def _object_func(params,
data,
model_func,
sel_dist,
theta,
lower_bound=None,
upper_bound=None,
verbose=0,
multinom=False,
flush_delay=0,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_stream=sys.stdout,
store_thetas=False):
"""
Objective function for optimization.
"""
global _counter
_counter += 1
# Deal with fixed parameters
params_up = Inference._project_params_up(params, fixed_params)
# Check our parameter bounds
if lower_bound is not None:
for pval, bound in zip(params_up, lower_bound):
if bound is not None and pval < bound:
return -_out_of_bounds_val / ll_scale
if upper_bound is not None:
for pval, bound in zip(params_up, upper_bound):
if bound is not None and pval > bound:
return -_out_of_bounds_val / ll_scale
ns = data.sample_sizes
all_args = [params_up, ns, sel_dist, theta] + list(func_args)
sfs = model_func(*all_args, **func_kwargs)
if multinom:
result = Inference.ll_multinom(sfs, data)
else:
result = Inference.ll(sfs, data)
if store_thetas:
global _theta_store
_theta_store[tuple(params)] = optimal_sfs_scaling(sfs, data)
# Bad result
if numpy.isnan(result):
result = _out_of_bounds_val
if (verbose > 0) and (_counter % verbose == 0):
param_str = 'array([%s])' % (', '.join(
['%- 12g' % v for v in params_up]))
output_stream.write('%-8i, %-12g, %s%s' %
(_counter, result, param_str, os.linesep))
Misc.delayed_flush(delay=flush_delay)
return -result / ll_scale
def optimize(p0,
data1,
data2,
cache1,
cache2,
model_func,
sel_dist,
scal_fac1,
scal_fac2,
theta1,
theta2,
lower_bound=None,
upper_bound=None,
verbose=0,
flush_delay=0.5,
epsilon=1e-3,
gtol=1e-5,
multinom=False,
maxiter=None,
full_output=False,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_file=None):
if output_file:
output_stream = file(output_file, 'w')
else:
output_stream = sys.stdout
args = (data1, data2, cache1, cache2, model_func, sel_dist, scal_fac1,
scal_fac2, theta1, theta2, lower_bound, upper_bound, verbose,
multinom, flush_delay, func_args, func_kwargs, fixed_params,
ll_scale, output_stream)
p0 = Inference._project_params_down(p0, fixed_params)
outputs = scipy.optimize.fmin_bfgs(_object_func,
p0,
epsilon=epsilon,
args=args,
gtol=gtol,
full_output=True,
disp=False,
maxiter=maxiter)
xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = outputs
xopt = Inference._project_params_up(xopt, fixed_params)
if output_file:
output_stream.close()
if not full_output:
return [-fopt, xopt]
else:
return xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag
def optimize(p0,
data,
model_func,
sel_dist,
theta,
lower_bound=None,
upper_bound=None,
verbose=0,
flush_delay=0.5,
epsilon=1e-3,
gtol=1e-5,
multinom=False,
maxiter=None,
full_output=False,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_file=None):
"""
optimizer for use with distributions where log transformations do not work,
e.g. when gamma is positive and negative
"""
if output_file:
output_stream = file(output_file, 'w')
else:
output_stream = sys.stdout
args = (data, model_func, sel_dist, theta, lower_bound, upper_bound,
verbose, multinom, flush_delay, func_args, func_kwargs,
fixed_params, ll_scale, output_stream)
p0 = _project_params_down(p0, fixed_params)
outputs = scipy.optimize.fmin_bfgs(_object_func,
p0,
epsilon=epsilon,
args=args,
gtol=gtol,
full_output=True,
disp=False,
maxiter=maxiter)
xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = outputs
xopt = Inference._project_params_up(xopt, fixed_params)
if output_file:
output_stream.close()
if not full_output:
return xopt
else:
return xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag
def optimize_cons(p0,
data,
model_func,
sel_dist,
theta,
lower_bound=None,
upper_bound=None,
verbose=0,
flush_delay=0.5,
epsilon=1e-4,
constraint=None,
gtol=1e-6,
multinom=False,
maxiter=None,
full_output=False,
func_args=[],
func_kwargs={},
fixed_params=None,
ll_scale=1,
output_file=None):
"""
Constrained optimization needs a constraint function and bounds.
