def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
step = function.step(beta)
betanew = betaold = beta
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
step = function.step(betanew)
betaold = betanew
betanew = betaold - step * function.grad(betaold)
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f.append(function.f(betanew))
if maths.norm(betanew - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
self.info_set(Info.fvalue, f)
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array, p-by-1. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
aold = anew = 1.0
thetaold = thetanew = beta
betanew = betaold = beta
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
step = function.step(betanew)
betaold = betanew
thetaold = thetanew
aold = anew
thetanew = betaold - step * function.grad(betaold)
anew = (1.0 + np.sqrt(4.0 * aold * aold + 1.0)) / 2.0
betanew = thetanew + (aold - 1.0) * (thetanew - thetaold) / anew
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.func_val):
f.append(function.f(betanew))
if maths.norm(betanew - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, functions, xy):
"""Finds the minimum of two functions with associated proximal
operators.
Parameters
----------
functions : List or tuple with two Functions or a SplittableFunction.
The two functions.
xy : List or tuple with two elements, numpy arrays. The starting points
for the minimisation.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
funcs = [functions.g, functions.h]
x_new = xy[0]
y_new = xy[1]
z_new = x_new.copy()
u_new = y_new.copy()
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
x_old = x_new
z_old = z_new
u_old = u_new
if isinstance(funcs[0], properties.ProximalOperator):
x_new = funcs[0].prox(z_old - u_old)
else:
x_new = funcs[0].proj(z_old - u_old)
y_new = x_new # TODO: Allow a linear operator here.
if isinstance(funcs[1], properties.ProximalOperator):
z_new = funcs[1].prox(y_new + u_old)
else:
z_new = funcs[1].proj(y_new + u_old)
# The order here is important! Do not change!
u_new = (y_new - z_new) + u_old
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
fval = funcs[0].f(z_new) + funcs[1].f(z_new)
f.append(fval)
if not self.simulation:
if i == 1:
if maths.norm(x_new - x_old) < self.eps \
and i >= self.min_iter:
# print "Stopping criterion kicked in!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else:
if maths.norm(x_new - x_old) / maths.norm(x_old) < self.eps \
and i >= self.min_iter:
# print "Stopping criterion kicked in!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
# Update the penalty parameter, rho, dynamically.
if self.mu > 1.0:
r = x_new - z_new
s = (z_new - z_old) * -self.rho
norm_r = maths.norm(r)
norm_s = maths.norm(s)
# print "norm(r): ", norm_r, ", norm(s): ", norm_s, ", rho:", \
# self.rho
if norm_r > self.mu * norm_s:
self.rho *= self.tau
u_new *= 1.0 / self.tau # Rescale dual variable.
elif norm_s > self.mu * norm_r:
self.rho /= self.tau
u_new *= self.tau # Rescale dual variable.
# Update the penalty parameter in the functions.
functions.set_rho(self.rho)
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return z_new
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
step = function.step(beta)
betanew = betaold = beta
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
step = function.step(betanew)
betaold = betanew
betanew = function.prox(betaold - step * function.grad(betaold),
step,
eps=1.0 / (float(i) ** (2.0 + consts.FLOAT_EPSILON)),
max_iter=self.max_iter)
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f.append(function.f(betanew))
if self.callback is not None:
self.callback(locals())
if (1.0 / step) * maths.norm(betanew - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, function, beta):
# Copy the allowed info keys for FISTA.
fista_info = list()
for nfo in self.info_copy():
if nfo in FISTA.INFO_PROVIDED:
fista_info.append(nfo)
# if not self.fista_info.allows(Info.num_iter):
# self.fista_info.add_key(Info.num_iter)
# Create the inner algorithm.
algorithm = FISTA(eps=self.eps,
max_iter=self.max_iter, min_iter=self.min_iter,
info=fista_info)
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.mu_start is None:
mu = [function.estimate_mu(beta)]
else:
mu = [self.mu_start]
function.set_mu(self.mu_min)
tmin = function.step(beta)
function.set_mu(mu[0])
max_eps = function.eps_max(mu[0])
G = min(max_eps, function.eps_opt(mu[0]))
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.gap):
Gval = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
i = 0
while True:
stop = False
tnew = function.step(beta)
eps_plus = min(max_eps, function.eps_opt(mu[-1]))
# print "current iterations: ", self.num_iter, \
# ", iterations left: ", self.max_iter - self.num_iter
algorithm.set_params(step=tnew, eps=eps_plus,
max_iter=self.max_iter - self.num_iter,
conesta_stop=None)
# conesta_stop=[self.mu_min])
# self.fista_info.clear()
beta = algorithm.run(function, beta)
#print "CONESTA loop", i, "FISTA=",self.fista_info[Info.num_iter], "TOT iter:", self.num_iter
self.num_iter += algorithm.num_iter
if Info.time in algorithm.info:
tval = algorithm.info_get(Info.time)
if Info.fvalue in algorithm.info:
fval = algorithm.info_get(Info.fvalue)
self.mu_min = min(self.mu_min, mu[-1])
tmin = min(tmin, tnew)
old_mu = function.set_mu(self.mu_min)
# Take one ISTA step for use in the stopping criterion.
beta_tilde = function.prox(beta - tmin * function.grad(beta),
tmin)
function.set_mu(old_mu)
if (1.0 / tmin) * maths.norm(beta - beta_tilde) < self.eps:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
stop = True
if self.num_iter >= self.max_iter:
stop = True
if self.info_requested(Info.time):
gap_time = utils.time_cpu()
if self.dynamic:
G_new = function.gap(beta, eps=eps_plus,
max_iter=self.max_iter - self.num_iter)
# TODO: Warn if G_new < 0.
G_new = abs(G_new) # Just in case ...
