| 1 | """ |
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| 2 | BumpsFitting module runs the bumps optimizer. |
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| 3 | """ |
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| 4 | import os |
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| 5 | from datetime import timedelta, datetime |
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| 6 | |
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| 7 | import numpy |
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| 8 | |
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| 9 | from bumps import fitters |
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| 10 | from bumps.mapper import SerialMapper, MPMapper |
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| 11 | from bumps import parameter |
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| 12 | |
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| 13 | # TODO: remove globals from interface to bumps options! |
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| 14 | # Default bumps to use the levenberg-marquardt optimizer |
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| 15 | from bumps.fitproblem import FitProblem |
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| 16 | fitters.FIT_DEFAULT = 'lm' |
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| 17 | |
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| 18 | from sas.fit.AbstractFitEngine import FitEngine |
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| 19 | from sas.fit.AbstractFitEngine import FResult |
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| 20 | from sas.fit.expression import compile_constraints |
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| 21 | |
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| 22 | class Progress(object): |
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| 23 | def __init__(self, history, max_step, pars, dof): |
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| 24 | remaining_time = int(history.time[0]*(float(max_step)/history.step[0]-1)) |
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| 25 | # Depending on the time remaining, either display the expected |
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| 26 | # time of completion, or the amount of time remaining. Use precision |
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| 27 | # appropriate for the duration. |
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| 28 | if remaining_time >= 1800: |
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| 29 | completion_time = datetime.now() + timedelta(seconds=remaining_time) |
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| 30 | if remaining_time >= 36000: |
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| 31 | time = completion_time.strftime('%Y-%m-%d %H:%M') |
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| 32 | else: |
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| 33 | time = completion_time.strftime('%H:%M') |
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| 34 | else: |
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| 35 | if remaining_time >= 3600: |
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| 36 | time = '%dh %dm'%(remaining_time//3600, (remaining_time%3600)//60) |
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| 37 | elif remaining_time >= 60: |
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| 38 | time = '%dm %ds'%(remaining_time//60, remaining_time%60) |
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| 39 | else: |
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| 40 | time = '%ds'%remaining_time |
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| 41 | chisq = "%.3g"%(2*history.value[0]/dof) |
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| 42 | step = "%d of %d"%(history.step[0], max_step) |
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| 43 | header = "=== Steps: %s chisq: %s ETA: %s\n"%(step, chisq, time) |
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| 44 | parameters = ["%15s: %-10.3g%s"%(k,v,("\n" if i%3==2 else " | ")) |
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| 45 | for i,(k,v) in enumerate(zip(pars,history.point[0]))] |
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| 46 | self.msg = "".join([header]+parameters) |
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| 47 | |
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| 48 | def __str__(self): |
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| 49 | return self.msg |
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| 50 | |
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| 51 | |
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| 52 | class BumpsMonitor(object): |
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| 53 | def __init__(self, handler, max_step, pars, dof): |
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| 54 | self.handler = handler |
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| 55 | self.max_step = max_step |
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| 56 | self.pars = pars |
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| 57 | self.dof = dof |
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| 58 | |
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| 59 | def config_history(self, history): |
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| 60 | history.requires(time=1, value=2, point=1, step=1) |
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| 61 | |
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| 62 | def __call__(self, history): |
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| 63 | if self.handler is None: return |
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| 64 | self.handler.set_result(Progress(history, self.max_step, self.pars, self.dof)) |
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| 65 | self.handler.progress(history.step[0], self.max_step) |
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| 66 | if len(history.step)>1 and history.step[1] > history.step[0]: |
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| 67 | self.handler.improvement() |
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| 68 | self.handler.update_fit() |
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| 69 | |
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| 70 | class ConvergenceMonitor(object): |
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| 71 | """ |
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| 72 | ConvergenceMonitor contains population summary statistics to show progress |
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| 73 | of the fit. This is a list [ (best, 0%, 25%, 50%, 75%, 100%) ] or |
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| 74 | just a list [ (best, ) ] if population size is 1. |
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| 75 | """ |
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| 76 | def __init__(self): |
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| 77 | self.convergence = [] |
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| 78 | |
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| 79 | def config_history(self, history): |
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| 80 | history.requires(value=1, population_values=1) |
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| 81 | |
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| 82 | def __call__(self, history): |
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| 83 | best = history.value[0] |
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| 84 | try: |
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| 85 | p = history.population_values[0] |
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| 86 | n,p = len(p), numpy.sort(p) |
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| 87 | QI,Qmid, = int(0.2*n),int(0.5*n) |
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| 88 | self.convergence.append((best, p[0],p[QI],p[Qmid],p[-1-QI],p[-1])) |
