def estimate_confidence_bound_with_cvx(f_mat, num_reads_in_bams, fixed_i,
mle_estimate, bound_type, alpha):
if bound_type == 'lb': bound_type = 'LOWER'
if bound_type == 'ub': bound_type = 'UPPER'
assert bound_type in ('LOWER',
'UPPER'), ("Improper bound type '%s'" % bound_type)
expected_array, observed_array = f_mat.expected_and_observed(
bam_cnts=num_reads_in_bams)
from cvxpy import matrix, variable, geq, log, eq, program, maximize, minimize, sum
mle_log_lhd = calc_lhd(mle_estimate, observed_array, expected_array)
lower_lhd_bound = mle_log_lhd - chi2.ppf(1 - alpha, 1) / 2.
free_indices = set(range(expected_array.shape[1])) - set((fixed_i, ))
Xs = matrix(observed_array)
ps = matrix(expected_array)
thetas = variable(ps.shape[1])
constraints = [
geq(Xs * log(ps * thetas), lower_lhd_bound),
eq(sum(thetas), 1),
geq(thetas, 0)
]
if bound_type == 'UPPER':
p = program(maximize(thetas[fixed_i, 0]), constraints)
else:
p = program(minimize(thetas[fixed_i, 0]), constraints)
p.options['maxiters'] = 1500
value = p.solve(quiet=not DEBUG_OPTIMIZATION)
thetas_values = numpy.array(thetas.value.T.tolist()[0])
log_lhd = calc_lhd(thetas_values, observed_array, expected_array)
return chi2.sf(2 * (mle_log_lhd - log_lhd), 1), value
def mincost_maxflow( flownx, cost='cost', DELTA=None ) :
if DELTA is None : DELTA = DEFAULT_DELTA
total_flow = compute_totalflow( flownx )
total_cost = compute_totalcost( flownx, cost=cost )
constraints = collect_constraints( flownx )
prog1 = cvxpy.program( cvxpy.maximize( total_flow ), constraints )
max_flow = prog1.solve()
constraints2 = [ c for c in constraints ]
constraints2.append( cvxpy.geq( total_flow, max_flow - DELTA ) )
prog2 = cvxpy.program( cvxpy.minimize( total_cost ), constraints2 )
res = prog2.solve()
return res
def estimate_transcript_frequencies_with_cvxopt(observed_array,
expected_array,
sparse_penalty,
sparse_index,
verbose=False):
from cvxpy import matrix, variable, geq, log, eq, program, maximize, \
minimize, sum, quad_over_lin
Xs = matrix(observed_array)
ps = matrix(expected_array)
thetas = variable(ps.shape[1])
constraints = [eq(sum(thetas), 1), geq(thetas, 0)]
if sparse_penalty == None:
p = program(maximize(Xs * log(ps * thetas)), constraints)
else:
p = program(
maximize(Xs * log(ps * thetas) - sparse_penalty *
quad_over_lin(1., thetas[sparse_index, 0])), constraints)
p.options['maxiters'] = 1500
value = p.solve(quiet=not verbose)
thetas_values = numpy.array(thetas.value.T.tolist()[0])
return thetas_values
def _FindMtdf(self, c_range=(1e-6, 1e-2), bounds=None,
normalization=DeltaGNormalization.DEFAULT):
"""Find the MTDF (Maximal Thermodynamic Driving Force).
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A 3 tuple (optimal dGfs, optimal concentrations, optimal mtdf).
"""
ln_conc, motive_force_lb, constraints = self._MakeMtdfProblem(
c_range, bounds)
program = cvxpy.program(cvxpy.maximize(motive_force_lb), constraints)
program.solve(quiet=True)
return ln_conc.value, program.objective.value
def _FindMtdf(self,
c_range=(1e-6, 1e-2),
bounds=None,
normalization=DeltaGNormalization.DEFAULT):
"""Find the MTDF (Maximal Thermodynamic Driving Force).
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A 3 tuple (optimal dGfs, optimal concentrations, optimal mtdf).
"""
ln_conc, motive_force_lb, constraints = self._MakeMtdfProblem(
c_range, bounds)
program = cvxpy.program(cvxpy.maximize(motive_force_lb), constraints)
program.solve(quiet=True)
return ln_conc.value, program.objective.value
def f_Gamma(c):
# For fixed c solve the semidefinite program for Algorithm 4
# Variable Gamma_plus (for \Gamma_{i+1})
d_plus = variable(2*n, 1, name='d_plus')
# Constraints
constr = []
for j in range(2*n):
ctt = less_equals(d_plus[j,0], 0)
constr.append( less_equals(-d_plus[j,0], 0))
constr.append( less_equals( d_plus[j,0], d[j,0]))
constr.append( less_equals( J[j,j]*d_plus[j,0] + sum([abs(J[i,j])*d_plus[i,0] for i in range(2*n)]) - abs(J[j,j])*d_plus[j,0], c* d_plus[j,0]))
# Objective function
obj = geo_mean(d_plus)
# Find solution
p = program(maximize(obj), constr)
return d_plus.value
def GetTotalReactionEnergy(self, min_driving_force=0, maximize=True):
"""
Maximizes the total pathway dG' (i.e. minimize energetic cost).
