[numpy.isinf(kl_g_C_u)] = 0
obj = -link_ll + kl
u_g = numpy.zeros_like(u)
u_g[:3 * n_u] = numpy.matmul(link_g.ravel().reshape(1, -1), A_u_g)
u_g[3 * n_u:] = numpy.matmul(link_g.ravel().reshape(1, -1), L_u_g)
u_g = u_g + kl_g
theta_g = numpy.zeros_like(c_theta)
for i in range(0, len(theta_g)):
theta_g[i] = numpy.matmul(numpy.matmul(link_g.ravel().reshape(1, -1), C_u_g),
C_g_u[i].ravel().reshape(-1, 1))[0][0] + \
numpy.matmul(numpy.matmul(link_g.ravel().reshape(1, -1), C_wu_g),
C_g_wu[i].ravel().reshape(-1, 1))[0][0] + \
numpy.matmul(numpy.matmul(link_g.ravel().reshape(1, -1), C_diag_w_g),
C_g_diag_w[i].ravel().reshape(-1, 1))[0][0] + \
numpy.matmul(kl_g_C_u.ravel().reshape(1, -1), C_g_u[i].ravel().reshape(-1, 1))[0][0]
mu_C_u_g = numpy.matmul(link_g.ravel().reshape(1, -1), C_u_g).reshape(3*n_u, 3*n_u) * C_g_u[8] + \
kl_g_C_u.reshape(3*n_u, 3*n_u) * C_g_u[8]
sigma_C_u_g = numpy.matmul(link_g.ravel().reshape(1, -1), C_u_g).reshape(3*n_u, 3*n_u) * C_g_u[9] + \
kl_g_C_u.reshape(3*n_u, 3*n_u) * C_g_u[9]
mu_C_wu_g = numpy.matmul(link_g.ravel().reshape(1, -1), C_wu_g).reshape(
-1, 3 * n_u) * C_g_wu[8]
sigma_C_wu_g = numpy.matmul(link_g.ravel().reshape(1, -1), C_wu_g).reshape(
-1, 3 * n_u) * C_g_wu[9]
mu_g = numpy.sum(mu_C_u_g[numpy.arange(0, n_u)*3, :], axis=1).ravel()*2 + \
numpy.sum(mu_C_u_g[numpy.arange(0, n_u)*3+1, :], axis=1).ravel()*2 + \
numpy.sum(mu_C_u_g[numpy.arange(0, n_u)*3+2, :], axis=1).ravel()*2 + \
numpy.sum(mu_C_wu_g[:, numpy.arange(0, n_u)*3], axis=0).ravel() + \
numpy.sum(mu_C_wu_g[:, numpy.arange(0, n_u)*3+1], axis=0).ravel() + \
numpy.sum(mu_C_wu_g[:, numpy.arange(0, n_u)*3+2], axis=0).ravel()
sigma_g = numpy.sum(sigma_C_u_g[numpy.arange(0, n_u)*3, :], axis=1).ravel()*2 + \
numpy.sum(sigma_C_u_g[numpy.arange(0, n_u)*3+1, :], axis=1).ravel()*2 + \
numpy.sum(sigma_C_u_g[numpy.arange(0, n_u)*3+2, :], axis=1).ravel()*2 + \
numpy.sum(sigma_C_wu_g[:, numpy.arange(0, n_u)*3], axis=0).ravel() + \
numpy.sum(sigma_C_wu_g[:, numpy.arange(0, n_u)*3+1], axis=0).ravel() + \
numpy.sum(sigma_C_wu_g[:, numpy.arange(0, n_u)*3+2], axis=0).ravel()
obj_g = numpy.hstack([theta_g, u_g, mu_g, sigma_g])
obj_g[numpy.isnan(obj_g)] = 0
obj_g[numpy.isinf(obj_g)] = 0
# print(numpy.array([numpy.sum(ln_s)/n_y, link_ll/n_y, -link_ll, kl, obj]))
return obj, numpy.hstack([theta_g, u_g, mu_g, sigma_g,
mu_shift_g]), -link_ll, -kl
def match_prior(mu_shift_0,
C_u,
sample_size=128,
lr=1e-3,
beta_1=0.9,
beta_2=0.999,
maxiter=int(1024),
factr=1e-4):
n_u = int(numpy.shape(C_u)[0] / 3)
L = []
m = numpy.zeros(6)
v = numpy.zeros(6)
mu_shift = mu_shift_0
fin_mu_shift = mu_shift_0
fin_L = None
for i in range(0, maxiter):
w = scipy.stats.multivariate_normal(mean=numpy.zeros(3 * n_u),
cov=C_u).rvs(sample_size)
L.append(prior_error(mu_shift, w, n_u))
g = prior_error_grad(mu_shift, w, n_u)
g[numpy.isnan(g)] = 0.0
g[numpy.isinf(g)] = 0.0
m = beta_1 * m + (1 - beta_1) * g
v = beta_2 * v + (1 - beta_2) * g * g
mu_shift = mu_shift - lr * m / (v**0.5 + eps)
if len(L) >= 2:
if L[-1] < numpy.min(L[:-1]):
fin_L = L[-1].copy()
fin_mu_shift = mu_shift.copy()
if len(L) > 32:
previous_opt = numpy.min(L.copy()[:-32])