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| 89 | except: |
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| 90 | self.convergence.append((best, best,best,best,best,best)) |
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| 91 | |
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| 92 | |
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| 93 | # Note: currently using bumps parameters for each parameter object so that |
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| 94 | # a SasFitness can be used directly in bumps with the usual semantics. |
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| 95 | # The disadvantage of this technique is that we need to copy every parameter |
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| 96 | # back into the model each time the function is evaluated. We could instead |
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| 97 | # define reference parameters for each sas parameter, but then we would not |
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| 98 | # be able to express constraints using python expressions in the usual way |
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| 99 | # from bumps, and would instead need to use string expressions. |
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| 100 | class SasFitness(object): |
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| 101 | """ |
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| 102 | Wrap SAS model as a bumps fitness object |
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| 103 | """ |
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| 104 | def __init__(self, model, data, fitted=[], constraints={}, |
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| 105 | initial_values=None, **kw): |
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| 106 | self.name = model.name |
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| 107 | self.model = model.model |
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| 108 | self.data = data |
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| 109 | if self.data.smearer is not None: |
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| 110 | self.data.smearer.model = self.model |
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| 111 | self._define_pars() |
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| 112 | self._init_pars(kw) |
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| 113 | if initial_values is not None: |
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| 114 | self._reset_pars(fitted, initial_values) |
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| 115 | self.constraints = dict(constraints) |
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| 116 | self.set_fitted(fitted) |
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| 117 | self.update() |
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| 118 | |
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| 119 | def _reset_pars(self, names, values): |
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| 120 | for k,v in zip(names, values): |
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| 121 | self._pars[k].value = v |
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| 122 | |
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| 123 | def _define_pars(self): |
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| 124 | self._pars = {} |
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| 125 | for k in self.model.getParamList(): |
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| 126 | name = ".".join((self.name,k)) |
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| 127 | value = self.model.getParam(k) |
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| 128 | bounds = self.model.details.get(k,["",None,None])[1:3] |
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| 129 | self._pars[k] = parameter.Parameter(value=value, bounds=bounds, |
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| 130 | fixed=True, name=name) |
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| 131 | #print parameter.summarize(self._pars.values()) |
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| 132 | |
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| 133 | def _init_pars(self, kw): |
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| 134 | for k,v in kw.items(): |
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| 135 | # dispersion parameters initialized with _field instead of .field |
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| 136 | if k.endswith('_width'): k = k[:-6]+'.width' |
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| 137 | elif k.endswith('_npts'): k = k[:-5]+'.npts' |
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| 138 | elif k.endswith('_nsigmas'): k = k[:-7]+'.nsigmas' |
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| 139 | elif k.endswith('_type'): k = k[:-5]+'.type' |
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| 140 | if k not in self._pars: |
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| 141 | formatted_pars = ", ".join(sorted(self._pars.keys())) |
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| 142 | raise KeyError("invalid parameter %r for %s--use one of: %s" |
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| 143 | %(k, self.model, formatted_pars)) |
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| 144 | if '.' in k and not k.endswith('.width'): |
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| 145 | self.model.setParam(k, v) |
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| 146 | elif isinstance(v, parameter.BaseParameter): |
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| 147 | self._pars[k] = v |
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| 148 | elif isinstance(v, (tuple,list)): |
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| 149 | low, high = v |
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| 150 | self._pars[k].value = (low+high)/2 |
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| 151 | self._pars[k].range(low,high) |
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| 152 | else: |
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| 153 | self._pars[k].value = v |
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| 154 | |
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| 155 | def set_fitted(self, param_list): |
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| 156 | """ |
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| 157 | Flag a set of parameters as fitted parameters. |
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| 158 | """ |
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| 159 | for k,p in self._pars.items(): |
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| 160 | p.fixed = (k not in param_list or k in self.constraints) |
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| 161 | self.fitted_par_names = [k for k in param_list if k not in self.constraints] |
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| 162 | self.computed_par_names = [k for k in param_list if k in self.constraints] |
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| 163 | self.fitted_pars = [self._pars[k] for k in self.fitted_par_names] |
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| 164 | self.computed_pars = [self._pars[k] for k in self.computed_par_names] |
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| 165 | |
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| 166 | # ===== Fitness interface ==== |
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| 167 | def parameters(self): |
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| 168 | return self._pars |
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| 169 | |
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| 170 | def update(self): |
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| 171 | for k,v in self._pars.items(): |
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| 172 | #print "updating",k,v,v.value |