if G_new < G:
G = G_new
else:
G = self.tau * G
else: # Static
G = self.tau * G
if self.info_requested(Info.time):
gap_time = utils.time_cpu() - gap_time
tval[-1] += gap_time
t = t + tval
if self.info_requested(Info.fvalue):
f = f + fval
if self.info_requested(Info.gap):
Gval.append(G)
if (G <= consts.TOLERANCE and mu[-1] <= consts.TOLERANCE) or stop:
break
mu_new = min(mu[-1], function.mu_opt(G))
self.mu_min = min(self.mu_min, mu_new)
if self.info_requested(Info.mu):
mu = mu + [max(self.mu_min, mu_new)] * len(fval)
else:
mu.append(max(self.mu_min, mu_new))
function.set_mu(mu_new)
i = i + 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i + 1)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.gap):
self.info_set(Info.gap, Gval)
if self.info_requested(Info.mu):
self.info_set(Info.mu, mu)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, X, y, beta=None):
"""Find the minimiser of the associated function, starting at beta.
Parameters
----------
X : Numpy array, shape n-by-p. The matrix X with independent
variables.
y : Numpy array, shape n-by-1. The response variable y.
beta : Numpy array. Optional starting point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
n, p = X.shape
if beta is None:
beta = self.start_vector.get_weights(p)
else:
beta = beta.copy()
function = functions.CombinedFunction()
function.add_loss(functions.losses.LinearRegression(X, y, mean=False))
function.add_prox(penalties.L1(l=self.l))
xTx = np.sum(X ** 2.0, axis=0)
if self.mean:
xTx *= 1.0 / float(n)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# The update has an error that propagates. This resets the
# approximation. We may not need to do this at every iteration.
y_Xbeta = y - np.dot(X, beta)
betaold = beta.copy()
for j in range(p):
xj = X[:, [j]]
betaj = beta[j, 0]
if xTx[j] < consts.TOLERANCE: # Avoid division-by-zero.
bj = 0.0
else:
bj = np.dot(xj.T, y_Xbeta + xj * betaj)[0, 0]
if self.mean:
bj /= float(n)
if j < self.penalty_start:
bj = bj / xTx[j]
else:
# Soft thresholding.
bj = np.sign(bj) \
* max(0.0, (abs(bj) - self.l) / xTx[j])
y_Xbeta -= xj * (bj - betaj) # Update X.beta.
beta[j] = bj # Save result.
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
f_ = self._f(y_Xbeta, y, beta)
f.append(f_)
# print "err:", maths.norm(beta - betaold)
if maths.norm(beta - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
# print "iterations: ", i
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray
The initial point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
if self.info_requested(Info.func_val):
func_val = []
if self.info_requested(Info.iterates):
iterates = []
# Keep track of the best value (MAP approximation)
lnprob_max = -np.inf
x_max = x
if self.info_requested(Info.func_val):
# Initial value from start vector
f = [-function.f(x)]
lnprob_max = f[-1]
# Number of parameters
p = x.size
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Sample a proposal point
y = np.copy(x)
for i in range(p):
y = self.Q.random_sample(y, i, copy=False)
lnprob_x = -function.f(y)
if self.info_requested(Info.func_val):
f.append(lnprob_x)
x = y
# Store best sample visited
if lnprob_x > lnprob_max:
lnprob_max = lnprob_x
x_max = x
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if it % self.thinning == 0:
if self.info_requested(Info.iterates):
iterates.append(x)
if self.info_requested(Info.func_val):
func_val.append(lnprob_x)
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return x_max
def run(self, function, beta):
# Copy the allowed info keys for FISTA.
fista_info = list()
for nfo in self.info_copy():
if nfo in FISTA.INFO_PROVIDED:
fista_info.append(nfo)
# CONESTA always asks for the gap.
if Info.gap not in fista_info:
fista_info.append(Info.gap)
# Create the inner algorithm.
algorithm = FISTA(use_gap=True, info=fista_info, eps=self.eps,
max_iter=self.max_iter, min_iter=self.min_iter)
# Not ok until the end.
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
# Time the init computation (essentialy Lipchitz constant in mu_opt).
if self.info_requested(Info.time):
init_time = utils.time_cpu()
# Compute current gap, precision eps (gap decreased by tau) and mu.
function.set_mu(consts.TOLERANCE)
gap = function.gap(beta, eps=self.eps, max_iter=self.max_iter)
eps = self.tau * abs(gap)
# Warning below if gap < -consts.TOLERANCE: See Special case 1
gM = function.eps_max(1.0)
loop = True
# Special case 1: gap is very small: stopping criterion satisfied
if gap < self.eps: # "- mu * gM" has been removed since mu == 0
warnings.warn(
"Stopping criterion satisfied before the first iteration."
" Either beta_start a the solution (given eps)."
" If beta_start is null the problem might be over-penalized. "
" Then try smaller penalization.")
loop = False
# Special case 2: gap infinite or NaN => eps is not finite or NaN
# => mu is NaN etc. Force eps to a large value, to force some FISTA
# iteration to getbetter starting point
if not np.isfinite(eps):
eps = self.eps_max
if loop: # mu is useless if loop is False
mu = function.mu_opt(eps)
function.set_mu(mu)
#gM = function.eps_max(1.0)
# Initialise info variables. Info variables have the suffix "_".
if self.info_requested(Info.time):
t_ = []
init_time = utils.time_cpu() - init_time
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f_ = []
if self.info_requested(Info.gap):
gap_ = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
if self.info_requested(Info.mu):
mu_ = []
i = 0 # Iteration counter.
