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redid constraints
This commit is contained in:
Max Zwiessele
parent
c0eddf29e7
commit
a264cdaa98
8 changed files with 290 additions and 190 deletions
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@ -17,10 +17,10 @@ import itertools
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# import numdifftools as ndt
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class Model(Parameterized):
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_fail_count = 0 # Count of failed optimization steps (see objective)
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_allowed_failures = 10 # number of allowed failures
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_fail_count = 0 # Count of failed optimization steps (see objective)
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_allowed_failures = 10 # number of allowed failures
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def __init__(self, name):
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super(Model, self).__init__(name)#Parameterized.__init__(self)
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super(Model, self).__init__(name) # Parameterized.__init__(self)
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self.priors = []
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self._priors = ParameterIndexOperations()
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self.optimization_runs = []
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@ -30,10 +30,10 @@ class Model(Parameterized):
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def log_likelihood(self):
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def _log_likelihood_gradients(self):
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#def dK_d(self, param, dL_dK, X, X2)
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# def dK_d(self, param, dL_dK, X, X2)
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g = np.zeros(self.size)
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try:
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#[g.__setitem__(s, self.gradient_mapping[p]().flat) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
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# [g.__setitem__(s, self.gradient_mapping[p]().flat) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
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[p._collect_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
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except ValueError:
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raise ValueError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name)
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@ -100,7 +100,7 @@ class Model(Parameterized):
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if len(tie_matches) > 1:
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raise ValueError, "cannot place prior across multiple ties"
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elif len(tie_matches) == 1:
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which = which[:1] # just place a prior object on the first parameter
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which = which[:1] # just place a prior object on the first parameter
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# check constraints are okay
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@ -168,14 +168,14 @@ class Model(Parameterized):
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Make this draw from the prior if one exists, else draw from N(0,1)
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"""
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# first take care of all parameters (from N(0,1))
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#x = self._get_params_transformed()
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# x = self._get_params_transformed()
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x = np.random.randn(self.size_transformed)
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x = self._untransform_params(x)
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# now draw from prior where possible
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if self.priors is not None and len(self.priors):
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[np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
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self._set_params(x)
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#self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
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# self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
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def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
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"""
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@ -220,8 +220,8 @@ class Model(Parameterized):
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job = pool.apply_async(opt_wrapper, args=(self,), kwds=kwargs)
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jobs.append(job)
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pool.close() # signal that no more data coming in
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pool.join() # wait for all the tasks to complete
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pool.close() # signal that no more data coming in
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pool.join() # wait for all the tasks to complete
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except KeyboardInterrupt:
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print "Ctrl+c received, terminating and joining pool."
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pool.terminate()
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@ -378,7 +378,7 @@ class Model(Parameterized):
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def optimize_SGD(self, momentum=0.1, learning_rate=0.01, iterations=20, **kwargs):
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# assert self.Y.shape[1] > 1, "SGD only works with D > 1"
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sgd = SGD.StochasticGD(self, iterations, learning_rate, momentum, **kwargs) # @UndefinedVariable
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sgd = SGD.StochasticGD(self, iterations, learning_rate, momentum, **kwargs) # @UndefinedVariable
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sgd.run()
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self.optimization_runs.append(sgd)
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@ -412,7 +412,7 @@ class Model(Parameterized):
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gradient = self.objective_function_gradients(x)
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numerical_gradient = (f1 - f2) / (2 * dx)
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global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient==0, 1e-32, gradient)))
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global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
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return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() < tolerance)
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else:
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@ -444,18 +444,18 @@ class Model(Parameterized):
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return
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gradient = self.objective_function_gradients(x)
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np.where(gradient==0, 1e-312, gradient)
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np.where(gradient == 0, 1e-312, gradient)
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ret = True
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for i, ind in enumerate(param_list):
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xx = x.copy()
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xx[ind] += step
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xx[i] += step
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f1 = self.objective_function(xx)
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xx[ind] -= 2.*step
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xx[i] -= 2.*step
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f2 = self.objective_function(xx)
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numerical_gradient = (f1 - f2) / (2 * step)
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ratio = (f1 - f2) / (2 * step * gradient[ind])
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difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
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ratio = (f1 - f2) / (2 * step * gradient[i])
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difference = np.abs((f1 - f2) / 2 / step - gradient[i])
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if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
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formatted_name = "\033[92m {0} \033[0m".format(names[ind])
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@ -466,7 +466,7 @@ class Model(Parameterized):
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r = '%.6f' % float(ratio)
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d = '%.6f' % float(difference)
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g = '%.6f' % gradient[ind]
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g = '%.6f' % gradient[i]
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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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@ -517,10 +517,10 @@ class Model(Parameterized):
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alpha = 0
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stop = False
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#Handle **kwargs
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# Handle **kwargs
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ep_args = {}
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for arg in kwargs.keys():
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if arg in ('epsilon','power_ep'):
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if arg in ('epsilon', 'power_ep'):
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ep_args[arg] = kwargs[arg]
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del kwargs[arg]
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@ -528,7 +528,7 @@ class Model(Parameterized):
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last_approximation = self.likelihood.copy()
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last_params = self._get_params()
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if len(ep_args) == 2:
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self.update_likelihood_approximation(epsilon=ep_args['epsilon'],power_ep=ep_args['power_ep'])
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self.update_likelihood_approximation(epsilon=ep_args['epsilon'], power_ep=ep_args['power_ep'])
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elif len(ep_args) == 1:
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if ep_args.keys()[0] == 'epsilon':
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self.update_likelihood_approximation(epsilon=ep_args['epsilon'])
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@ -540,8 +540,8 @@ class Model(Parameterized):
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ll_change = new_ll - last_ll
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if ll_change < 0:
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self.likelihood = last_approximation # restore previous likelihood approximation
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self._set_params(last_params) # restore model parameters
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self.likelihood = last_approximation # restore previous likelihood approximation
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self._set_params(last_params) # restore model parameters
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print "Log-likelihood decrement: %s \nLast likelihood update discarded." % ll_change
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stop = True
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else:
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