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| 173 | self.model.setParam(k,v.value) |
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| 174 | self._dirty = True |
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| 175 | |
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| 176 | def _recalculate(self): |
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| 177 | if self._dirty: |
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| 178 | self._residuals, self._theory \ |
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| 179 | = self.data.residuals(self.model.evalDistribution) |
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| 180 | self._dirty = False |
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| 181 | |
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| 182 | def numpoints(self): |
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| 183 | return numpy.sum(self.data.idx) # number of fitted points |
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| 184 | |
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| 185 | def nllf(self): |
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| 186 | return 0.5*numpy.sum(self.residuals()**2) |
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| 187 | |
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| 188 | def theory(self): |
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| 189 | self._recalculate() |
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| 190 | return self._theory |
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| 191 | |
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| 192 | def residuals(self): |
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| 193 | self._recalculate() |
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| 194 | return self._residuals |
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| 195 | |
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| 196 | # Not implementing the data methods for now: |
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| 197 | # |
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| 198 | # resynth_data/restore_data/save/plot |
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| 199 | |
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| 200 | class ParameterExpressions(object): |
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| 201 | def __init__(self, models): |
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| 202 | self.models = models |
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| 203 | self._setup() |
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| 204 | |
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| 205 | def _setup(self): |
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| 206 | exprs = {} |
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| 207 | for M in self.models: |
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| 208 | exprs.update((".".join((M.name, k)), v) for k, v in M.constraints.items()) |
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| 209 | if exprs: |
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| 210 | symtab = dict((".".join((M.name, k)), p) |
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| 211 | for M in self.models |
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| 212 | for k,p in M.parameters().items()) |
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| 213 | self.update = compile_constraints(symtab, exprs) |
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| 214 | else: |
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| 215 | self.update = lambda: 0 |
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| 216 | |
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| 217 | def __call__(self): |
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| 218 | self.update() |
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| 219 | |
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| 220 | def __getstate__(self): |
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| 221 | return self.models |
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| 222 | |
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| 223 | def __setstate__(self, state): |
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| 224 | self.models = state |
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| 225 | self._setup() |
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| 226 | |
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| 227 | class BumpsFit(FitEngine): |
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| 228 | """ |
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| 229 | Fit a model using bumps. |
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| 230 | """ |
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| 231 | def __init__(self): |
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| 232 | """ |
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| 233 | Creates a dictionary (self.fit_arrange_dict={})of FitArrange elements |
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| 234 | with Uid as keys |
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| 235 | """ |
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| 236 | FitEngine.__init__(self) |
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| 237 | self.curr_thread = None |
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| 238 | |
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| 239 | def fit(self, msg_q=None, |
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| 240 | q=None, handler=None, curr_thread=None, |
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| 241 | ftol=1.49012e-8, reset_flag=False): |
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| 242 | # Build collection of bumps fitness calculators |
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| 243 | models = [SasFitness(model=M.get_model(), |
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| 244 | data=M.get_data(), |
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| 245 | constraints=M.constraints, |
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| 246 | fitted=M.pars, |
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| 247 | initial_values=M.vals if reset_flag else None) |
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| 248 | for M in self.fit_arrange_dict.values() |
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| 249 | if M.get_to_fit()] |
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| 250 | if len(models) == 0: |
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| 251 | raise RuntimeError("Nothing to fit") |
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| 252 | problem = FitProblem(models) |
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| 253 | |
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| 254 | # TODO: need better handling of parameter expressions and bounds constraints |
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| 255 | # so that they are applied during polydispersity calculations. This |
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| 256 | # will remove the immediate need for the setp_hook in bumps, though |
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| 257 | # bumps may still need something similar, such as a sane class structure |
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| 258 | # which allows a subclass to override setp. |
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| 259 | problem.setp_hook = ParameterExpressions(models) |
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| 260 | |
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| 261 | # Run the fit |
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| 262 | result = run_bumps(problem, handler, curr_thread) |
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| 263 | if handler is not None: |
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| 264 | handler.update_fit(last=True) |
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| 265 | |