while loop:
converged = False
# Current precision.
eps_mu = max(eps, self.eps) - mu * gM
# Set current parameters to algorithm.
algorithm.set_params(eps=eps_mu,
max_iter=self.max_iter - self.num_iter)
# Run FISTA.
beta = algorithm.run(function, beta)
# Update global iteration counter.
self.num_iter += algorithm.num_iter
# Get info from algorithm.
if Info.time in algorithm.info and \
self.info_requested(Info.time):
t_ += algorithm.info_get(Info.time)
if i == 0: # Add init time to first iteration.
t_[0] += init_time
if Info.func_val in algorithm.info and \
self.info_requested(Info.func_val):
f_ += algorithm.info_get(Info.func_val)
elif Info.fvalue in algorithm.info and \
self.info_requested(Info.fvalue):
f_ += algorithm.info_get(Info.fvalue)
if self.info_requested(Info.mu):
mu_ += [mu] * algorithm.num_iter
if self.info_requested(Info.gap):
gap_ += algorithm.info_get(Info.gap)
# Obtain the gap from the last FISTA run. May be small and negative
# close to machine epsilon.
gap_mu = abs(algorithm.info_get(Info.gap)[-1])
# TODO: Warn if gap_mu < -consts.TOLERANCE.
if not self.simulation:
if gap_mu + mu * gM < self.eps:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
converged = True
if self.callback is not None:
self.callback(locals())
if self.info_requested(Info.verbose):
print("CONESTA ite:%i, gap_mu: %g, eps: %g, mu: %g, "
"eps_mu: %g" % (i, gap_mu, eps, mu, eps_mu))
# Stopping criteria.
if (converged or self.num_iter >= self.max_iter) \
and self.num_iter >= self.min_iter:
break
# Update the precision eps.
# eps = self.tau * (gap_mu + mu * gM)
eps = max(self.eps, self.tau * (gap_mu + mu * gM))
# Compute and update mu.
# mu = max(self.mu_min, min(function.mu_opt(eps), mu))
mu = min(function.mu_opt(eps), mu)
function.set_mu(mu)
i = i + 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, self.num_iter)
if self.info_requested(Info.continuations):
self.info_set(Info.continuations, i + 1)
if self.info_requested(Info.time):
self.info_set(Info.time, t_)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f_)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f_)
if self.info_requested(Info.gap):
self.info_set(Info.gap, gap_)
if self.info_requested(Info.mu):
self.info_set(Info.mu, mu_)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, X, y, beta=None):
"""Find the minimiser of the associated function, starting at beta.
Parameters
----------
X : Numpy array, shape n-by-p. The matrix X with independent
variables.
y : Numpy array, shape n-by-1. The response variable y.
beta : Numpy array. Optional starting point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
n, p = X.shape
if beta is None:
beta = self.start_vector.get_weights(p)
else:
beta = beta.copy()
xTx = np.sum(X**2, axis=0)
if self.mean:
xTx *= 1.0 / float(n)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# The update has an error that propagates. This resets the
# approximation. We may not need to do this at every iteration.
Xbeta_y = np.dot(X, beta) - y
betaold = beta.copy()
for j in range(p):
xj = X[:, [j]]
betaj = beta[j]
# Solve for beta[j].
if xTx[j] < consts.TOLERANCE: # Avoid division-by-zero.
bj = 0.0
else:
# Intercept.
S0 = np.dot(xj.T, Xbeta_y - xj * betaj)[0, 0]
if self.mean:
S0 /= float(n)
if j < self.penalty_start:
bj = -S0 / xTx[j]
else:
if S0 > self.l:
bj = (self.l - S0) / xTx[j]
elif S0 < -self.l:
bj = (-self.l - S0) / xTx[j]
else:
bj = 0.0
Xbeta_y += xj * (bj - betaj) # Update X.beta.
beta[j] = bj # Save result.
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
f_ = self._f(Xbeta_y, y, beta)
f.append(f_)
# print "f:", f[-1]
# print "err:", maths.norm(beta - betaold)
if maths.norm(beta - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
# print "iterations: ", i
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray, shape (p, 1) or (p, num_walkers)
The initial point.
"""
if x.ndim > 2:
raise ValueError("The staring points must be a numpy array of "
"shape (p, 1) or (p, num_walkers).")
if x.ndim < 2: # Make x a column vector
x = np.atleast_2d(x)
if x.shape[0] < x.shape[1]:
x = x.T
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
p = x.shape[0] # Dimension of parameter space
num_walkers = max(p + 1, self.num_walkers)
if self.info_requested(Info.func_val):
func_val = []
for i in range(num_walkers):
func_val.append([])
if self.info_requested(Info.iterates):
iterates = []
for i in range(num_walkers):
iterates.append([])
# For the acceptance rate
accepted = [0] * num_walkers
# Initial value from start vector
lnprob_x = [0] * num_walkers
lnprob_y = [0] * num_walkers
if x.shape[1] < num_walkers:
X = np.zeros((p, num_walkers))
X[:, :x.shape[1]] = x
for i in range(num_walkers - x.shape[1]):
if x.shape[1] > 1:
rnd_col = np.random.randint(x.shape[1])
x_ = x[:, [rnd_col]]
else:
x_ = x
X[:, [x.shape[1] + i]] = self.Q.random_sample(x)
elif x.shape[1] == num_walkers:
X = x
else:
raise ValueError("The number of parameters is greater than the "
"number of walkers. This should not be able to "
"happen here. Please report this error so that "
"we can fix it!")
