Source Code for Module SloppyCell.Optimization

  1  import copy 
  2  import sys 
  3   
  4  import logging 
  5  logger = logging.getLogger('Optimization') 
  6   
  7  import scipy 
  8   
  9  import SloppyCell.KeyedList_mod as KeyedList_mod 
 10  import SloppyCell.Utility as Utility 
 11  KeyedList = KeyedList_mod.KeyedList 
 12   
 13 -def fmin_powell_log_params(m, params, *args, **kwargs): 
 14      func = m.cost_log_params 
 15   
 16      pmin = scipy.optimize.fmin_powell(func, scipy.log(params),  
 17                                        *args, **kwargs) 
 18      if isinstance(params, KeyedList): 
 19          pout = params.copy() 
 20          pout.update(scipy.exp(pmin)) 
 21          return pout 
 22      else: 
 23          return scipy.exp(pmin) 
 24   
 25 -def fmin_log_params(m, params, *args, **kwargs): 
 26      def func(log_params): 
 27          try: 
 28              return m.cost_log_params(log_params) 
 29          except Utility.SloppyCellException: 
 30              logger.warn('Exception in cost evaluation. Cost set to inf.') 
 31              return scipy.inf 
 32   
 33      pmin = scipy.optimize.fmin(func, scipy.log(params), 
 34                                 *args, **kwargs) 
 35   
 36      if isinstance(params, KeyedList): 
 37          pout = params.copy() 
 38          pout.update(scipy.exp(pmin)) 
 39          return pout 
 40      else: 
 41          return scipy.exp(pmin) 
 42   
 43 -def fmin(m, params, *args, **kwargs): 
 44      def func(params): 
 45          try: 
 46              return m.cost(params) 
 47          except Utility.SloppyCellException: 
 48              logger.warn('Exception in cost evaluation. Cost set to inf.') 
 49              return scipy.inf 
 50   
 51      pmin = scipy.optimize.fmin(func, params, *args, **kwargs) 
 52   
 53      if isinstance(params, KeyedList): 
 54          pout = params.copy() 
 55          pout.update(pmin) 
 56          return pout 
 57      else: 
 58          return pmin 
 59   
 60   
 61 -def fmin_xform(m, params, xforms, invforms, *args, **kwargs): 
 62      """ 
 63      Nelder-Mead the cost over an arbitrary transform on the parameters. 
 64   
 65      m         Model to minimize the cost for 
 66      params    initial parameter estimate 
 67      xforms    sequence of transforms (of length, len(params)) to apply to the  
 68                parameters before optimizing 
 69      invforms  sequences of inverse transforms to get back to straight parameters 
 70      *args     passed on to scipy.optimize.fmin 
 71      **kwargs  passed on to scipy.optimize.fmin 
 72                For information on these, consult help(scipy.optimize.fmin) 
 73      """ 
 74      def func(xp, invforms): 
 75          p = [inv(xp_val) for (xp_val, inv) in zip(xp, invforms)] 
 76          try: 
 77              return m.cost(p) 
 78          except Utility.SloppyCellException: 
 79              logger.warn('Exception in cost evaluation. Cost set to inf.') 
 80              return scipy.inf 
 81   
 82      params = scipy.array([x(xp_val) for (xp_val, x) in zip(params, xforms)]) 
 83      pmin = scipy.optimize.fmin(func, params, args = (invforms,), 
 84                                 *args, **kwargs) 
 85   
 86      pmin = [inv(xp_val) for (xp_val, inv) in zip(pmin, invforms)] 
 87      if isinstance(params, KeyedList): 
 88          pout = params.copy() 
 89          pout.update(pmin) 
 90          return pout 
 91      else: 
 92          return pmin 
 93   
 94  import SloppyCell.lmopt as lmopt 
 95 -def fmin_lm_log_params(m, params, *args, **kwargs): 
 96      """ 
 97      Minimize the cost of a model using Levenberg-Marquadt in terms of log  
 98      parameters. 
 99   
100      The *args and **kwargs represent additional parmeters that will be passed to 
101      the optimization algorithm. For your convenience, the docstring of that 
102      function is appended below: 
103   
104      """ 
105      jac = lambda lp: scipy.asarray(m.jacobian_log_params_sens(lp)) 
106      sln = lmopt.fmin_lm(f=m.res_log_params, x0=scipy.log(params), fprime=jac, 
107                          *args, **kwargs) 
108      if isinstance(params, KeyedList): 
109          pout = params.copy() 
110          pout.update(scipy.exp(sln)) 
111          return pout 
112      else: 
113          return scipy.exp(sln) 
114  fmin_lm_log_params.__doc__ = fmin_lm_log_params.__doc__ + lmopt.fmin_lm.__doc__ 
115   
116 -def leastsq_log_params(m, params, *args, **kwargs): 
117      # Dfun = m.jacobian_log_params_sens 
118      func = m.res_log_params 
119   
120      pmin, msg = scipy.optimize.leastsq(func, scipy.log(params), *args, **kwargs) 
121   
122      if isinstance(params, KeyedList): 
123          pout = params.copy() 
124          pout.update(scipy.exp(pmin)) 
125          return pout 
126      else: 
127          return scipy.exp(pmin) 
128