"""
if output_file:
output_stream = file(output_file, 'w')
else:
output_stream = sys.stdout
if not (lower_bound is None):
lower_bound_a = lower_bound + [0]
if not (upper_bound is None):
upper_bound_a = upper_bound + [numpy.inf]
args = (data, model_func, sel_dist, theta, lower_bound, upper_bound,
verbose, multinom, flush_delay, func_args, func_kwargs,
fixed_params, ll_scale, output_stream)
p0 = Inference._project_params_down(p0, fixed_params)
####make sure to define consfunc and bnds ####
if (not lower_bound is None) and (not upper_bound is None):
bnds = tuple((x, y) for x, y in zip(lower_bound, upper_bound))
outputs = scipy.optimize.fmin_slsqp(_object_func,
p0,
bounds=bnds,
args=args,
f_eqcons=constraint,
epsilon=epsilon,
iter=maxiter,
full_output=True,
disp=False)
xopt, fopt, func_calls, grad_calls, warnflag = outputs
xopt = Inference._project_params_up(xopt, fixed_params)
if output_file:
output_stream.close()
if not full_output:
return [-fopt, xopt]
else:
return xopt, fopt, func_calls, grad_calls, warnflag
def _object_func(params,
data1,
data2,
cache1,
cache2,
model_func,
sel_dist,
scal_fac1,
scal_fac2,
theta1,
theta2,
lower_bound=None,
upper_bound=None,
verbose=0,
multinom=False,
flush_delay=0,
func_args=[],
func_kwargs={},
fixed_params1=None,
fixed_params2=None,
ll_scale=1,
output_stream=sys.stdout,
store_thetas=False):
"""
Objective function for optimization.
"""
global _counter
_counter += 1
# Scaling factors scales sel_dist differently for species 1 and species 2
sel_dist1 = copy_func(
sel_dist, defaults=scal_fac1) # scal_fac1 should be 2*Nea of pop 1
sel_dist2 = copy_func(
sel_dist, defaults=scal_fac2) # scal_fac2 should be 4*Nea of pop 2
# Deal with fixed parameters
params_up1 = Inference._project_params_up(params, fixed_params1)
params_up2 = Inference._project_params_up(params, fixed_params2)
# Check our parameter bounds
if lower_bound is not None:
for pval, bound in zip(params_up1, lower_bound):
if bound is not None and pval < bound:
return -_out_of_bounds_val / ll_scale
if upper_bound is not None:
for pval, bound in zip(params_up1, upper_bound):
if bound is not None and pval > bound:
return -_out_of_bounds_val / ll_scale
ns1 = data1.sample_sizes
ns2 = data2.sample_sizes
all_args1 = [params_up1, ns1, sel_dist1, theta1, cache1] + list(func_args)
all_args2 = [params_up2, ns2, sel_dist2, theta2, cache2] + list(func_args)
# Pass the pts argument via keyword, but don't alter the passed-in
# func_kwargs
#func_kwargs = func_kwargs.copy()
#func_kwargs['pts'] = pts
sfs1 = model_func(*all_args1, **func_kwargs)
sfs2 = model_func(*all_args2, **func_kwargs)
if multinom:
result = Inference.ll_multinom(sfs1, data1) + Inference.ll_multinom(
sfs2, data2)
else:
result = Inference.ll(sfs1, data1) + Inference.ll(sfs2, data2)
# Bad result
if numpy.isnan(result):
result = _out_of_bounds_val
if (verbose > 0) and (_counter % verbose == 0):
param_str = 'array([%s])' % (', '.join(
['%- 12g' % v for v in params_up1]))
output_stream.write('%-8i, %-12g, %s%s' %
(_counter, result, param_str, os.linesep))
Misc.delayed_flush(delay=flush_delay)
return -result / ll_scale