for i in range(num_walkers):
lnprob_x[i] = -function.f(X[:, [i]])
# Keep track of best value (sort of like MAP approximations)
lnprob_max = [-np.inf] * num_walkers
X_max = X
import warnings
warnings.filterwarnings('error')
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Select another walker randomly for each walker
walker_idx = np.mod(
np.arange(num_walkers) + np.floor(self.random_state.rand() *
(num_walkers - 1)) + 1,
num_walkers).astype(np.int)
# Sample stretch move size
z = ((self.step_size - 1.0) *
self.random_state.rand(num_walkers) + 1)**2.0 \
/ self.step_size
# Construct proposal points for all walkers
Xj = X[:, walker_idx]
Y = Xj + z * (X - Xj)
ln_r = np.log(self.random_state.rand(num_walkers))
for i in range(num_walkers):
y = Y[:, [i]]
lnprob_y[i] = -function.f(y)
if (lnprob_y[i] == -np.inf) or (lnprob_x[i] == -np.inf):
ln_q_i = -np.inf
else:
ln_q_i = (p - 1) * np.log(z[i]) + lnprob_y[i] - lnprob_x[i]
if ln_r[i] <= ln_q_i:
X[:, [i]] = y
lnprob_x[i] = lnprob_y[i]
accepted[i] += 1
# Store best sample visited
if lnprob_x[i] > lnprob_max[i]:
lnprob_max[i] = lnprob_x[i]
X_max = X[:, [i]]
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if (it - 1) % self.thinning == 0:
if self.info_requested(Info.iterates):
for i in range(num_walkers):
iterates[i].append(X[:, [i]])
if self.info_requested(Info.func_val):
for i in range(num_walkers):
func_val[i].append(lnprob_x[i])
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.acceptance_rate):
acceptance_rate = []
for i in range(num_walkers):
acceptance_rate.append(accepted[i] / float(it))
self.info_set(Info.acceptance_rate, acceptance_rate)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return X_max
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray
The initial point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
if self.info_requested(Info.func_val):
func_val = []
if self.info_requested(Info.iterates):
iterates = []
# Keep track of the best value (MAP approximation)
lnprob_max = -np.inf
x_max = x
if self.info_requested(Info.func_val):
# Initial value from start vector
f = [-function.f(x)]
lnprob_max = f[-1]
# Number of parameters
p = x.size
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Sample a proposal point
y = np.copy(x)
for i in range(p):
y = self.Q.random_sample(y, i, copy=False)
lnprob_x = -function.f(y)
if self.info_requested(Info.func_val):
f.append(lnprob_x)
x = y
# Store best sample visited
if lnprob_x > lnprob_max:
lnprob_max = lnprob_x
x_max = x
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if it % self.thinning == 0:
if self.info_requested(Info.iterates):
iterates.append(x)
if self.info_requested(Info.func_val):
func_val.append(lnprob_x)
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return x_max
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray
The initial point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
if self.info_requested(Info.func_val):
func_val = []
if self.info_requested(Info.iterates):
iterates = []
# Keep track of best value (MAP approximation)
lnprob_max = -np.inf
x_max = x
# For the acceptance rate
accepted = 0
# Initial value from start vector
lnprob_x = -function.f(x)
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Sample a proposal point
y = self.Q.random_sample(x)
lnprob_y = -function.f(y)
ln_q = lnprob_y - lnprob_x
# Compute transition probabilities
if not self.Q.is_symmetric():
ln_q_y_x = self.Q.transition_lnprob(y, x) # Move from x to y
ln_q_x_y = self.Q.transition_lnprob(x, y) # Move from y to x
ln_q += ln_q_x_y - ln_q_y_x
log_r = np.log(self.random_state.rand())
if ln_q >= 0.0 or ln_q >= log_r:
x = y
lnprob_x = lnprob_y
accepted += 1
# Store best sample visited
if lnprob_x > lnprob_max:
lnprob_max = lnprob_x
x_max = x
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if it % self.thinning == 0:
if self.info_requested(Info.iterates):
iterates.append(x)
if self.info_requested(Info.func_val):
func_val.append(lnprob_x)
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.acceptance_rate):
self.info_set(Info.acceptance_rate, accepted / float(it))
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return x_max
def run(self, function, beta):
# Copy the allowed info keys for FISTA.
fista_info = list()
for nfo in self.info_copy():
if nfo in FISTA.INFO_PROVIDED:
fista_info.append(nfo)
# CONESTA always asks for the gap.
if Info.gap not in fista_info:
fista_info.append(Info.gap)
# Create the inner algorithm.
algorithm = FISTA(use_gap=True, info=fista_info, eps=self.eps,
max_iter=self.max_iter, min_iter=self.min_iter)
# Not ok until the end.
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
# Time the init computation (essentialy Lipchitz constant in mu_opt).
if self.info_requested(Info.time):
init_time = utils.time_cpu()
# Compute current gap, precision eps (gap decreased by tau) and mu.
function.set_mu(consts.TOLERANCE)
gap = function.gap(beta, eps=self.eps, max_iter=self.max_iter)
eps = self.tau * abs(gap)
# Warning below if gap < -consts.TOLERANCE: See Special case 1
gM = function.eps_max(1.0)
loop = True
# Special case 1: gap is very small: stopping criterion satisfied
if gap < self.eps: # "- mu * gM" has been removed since mu == 0
warnings.warn(
"Stopping criterion satisfied before the first iteration."
" Either beta_start a the solution (given eps)."
