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https://github.com/SheffieldML/GPy.git
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Merge branch 'master' of github.com:SheffieldML/GPy into debug
This commit is contained in:
James Hensman
commit
f92ff10e32
4 changed files with 68 additions and 40 deletions
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@ -308,9 +308,7 @@ class model(parameterised):
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numerical_gradient = (f1-f2)/(2*dx)
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ratio = (f1-f2)/(2*np.dot(dx,gradient))
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if verbose:
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#print "gradient = ",gradient
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#print "numerical gradient = ",numerical_gradient
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print " Gradient ratio = ", ratio, '\n'
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print "Gradient ratio = ", ratio, '\n'
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sys.stdout.flush()
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if (np.abs(1.-ratio)<tolerance) and not np.isnan(ratio):
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@ -319,10 +317,24 @@ class model(parameterised):
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else:
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if verbose:
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print "Global check failed. Testing individual gradients\n"
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try:
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names = self.extract_param_names()
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except NotImplementedError:
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names = ['Variable %i'%i for i in range(len(x))]
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try:
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names = self.extract_param_names()
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except NotImplementedError:
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names = ['Variable %i'%i for i in range(len(x))]
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# Prepare for pretty-printing
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header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical']
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max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
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float_len = 10
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cols = [max_names]
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cols.extend([max(float_len, len(header[i])) for i in range(1, len(header))])
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cols = np.array(cols) + 5
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header_string = ["{h:^{col}}".format(h = header[i], col = cols[i]) for i in range(len(cols))]
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header_string = map(lambda x: '|'.join(x), [header_string])
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separator = '-'*len(header_string[0])
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print '\n'.join([header_string[0], separator])
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for i in range(len(x)):
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xx = x.copy()
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xx[i] += step
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@ -338,11 +350,20 @@ class model(parameterised):
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numerical_gradient = (f1-f2)/(2*step)
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ratio = (f1-f2)/(2*step*gradient)
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difference = np.abs((f1-f2)/2/step - gradient)
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if verbose:
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print "{0:10s} ratio: {1:15f} difference: {2:15f} analytical: {3:15f} numerical: {4:15f}".format(names[i], float(ratio), float(difference), gradient, float(numerical_gradient)),
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if (np.abs(ratio-1)<tolerance):
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print " "+'\033[92m' + u"\u2713" + '\033[0m' # green chackmark
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formatted_name = "\033[92m {0} \033[0m".format(names[i])
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else:
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print " "+'\033[91m' + u"\u2717" + '\033[0m' # red crossmark
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formatted_name = "\033[91m {0} \033[0m".format(names[i])
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r = '%.6f' % float(ratio)
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d = '%.6f' % float(difference)
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g = '%.6f' % gradient
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ng = '%.6f' % float(numerical_gradient)
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grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4])
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print grad_string
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print ''
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return False
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return True
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@ -3,7 +3,13 @@
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from scipy import optimize
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# import rasmussens_minimize as rasm
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try:
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import rasmussens_minimize as rasm
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rasm_available = True
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except ImportError:
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rasm_available = False
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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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@ -38,7 +44,7 @@ class Optimizer():
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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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self.opt(**kwargs)
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@ -159,43 +165,45 @@ class opt_simplex(Optimizer):
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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 Conjugate Gradient"
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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 Conjugate Gradient"
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# def opt(self):
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# """
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# Run Rasmussen's Conjugate Gradient optimizer
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# """
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def opt(self, 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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# 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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assert 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_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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opt_result = rasm.minimize(self.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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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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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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if rasm_available:
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optimizers['rasmussen'] = opt_rasm
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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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@ -22,7 +22,7 @@ class warpedGP(GP_regression):
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if warping_function == None:
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self.warping_function = TanhWarpingFunction(warping_terms)
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# self.warping_params = np.random.randn(self.warping_function.n_terms, 3)
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self.warping_params = np.ones((self.warping_function.n_terms, 3))*1.0 # TODO better init
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self.warping_params = np.ones((self.warping_function.n_terms, 3))*0.0 # TODO better init
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self.warp_params_shape = (self.warping_function.n_terms, 3) # todo get this from the subclass
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self.Z = Y.copy()
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@ -1,5 +1,4 @@
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import os
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import posix
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import pylab as pb
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import numpy as np
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import GPy
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