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 _object_func(params, data, model_func, pts, lower_bound=None, upper_bound=None, verbose=0, multinom=True, 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 = _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] + 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 sfs = model_func(*all_args, **func_kwargs) if multinom: result = ll_multinom(sfs, data) else: result = 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 _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