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https://github.com/SheffieldML/GPy.git
synced 2026-06-26 15:49:40 +02:00
[pickle] load errors bc of kernel changes, backwards compatibility issues fixed
This commit is contained in:
Max Zwiessele
parent
6f9c5042f9
commit
850c10beaa
4 changed files with 42 additions and 53 deletions
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@ -27,10 +27,10 @@ class Optimizer(object):
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:rtype: optimizer object.
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"""
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def __init__(self, x_init, messages=False, model=None, max_f_eval=1e4, max_iters=1e3,
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def __init__(self, x_init=None, messages=False, max_f_eval=1e4, max_iters=1e3,
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ftol=None, gtol=None, xtol=None, bfgs_factor=None):
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self.opt_name = None
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self.x_init = x_init
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#x_init = x_init
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# Turning messages off and using internal structure for print outs:
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self.messages = False #messages
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self.f_opt = None
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@ -45,7 +45,6 @@ class Optimizer(object):
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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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self.model = model
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def run(self, **kwargs):
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start = dt.datetime.now()
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@ -53,7 +52,7 @@ class Optimizer(object):
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end = dt.datetime.now()
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self.time = str(end - start)
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def opt(self, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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raise NotImplementedError("this needs to be implemented to use the optimizer class")
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def __str__(self):
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@ -64,12 +63,16 @@ class Optimizer(object):
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diagnostics += "Time elapsed: \t\t\t\t %s\n" % self.time
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return diagnostics
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def __getstate__(self):
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return []
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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, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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"""
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Run the TNC optimizer
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@ -87,7 +90,7 @@ class opt_tnc(Optimizer):
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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(f_fp, self.x_init, messages=self.messages,
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opt_result = optimize.fmin_tnc(f_fp, 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 = f_fp(self.x_opt)[0]
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@ -99,7 +102,7 @@ class opt_lbfgsb(Optimizer):
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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, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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"""
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Run the optimizer
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@ -123,7 +126,7 @@ class opt_lbfgsb(Optimizer):
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if self.bfgs_factor is not None:
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opt_dict['factr'] = self.bfgs_factor
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opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint=iprint,
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opt_result = optimize.fmin_l_bfgs_b(f_fp, x_init, iprint=iprint,
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maxfun=self.max_iters, **opt_dict)
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self.x_opt = opt_result[0]
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self.f_opt = f_fp(self.x_opt)[0]
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@ -133,13 +136,13 @@ class opt_lbfgsb(Optimizer):
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#a more helpful error message is available in opt_result in the Error case
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if opt_result[2]['warnflag']==2:
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self.status = 'Error' + str(opt_result[2]['task'])
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class opt_bfgs(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 = "BFGS (Scipy implementation)"
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def opt(self, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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"""
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Run the optimizer
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@ -154,7 +157,7 @@ class opt_bfgs(Optimizer):
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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_bfgs(f, self.x_init, fp, disp=self.messages,
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opt_result = optimize.fmin_bfgs(f, x_init, fp, disp=self.messages,
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maxiter=self.max_iters, full_output=True, **opt_dict)
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self.x_opt = opt_result[0]
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self.f_opt = f_fp(self.x_opt)[0]
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@ -166,7 +169,7 @@ class opt_simplex(Optimizer):
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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, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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"""
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The simplex optimizer does not require gradients.
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"""
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@ -181,7 +184,7 @@ class opt_simplex(Optimizer):
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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(f, self.x_init, (), disp=self.messages,
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opt_result = optimize.fmin(f, 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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@ -196,7 +199,7 @@ class opt_rasm(Optimizer):
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Optimizer.__init__(self, *args, **kwargs)
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self.opt_name = "Rasmussen's Conjugate Gradient"
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def opt(self, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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"""
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Run Rasmussen's Conjugate Gradient optimizer
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"""
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@ -213,7 +216,7 @@ class opt_rasm(Optimizer):
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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, f_fp, (), messages=self.messages,
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opt_result = rasm.minimize(x_init, 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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@ -230,11 +233,11 @@ class opt_SCG(Optimizer):
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self.opt_name = "Scaled Conjugate Gradients"
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def opt(self, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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assert not f is None
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assert not fp is None
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opt_result = SCG(f, fp, self.x_init, display=self.messages,
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opt_result = SCG(f, fp, x_init, display=self.messages,
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maxiters=self.max_iters,
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max_f_eval=self.max_f_eval,
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xtol=self.xtol, ftol=self.ftol,
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@ -245,7 +248,7 @@ class opt_SCG(Optimizer):
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self.f_opt = self.trace[-1]
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self.funct_eval = opt_result[2]
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self.status = opt_result[3]
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class Opt_Adadelta(Optimizer):
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def __init__(self, step_rate=0.1, decay=0.9, momentum=0, *args, **kwargs):
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Optimizer.__init__(self, *args, **kwargs)
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@ -254,13 +257,13 @@ class Opt_Adadelta(Optimizer):
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self.decay = decay
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self.momentum = momentum
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def opt(self, f_fp=None, f=None, fp=None):
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def opt(self, x_init, f_fp=None, f=None, fp=None):
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assert not fp is None
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import climin
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opt = climin.adadelta.Adadelta(self.x_init, fp, step_rate=self.step_rate, decay=self.decay, momentum=self.momentum)
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opt = climin.adadelta.Adadelta(x_init, fp, step_rate=self.step_rate, decay=self.decay, momentum=self.momentum)
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for info in opt:
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if info['n_iter']>=self.max_iters:
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self.x_opt = opt.wrt
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