diff --git a/GPy/core/gp.py b/GPy/core/gp.py index 42dab1b8..0e93cd99 100644 --- a/GPy/core/gp.py +++ b/GPy/core/gp.py @@ -93,6 +93,22 @@ class GP(Model): self.link_parameter(self.kern) self.link_parameter(self.likelihood) + def set_X(self,X): + # TODO: it does not work with BGPLVM + if isinstance(X, ObsAr): + self.X = X + else: + self.X = ObsAr(X) + + def set_Y(self,Y): + if self.normalizer is not None: + self.normalizer.scale_by(Y) + self.Y_normalized = ObsAr(self.normalizer.normalize(Y)) + self.Y = Y + else: + self.Y = ObsAr(Y) + self.Y_normalized = self.Y + def parameters_changed(self): self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.Y_metadata) self.likelihood.update_gradients(self.grad_dict['dL_dthetaL']) diff --git a/GPy/core/mapping.py b/GPy/core/mapping.py index 8cfd9ad0..049f1699 100644 --- a/GPy/core/mapping.py +++ b/GPy/core/mapping.py @@ -13,11 +13,7 @@ class Mapping(Parameterized): def __init__(self, input_dim, output_dim, name='mapping'): self.input_dim = input_dim self.output_dim = output_dim - super(Mapping, self).__init__(name=name) - # Model.__init__(self) - # All leaf nodes should call self._set_params(self._get_params()) at - # the end def f(self, X): raise NotImplementedError @@ -56,7 +52,8 @@ class Mapping(Parameterized): Can plot only part of the data and part of the posterior functions using which_data and which_functions - This is a convenience function: arguments are passed to GPy.plotting.matplot_dep.models_plots.plot_mapping + This is a convenience function: arguments are passed to + GPy.plotting.matplot_dep.models_plots.plot_mapping """ if "matplotlib" in sys.modules: @@ -66,7 +63,10 @@ class Mapping(Parameterized): raise NameError, "matplotlib package has not been imported." class Bijective_mapping(Mapping): - """This is a mapping that is bijective, i.e. you can go from X to f and also back from f to X. The inverse mapping is called g().""" + """ + This is a mapping that is bijective, i.e. you can go from X to f and + also back from f to X. The inverse mapping is called g(). + """ def __init__(self, input_dim, output_dim, name='bijective_mapping'): super(Bijective_apping, self).__init__(name=name) @@ -77,7 +77,11 @@ class Bijective_mapping(Mapping): from model import Model class Mapping_check_model(Model): - """This is a dummy model class used as a base class for checking that the gradients of a given mapping are implemented correctly. It enables checkgradient() to be called independently on each mapping.""" + """ + This is a dummy model class used as a base class for checking that the + gradients of a given mapping are implemented correctly. It enables + checkgradient() to be called independently on each mapping. + """ def __init__(self, mapping=None, dL_df=None, X=None): num_samples = 20 if mapping==None: diff --git a/GPy/core/parameterization/param.py b/GPy/core/parameterization/param.py index cb9b0cfe..c0836355 100644 --- a/GPy/core/parameterization/param.py +++ b/GPy/core/parameterization/param.py @@ -53,7 +53,9 @@ class Param(Parameterizable, ObsAr): return obj def __init__(self, name, input_array, default_constraint=None, *a, **kw): + self._in_init_ = True super(Param, self).__init__(name=name, default_constraint=default_constraint, *a, **kw) + self._in_init_ = False def build_pydot(self,G): import pydot diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py index 8d1efb1c..67d74626 100644 --- a/GPy/core/parameterization/parameter_core.py +++ b/GPy/core/parameterization/parameter_core.py @@ -18,7 +18,7 @@ import numpy as np import re import logging -__updated__ = '2014-09-22' +__updated__ = '2014-10-09' class HierarchyError(Exception): """ @@ -99,7 +99,7 @@ class Observable(object): :param bool trigger_parent: Whether to trigger the parent, after self has updated """ - if not self.update_model(): + if not self.update_model() or self._in_init_: #print "Warning: updates are off, updating the model will do nothing" return self._trigger_params_changed(trigger_parent) diff --git a/GPy/core/parameterization/priors.py b/GPy/core/parameterization/priors.py index ddc4d02f..1805f2ec 100644 --- a/GPy/core/parameterization/priors.py +++ b/GPy/core/parameterization/priors.py @@ -52,7 +52,7 @@ class Gaussian(Prior): self.constant = -0.5 * np.log(2 * np.pi * self.sigma2) def __str__(self): - return "N(" + str(np.round(self.mu)) + ', ' + str(np.round(self.sigma2)) + ')' + return "N({:.2g}, {:.2g})".format(self.mu, self.sigma) def lnpdf(self, x): return self.constant - 0.5 * np.square(x - self.mu) / self.sigma2 @@ -82,7 +82,7 @@ class Uniform(Prior): self.upper = float(upper) def __str__(self): - return "[" + str(np.round(self.lower)) + ', ' + str(np.round(self.upper)) + ']' + return "[{:.2g}, {:.2g}]".format(self.lower, self.upper) def lnpdf(self, x): region = (x>=self.lower) * (x<=self.upper) @@ -122,7 +122,7 @@ class LogGaussian(Prior): self.constant = -0.5 * np.log(2 * np.pi * self.sigma2) def __str__(self): - return "lnN(" + str(np.round(self.mu)) + ', ' + str(np.round(self.sigma2)) + ')' + return "lnN({:.2g}, {:.2g})".format(self.mu, self.sigma) def lnpdf(self, x): return self.constant - 0.5 * np.square(np.log(x) - self.mu) / self.sigma2 - np.log(x) @@ -220,7 +220,7 @@ class Gamma(Prior): self.constant = -gammaln(self.a) + a * np.log(b) def __str__(self): - return "Ga(" + str(np.round(self.a)) + ', ' + str(np.round(self.b)) + ')' + return "Ga({:.2g}, {:.2g})".format(self.a, self.b) def summary(self): ret = {"E[x]": self.a / self.b, \ @@ -254,7 +254,7 @@ class Gamma(Prior): b = E / V return Gamma(a, b) -class inverse_gamma(Prior): +class InverseGamma(Prior): """ Implementation of the inverse-Gamma probability function, coupled with random variables. @@ -265,6 +265,7 @@ class inverse_gamma(Prior): """ domain = _POSITIVE + _instances = [] def __new__(cls, a, b): # Singleton: if cls._instances: cls._instances[:] = [instance for instance in cls._instances if instance()] @@ -280,7 +281,7 @@ class inverse_gamma(Prior): self.constant = -gammaln(self.a) + a * np.log(b) def __str__(self): - return "iGa(" + str(np.round(self.a)) + ', ' + str(np.round(self.b)) + ')' + return "iGa({:.2g}, {:.2g})".format(self.a, self.b) def lnpdf(self, x): return self.constant - (self.a + 1) * np.log(x) - self.b / x @@ -290,3 +291,65 @@ class inverse_gamma(Prior): def rvs(self, n): return 1. / np.random.gamma(scale=1. / self.b, shape=self.a, size=n) + +class HalfT(Prior): + """ + Implementation of the half student t probability function, coupled with random variables. + + :param A: scale parameter + :param nu: degrees of freedom + + """ + domain = _POSITIVE + _instances = [] + def __new__(cls, A, nu): # Singleton: + if cls._instances: + cls._instances[:] = [instance for instance in cls._instances if instance()] + for instance in cls._instances: + if instance().A == A and instance().nu == nu: + return instance() + o = super(Prior, cls).