current_opt = numpy.min(L.copy()[-32:])
if previous_opt - current_opt <= numpy.abs(previous_opt * factr):
break
print(
'============================================================================='
)
print('Prior Matched: ')
print('Total Iterations: ' + str(i),
', Loss: ' + str(fin_L) + ', Mu_Shift:' + str(fin_mu_shift))
print(
'============================================================================='
)
return fin_mu_shift
def get_calibration(t_test, mu_test, sigma_test, mu_w, cov_w, mu_shift):
n_y = numpy.shape(mu_test)[0]
n_t = numpy.shape(t_test)[1]
q_hat = numpy.zeros((n_y, n_t))
s_hat = numpy.zeros((n_y, n_t))
for i in range(0, n_y):
ln_s = scipy.stats.norm.logpdf(x=t_test,
loc=mu_test[i, :],
scale=sigma_test[i, :]).reshape(-1, 1)
feature_q = numpy.hstack([
scipy.stats.norm.logcdf(x=t_test,
loc=mu_test[i, :],
scale=sigma_test[i, :]).reshape(-1, 1),
scipy.stats.norm.logsf(x=t_test,
loc=mu_test[i, :],
scale=sigma_test[i, :]).reshape(-1, 1),
numpy.ones((n_t, 1))
])
w_sample = scipy.stats.multivariate_normal.rvs(size=1024,
mean=mu_w[i, :],
cov=cov_w[i, :, :])
w_sample[:, 0] = -numpy.exp(w_sample[:, 0] / mu_shift[0] + mu_shift[1])
w_sample[:, 1] = numpy.exp(w_sample[:, 1] / mu_shift[2] + mu_shift[3])
w_sample[:, 2] = w_sample[:, 2] / mu_shift[4] + mu_shift[5]
raw_prod = numpy.matmul(feature_q, w_sample.transpose())
MAX = raw_prod.copy()
MAX[MAX < 0] = 0
q_hat[i, :] = numpy.mean(numpy.exp(-MAX) /
(numpy.exp(-MAX) + numpy.exp(raw_prod - MAX)),
axis=1).ravel()
tmp_de = numpy.where(
raw_prod <= 0, 2 * numpy.log(1 + numpy.exp(raw_prod)),
2 * (raw_prod + numpy.log(1 + 1 / numpy.exp(raw_prod))))
ln_s_hat = (raw_prod + numpy.log(
(w_sample[:, 0] + w_sample[:, 1]) *
numpy.exp(feature_q[:, 0].reshape(-1, 1)) - w_sample[:, 0]) -
feature_q[:, 0].reshape(-1, 1) -
feature_q[:, 1].reshape(-1, 1) - tmp_de) + ln_s
mc_s_hat = numpy.exp(ln_s_hat)
mc_s_hat[numpy.isnan(mc_s_hat)] = 0
mc_s_hat[numpy.isinf(mc_s_hat)] = 0
s_hat[i, :] = numpy.mean(mc_s_hat, axis=1).ravel()
return s_hat, q_hat
def polyinterp(points, doPlot=None, xminBound=None, xmaxBound=None):
""" polynomial interpolation
Parameters
----------
points: shape(pointNum, 3), three columns represents x, f, g
doPolot: set to 1 to plot, default 0
xmin: min value that brackets minimum (default: min of points)
xmax: max value that brackets maximum (default: max of points)
set f or g to sqrt(-1)=1j if they are not known
the order of the polynomial is the number of known f and g values minus 1
Returns
-------
minPos:
fmin:
"""
if doPlot == None:
doPlot = 0
nPoints = points.shape[0]
order = np.sum(np.imag(points[:, 1:3]) == 0) -1
# code for most common case: cubic interpolation of 2 points
if nPoints == 2 and order == 3 and doPlot == 0:
[minVal, minPos] = [np.min(points[:,0]), np.argmin(points[:,0])]
notMinPos = 1 - minPos
d1 = points[minPos,2] + points[notMinPos,2] - 3*(points[minPos,1]-\
points[notMinPos,1])/(points[minPos,0]-points[notMinPos,0])
t_d2 = d1**2 - points[minPos,2]*points[notMinPos,2]
if t_d2 > 0:
d2 = np.sqrt(t_d2)
else:
d2 = np.sqrt(-t_d2) * np.complex(0,1)
if np.isreal(d2):
t = points[notMinPos,0] - (points[notMinPos,0]-points[minPos,0])*\