" If beta_start is null the problem might be over-penalized. "
" Then try smaller penalization.")
loop = False
# Special case 2: gap infinite or NaN => eps is not finite or NaN
# => mu is NaN etc. Force eps to a large value, to force some FISTA
# iteration to getbetter starting point
if not np.isfinite(eps):
eps = self.eps_max
if loop: # mu is useless if loop is False
mu = function.mu_opt(eps)
function.set_mu(mu)
#gM = function.eps_max(1.0)
# Initialise info variables. Info variables have the suffix "_".
if self.info_requested(Info.time):
t_ = []
init_time = utils.time_cpu() - init_time
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f_ = []
if self.info_requested(Info.gap):
gap_ = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
if self.info_requested(Info.mu):
mu_ = []
i = 0 # Iteration counter.
while loop:
converged = False
# Current precision.
eps_mu = max(eps, self.eps) - mu * gM
# Set current parameters to algorithm.
algorithm.set_params(eps=eps_mu,
max_iter=self.max_iter - self.num_iter)
# Run FISTA.
beta = algorithm.run(function, beta)
# Update global iteration counter.
self.num_iter += algorithm.num_iter
# Get info from algorithm.
if Info.time in algorithm.info and \
self.info_requested(Info.time):
t_ += algorithm.info_get(Info.time)
if i == 0: # Add init time to first iteration.
t_[0] += init_time
if Info.func_val in algorithm.info and \
self.info_requested(Info.func_val):
f_ += algorithm.info_get(Info.func_val)
elif Info.fvalue in algorithm.info and \
self.info_requested(Info.fvalue):
f_ += algorithm.info_get(Info.fvalue)
if self.info_requested(Info.mu):
mu_ += [mu] * algorithm.num_iter
if self.info_requested(Info.gap):
gap_ += algorithm.info_get(Info.gap)
# Obtain the gap from the last FISTA run. May be small and negative
# close to machine epsilon.
gap_mu = abs(algorithm.info_get(Info.gap)[-1])
# TODO: Warn if gap_mu < -consts.TOLERANCE.
if not self.simulation:
if gap_mu + mu * gM < self.eps:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
converged = True
if self.callback is not None:
self.callback(locals())
if self.info_requested(Info.verbose):
print("CONESTA ite:%i, gap_mu: %g, eps: %g, mu: %g, "
"eps_mu: %g" % (i, gap_mu, eps, mu, eps_mu))
# Stopping criteria.
if (converged or self.num_iter >= self.max_iter) \
and self.num_iter >= self.min_iter:
break
# Update the precision eps.
# eps = self.tau * (gap_mu + mu * gM)
eps = max(self.eps, self.tau * (gap_mu + mu * gM))
# Compute and update mu.
# mu = max(self.mu_min, min(function.mu_opt(eps), mu))
mu = min(function.mu_opt(eps), mu)
function.set_mu(mu)
i = i + 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, self.num_iter)
if self.info_requested(Info.continuations):
self.info_set(Info.continuations, i + 1)
if self.info_requested(Info.time):
self.info_set(Info.time, t_)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f_)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f_)
if self.info_requested(Info.gap):
self.info_set(Info.gap, gap_)
if self.info_requested(Info.mu):
self.info_set(Info.mu, mu_)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
z = betanew = betaold = beta
if self.info_requested(Info.time):
t_ = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f_ = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
if self.info_requested(Info.gap):
gap_ = []
if self.return_best:
best_f = np.inf
best_beta = None
#print("########", max(self.min_iter, self.max_iter) + 1)
for i in range(1, max(self.min_iter, self.max_iter) + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)
step = function.step(z)
betaold = betanew
betanew = function.prox(z - step * function.grad(z),
step,
eps=1.0 / (float(i) ** (4.0 + consts.FLOAT_EPSILON)),
max_iter=self.max_iter)
if self.info_requested(Info.time):
t_.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
func_val = function.f(betanew)
f_.append(func_val)
if self.return_best and func_val < best_f:
best_f = func_val
best_beta = betanew
if self.callback is not None:
self.callback(locals())
if self.use_gap:
gap = function.gap(betanew,
eps=self.eps,
max_iter=self.max_iter)
# TODO: Warn if G_new < -consts.TOLERANCE.
gap = abs(gap) # May happen close to machine epsilon.
if self.info_requested(Info.gap):
gap_.append(gap)
if not self.simulation:
if self.info_requested(Info.verbose):
print("FISTA ite:%i, gap:%g" % (i, gap))
if gap < self.eps:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else:
if not self.simulation:
eps_cur = maths.norm(betanew - z)
if self.info_requested(Info.verbose):
print("FISTA ite: %i, eps_cur:%g" % (i, eps_cur))
if step > 0.0:
if (1.0 / step) * eps_cur < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else: # TODO: Fix this!
if maths.norm(betanew - z) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t_)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f_)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f_)
if self.info_requested(Info.gap):
self.info_set(Info.gap, gap_)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
if self.return_best and best_beta is not None:
return best_beta
else:
return betanew
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
step = function.step(beta)
betanew = betaold = beta
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
step = function.step(betanew)
betaold = betanew
betanew = function.prox(betaold - step * function.grad(betaold),
step,
eps=1.0 / (float(i) ** (2.0 + consts.FLOAT_EPSILON)),
max_iter=self.max_iter)
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f.append(function.f(betanew))
if self.callback is not None:
self.callback(locals())
if (1.0 / step) * maths.norm(betanew - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
z = betanew = betaold = beta
if self.info_requested(Info.time):
t_ = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f_ = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
if self.info_requested(Info.gap):
gap_ = []
if self.return_best:
best_f = np.inf
best_beta = None
#print("########", max(self.min_iter, self.max_iter) + 1)
for i in range(1, max(self.min_iter, self.max_iter) + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)
step = function.step(z)
betaold = betanew
betanew = function.prox(z - step * function.grad(z),
step,
eps=1.0 / (float(i) ** (4.0 + consts.FLOAT_EPSILON)),
max_iter=self.max_iter)
if self.info_requested(Info.time):
t_.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
func_val = function.f(betanew)
f_.append(func_val)
if self.return_best and func_val < best_f:
best_f = func_val
best_beta = betanew
if self.callback is not None:
self.callback(locals())
if self.use_gap:
gap = function.gap(betanew,
eps=self.eps,
max_iter=self.max_iter)
# TODO: Warn if G_new < -consts.TOLERANCE.
gap = abs(gap) # May happen close to machine epsilon.