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| 266 | # TODO: shouldn't reference internal parameters of fit problem |
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| 267 | varying = problem._parameters |
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| 268 | # collect the results |
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| 269 | all_results = [] |
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| 270 | for M in problem.models: |
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| 271 | fitness = M.fitness |
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| 272 | fitted_index = [varying.index(p) for p in fitness.fitted_pars] |
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| 273 | param_list = fitness.fitted_par_names + fitness.computed_par_names |
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| 274 | R = FResult(model=fitness.model, data=fitness.data, |
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| 275 | param_list=param_list) |
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| 276 | R.theory = fitness.theory() |
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| 277 | R.residuals = fitness.residuals() |
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| 278 | R.index = fitness.data.idx |
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| 279 | R.fitter_id = self.fitter_id |
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| 280 | # TODO: should scale stderr by sqrt(chisq/DOF) if dy is unknown |
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| 281 | R.success = result['success'] |
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| 282 | if R.success: |
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| 283 | R.stderr = numpy.hstack((result['stderr'][fitted_index], |
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| 284 | numpy.NaN*numpy.ones(len(fitness.computed_pars)))) |
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| 285 | R.pvec = numpy.hstack((result['value'][fitted_index], |
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| 286 | [p.value for p in fitness.computed_pars])) |
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| 287 | R.fitness = numpy.sum(R.residuals**2)/(fitness.numpoints() - len(fitted_index)) |
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| 288 | else: |
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| 289 | R.stderr = numpy.NaN*numpy.ones(len(param_list)) |
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| 290 | R.pvec = numpy.asarray( [p.value for p in fitness.fitted_pars+fitness.computed_pars]) |
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| 291 | R.fitness = numpy.NaN |
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| 292 | R.convergence = result['convergence'] |
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| 293 | if result['uncertainty'] is not None: |
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| 294 | R.uncertainty_state = result['uncertainty'] |
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| 295 | all_results.append(R) |
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| 296 | |
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| 297 | if q is not None: |
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| 298 | q.put(all_results) |
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| 299 | return q |
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| 300 | else: |
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| 301 | return all_results |
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| 302 | |
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| 303 | def run_bumps(problem, handler, curr_thread): |
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| 304 | def abort_test(): |
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| 305 | if curr_thread is None: return False |
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| 306 | try: curr_thread.isquit() |
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| 307 | except KeyboardInterrupt: |
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| 308 | if handler is not None: |
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| 309 | handler.stop("Fitting: Terminated!!!") |
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| 310 | return True |
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| 311 | return False |
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| 312 | |
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| 313 | fitopts = fitters.FIT_OPTIONS[fitters.FIT_DEFAULT] |
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| 314 | fitclass = fitopts.fitclass |
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| 315 | options = fitopts.options.copy() |
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| 316 | max_step = fitopts.options.get('steps', 0) + fitopts.options.get('burn', 0) |
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| 317 | pars = [p.name for p in problem._parameters] |
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| 318 | #x0 = numpy.asarray([p.value for p in problem._parameters]) |
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| 319 | options['monitors'] = [ |
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| 320 | BumpsMonitor(handler, max_step, pars, problem.dof), |
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| 321 | ConvergenceMonitor(), |
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| 322 | ] |
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| 323 | fitdriver = fitters.FitDriver(fitclass, problem=problem, |
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| 324 | abort_test=abort_test, **options) |
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| 325 | omp_threads = int(os.environ.get('OMP_NUM_THREADS','0')) |
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| 326 | mapper = MPMapper if omp_threads == 1 else SerialMapper |
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| 327 | fitdriver.mapper = mapper.start_mapper(problem, None) |
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| 328 | #import time; T0 = time.time() |
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| 329 | try: |
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| 330 | best, fbest = fitdriver.fit() |
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| 331 | except: |
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| 332 | import traceback; traceback.print_exc() |
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| 333 | raise |
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| 334 | finally: |
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| 335 | mapper.stop_mapper(fitdriver.mapper) |
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| 336 | |
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| 337 | |
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| 338 | convergence_list = options['monitors'][-1].convergence |
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| 339 | convergence = (2*numpy.asarray(convergence_list)/problem.dof |
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| 340 | if convergence_list else numpy.empty((0,1),'d')) |
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| 341 | |
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| 342 | success = best is not None |
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| 343 | return { |
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| 344 | 'value': best if success else None, |
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| 345 | 'stderr': fitdriver.stderr() if success else None, |
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| 346 | 'success': success, |
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| 347 | 'convergence': convergence, |
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| 348 | 'uncertainty': getattr(fitdriver.fitter, 'state', None), |
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| 349 | } |
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| 350 | |
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