((points[notMinPos,2]+d2-d1)/(points[notMinPos,2]-\
points[minPos,2]+2*d2))
minPos = np.min([np.max([t,points[minPos,0]]), points[notMinPos,0]])
else:
minPos = np.mean(points[:,0])
fmin = minVal
return (minPos, fmin)
xmin = np.min(points[:,0])
xmax = np.max(points[:,0])
# compute bounds of interpolation area
if xminBound == None:
xminBound = xmin
if xmaxBound == None:
xmaxBound = xmax
# constraints based on available function values
A = np.zeros((0, order+1))
b = np.zeros((0, 1))
for i in range(nPoints):
if np.imag(points[i,1]) == 0:
constraint = np.zeros(order+1)
for j in np.arange(order,-1,-1):
constraint[order-j] = points[i,0]**j
A = np.vstack((A, constraint))
b = np.append(b, points[i,1])
# constraints based on availabe derivatives
for i in range(nPoints):
if np.isreal(points[i,2]):
constraint = np.zeros(order+1)
for j in range(1,order+1):
constraint[j-1] = (order-j+1)* points[i,0]**(order-j)
A = np.vstack((A, constraint))
b = np.append(b,points[i,2])
# find interpolating polynomial
params = np.linalg.solve(A, b)
# compute critical points
dParams = np.zeros(order)
for i in range(params.size-1):
dParams[i] = params[i] * (order-i)
if np.any(np.isinf(dParams)):
cp = np.concatenate((np.array([xminBound, xmaxBound]), points[:,0]))
else:
cp = np.concatenate((np.array([xminBound, xmaxBound]), points[:,0], \
np.roots(dParams)))
# test critical points
fmin = np.infty;
minPos = (xminBound + xmaxBound)/2.
for xCP in cp:
if np.imag(xCP) == 0 and xCP >= xminBound and xCP <= xmaxBound:
fCP = np.polyval(params, xCP)
if np.imag(fCP) == 0 and fCP < fmin:
minPos = np.double(np.real(xCP))
fmin = np.double(np.real(fCP))
# plot situation (omit this part for now since we are not going to use it
# anyway)
return (minPos, fmin)
def isLegal(v):
return np.sum(np.any(np.imag(v)))==0 and np.sum(np.isnan(v))==0 and \
np.sum(np.isinf(v))==0
#####################################################################
# fit model to galaxy shape parameters
#
# re - [0, infty], transformation log
# ab - [0, 1], transformation log (ab / (1 - ab))
# phi - [0, 180], transformation log (phi / (180 - phi))
#
######################################################################
print "fitting galaxy shape"
shape_df = np.row_stack([ coadd_df[['expRad_r', 'expAB_r', 'expPhi_r']].values,
coadd_df[['deVRad_r', 'deVAB_r', 'deVPhi_r']].values ])[::3,:]
shape_df[:,0] = np.log(shape_df[:,0])
shape_df[:,1] = np.log(shape_df[:,1]) - np.log(1.-shape_df[:,1])
shape_df[:,2] = shape_df[:,2] * (np.pi / 180.)
bad_idx = np.any(np.isinf(shape_df), axis=1)
shape_df = shape_df[~bad_idx,:]
gal_re_mog = fit_mog(shape_df[:,0], mog_class = GalRadiusMoG, max_comps=50)
gal_ab_mog = fit_mog(shape_df[:,1], mog_class = GalAbMoG, max_comps=50)
with open('gal_re_mog.pkl', 'wb') as f:
pickle.dump(gal_re_mog, f)
with open('gal_ab_mog.pkl', 'wb') as f:
pickle.dump(gal_ab_mog, f)
#####################################################################
# fit star => galaxy proposal distributions
#
# re - [0, infty], transformation log
def to_log_nanomaggies(mags):
fluxes = np.log(mags2nanomaggies(mags))
bad_idx = np.any(np.isinf(fluxes), axis=1)
return fluxes[~bad_idx,:]
