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[pickling] _src -> src
This commit is contained in:
Max Zwiessele
parent
b236896fbd
commit
4cd16a86b4
9 changed files with 69 additions and 34 deletions
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@ -40,18 +40,28 @@ def load(file_or_path):
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:param file_name: path/to/file.pickle
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"""
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# This is the pickling pain when changing _src -> src
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try:
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import cPickle as pickle
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if isinstance(file_or_path, basestring):
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with open(file_or_path, 'rb') as f:
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m = pickle.load(f)
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else:
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m = pickle.load(file_or_path)
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except:
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import pickle
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if isinstance(file_or_path, str):
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with open(file_or_path, 'rb') as f:
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m = pickle.load(f)
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else:
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m = pickle.load(file_or_path)
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try:
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import cPickle as pickle
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if isinstance(file_or_path, basestring):
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with open(file_or_path, 'rb') as f:
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m = pickle.load(f)
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else:
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m = pickle.load(file_or_path)
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except:
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import pickle
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if isinstance(file_or_path, str):
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with open(file_or_path, 'rb') as f:
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m = pickle.load(f)
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else:
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m = pickle.load(file_or_path)
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except ImportError:
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import sys
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import inspect
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sys.modules['GPy.kern._src'] = kern.src
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for name, module in inspect.getmembers(kern.src):
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if not name.startswith('_'):
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sys.modules['GPy.kern._src.{}'.format(name)] = module
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m = load(file_or_path)
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return m
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@ -121,6 +121,10 @@ class GP(Model):
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# W_{pp} := \texttt{Woodbury inv}
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# p := _predictive_variable
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def __setstate__(self, state):
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self.mean_function = None
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super(GP, self).__setstate__(state)
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@property
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def _predictive_variable(self):
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return self.X
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@ -459,7 +463,7 @@ class GP(Model):
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m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs)
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if self.normalizer is not None:
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m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
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def sim_one_dim(m, v):
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if not full_cov:
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return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T
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@ -30,6 +30,10 @@ class Model(Parameterized):
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self.obj_grads = None
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self.add_observer(self.tie, self.tie._parameters_changed_notification, priority=-500)
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def __setstate__(self, state):
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self.obj_grads = None
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super(Model, self).__setstate__(state)
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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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@ -90,7 +90,7 @@ class Param(Parameterizable, ObsAr):
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self._original_ = getattr(obj, '_original_', None)
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self._name = getattr(obj, '_name', None)
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self._gradient_array_ = getattr(obj, '_gradient_array_', None)
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self._update_on = getattr(obj, '_update_on', None)
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self.__update_on = getattr(obj, '__update_on', None)
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self.constraints = getattr(obj, 'constraints', None)
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self.priors = getattr(obj, 'priors', None)
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@ -34,6 +34,15 @@ class Updateable(Observable):
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p.traverse(turn_updates)
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self.trigger_update()
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@property
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def _update_on(self):
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if not hasattr(self, '__update_on'):
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self.__update_on = True
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return self.__update_on
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@_update_on.setter
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def _update_on(self, update):
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self.__update_on = update
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def toggle_update(self):
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print("deprecated: toggle_update was renamed to update_toggle for easier access")
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self.update_toggle()
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@ -27,7 +27,7 @@ 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, model=None, 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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@ -133,7 +133,7 @@ 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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@ -245,7 +245,7 @@ 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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@ -256,11 +256,11 @@ class Opt_Adadelta(Optimizer):
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def opt(self, 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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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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@ -1,10 +1,8 @@
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"""
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Kernel module the kernels to sit in.
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.. automodule:: .src
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:members:
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:private-members:
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"""
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from . import src
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from .src.kern import Kern
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from .src.add import Add
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from .src.prod import Prod
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@ -54,13 +54,22 @@ class Kern(Parameterized):
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self.active_dims = active_dims
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self._all_dims_active = np.atleast_1d(active_dims).astype(int)
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assert self._all_dims_active.size == self.input_dim, "input_dim={} does not match len(active_dim)={}, _all_dims_active={}".format(self.input_dim, self._all_dims_active.size, self._all_dims_active)
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assert self._all_dims_active.size == self.input_dim, "input_dim={} does not match len(active_dim)={}, active_dim={}".format(self.input_dim, self._all_dims_active.size, self._all_dims_active)
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self._sliced_X = 0
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self.useGPU = self._support_GPU and useGPU
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from .psi_comp import PSICOMP_GH
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self.psicomp = PSICOMP_GH()
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self.psicomp = PSICOMP_GH()
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@property
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def _all_dims_active(self):
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if not hasattr(self, '__all_dims_active'):
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self.__all_dims_active = np.asanyarray(self.active_dims)
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return self.__all_dims_active
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@_all_dims_active.setter
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def _all_dims_active(self, active_dims):
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self.__all_dims_active = np.asanyarray(active_dims)
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@property
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def _effective_input_dim(self):
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@ -211,15 +220,15 @@ class Kern(Parameterized):
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def get_most_significant_input_dimensions(self, which_indices=None):
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"""
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Determine which dimensions should be plotted
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Returns the top three most signification input dimensions
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if less then three dimensions, the non existing dimensions are
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labeled as None, so for a 1 dimensional input this returns
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(0, None, None).
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:param which_indices: force the indices to be the given indices.
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:type which_indices: int or tuple(int,int) or tuple(int,int,int)
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:param which_indices: force the indices to be the given indices.
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:type which_indices: int or tuple(int,int) or tuple(int,int,int)
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"""
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if which_indices is None:
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which_indices = np.argsort(self.input_sensitivity())[::-1][:3]
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@ -235,7 +244,7 @@ class Kern(Parameterized):
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input_1, input_2 = which_indices, None
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except ValueError:
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# which_indices was a list or array like with only one int
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input_1, input_2 = which_indices[0], None
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input_1, input_2 = which_indices[0], None
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return input_1, input_2, input_3
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@ -47,12 +47,13 @@ class RBF(Stationary):
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return dc
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def __setstate__(self, state):
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self.use_invLengthscale = False
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return super(RBF, self).__setstate__(state)
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def spectrum(self, omega):
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assert self.input_dim == 1 #TODO: higher dim spectra?
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return self.variance*np.sqrt(2*np.pi)*self.lengthscale*np.exp(-self.lengthscale*2*omega**2/2)
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def parameters_changed(self):
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if self.use_invLengthscale: self.lengthscale[:] = 1./np.sqrt(self.inv_l+1e-200)
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super(RBF,self).parameters_changed()
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@ -85,7 +86,7 @@ class RBF(Stationary):
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def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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return self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[3:]
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def update_gradients_diag(self, dL_dKdiag, X):
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super(RBF,self).update_gradients_diag(dL_dKdiag, X)
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if self.use_invLengthscale: self.inv_l.gradient =self.lengthscale.gradient*(self.lengthscale**3/-2.)
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