if self.info_requested(Info.gap):
gap_.append(gap)
if not self.simulation:
if self.info_requested(Info.verbose):
print("FISTA ite:%i, gap:%g" % (i, gap))
if gap < self.eps:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else:
if not self.simulation:
eps_cur = maths.norm(betanew - z)
if self.info_requested(Info.verbose):
print("FISTA ite: %i, eps_cur:%g" % (i, eps_cur))
if step > 0.0:
if (1.0 / step) * eps_cur < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else: # TODO: Fix this!
if maths.norm(betanew - z) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t_)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f_)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f_)
if self.info_requested(Info.gap):
self.info_set(Info.gap, gap_)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
if self.return_best and best_beta is not None:
return best_beta
else:
return betanew
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function. The function to minimise.
beta : Numpy array. The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
# step = function.step(beta)
z = betanew = betaold = beta
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
for i in xrange(1, max(self.min_iter, self.max_iter) + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)
step = function.step(z)
betaold = betanew
betanew = function.prox(z - step * function.grad(z),
step)
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
f.append(function.f(betanew))
if self.conesta_stop is not None:
mu_min = self.conesta_stop[0]
# print "mu_min:", mu_min
mu_old = function.set_mu(mu_min)
# print "mu_old:", mu_old
stop_step = function.step(betanew)
# print "step :", step
# Take one ISTA step for use in the stopping criterion.
stop_z = function.prox(betanew - stop_step \
* function.grad(betanew),
stop_step)
function.set_mu(mu_old)
# print "err :", maths.norm(betanew - z)
# print "sc err:", (1.0 / step) * maths.norm(betanew - z)
# print "eps :", self.eps
if (1. / stop_step) * maths.norm(betanew - stop_z) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else:
if step > 0.0:
if (1.0 / step) * maths.norm(betanew - z) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else: # TODO: Fix this!
if maths.norm(betanew - z) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray
The initial point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
if self.info_requested(Info.func_val):
func_val = []
if self.info_requested(Info.iterates):
iterates = []
# Keep track of best value (MAP approximation)
lnprob_max = -np.inf
x_max = x
# For the acceptance rate
accepted = 0
# Initial value from start vector
lnprob_x = -function.f(x)
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Sample a proposal point
y = self.Q.random_sample(x)
lnprob_y = -function.f(y)
ln_q = lnprob_y - lnprob_x
# Compute transition probabilities
if not self.Q.is_symmetric():
ln_q_y_x = self.Q.transition_lnprob(y, x) # Move from x to y
ln_q_x_y = self.Q.transition_lnprob(x, y) # Move from y to x
ln_q += ln_q_x_y - ln_q_y_x
log_r = np.log(self.random_state.rand())
if ln_q >= 0.0 or ln_q >= log_r:
x = y
lnprob_x = lnprob_y
accepted += 1
# Store best sample visited
if lnprob_x > lnprob_max:
lnprob_max = lnprob_x
x_max = x
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if it % self.thinning == 0:
if self.info_requested(Info.iterates):
iterates.append(x)
if self.info_requested(Info.func_val):
func_val.append(lnprob_x)
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.acceptance_rate):
self.info_set(Info.acceptance_rate, accepted / float(it))
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return x_max
def run(self, X, y, beta=None):
"""Find the minimiser of the associated function, starting at beta.
Parameters
----------
X : Numpy array, shape n-by-p. The matrix X with independent
variables.
y : Numpy array, shape n-by-1. The response variable y.
beta : Numpy array. Optional starting point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
n, p = X.shape
if beta is None:
beta = self.start_vector.get_vector(p)
else:
beta = beta.copy()
function = functions.CombinedFunction()
function.add_function(functions.losses.LinearRegression(X, y,
mean=False))
function.add_prox(penalties.L1(l=self.l))
xTx = np.sum(X ** 2.0, axis=0)
if self.mean:
xTx *= 1.0 / float(n)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# The update has an error that propagates. This resets the
# approximation. We may not need to do this at every iteration.
y_Xbeta = y - np.dot(X, beta)
betaold = beta.copy()
for j in range(p):
xj = X[:, [j]]
betaj = beta[j, 0]
if xTx[j] < consts.TOLERANCE: # Avoid division-by-zero.
bj = 0.0
else:
bj = np.dot(xj.T, y_Xbeta + xj * betaj)[0, 0]
if self.mean:
bj /= float(n)
if j < self.penalty_start:
bj = bj / xTx[j]
else:
# Soft thresholding.
bj = np.sign(bj) \
* max(0.0, (abs(bj) - self.l) / xTx[j])
y_Xbeta -= xj * (bj - betaj) # Update X.beta.
beta[j] = bj # Save result.
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
f_ = self._f(y_Xbeta, y, beta)
f.append(f_)
# print "err:", maths.norm(beta - betaold)
if maths.norm(beta - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
# print "iterations: ", i
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, function, x, **kwargs):
"""This function returns the sample with highest probability over the
distribution defined by the given negative log-likelihood function.
Note: The procedure returns the sample that had the maximum value of
exp(-function.f(x)),
which corresponds to the sample that had the minimum value of
function.f(x),
i.e., the function represents the negative log-likelihood of the
distribution function.
Parameters
----------
function : parsimony.functions.properties.Function
The negative log-likelihood function to minimise.
x : numpy.ndarray, shape (p, 1) or (p, num_walkers)
The initial point.
