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inference
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Nicolo Fusi
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5 changed files with 606 additions and 0 deletions
201
GPy/inference/optimization.py
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201
GPy/inference/optimization.py
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from scipy import optimize
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# import rasmussens_minimize as rasm
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import pdb
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import pylab as pb
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import datetime as dt
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class Optimizer():
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def __init__(self, x_init, f_fp, f, fp , messages = False, max_f_eval = 1e4, ftol = None, gtol = None, xtol = None):
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"""
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Superclass for all the optimizers.
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Arguments:
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x_init: initial set of parameters
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f_fp: function that returns the function AND the gradients at the same time
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f: function to optimize
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fp: gradients
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messages: print messages from the optimizer? (True | False)
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max_f_eval: maximum number of function evaluations
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"""
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self.opt_name = None
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self.f_fp = f_fp
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self.f = f
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self.fp = fp
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self.x_init = x_init
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self.messages = messages
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self.f_opt = None
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self.x_opt = None
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self.funct_eval = None
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self.status = None
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self.max_f_eval = int(max_f_eval)
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self.trace = None
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self.time = "Not available"
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self.xtol = xtol
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self.gtol = gtol
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self.ftol = ftol
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def run(self):
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start = dt.datetime.now()
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self.opt()
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end = dt.datetime.now()
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self.time = str(end-start)
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def opt(self):
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raise NotImplementedError, "this needs to be implemented to utilise the optimizer class"
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def plot(self):
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if self.trace == None:
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print "No trace present so I can't plot it. Please check that the optimizer actually supplies a trace."
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else:
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pb.figure()
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pb.plot(self.trace)
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pb.xlabel('Iteration')
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pb.ylabel('f(x)')
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def diagnostics(self):
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print "Optimizer: \t\t\t\t %s" % self.opt_name
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print "f(x_opt): \t\t\t\t %.3f" % self.f_opt
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print "Number of function evaluations: \t %d" % self.funct_eval
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print "Optimization status: \t\t\t %s" % self.status
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print "Time elapsed: \t\t\t\t %s" % self.time
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class opt_tnc(Optimizer):
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def __init__(self, *args, **kwargs):
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Optimizer.__init__(self, *args, **kwargs)
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self.opt_name = "TNC (Scipy implementation)"
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def opt(self):
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"""
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Run the TNC optimizer
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"""
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tnc_rcstrings = ['Local minimum', 'Converged', 'XConverged', 'Maximum number of f evaluations reached',
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'Line search failed', 'Function is constant']
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assert self.f_fp != None, "TNC requires f_fp"
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opt_dict = {}
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if self.xtol is not None:
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opt_dict['xtol'] = self.xtol
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if self.ftol is not None:
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opt_dict['ftol'] = self.ftol
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if self.gtol is not None:
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opt_dict['pgtol'] = self.gtol
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opt_result = optimize.fmin_tnc(self.f_fp, self.x_init, messages = self.messages,
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maxfun = self.max_f_eval, **opt_dict)
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self.x_opt = opt_result[0]
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self.f_opt = self.f_fp(self.x_opt)[0]
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self.funct_eval = opt_result[1]
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self.status = tnc_rcstrings[opt_result[2]]
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class opt_lbfgsb(Optimizer):
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def __init__(self, *args, **kwargs):
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Optimizer.__init__(self, *args, **kwargs)
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self.opt_name = "L-BFGS-B (Scipy implementation)"
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def opt(self):
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"""
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Run the optimizer
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"""
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rcstrings = ['Converged', 'Maximum number of f evaluations reached', 'Error']
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assert self.f_fp != None, "BFGS requires f_fp"
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if self.messages:
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iprint = 1
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else:
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iprint = -1
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opt_dict = {}
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if self.xtol is not None:
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print "WARNING: l-bfgs-b doesn't have an xtol arg, so I'm going to ignore it"
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if self.ftol is not None:
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print "WARNING: l-bfgs-b doesn't have an ftol arg, so I'm going to ignore it"
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if self.gtol is not None:
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opt_dict['pgtol'] = self.gtol
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opt_result = optimize.fmin_l_bfgs_b(self.f_fp, self.x_init, iprint = iprint,
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maxfun = self.max_f_eval, **opt_dict)
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self.x_opt = opt_result[0]
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self.f_opt = self.f_fp(self.x_opt)[0]
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self.funct_eval = opt_result[2]['funcalls']
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self.status = rcstrings[opt_result[2]['warnflag']]
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class opt_simplex(Optimizer):
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def __init__(self, *args, **kwargs):
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Optimizer.__init__(self, *args, **kwargs)
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self.opt_name = "Nelder-Mead simplex routine (via Scipy)"
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def opt(self):
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"""
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The simplex optimizer does not require gradients, which
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is great during development. Otherwise it's a bit slow.
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"""
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statuses = ['Converged', 'Maximum number of function evaluations made','Maximum number of iterations reached']
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opt_dict = {}
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if self.xtol is not None:
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opt_dict['xtol'] = self.xtol
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if self.ftol is not None:
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opt_dict['ftol'] = self.ftol
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if self.gtol is not None:
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print "WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it"
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opt_result = optimize.fmin(self.f, self.x_init, (), disp = self.messages,
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maxfun = self.max_f_eval, full_output=True, **opt_dict)
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self.x_opt = opt_result[0]
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self.f_opt = opt_result[1]
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self.funct_eval = opt_result[3]
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self.status = statuses[opt_result[4]]
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self.trace = None
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# class opt_rasm(Optimizer):
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# def __init__(self, *args, **kwargs):
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# Optimizer.__init__(self, *args, **kwargs)
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# self.opt_name = "Rasmussen's SCG"
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# def opt(self):
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# """
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# Run Rasmussen's SCG optimizer
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# """
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# assert self.f_fp != None, "Rasmussen's minimizer requires f_fp"
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# statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached',
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# 'NaNs in optimization']
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# opt_dict = {}
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# if self.xtol is not None:
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# print "WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it"
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# if self.ftol is not None:
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# print "WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it"
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# if self.gtol is not None:
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# print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
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# opt_result = rasm.minimize(self.x_init, self.f_fp, (), messages = self.messages,
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# maxnumfuneval = self.max_f_eval)
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# self.x_opt = opt_result[0]
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# self.f_opt = opt_result[1][-1]
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# self.funct_eval = opt_result[2]
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# self.status = statuses[opt_result[3]]
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# self.trace = opt_result[1]
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def get_optimizer(f_min):
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optimizers = {'fmin_tnc': opt_tnc,
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# 'rasmussen': opt_rasm,
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'simplex': opt_simplex,
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'lbfgsb': opt_lbfgsb}
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for opt_name in optimizers.keys():
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if opt_name.lower().find(f_min.lower()) != -1:
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return optimizers[opt_name]
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raise KeyError('No optimizer was found matching the name: %s' % f_min)
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