__new__(cls, A, nu) + cls._instances.append(weakref.ref(o)) + return cls._instances[-1]() + def __init__(self, A, nu): + self.A = float(A) + self.nu = float(nu) + self.constant = gammaln(.5*(self.nu+1.)) - gammaln(.5*self.nu) - .5*np.log(np.pi*self.A*self.nu) + + def __str__(self): + return "hT({:.2g}, {:.2g})".format(self.A, self.nu) + + def lnpdf(self,theta): + return (theta>0) * ( self.constant -.5*(self.nu+1) * np.log( 1.+ (1./self.nu) * (theta/self.A)**2 ) ) + + #theta = theta if isinstance(theta,np.ndarray) else np.array([theta]) + #lnpdfs = np.zeros_like(theta) + #theta = np.array([theta]) + #above_zero = theta.flatten()>1e-6 + #v = self.nu + #sigma2=self.A + #stop + #lnpdfs[above_zero] = (+ gammaln((v + 1) * 0.5) + # - gammaln(v * 0.5) + # - 0.5*np.log(sigma2 * v * np.pi) + # - 0.5*(v + 1)*np.log(1 + (1/np.float(v))*((theta[above_zero][0]**2)/sigma2)) + #) + #return lnpdfs + + def lnpdf_grad(self,theta): + theta = theta if isinstance(theta,np.ndarray) else np.array([theta]) + grad = np.zeros_like(theta) + above_zero = theta>1e-6 + v = self.nu + sigma2=self.A + grad[above_zero] = -0.5*(v+1)*(2*theta[above_zero])/(v*sigma2 + theta[above_zero][0]**2) + return grad + + def rvs(self, n): + #return np.random.randn(n) * self.sigma + self.mu + from scipy.stats import t + #[np.abs(x) for x in t.rvs(df=4,loc=0,scale=50, size=10000)]) + ret = t.rvs(self.nu,loc=0,scale=self.A, size=n) + ret[ret<0] = 0 + return ret + diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py index 6b923609..8ea0d4c6 100644 --- a/GPy/core/sparse_gp.py +++ b/GPy/core/sparse_gp.py @@ -9,6 +9,7 @@ from .. import likelihoods from parameterization.variational import VariationalPosterior import logging +from GPy.inference.latent_function_inference.posterior import Posterior logger = logging.getLogger("sparse gp") class SparseGP(GP): @@ -34,12 +35,18 @@ class SparseGP(GP): """ - def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp', Y_metadata=None, normalizer=False): + def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, + name='sparse gp', Y_metadata=None, normalizer=False, + missing_data=False): + + self.missing_data = missing_data + if self.missing_data: + self.ninan = ~np.isnan(Y) #pick a sensible inference method if inference_method is None: if isinstance(likelihood, likelihoods.Gaussian): - inference_method = var_dtc.VarDTC() + inference_method = var_dtc.VarDTC(limit=1 if not self.missing_data else Y.shape[1]) else: #inference_method = ?? raise NotImplementedError, "what to do what to do?" @@ -51,44 +58,200 @@ class SparseGP(GP): GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer) logger.info("Adding Z as parameter") self.link_parameter(self.Z, index=0) + if self.missing_data: + self.Ylist = [] + overall = self.Y_normalized.shape[1] + m_f = lambda i: "Precomputing Y for missing data: {: >7.2%}".format(float(i+1)/overall) + message = m_f(-1) + print message, + for d in xrange(overall): + self.Ylist.append(self.Y_normalized[self.ninan[:, d], d][:, None]) + print ' '*(len(message)+1) + '\r', + message = m_f(d) + print message, + print '' + def has_uncertain_inputs(self): return isinstance(self.X, VariationalPosterior) - def parameters_changed(self): - self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata) - self.likelihood.update_gradients(self.grad_dict['dL_dthetaL']) - if isinstance(self.X, VariationalPosterior): + def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None): + """ + This is the standard part, which usually belongs in parameters_changed. + + For automatic handling of subsampling (such as missing_data, stochastics etc.), we need to put this into an inner + loop, in order to ensure a different handling of gradients etc of different + subsets of data. + + The dict in current_values will be passed aroung as current_values for + the rest of the algorithm, so this is the place to store current values, + such as subsets etc, if necessary. + + If Lm and dL_dKmm can be precomputed (or only need to be computed once) + pass them in here, so they will be passed to the inference_method. + + subset_indices is a dictionary of indices. you can put the indices however you + like them into this dictionary for inner use of the indices inside the + algorithm. + """ + posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=None) + current_values = {} + likelihood.update_gradients(grad_dict['dL_dthetaL']) + current_values['likgrad'] = likelihood.gradient.copy() + if subset_indices is None: + subset_indices = {} + if isinstance(X, VariationalPosterior): #gradients wrt kernel - dL_dKmm = self.grad_dict['dL_dKmm'] - self.kern.update_gradients_full(dL_dKmm, self.Z, None) - target = self.kern.gradient.copy() - self.kern.update_gradients_expectations(variational_posterior=self.X, - Z=self.Z, - dL_dpsi0=self.grad_dict['dL_dpsi0'], - dL_dpsi1=self.grad_dict['dL_dpsi1'], - dL_dpsi2=self.grad_dict['dL_dpsi2']) - self.kern.gradient += target + dL_dKmm = grad_dict['dL_dKmm'] + kern.update_gradients_full(dL_dKmm, Z, None) + current_values['kerngrad'] = kern.gradient.copy() + kern.update_gradients_expectations(variational_posterior=X, + Z=Z, + dL_dpsi0=grad_dict['dL_dpsi0'], + dL_dpsi1=grad_dict['dL_dpsi1'], + dL_dpsi2=grad_dict['dL_dpsi2']) + current_values['kerngrad'] += kern.gradient #gradients wrt Z - self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z) - self.Z.gradient += self.kern.gradients_Z_expectations( - self.grad_dict['dL_dpsi0'], - self.grad_dict['dL_dpsi1'], - self.grad_dict['dL_dpsi2'], - Z=self.Z, - variational_posterior=self.X) + current_values['Zgrad'] = kern.gradients_X(dL_dKmm, Z) + current_values['Zgrad'] += kern.gradients_Z_expectations( + grad_dict['dL_dpsi0'], + grad_dict['dL_dpsi1'], + grad_dict['dL_dpsi2'], + Z=Z, + variational_posterior=X) else: #gradients wrt kernel - self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X) - target = self.kern.gradient.copy() - self.kern.update_gradients_full(self.grad_dict['dL_dKnm'], self.X, self.Z) - target += self.kern.gradient - self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None) - self.kern.gradient += target + kern.update_gradients_diag(grad_dict['dL_dKdiag'], X) + current_values['kerngrad'] = kern.gradient.copy() + kern.update_gradients_full(grad_dict['dL_dKnm'], X, Z) + current_values['kerngrad'] += kern.gradient + kern.update_gradients_full(grad_dict['dL_dKmm'], Z, None) + current_values['kerngrad'] += kern.gradient #gradients wrt Z - self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z) - self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X) + current_values['Zgrad'] = kern.gradients_X(grad_dict['dL_dKmm'], Z) + current_values['Zgrad'] += kern.gradients_X(grad_dict['dL_dKnm'].T, Z, X) + return posterior, log_marginal_likelihood, grad_dict, current_values, subset_indices + + def _inner_take_over_or_update(self, full_values=None, current_values=None, value_indices=None): + """ + This is for automatic updates of values in the inner loop of missing + data handling. Both arguments are dictionaries and the values in + full_values will be updated by the current_gradients. + + If a key from current_values does not exist in full_values, it will be + initialized to the value in current_values. + + If there is indices needed for the update, value_indices can be