"""
if x.ndim > 2:
raise ValueError("The staring points must be a numpy array of "
"shape (p, 1) or (p, num_walkers).")
if x.ndim < 2: # Make x a column vector
x = np.atleast_2d(x)
if x.shape[0] < x.shape[1]:
x = x.T
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
time = []
p = x.shape[0] # Dimension of parameter space
num_walkers = max(p + 1, self.num_walkers)
if self.info_requested(Info.func_val):
func_val = []
for i in range(num_walkers):
func_val.append([])
if self.info_requested(Info.iterates):
iterates = []
for i in range(num_walkers):
iterates.append([])
# For the acceptance rate
accepted = [0] * num_walkers
# Initial value from start vector
lnprob_x = [0] * num_walkers
lnprob_y = [0] * num_walkers
if x.shape[1] < num_walkers:
X = np.zeros((p, num_walkers))
X[:, :x.shape[1]] = x
for i in range(num_walkers - x.shape[1]):
if x.shape[1] > 1:
rnd_col = np.random.randint(x.shape[1])
x_ = x[:, [rnd_col]]
else:
x_ = x
X[:, [x.shape[1] + i]] = self.Q.random_sample(x)
elif x.shape[1] == num_walkers:
X = x
else:
raise ValueError("The number of parameters is greater than the "
"number of walkers. This should not be able to "
"happen here. Please report this error so that "
"we can fix it!")
for i in range(num_walkers):
lnprob_x[i] = -function.f(X[:, [i]])
# Keep track of best value (sort of like MAP approximations)
lnprob_max = [-np.inf] * num_walkers
X_max = X
import warnings
warnings.filterwarnings('error')
for it in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# Select another walker randomly for each walker
walker_idx = np.mod(np.arange(num_walkers) +
np.floor(self.random_state.rand() *
(num_walkers - 1)) + 1,
num_walkers).astype(np.int)
# Sample stretch move size
z = ((self.step_size - 1.0) *
self.random_state.rand(num_walkers) + 1)**2.0 \
/ self.step_size
# Construct proposal points for all walkers
Xj = X[:, walker_idx]
Y = Xj + z * (X - Xj)
ln_r = np.log(self.random_state.rand(num_walkers))
for i in range(num_walkers):
y = Y[:, [i]]
lnprob_y[i] = -function.f(y)
if (lnprob_y[i] == -np.inf) or (lnprob_x[i] == -np.inf):
ln_q_i = -np.inf
else:
ln_q_i = (p - 1) * np.log(z[i]) + lnprob_y[i] - lnprob_x[i]
if ln_r[i] <= ln_q_i:
X[:, [i]] = y
lnprob_x[i] = lnprob_y[i]
accepted[i] += 1
# Store best sample visited
if lnprob_x[i] > lnprob_max[i]:
lnprob_max[i] = lnprob_x[i]
X_max = X[:, [i]]
if self.info_requested(Info.time):
time.append(utils.time_cpu() - tm)
if (it - 1) % self.thinning == 0:
if self.info_requested(Info.iterates):
for i in range(num_walkers):
iterates[i].append(X[:, [i]])
if self.info_requested(Info.func_val):
for i in range(num_walkers):
func_val[i].append(lnprob_x[i])
if self.callback is not None:
self.callback(locals())
self.num_iter = it
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, it)
if self.info_requested(Info.time):
self.info_set(Info.time, time)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, func_val)
if self.info_requested(Info.iterates):
self.info_set(Info.iterates, iterates)
if self.info_requested(Info.acceptance_rate):
acceptance_rate = []
for i in range(num_walkers):
acceptance_rate.append(accepted[i] / float(it))
self.info_set(Info.acceptance_rate, acceptance_rate)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return X_max
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : Function
The function to minimise.
beta : numpy array
The start vector.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
betanew = betaold = beta
if self.use_gradient and hasattr(function, "grad"):
function_grad = function.grad
else:
function_grad = function.subgrad
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
fbest = np.inf
betabest = None
for i in xrange(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
betaold = betanew
subgrad = function_grad(betaold)
step = self.step_size(i, betaold, subgrad)
betanew = betaold - step * subgrad
fval = None
if self.use_best_f:
fval = function.f(betanew)
if fval < fbest:
fbest = fval
betabest = betanew
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.func_val):
if self.use_best_f:
f.append(fbest)
else:
if fval is None:
f.append(function.f(betanew))
else:
f.append(fval)
if maths.norm(betanew - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.func_val):
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
if self.use_best_f:
return betabest
else:
return betanew
def run(self, X, y, beta=None):
"""Find the minimiser of the associated function, starting at beta.
Parameters
----------
X : Numpy array, shape n-by-p. The matrix X with independent
variables.
y : Numpy array, shape n-by-1. The response variable y.
beta : Numpy array. Optional starting point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
n, p = X.shape
if beta is None:
beta = self.start_vector.get_weights(p)
else:
beta = beta.copy()
xTx = np.sum(X ** 2.0, axis=0)
if self.mean:
xTx *= 1.0 / float(n)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
# The update has an error that propagates. This resets the
# approximation. We may not need to do this at every iteration.
Xbeta_y = np.dot(X, beta) - y
betaold = beta.copy()
for j in range(p):
xj = X[:, [j]]
betaj = beta[j]
# Solve for beta[j].
if xTx[j] < consts.TOLERANCE: # Avoid division-by-zero.
bj = 0.0
else:
# Intercept.
S0 = np.dot(xj.T, Xbeta_y - xj * betaj)[0, 0]
if self.mean:
S0 /= float(n)
if j < self.penalty_start:
bj = -S0 / xTx[j]
else:
if S0 > self.l:
bj = (self.l - S0) / xTx[j]
elif S0 < -self.l:
bj = (-self.l - S0) / xTx[j]
else:
bj = 0.0
Xbeta_y += xj * (bj - betaj) # Update X.beta.
beta[j] = bj # Save result.