Arguments:
min_driving_force - the lower limit on each reaction's driving force
(it is common to provide the optimize driving force
in order to find the concentrations that minimize the
cost, without affecting the MTDF).
maximize - if True then finds the maximal total dG.
if False then finds the minimal total dG.
"""
ln_conc, constraints, total_g = self._GetTotalReactionEnergy(
self.c_range, self.bounds, min_driving_force)
if maximize:
objective = cvxpy.maximize(total_g)
else:
objective = cvxpy.minimize(total_g)
program = cvxpy.program(objective, constraints)
program.solve(quiet=True)
return ln_conc.value, program.objective.value
def GetTotalReactionEnergy(self, min_driving_force=0, maximize=True):
"""
Maximizes the total pathway dG' (i.e. minimize energetic cost).
Arguments:
min_driving_force - the lower limit on each reaction's driving force
(it is common to provide the optimize driving force
in order to find the concentrations that minimize the
cost, without affecting the MTDF).
maximize - if True then finds the maximal total dG.
if False then finds the minimal total dG.
"""
ln_conc, constraints, total_g = self._GetTotalReactionEnergy(
self.c_range, self.bounds, min_driving_force)
if maximize:
objective = cvxpy.maximize(total_g)
else:
objective = cvxpy.minimize(total_g)
program = cvxpy.program(objective, constraints)
program.solve(quiet=True)
return ln_conc.value, program.objective.value
def FindMTDF(self, concentration_bounds=None, normalization=None):
"""Finds the MTDF.
Args:
bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
normalization = normalization or self.DeltaGNormalization.DEFAULT
# Constrain concentrations
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [
cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))
]
# Make the objective
motive_force_lb = cvxpy.variable(name='B')
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0))
else:
motive_force = self.DeltaGNormalization.NormalizeDGByFlux(
curr_dgr, flux, normalization)
constr.append(cvxpy.geq(motive_force, motive_force_lb))
objective = cvxpy.maximize(motive_force_lb)
problem = cvxpy.program(objective, constr, name='MTDF_OPT')
problem.solve(quiet=True)
"""
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
mtdf = float(motive_force_lb.value)
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = MTDFOptimizedPathway(
self._model,
self._thermo,
my_bounds,
optimal_value=mtdf,
optimal_ln_metabolite_concentrations=opt_ln_conc)
result.SetNormalization(normalization)
return result
def maxflow( flownx ) :
total_flow = compute_totalflow( flownx )
constraints = collect_constraints( flownx )
prog = cvxpy.program( cvxpy.maximize( total_flow ), constraints )
max_flow = prog.solve()
return max_flow
def FindMTDF(self, concentration_bounds=None, normalization=None):
"""Finds the MTDF.
Args:
bounds: the Bounds objects setting concentration bounds.
"""
my_bounds = concentration_bounds or self.DefaultConcentrationBounds()
normalization = normalization or self.DeltaGNormalization.DEFAULT
# Constrain concentrations
ln_conc = cvxpy.variable(m=1, n=self.Ncompounds, name='lnC')
ln_conc_lb, ln_conc_ub = my_bounds.GetLnBounds(self.compounds)
constr = [cvxpy.geq(ln_conc, cvxpy.matrix(ln_conc_lb)),
cvxpy.leq(ln_conc, cvxpy.matrix(ln_conc_ub))]
# Make the objective
motive_force_lb = cvxpy.variable(name='B')
my_dG0_r_primes = np.matrix(self.dG0_r_prime)
# Make flux-based constraints on reaction free energies.
# All reactions must have negative dGr in the direction of the flux.
# Reactions with a flux of 0 must be in equilibrium.
S = np.matrix(self.S)
for i, flux in enumerate(self.fluxes):
curr_dg0 = my_dG0_r_primes[0, i]
# if the dG0 is unknown, this reaction imposes no new constraints
if np.isnan(curr_dg0):
continue
rcol = cvxpy.matrix(S[:, i])
curr_dgr = curr_dg0 + RT * ln_conc * rcol
if flux == 0:
constr.append(cvxpy.eq(curr_dgr, 0))
else:
motive_force = self.DeltaGNormalization.NormalizeDGByFlux(
curr_dgr, flux, normalization)
constr.append(cvxpy.geq(motive_force, motive_force_lb))
objective = cvxpy.maximize(motive_force_lb)
problem = cvxpy.program(objective, constr, name='MTDF_OPT')
problem.solve(quiet=True)
"""
if status != 'optimal':
status = optimized_pathway.OptimizationStatus.Infeasible(
'Pathway infeasible given bounds.')
return MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimization_status=status)
"""
mtdf = float(motive_force_lb.value)
opt_ln_conc = np.matrix(np.array(ln_conc.value))
result = MTDFOptimizedPathway(
self._model, self._thermo,
my_bounds, optimal_value=mtdf,
optimal_ln_metabolite_concentrations=opt_ln_conc)
result.SetNormalization(normalization)
return result