used for + that. If value_indices has the same key, as current_values, the update + in full_values will be indexed by the indices in value_indices. + + grads: + dictionary of standing gradients (you will have to carefully make sure, that + the ordering is right!). The values in here will be updated such that + full_values[key] += current_values[key] forall key in full_gradients.keys() + + gradients: + dictionary of gradients in the current set of parameters. + + value_indices: + dictionary holding indices for the update in full_values. + if the key exists the update rule is: + full_values[key][value_indices[key]] += current_values[key] + """ + for key in current_values.keys(): + if value_indices is not None and value_indices.has_key(key): + index = value_indices[key] + else: + index = slice(None) + if full_values.has_key(key): + full_values[key][index] += current_values[key] + else: + full_values[key] = current_values[key] + + def _inner_values_update(self, current_values): + """ + This exists if there is more to do with the current values. + It will be called allways in the inner loop, so that + you can do additional inner updates for the inside of the missing data + loop etc. This can also be used for stochastic updates, when only working on + one dimension of the output. + """ + pass + + def _outer_values_update(self, full_values): + """ + Here you put the values, which were collected before in the right places. + E.g. set the gradients of parameters, etc. + """ + self.likelihood.gradient = full_values['likgrad'] + self.kern.gradient = full_values['kerngrad'] + self.Z.gradient = full_values['Zgrad'] + + def _outer_init_full_values(self): + """ + If full_values has indices in values_indices, we might want to initialize + the full_values differently, so that subsetting is possible. + + Here you can initialize the full_values for the values needed. + + Keep in mind, that if a key does not exist in full_values when updating + values, it will be set (so e.g. for Z there is no need to initialize Zgrad, + as there is no subsetting needed. For X in BGPLVM on the other hand we probably need + to initialize the gradients for the mean and the variance in order to + have the full gradient for indexing) + """ + return {} + + def _outer_loop_for_missing_data(self): + Lm = None + dL_dKmm = None + Kmm = None + + self._log_marginal_likelihood = 0 + full_values = self._outer_init_full_values() + + woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim)) + woodbury_vector = np.zeros((self.num_inducing, self.output_dim)) + m_f = lambda i: "Inference with missing data: {: >7.2%}".format(float(i+1)/self.output_dim) + message = m_f(-1) + print message, + for d in xrange(self.output_dim): + ninan = self.ninan[:, d] + + print ' '*(len(message)) + '\r', + message = m_f(d) + print message, + + posterior, log_marginal_likelihood, \ + grad_dict, current_values, value_indices = self._inner_parameters_changed( + self.kern, self.X[ninan], + self.Z, self.likelihood, + self.Ylist[d], self.Y_metadata, + Lm, dL_dKmm, + subset_indices=dict(outputs=d, samples=ninan)) + + self._inner_take_over_or_update(full_values, current_values, value_indices) + self._inner_values_update(current_values) + + Lm = posterior.K_chol + dL_dKmm = grad_dict['dL_dKmm'] + Kmm = posterior._K + woodbury_inv[:, :, d] = posterior.woodbury_inv + woodbury_vector[:, d:d+1] = posterior.woodbury_vector + self._log_marginal_likelihood += log_marginal_likelihood + print '' + + self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, + K=Kmm, mean=None, cov=None, K_chol=Lm) + self._outer_values_update(full_values) + + def parameters_changed(self): + if self.missing_data: + self._outer_loop_for_missing_data() + else: + self.posterior, self._log_marginal_likelihood, self.grad_dict, full_values, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata) + self._outer_values_update(full_values) def _raw_predict(self, Xnew, full_cov=False, kern=None): """ diff --git a/GPy/core/sparse_gp_mpi.py b/GPy/core/sparse_gp_mpi.py index e7faf7a8..d0008e58 100644 --- a/GPy/core/sparse_gp_mpi.py +++ b/GPy/core/sparse_gp_mpi.py @@ -34,15 +34,15 @@ class SparseGP_MPI(SparseGP): """ - def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None, normalizer=False): + def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None, normalizer=False, missing_data=False): self._IN_OPTIMIZATION_ = False if mpi_comm != None: if inference_method is None: inference_method = VarDTC_minibatch(mpi_comm=mpi_comm) else: assert isinstance(inference_method, VarDTC_minibatch), 'inference_method has to support MPI!' - - super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer) + + super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer, missing_data=missing_data) self.update_model(False) self.link_parameter(self.X, index=0) if variational_prior is not None: @@ -75,18 +75,18 @@ class SparseGP_MPI(SparseGP): return dc #===================================================== - # The MPI parallelization + # The MPI parallelization # - can move to model at some point #===================================================== - + @SparseGP.optimizer_array.setter def optimizer_array(self, p): if self.mpi_comm != None: if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0: self.mpi_comm.Bcast(np.int32(1),root=0) - self.mpi_comm.Bcast(p, root=0) + self.mpi_comm.Bcast(p, root=0) SparseGP.optimizer_array.fset(self,p) - + def optimize(self, optimizer=None, start=None, **kwargs): self._IN_OPTIMIZATION_ = True if self.mpi_comm==None: diff --git a/GPy/core/svigp.py b/GPy/core/svigp.py index 60e8371c..f78618eb 100644 --- a/GPy/core/svigp.py +++ b/GPy/core/svigp.py @@ -42,10 +42,10 @@ class SVIGP(GP): """ - def __init__(self, X, likelihood, kernel, Z, q_u=None, batchsize=10, X_variance=None): - GP.__init__(self, X, likelihood, kernel, normalize_X=False) + def __init__(self, X, Y, kernel, Z, q_u=None, batchsize=10): + raise NotImplementedError, "This is a work in progress, see github issue " + GP.__init__(self, X, Y, kernel) self.batchsize=batchsize - self.Y = self.likelihood.Y.copy() self.Z = Z self.num_inducing = Z.shape[0] @@ -57,15 +57,9 @@ class SVIGP(GP): self.param_steplength = 1e-5 self.momentum = 0.9 - if X_variance is None: - self.has_uncertain_inputs = False - else: - self.has_uncertain_inputs = True - self.X_variance = X_variance - - if q_u is None: q_u = np.hstack((np.random.randn(self.num_inducing*self.output_dim),-.5*np.eye(self.num_inducing).flatten())) + self.set_vb_param(q_u) self._permutation = np.random.permutation(self.num_data) @@ -76,82 +70,25 @@ class SVIGP(GP): self._grad_trace = [] #set the adaptive steplength parameters - self.hbar_t = 0.0 - self.tau_t = 100.0 - self.gbar_t = 0.0 - self.gbar_t1 = 0.0 - self.gbar_t2 = 0.0 - self.hbar_tp = 0.0 - self.tau_tp = 10000.0 - self.gbar_tp = 0.0 - self.adapt_param_steplength = True - self.adapt_vb_steplength = True - self._param_steplength_trace = [] - self._vb_steplength_trace = [] - -# def _getstate(self): -# steplength_params = [self.hbar_t, self.tau_t, self.gbar_t, self.gbar_t1, self.gbar_t2, self.hbar_tp, self.tau_tp, self.gbar_tp, self.adapt_param_steplength, self.adapt_vb_steplength, self.vb_steplength, self.param_steplength] -# return GP._getstate(self) + \ -# [self.get_vb_param(), -# self.Z, -# self.num_inducing, -# self.has_uncertain_inputs, -# self.X_variance, -# self.X_batch, -# self.X_variance_batch, -# steplength_params, -# self.batchcounter, -# self.batchsize, -# self.epochs, -# self.momentum, -# self.data_prop, -# self._param_trace, -# self._param_steplength_trace, -# self._vb_steplength_trace, -# self._ll_trace, -# self._grad_trace, -# self.Y, -# self._permutation, -# self.iterations -# ] -# -# def _setstate(self, state): -# self.iterations = state.pop() -# self._permutation = state.pop() -# self.Y = state.pop() -# self._grad_trace = state.pop() -# self._ll_trace = state.pop() -# self._vb_steplength_trace = state.pop() -# self._param_steplength_trace = state.pop() -# self._param_trace = state.pop() -# self.data_prop = state.pop() -# self.momentum = state.pop() -# self.epochs = state.pop() -# self.batchsize = state.pop() -# self.batchcounter = state.pop() -# steplength_params = state.pop() -# (self.hbar_t, self.tau_t, self.gbar_t, self.gbar_t1, self.gbar_t2, self.hbar_tp, self.tau_tp, self.gbar_tp, self.adapt_param_steplength, self.adapt_vb_steplength, self.vb_steplength, self.param_steplength) = steplength_params -# self.X_variance_batch = state.pop() -# self.X_batch = state.pop() -# self.X_variance = state.pop() -# self.has_uncertain_inputs = state.pop() -# self.num_inducing = state.pop() -# self.Z = state.pop() -# vb_param = state.pop() -# GP._setstate(self, state) -# self.set_vb_param(vb_param) + #self.hbar_t = 0.0 + #self.tau_t = 100.0 + #self.gbar_t = 0.0 + #self.gbar_t1 = 0.0 + #self.gbar_t2 = 0.0 + #self.hbar_tp = 0.0 + #self.tau_tp = 10000.0 + #self.gbar_tp = 0.0 + #self.adapt_param_steplength = True + #self.adapt_vb_steplength = True + #self._param_steplength_trace = [] + #self._vb_steplength_trace = [] def _compute_kernel_matrices(self): # kernel computations, using BGPLVM notation self.Kmm = self.kern.K(self.Z) - if self.has_uncertain_inputs: - self.psi0 = self.kern.psi0(self.Z, self.X_batch, self.X_variance_batch) - self.psi1 = self.kern.psi1(self.Z, self.X_batch, self.X_variance_batch) - self.psi2 = self.kern.psi2(self.Z, self.X_batch, self.X_variance_batch) - else: - self.psi0 = self.kern.Kdiag(self.X_batch) - self.psi1 = self.kern.K(self.X_batch, self.Z) - self.psi2 = None + self.psi0 = self.kern.Kdiag(self.X_batch) + self.psi1 = self.kern.K(self.X_batch, self.Z) + self.psi2 = None def dL_dtheta(self): dL_dtheta = self.kern._param_grad_helper(self.dL_dKmm, self.Z) @@ -179,7 +116,7 @@ class SVIGP(GP): def load_batch(self): """ - load a batch of data (set self.X_batch and self.likelihood.Y from self.X, self.Y) + load a batch of data (set self.X_batch and self.Y_batch from self.X, self.Y) """ #if we've seen all the data, start again with them in a new random order @@ -191,9 +128,7 @@ class SVIGP(GP): this_perm = self._permutation[self.batchcounter:self.batchcounter+self.batchsize] self.X_batch = self.X[this_perm] - self.likelihood.set_data(self.Y[this_perm]) - if self.has_uncertain_inputs: - self.X_variance_batch = self.X_variance[this_perm] + self.Y_batch = self.Y[this_perm] self.batchcounter += self.batchsize @@ -288,7 +223,9 @@ class SVIGP(GP): Compute the gradients of the lower bound wrt the canonical and Expectation parameters of u. - Note that the natural gradient in either is given by the gradient in the other (See Hensman et al 2012 Fast Variational inference in the conjugate exponential Family) + Note that the natural gradient in either is given by the gradient in + the other (See Hensman et al 2012 Fast Variational inference in the + conjugate exponential Family) """ # Gradient for eta diff --git a/GPy/defaults.cfg b/GPy/defaults.cfg index 50cc1107..306543ed 100644 --- a/GPy/defaults.cfg +++ b/GPy/defaults.cfg @@ -19,4 +19,9 @@ dir=$HOME/tmp/GPy-datasets/ # if you have an anaconda python installation please specify it here. installed = False location = None -MKL = False # set this to true if you have the MKL optimizations installed + # set this to true if you have the MKL optimizations installed: +MKL = False + +[weave] +#if true, try to use weave, and fall back to numpy. if false, just use numpy. +working = True diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index a640e360..94e83ff1 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -23,9 +23,6 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan X = _np.random.rand(num_inputs, input_dim) lengthscales = _np.random.rand(input_dim) k = GPy.kern.RBF(input_dim, .5, lengthscales, ARD=True) - ##+ GPy.kern.white(input_dim, 0.01) - #) - #k = GPy.kern.Linear(input_dim, ARD=1)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001) K = k.K(X) Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T @@ -159,7 +156,6 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, Q=4 def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k): import GPy from matplotlib import pyplot as plt - from ..util.misc import param_to_array import numpy as np _np.random.seed(0) @@ -177,7 +173,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, fig, (latent_axes, sense_axes) = plt.subplots(1, 2) m.plot_latent(ax=latent_axes, labels=m.data_labels) data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:])) - lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable + lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels) raw_input('Press enter to finish') plt.close(fig) @@ -186,8 +182,6 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k): import GPy from matplotlib import pyplot as plt - from ..util.misc import param_to_array - import numpy as np _np.random.seed(0) data = GPy.util.datasets.oil() @@ -204,7 +198,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40 fig, (latent_axes, sense_axes) = plt.subplots(1, 2) m.plot_latent(ax=latent_axes, labels=m.data_labels) data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:])) - lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable + lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels) raw_input('Press enter to finish') plt.close(fig) @@ -228,10 +222,10 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False): Ylist = [Y1, Y2, Y3] if plot_sim: - import pylab + from matplotlib import pyplot as plt import matplotlib.cm as cm import itertools - fig = pylab.figure("MRD Simulation Data", figsize=(8, 6)) + fig = plt.figure("MRD Simulation Data", figsize=(8, 6)) fig.clf() ax = fig.add_subplot(2, 1, 1) labls = slist_names @@ -242,29 +236,11 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False): ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i) ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable ax.set_title("Y{}".format(i + 1)) - pylab.draw() - pylab.tight_layout() + plt.draw() + plt.tight_layout() return slist, [S1, S2, S3], Ylist -def _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS): - S1 = _np.hstack([s1, sS]) - S2 = _np.hstack([s2, s3, sS]) - S3 = _np.hstack([s3, sS]) - Y1 = S1.dot(_np.random.randn(S1.shape[1], D1)) - Y2 = S2.dot(_np.random.randn(S2.shape[1], D2)) - Y3 = S3.dot(_np.random.randn(S3.shape[1], D3)) - Y1 += .3 * _np.random.randn(*Y1.shape) - Y2 += .2 * _np.random.randn(*Y2.shape) - Y3 += .25 * _np.random.randn(*Y3.shape) - Y1 -= Y1.mean(0) - Y2 -= Y2.mean(0) - Y3 -= Y3.mean(0) - Y1 /= Y1.std(0) - Y2 /= Y2.std(0) - Y3 /= Y3.std(0) - return Y1, Y2, Y3, S1, S2, S3 - def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False): _np.random.seed(1234) @@ -291,10 +267,10 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False): Ylist = [Y1, Y2, Y3] if plot_sim: - import pylab + from matplotlib import pyplot as plt import matplotlib.cm as cm import itertools - fig = pylab.figure("MRD Simulation Data", figsize=(8, 6)) + fig = plt.figure("MRD Simulation Data", figsize=(8, 6)) fig.clf() ax = fig.add_subplot(2, 1, 1) labls = slist_names @@ -305,28 +281,28 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False): ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i) ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable ax.set_title("Y{}".format(i + 1)) - pylab.draw() - pylab.tight_layout() + plt.draw() + plt.tight_layout() return slist, [S1, S2, S3], Ylist -# def bgplvm_simulation_matlab_compare(): -# from GPy.util.datasets import simulation_BGPLVM -# from GPy import kern -# from GPy.models import BayesianGPLVM -# -# sim_data = simulation_BGPLVM() -# Y = sim_data['Y'] -# mu = sim_data['mu'] -# num_inducing, [_, Q] = 3, mu.shape -# -# k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) -# m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, -# _debug=False) -# m.auto_scale_factor = True -# m['noise'] = Y.var() / 100. -# m['linear_variance'] = .01 -# return m +def _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS): + S1 = _np.hstack([s1, sS]) + S2 = _np.hstack([s2, s3, sS]) + S3 = _np.hstack([s3, sS]) + Y1 = S1.dot(_np.random.randn(S1.shape[1], D1)) + Y2 = S2.dot(_np.random.randn(S2.shape[1], D2)) + Y3 = S3.dot(_np.random.randn(S3.shape[1], D3)) + Y1 += .3 * _np.random.randn(*Y1.shape) + Y2 += .2 * _np.random.randn(*Y2.shape) + Y3 += .25 * _np.random.randn(*Y3.shape) + Y1 -= Y1.mean(0) + Y2 -= Y2.mean(0) + Y3 -= Y3.mean(0) + Y1 /= Y1.std(0) + Y2 /= Y2.std(0) + Y3 /= Y3.std(0) + return Y1, Y2, Y3, S1, S2, S3 def bgplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, @@ -391,16 +367,13 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1, Y = Ylist[0] k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) - inan = _np.random.binomial(1, .8, size=Y.shape).astype(bool) # 80% missing data + inan = _np.random.binomial(1, .2, size=Y.shape).astype(bool) # 80% missing data Ymissing = Y.copy() Ymissing[inan] = _np.nan m = BayesianGPLVM(Ymissing, Q, init="random", num_inducing=num_inducing, - inference_method=VarDTCMissingData(inan=inan), kernel=k) + kernel=k, missing_data=True) - m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape) - m.likelihood.variance = .01 - m.parameters_changed() m.Yreal = Y if optimize: diff --git a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py index 3aeb4fbb..7c8041ce 100644 --- a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py +++ b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py @@ -1,7 +1,6 @@ import numpy as np from ...util import diag from ...util.linalg import mdot, jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify, DSYR -from ...util.misc import param_to_array from ...core.parameterization.variational import VariationalPosterior from . import LatentFunctionInference from posterior import Posterior @@ -23,7 +22,7 @@ class EPDTC(LatentFunctionInference): self.get_YYTfactor.limit = limit def _get_trYYT(self, Y): - return param_to_array(np.sum(np.square(Y))) + return np.sum(np.square(Y)) def __getstate__(self): # has to be overridden, as Cacher objects cannot be pickled. @@ -44,7 +43,7 @@ class EPDTC(LatentFunctionInference): """ N, D = Y.shape if (N>=D): - return param_to_array(Y) + return Y else: return jitchol(tdot(Y)) diff --git a/GPy/inference/latent_function_inference/laplace.py b/GPy/inference/latent_function_inference/laplace.py index 1c153518..a815d433 100644 --- a/GPy/inference/latent_function_inference/laplace.py +++ b/GPy/inference/latent_function_inference/laplace.py @@ -12,7 +12,6 @@ import numpy as np from ...util.linalg import mdot, jitchol, dpotrs, dtrtrs, dpotri, symmetrify, pdinv -from ...util.misc import param_to_array from posterior import Posterior import warnings from scipy import optimize @@ -39,9 +38,6 @@ class Laplace(LatentFunctionInference): Returns a Posterior class containing essential quantities of the posterior """ - #make Y a normal array! - Y = param_to_array(Y) - # Compute K K = kern.K(X) @@ -153,7 +149,7 @@ class Laplace(LatentFunctionInference): #compute vital matrices C = np.dot(LiW12, K) - Ki_W_i = K - C.T.dot(C) + Ki_W_i = K - C.T.dot(C) #compute the log marginal log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata) - np.sum(np.log(np.diag(L))) diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py index 4f21bc29..b10ec832 100644 --- a/GPy/inference/latent_function_inference/var_dtc.py +++ b/GPy/inference/latent_function_inference/var_dtc.py @@ -6,7 +6,6 @@ from ...util.linalg import mdot, jitchol, backsub_both_sides, tdot, dtrtrs, dtrt from ...util import diag from ...core.parameterization.variational import VariationalPosterior import numpy as np -from ...util.misc import param_to_array from . import LatentFunctionInference log_2_pi = np.log(2*np.pi) import logging, itertools @@ -35,7 +34,7 @@ class VarDTC(LatentFunctionInference): self.get_YYTfactor.limit = limit def _get_trYYT(self, Y): - return param_to_array(np.sum(np.square(Y))) + return np.sum(np.square(Y)) def __getstate__(self): # has to be overridden, as Cacher objects cannot be pickled. @@ -56,14 +55,16 @@ class VarDTC(LatentFunctionInference): """ N, D = Y.shape if (N>=D): - return param_to_array(Y) + return Y.view(np.ndarray) else: return jitchol(tdot(Y)) def get_VVTfactor(self, Y, prec): return Y * prec # TODO chache this, and make it effective - def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None): + + + def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None, dL_dKmm=None): _, output_dim = Y.shape uncertain_inputs = isinstance(X, VariationalPosterior) @@ -85,8 +86,8 @@ class VarDTC(LatentFunctionInference): Kmm = kern.K(Z).copy() diag.add(Kmm, self.const_jitter) - Lm = jitchol(Kmm) - + if Lm is None: + Lm = jitchol(Kmm) # The rather complex computations of A, and the psi stats if uncertain_inputs: @@ -101,7 +102,6 @@ class VarDTC(LatentFunctionInference): else: psi0 = kern.Kdiag(X) psi1 = kern.K(X, Z) - psi2 = None if het_noise: tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1))) else: @@ -123,11 +123,12 @@ class VarDTC(LatentFunctionInference): delit = tdot(_LBi_Lmi_psi1Vf) data_fit = np.trace(delit) DBi_plus_BiPBi = backsub_both_sides(LB, output_dim * np.eye(num_inducing) + delit) - delit = -0.5 * DBi_plus_BiPBi - delit += -0.5 * B * output_dim - delit += output_dim * np.eye(num_inducing) - # Compute dL_dKmm - dL_dKmm = backsub_both_sides(Lm, delit) + if dL_dKmm is None: + delit = -0.5 * DBi_plus_BiPBi + delit += -0.5 * B * output_dim + delit += output_dim * np.eye(num_inducing) + # Compute dL_dKmm + dL_dKmm = backsub_both_sides(Lm, delit) # derivatives of L w.r.t. psi dL_dpsi0, dL_dpsi1, dL_dpsi2 = _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, @@ -209,6 +210,7 @@ class VarDTCMissingData(LatentFunctionInference): inan = np.isnan(Y) has_none = inan.any() self._inan = ~inan + inan = self._inan else: inan = self._inan has_none = True @@ -242,17 +244,15 @@ class VarDTCMissingData(LatentFunctionInference): self._subarray_indices = [[slice(None),slice(None)]] return [Y], [(Y**2).sum()] - def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None): + def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None): if isinstance(X, VariationalPosterior): uncertain_inputs = True psi0_all = kern.psi0(Z, X) psi1_all = kern.psi1(Z, X) - psi2_all = kern.psi2(Z, X) else: uncertain_inputs = False psi0_all = kern.Kdiag(X) psi1_all = kern.K(X, Z) - psi2_all = None Ys, traces = self._Y(Y) beta_all = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6) @@ -274,10 +274,11 @@ class VarDTCMissingData(LatentFunctionInference): Kmm = kern.K(Z).copy() diag.add(Kmm, self.const_jitter) #factor Kmm - Lm = jitchol(Kmm) + if Lm is None: + Lm = jitchol(Kmm) if uncertain_inputs: LmInv = dtrtri(Lm) - size = Y.shape[1] + size = len(Ys) next_ten = 0 for i, [y, v, trYYT] in enumerate(itertools.izip(Ys, self._inan.T, traces)): if ((i+1.)/size) >= next_ten: @@ -285,19 +286,19 @@ class VarDTCMissingData(LatentFunctionInference): next_ten += .1 if het_noise: beta = beta_all[i] else: beta = beta_all - VVT_factor = (y*beta) output_dim = 1#len(ind) psi0 = psi0_all[v] psi1 = psi1_all[v, :] - if uncertain_inputs: psi2 = psi2_all[v, :] + if uncertain_inputs: + psi2 = kern.psi2(Z, X[v, :]) else: psi2 = None num_data = psi1.shape[0] if uncertain_inputs: if het_noise: psi2_beta = psi2 * (beta.flatten().reshape(num_data, 1, 1)).sum(0) - else: psi2_beta = psi2.sum(0) * beta + else: psi2_beta = psi2 * (beta) A = LmInv.dot(psi2_beta.dot(LmInv.T)) else: if het_noise: tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1))) @@ -332,11 +333,11 @@ class VarDTCMissingData(LatentFunctionInference): dL_dpsi0_all[v] += dL_dpsi0 dL_dpsi1_all[v, :] += dL_dpsi1 if uncertain_inputs: - dL_dpsi2_all[v, :] += dL_dpsi2 + dL_dpsi2_all[v, :, :] += dL_dpsi2 # log marginal likelihood log_marginal += _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise, - psi0, A, LB, trYYT, data_fit,VVT_factor) + psi0, A, LB, trYYT, data_fit, VVT_factor) #put the gradients in the right places dL_dR += _compute_dL_dR(likelihood, @@ -395,7 +396,6 @@ def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, VVT_factor, C # subsume back into psi1 (==Kmn) dL_dpsi1 += 2.*np.dot(psi1, dL_dpsi2) dL_dpsi2 = None - return dL_dpsi0, dL_dpsi1, dL_dpsi2 diff --git a/GPy/inference/latent_function_inference/var_dtc_gpu.py b/GPy/inference/latent_function_inference/var_dtc_gpu.py index 3bd5c347..f7da9080 100644 --- a/GPy/inference/latent_function_inference/var_dtc_gpu.py +++ b/GPy/inference/latent_function_inference/var_dtc_gpu.py @@ -6,7 +6,6 @@ from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs from ...util import diag from ...core.parameterization.variational import VariationalPosterior import numpy as np -from ...util.misc import param_to_array from . import LatentFunctionInference log_2_pi = np.log(2*np.pi) @@ -32,18 +31,18 @@ class VarDTC_GPU(LatentFunctionInference): """ const_jitter = np.float64(1e-6) def __init__(self, batchsize=None, gpu_memory=4., limit=1): - + self.batchsize = batchsize self.gpu_memory = gpu_memory - + self.midRes = {} self.batch_pos = 0 # the starting position of the current mini-batch - + self.cublas_handle = gpu_init.cublas_handle - + # Initialize GPU caches self.gpuCache = None - + def _initGPUCache(self, kern, num_inducing, input_dim, output_dim, Y): ndata = Y.shape[0] if self.batchsize==None: @@ -75,10 +74,10 @@ class VarDTC_GPU(LatentFunctionInference): 'psi2p_gpu' :gpuarray.empty((num_inducing,num_inducing),np.float64,order='F'), } self.gpuCache['ones_gpu'].fill(1.0) - + YT_gpu = self.gpuCache['YT_gpu'] self._trYYT = cublas.cublasDdot(self.cublas_handle, YT_gpu.size, YT_gpu.gpudata, 1, YT_gpu.gpudata, 1) - + def _estimateMemoryOccupation(self, N, M, D): """ Estimate the best batch size. @@ -89,7 +88,7 @@ class VarDTC_GPU(LatentFunctionInference): unit: GB """ return (M+9.*M*M+3*M*D+N+2.*N*D)*8./1024./1024./1024., (4.+3.*M+D+3.*M*M)*8./1024./1024./1024. - + def _estimateBatchSize(self, kern, N, M, Q, D): """ Estimate the best batch size. @@ -104,11 +103,11 @@ class VarDTC_GPU(LatentFunctionInference): else: x0, x1 = 0.,0. y0, y1 = self._estimateMemoryOccupation(N, M, D) - + opt_batchsize = min(int((self.gpu_memory-y0-x0)/(x1+y1)), N) - + return opt_batchsize - + def _get_YYTfactor(self, Y): """ find a matrix L which satisfies LLT = YYT. @@ -117,10 +116,10 @@ class VarDTC_GPU(LatentFunctionInference): """ N, D = Y.shape if (N>=D): - return param_to_array(Y) + return Y.view(np.ndarray) else: return jitchol(tdot(Y)) - + def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs, het_noise): num_inducing, input_dim = Z.shape[0], Z.shape[1] num_data, output_dim = Y.shape @@ -130,7 +129,7 @@ class VarDTC_GPU(LatentFunctionInference): beta_gpu = self.gpuCache['beta_gpu'] YT_gpu = self.gpuCache['YT_gpu'] betaYT_gpu = self.gpuCache['betaYT_gpu'] - + beta_gpu.fill(beta) betaYT_gpu.fill(0.) cublas.cublasDaxpy(self.cublas_handle, betaYT_gpu.size, beta, YT_gpu.gpudata, 1, betaYT_gpu.gpudata, 1) @@ -140,7 +139,7 @@ class VarDTC_GPU(LatentFunctionInference): psi1Y_gpu.fill(0.) psi2_gpu.fill(0.) psi0_full = 0 - + for n_start in xrange(0,num_data,self.batchsize): n_end = min(self.batchsize+n_start, num_data) ndata = n_end - n_start @@ -156,35 +155,35 @@ class VarDTC_GPU(LatentFunctionInference): psi1p_gpu = kern.K(X_slice, Z) cublas.cublasDgemm(self.cublas_handle, 'T', 'T', num_inducing, output_dim, ndata, 1.0, psi1p_gpu.gpudata, ndata, betaYT_gpu_slice.gpudata, output_dim, 1.0, psi1Y_gpu.gpudata, num_inducing) - + psi0_full += psi0.sum() - + if uncertain_inputs: sum_axis(psi2_gpu,psi2p_gpu,1,1) else: cublas.cublasDgemm(self.cublas_handle, 'T', 'N', num_inducing, num_inducing, ndata, beta, psi1p_gpu.gpudata, ndata, psi1p_gpu.gpudata, ndata, 1.0, psi2_gpu.gpudata, num_inducing) - + psi0_full *= beta if uncertain_inputs: cublas.cublasDscal(self.cublas_handle, psi2_gpu.size, beta, psi2_gpu.gpudata, 1) - - else: + + else: psi2_full = np.zeros((num_inducing,num_inducing)) psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM psi0_full = 0. YRY_full = 0. - - for n_start in xrange(0,num_data,self.batchsize): + + for n_start in xrange(0,num_data,self.batchsize): n_end = min(self.batchsize+n_start, num_data) Y_slice = Y[n_start:n_end] X_slice = X[n_start:n_end] - + if het_noise: b = beta[n_start] YRY_full += np.inner(Y_slice, Y_slice)*b else: b = beta - + if uncertain_inputs: psi0 = kern.psi0(Z, X_slice) psi1 = kern.psi1(Z, X_slice) @@ -193,50 +192,50 @@ class VarDTC_GPU(LatentFunctionInference): psi0 = kern.Kdiag(X_slice) psi1 = kern.K(X_slice, Z) psi2_full += np.dot(psi1.T,psi1)*b - + psi0_full += psi0.sum()*b - psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM - + psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM + if not het_noise: YRY_full = trYYT*beta psi1Y_gpu.set(psi1Y_full) psi2_gpu.set(psi2_full) - + return psi0_full, YRY_full - + def inference_likelihood(self, kern, X, Z, likelihood, Y): """ The first phase of inference: Compute: log-likelihood, dL_dKmm - + Cached intermediate results: Kmm, KmmInv, """ - + num_inducing, input_dim = Z.shape[0], Z.shape[1] num_data, output_dim = Y.shape - + #see whether we've got a different noise variance for each datum beta = 1./np.fmax(likelihood.variance, 1e-6) het_noise = beta.size > 1 if het_noise: self.batchsize=0 - + self._initGPUCache(kern, num_inducing, input_dim, output_dim, Y) if isinstance(X, VariationalPosterior): uncertain_inputs = True else: uncertain_inputs = False - + psi1Y_gpu = self.gpuCache['psi1Y_gpu'] psi2_gpu = self.gpuCache['psi2_gpu'] - + psi0_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs, het_noise) - + #====================================================================== # Compute Common Components #====================================================================== - + Kmm = kern.K(Z).copy() Kmm_gpu = self.gpuCache['Kmm_gpu'] Kmm_gpu.set(np.asfortranarray(Kmm)) @@ -244,14 +243,14 @@ class VarDTC_GPU(LatentFunctionInference): ones_gpu = self.gpuCache['ones_gpu'] cublas.cublasDaxpy(self.cublas_handle, num_inducing, self.const_jitter, ones_gpu.gpudata, 1, Kmm_gpu.gpudata, num_inducing+1) # assert np.allclose(Kmm, Kmm_gpu.get()) - + # Lm = jitchol(Kmm) # Lm_gpu = self.gpuCache['Lm_gpu'] cublas.cublasDcopy(self.cublas_handle, Kmm_gpu.size, Kmm_gpu.gpudata, 1, Lm_gpu.gpudata, 1) culinalg.cho_factor(Lm_gpu,'L') # print np.abs(np.tril(Lm)-np.tril(Lm_gpu.get())).max() - + # Lambda = Kmm+psi2_full # LL = jitchol(Lambda) # @@ -261,7 +260,7 @@ class VarDTC_GPU(LatentFunctionInference): LL_gpu = Lambda_gpu culinalg.cho_factor(LL_gpu,'L') # print np.abs(np.tril(LL)-np.tril(LL_gpu.get())).max() - + # b,_ = dtrtrs(LL, psi1Y_full) # bbt_cpu = np.square(b).sum() # @@ -270,7 +269,7 @@ class VarDTC_GPU(LatentFunctionInference): cublas.cublasDtrsm(self.cublas_handle , 'L', 'L', 'N', 'N', num_inducing, output_dim, np.float64(1.0), LL_gpu.gpudata, num_inducing, b_gpu.gpudata, num_inducing) bbt = cublas.cublasDdot(self.cublas_handle, b_gpu.size, b_gpu.gpudata, 1, b_gpu.gpudata, 1) # print np.abs(bbt-bbt_cpu) - + # v,_ = dtrtrs(LL.T,b,lower=False) # vvt = np.einsum('md,od->mo',v,v) # LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right') @@ -288,7 +287,7 @@ class VarDTC_GPU(LatentFunctionInference): tr_LmInvPsi2LmInvT = float(strideSum(LmInvPsi2LmInvT_gpu, num_inducing+1).get()) # print np.abs(vvt-vvt_gpu.get()).max() # print np.abs(np.trace(LmInvPsi2LmInvT)-tr_LmInvPsi2LmInvT) - + # Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T # LmInvPsi2LLInvT= dtrtrs(Lm,Psi2LLInvT)[0] # KmmInvPsi2LLInvT = dtrtrs(Lm,LmInvPsi2LLInvT,trans=True)[0] @@ -303,7 +302,7 @@ class VarDTC_GPU(LatentFunctionInference): cublas.cublasDcopy(self.cublas_handle, KmmInvPsi2LLInvT_gpu.size, KmmInvPsi2LLInvT_gpu.gpudata, 1, KmmInvPsi2P_gpu.gpudata, 1) cublas.cublasDtrsm(self.cublas_handle , 'r', 'L', 'N', 'N', num_inducing, num_inducing, np.float64(1.0), LL_gpu.gpudata, num_inducing, KmmInvPsi2P_gpu.gpudata, num_inducing) # print np.abs(KmmInvPsi2P-KmmInvPsi2P_gpu.get()).max() - + # dL_dpsi2R = (output_dim*KmmInvPsi2P - vvt)/2. # dL_dpsi2 with R inside psi2 # dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu'] @@ -311,7 +310,7 @@ class VarDTC_GPU(LatentFunctionInference): cublas.cublasDaxpy(self.cublas_handle, KmmInvPsi2P_gpu.size, np.float64(-output_dim), KmmInvPsi2P_gpu.gpudata, 1, dL_dpsi2R_gpu.gpudata, 1) cublas.cublasDscal(self.cublas_handle, dL_dpsi2R_gpu.size, np.float64(-0.5), dL_dpsi2R_gpu.gpudata, 1) # print np.abs(dL_dpsi2R_gpu.get()-dL_dpsi2R).max() - + #====================================================================== # Compute log-likelihood #====================================================================== @@ -320,7 +319,7 @@ class VarDTC_GPU(LatentFunctionInference): else: logL_R = -num_data*np.log(beta) # logL_old = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-np.trace(LmInvPsi2LmInvT))+YRY_full-bbt)/2.-output_dim*(-np.log(np.diag(Lm)).sum()+np.log(np.diag(LL)).sum()) - + logdetKmm = float(logDiagSum(Lm_gpu,num_inducing+1).get()) logdetLambda = float(logDiagSum(LL_gpu,num_inducing+1).get()) logL = -(output_dim*(num_data*log_2_pi+logL_R+psi0_full-tr_LmInvPsi2LmInvT)+YRY_full-bbt)/2.+output_dim*(logdetKmm-logdetLambda) @@ -329,7 +328,7 @@ class VarDTC_GPU(LatentFunctionInference): #====================================================================== # Compute dL_dKmm #====================================================================== - + # dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2. # dL_dKmm_gpu = self.gpuCache['dL_dKmm_gpu'] @@ -341,24 +340,24 @@ class VarDTC_GPU(LatentFunctionInference): #====================================================================== # Compute the Posterior distribution of inducing points p(u|Y) #====================================================================== - + post = Posterior(woodbury_inv=KmmInvPsi2P_gpu.get(), woodbury_vector=v_gpu.get(), K=Kmm_gpu.get(), mean=None, cov=None, K_chol=Lm_gpu.get()) #====================================================================== # Compute dL_dthetaL for uncertian input and non-heter noise - #====================================================================== - + #====================================================================== + if not het_noise: dL_dthetaL = (YRY_full + output_dim*psi0_full - num_data*output_dim)/-2. dL_dthetaL += cublas.cublasDdot(self.cublas_handle,dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata,1,psi2_gpu.gpudata,1) dL_dthetaL += cublas.cublasDdot(self.cublas_handle,v_gpu.size, v_gpu.gpudata,1,psi1Y_gpu.gpudata,1) self.midRes['dL_dthetaL'] = -beta*dL_dthetaL - + return logL, dL_dKmm_gpu.get(), post def inference_minibatch(self, kern, X, Z, likelihood, Y): """ - The second phase of inference: Computing the derivatives over a minibatch of Y + The second phase of inference: Computing the derivatives over a minibatch of Y Compute: dL_dpsi0, dL_dpsi1, dL_dpsi2, dL_dthetaL return a flag showing whether it reached the end of Y (isEnd) """ @@ -370,10 +369,10 @@ class VarDTC_GPU(LatentFunctionInference): uncertain_inputs = True else: uncertain_inputs = False - + beta = 1./np.fmax(likelihood.variance, 1e-6) het_noise = beta.size > 1 - + n_start = self.batch_pos n_end = min(self.batchsize+n_start, num_data) if n_end==num_data: @@ -382,12 +381,12 @@ class VarDTC_GPU(LatentFunctionInference): else: isEnd = False self.batch_pos = n_end - + nSlice = n_end-n_start X_slice = X[n_start:n_end] if