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
f_ = self._f(Xbeta_y, y, beta)
f.append(f_)
# print "f:", f[-1]
# print "err:", maths.norm(beta - betaold)
if maths.norm(beta - betaold) < self.eps \
and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
# print "iterations: ", i
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, function, beta):
"""Find the minimiser of the given function, starting at beta.
Parameters
----------
function : parsimony.functions.properties.Function
The function to minimise.
beta : numpy.ndarray or list of numpy.ndarray
The start point.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
is_list = False
if isinstance(beta, list):
is_list = True
betanew = betaold = beta
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
step = function.step(betanew, iteration=i)
betaold = betanew
grad = function.grad(betaold)
if not is_list:
betanew = betaold - step * grad
else:
betanew = [betaold[i] - step * grad[i]
for i in range(len(betaold))]
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
f.append(function.f(betanew))
if not is_list:
err = maths.norm(betanew - betaold)
else:
err = np.sqrt(np.sum([np.sum((betanew[i] - betaold[i])**2.0)
for i in range(len(betanew))]))
if err < self.eps and i >= self.min_iter:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue) \
or self.info_requested(Info.func_val):
self.info_set(Info.fvalue, f)
self.info_set(Info.func_val, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return betanew
def run(self, function, beta=None):
"""The excessive gap method for strongly convex functions.
Parameters
----------
function : The function to minimise. It contains two parts, function.g
is the strongly convex part and function.h is the smoothed part
of the function.
beta : Numpy array. A start vector. This is normally not given, but
left None, since the start vector is computed by the algorithm.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
A = function.A()
u = [0] * len(A)
for i in xrange(len(A)):
u[i] = np.zeros((A[i].shape[0], 1))
# L = lambda_max(A'A) / (lambda_min(X'X) + k)
L = function.L()
if L < consts.TOLERANCE:
L = consts.TOLERANCE
mu = [2.0 * L]
function.set_mu(mu)
if beta is not None:
beta0 = beta
else:
beta0 = function.betahat(u) # u is zero here
beta = beta0
alpha = function.V(u, beta, L) # u is zero here
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.bound):
bound = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
k = 0
while True:
if self.info_requested(Info.time):
tm = utils.time_cpu()
tau = 2.0 / (float(k) + 3.0)
function.set_mu(mu[k])
alpha_hat = function.alpha(beta)
for i in xrange(len(alpha_hat)):
u[i] = (1.0 - tau) * alpha[i] + tau * alpha_hat[i]
mu.append((1.0 - tau) * mu[k])
betahat = function.betahat(u)
beta = (1.0 - tau) * beta + tau * betahat
alpha = function.V(u, betahat, L)
upper_limit = mu[k + 1] * function.M()
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
mu_old = function.get_mu()
function.set_mu(0.0)
f.append(function.f(beta))
function.set_mu(mu_old)
if self.info_requested(Info.bound):
# bound.append(2.0 * function.M() * mu[0] \
# / ((float(k) + 1.0) * (float(k) + 2.0)))
bound.append(upper_limit)
if upper_limit < self.eps and k >= self.min_iter - 1:
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
if k >= self.max_iter - 1 and k >= self.min_iter - 1:
break
k = k + 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, k + 1)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.mu):
self.info_set(Info.mu, mu)
if self.info_requested(Info.bound):
self.info_set(Info.bound, bound)
if self.info_requested(Info.beta):
self.info_set(Info.beta, beta0)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return beta
def run(self, functions, xy):
"""Finds the minimum of two functions with associated proximal
operators.
Parameters
----------
functions : List or tuple with two Functions or a SplittableFunction.
The two functions.
xy : List or tuple with two elements, numpy arrays. The starting points
for the minimisation.
"""
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
funcs = [functions.g, functions.h]
x_new = xy[0]
y_new = xy[1]
z_new = x_new.copy()
u_new = y_new.copy()
for i in range(1, self.max_iter + 1):
if self.info_requested(Info.time):
tm = utils.time_cpu()
x_old = x_new
z_old = z_new
u_old = u_new
if isinstance(funcs[0], properties.ProximalOperator):
x_new = funcs[0].prox(z_old - u_old)
else:
x_new = funcs[0].proj(z_old - u_old)
y_new = x_new # TODO: Allow a linear operator here.
if isinstance(funcs[1], properties.ProximalOperator):
z_new = funcs[1].prox(y_new + u_old)
else:
z_new = funcs[1].proj(y_new + u_old)
# The order here is important! Do not change!
u_new = (y_new - z_new) + u_old
if self.info_requested(Info.time):
t.append(utils.time_cpu() - tm)
if self.info_requested(Info.fvalue):
fval = funcs[0].f(z_new) + funcs[1].f(z_new)
f.append(fval)
if not self.simulation:
if i == 1:
if maths.norm(x_new - x_old) < self.eps \
and i >= self.min_iter:
# print "Stopping criterion kicked in!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
else:
if maths.norm(x_new - x_old) / maths.norm(x_old) < self.eps \
and i >= self.min_iter:
# print "Stopping criterion kicked in!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
# Update the penalty parameter, rho, dynamically.
if self.mu > 1.0:
r = x_new - z_new
s = (z_new - z_old) * -self.rho
norm_r = maths.norm(r)
norm_s = maths.norm(s)
# print "norm(r): ", norm_r, ", norm(s): ", norm_s, ", rho:", \
# self.rho
if norm_r > self.mu * norm_s:
self.rho *= self.tau
u_new *= 1.0 / self.tau # Rescale dual variable.
elif norm_s > self.mu * norm_r:
self.rho /= self.tau
u_new *= self.tau # Rescale dual variable.
# Update the penalty parameter in the functions.
functions.set_rho(self.rho)
self.num_iter = i
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, i)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return z_new