het_noise: beta = beta[n_start] # nSlice==1 - + if kern.useGPU: if not uncertain_inputs: psi0p_gpu = kern.Kdiag(X_slice) @@ -416,28 +415,28 @@ class VarDTC_GPU(LatentFunctionInference): psi1p_gpu.set(np.asfortranarray(psi1)) if uncertain_inputs: psi2p_gpu.set(np.asfortranarray(psi2)) - + #====================================================================== # Compute dL_dpsi #====================================================================== dL_dpsi2R_gpu = self.gpuCache['dL_dpsi2R_gpu'] - v_gpu = self.gpuCache['v_gpu'] + v_gpu = self.gpuCache['v_gpu'] dL_dpsi0_gpu = self.gpuCache['dL_dpsi0_gpu'] dL_dpsi1_gpu = self.gpuCache['dL_dpsi1_gpu'] dL_dpsi2_gpu = self.gpuCache['dL_dpsi2_gpu'] betaYT_gpu = self.gpuCache['betaYT_gpu'] betaYT_gpu_slice = betaYT_gpu[:,n_start:n_end] - + # Adjust to the batch size if dL_dpsi0_gpu.shape[0] > nSlice: dL_dpsi0_gpu = dL_dpsi0_gpu.ravel()[:nSlice] dL_dpsi1_gpu = dL_dpsi1_gpu.ravel()[:nSlice*num_inducing].reshape(nSlice,num_inducing) - + dL_dpsi0_gpu.fill(-output_dim *beta/2.) - + cublas.cublasDgemm(self.cublas_handle, 'T', 'T', nSlice, num_inducing, output_dim, 1.0, betaYT_gpu_slice.gpudata, output_dim, v_gpu.gpudata, num_inducing, 0., dL_dpsi1_gpu.gpudata, nSlice) - + if uncertain_inputs: cublas.cublasDcopy(self.cublas_handle, dL_dpsi2R_gpu.size, dL_dpsi2R_gpu.gpudata, 1, dL_dpsi2_gpu.gpudata, 1) cublas.cublasDscal(self.cublas_handle, dL_dpsi2_gpu.size, beta, dL_dpsi2_gpu.gpudata, 1) @@ -458,7 +457,7 @@ class VarDTC_GPU(LatentFunctionInference): dL_dpsi1 = dL_dpsi1_gpu else: dL_dpsi0 = dL_dpsi0_gpu.get() - dL_dpsi1 = dL_dpsi1_gpu.get() + dL_dpsi1 = dL_dpsi1_gpu.get() if uncertain_inputs: if kern.useGPU: dL_dpsi2 = dL_dpsi2_gpu @@ -480,4 +479,4 @@ class VarDTC_GPU(LatentFunctionInference): 'dL_dthetaL':dL_dthetaL} return isEnd, (n_start,n_end), grad_dict - + diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py index b9ecbb5c..ae25f3e3 100644 --- a/GPy/inference/latent_function_inference/var_dtc_parallel.py +++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py @@ -6,7 +6,6 @@ from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdi from ...util import diag from ...core.parameterization.variational import VariationalPosterior import numpy as np -from ...util.misc import param_to_array from . import LatentFunctionInference log_2_pi = np.log(2*np.pi) @@ -27,26 +26,26 @@ class VarDTC_minibatch(LatentFunctionInference): """ const_jitter = 1e-6 def __init__(self, batchsize=None, limit=1, mpi_comm=None): - + self.batchsize = batchsize self.mpi_comm = mpi_comm self.limit = limit - + # Cache functions from ...util.caching import Cacher self.get_trYYT = Cacher(self._get_trYYT, limit) self.get_YYTfactor = Cacher(self._get_YYTfactor, limit) - + self.midRes = {} self.batch_pos = 0 # the starting position of the current mini-batch self.Y_speedup = False # Replace Y with the cholesky factor of YY.T, but the posterior inference will be wrong - + def __getstate__(self): - # has to be overridden, as Cacher objects cannot be pickled. + # has to be overridden, as Cacher objects cannot be pickled. return self.batchsize, self.limit, self.Y_speedup def __setstate__(self, state): - # has to be overridden, as Cacher objects cannot be pickled. + # has to be overridden, as Cacher objects cannot be pickled. self.batchsize, self.limit, self.Y_speedup = state self.mpi_comm = None self.midRes = {} @@ -58,9 +57,9 @@ class VarDTC_minibatch(LatentFunctionInference): def set_limit(self, limit): self.get_trYYT.limit = limit self.get_YYTfactor.limit = limit - + def _get_trYYT(self, Y): - return param_to_array(np.sum(np.square(Y))) + return np.sum(np.square(Y)) def _get_YYTfactor(self, Y): """ @@ -70,19 +69,19 @@ class VarDTC_minibatch(LatentFunctionInference): """ N, D = Y.shape if (N>=D): - return param_to_array(Y) + return Y.view(np.ndarray) else: return jitchol(tdot(Y)) - + def gatherPsiStat(self, kern, X, Z, Y, beta, uncertain_inputs): - + het_noise = beta.size > 1 trYYT = self.get_trYYT(Y) if self.Y_speedup and not het_noise: Y = self.get_YYTfactor(Y) - - num_inducing = Z.shape[0] + + num_inducing = Z.shape[0] num_data, output_dim = Y.shape if self.batchsize == None: self.batchsize = num_data @@ -91,8 +90,8 @@ class VarDTC_minibatch(LatentFunctionInference): psi1Y_full = np.zeros((output_dim,num_inducing)) # DxM psi0_full = 0. YRY_full = 0. - - for n_start in xrange(0,num_data,self.batchsize): + + for n_start in xrange(0,num_data,self.batchsize): n_end = min(self.batchsize+n_start, num_data) if (n_end-n_start)==num_data: Y_slice = Y @@ -100,13 +99,13 @@ class VarDTC_minibatch(LatentFunctionInference): else: Y_slice = Y[n_start:n_end] X_slice = X[n_start:n_end] - + if het_noise: b = beta[n_start] YRY_full += np.inner(Y_slice, Y_slice)*b else: b = beta - + if uncertain_inputs: psi0 = kern.psi0(Z, X_slice) psi1 = kern.psi1(Z, X_slice) @@ -115,13 +114,13 @@ class VarDTC_minibatch(LatentFunctionInference): psi0 = kern.Kdiag(X_slice) psi1 = kern.K(X_slice, Z) psi2_full += np.dot(psi1.T,psi1)*b - + psi0_full += psi0.sum()*b - psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM + psi1Y_full += np.dot(Y_slice.T,psi1)*b # DxM if not het_noise: YRY_full = trYYT*beta - + if self.mpi_comm != None: psi0_all = np.array(psi0_full) psi1Y_all = psi1Y_full.copy() @@ -132,18 +131,18 @@ class VarDTC_minibatch(LatentFunctionInference): self.mpi_comm.Allreduce([psi2_full, MPI.DOUBLE], [psi2_all, MPI.DOUBLE]) self.mpi_comm.Allreduce([YRY_full, MPI.DOUBLE], [YRY_all, MPI.DOUBLE]) return psi0_all, psi1Y_all, psi2_all, YRY_all - + return psi0_full, psi1Y_full, psi2_full, YRY_full - + def inference_likelihood(self, kern, X, Z, likelihood, Y): """ The first phase of inference: Compute: log-likelihood, dL_dKmm - + Cached intermediate results: Kmm, KmmInv, """ - - num_data, output_dim = Y.shape + + num_data, output_dim = Y.shape input_dim = Z.shape[0] if self.mpi_comm != None: num_data_all = np.array(num_data,dtype=np.int32) @@ -154,7 +153,7 @@ class VarDTC_minibatch(LatentFunctionInference): uncertain_inputs = True else: uncertain_inputs = False - + #see whether we've got a different noise variance for each datum beta = 1./np.fmax(likelihood.variance, 1e-6) het_noise = beta.size > 1 @@ -162,28 +161,28 @@ class VarDTC_minibatch(LatentFunctionInference): self.batchsize = 1 psi0_full, psi1Y_full, psi2_full, YRY_full = self.gatherPsiStat(kern, X, Z, Y, beta, uncertain_inputs) - + #====================================================================== # Compute Common Components #====================================================================== - + Kmm = kern.K(Z).copy() diag.add(Kmm, self.const_jitter) KmmInv,Lm,LmInv,_ = pdinv(Kmm) - + LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T) Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT LInv,LL,LLInv,logdet_L = pdinv(Lambda) b = LLInv.dot(LmInv.dot(psi1Y_full.T)) bbt = np.square(b).sum() v = LmInv.T.dot(LLInv.T.dot(b)) - + dL_dpsi2R = LmInv.T.dot(-LLInv.T.dot(tdot(b)+output_dim*np.eye(input_dim)).dot(LLInv)+output_dim*np.eye(input_dim)).dot(LmInv)/2. - + # Cache intermediate results self.midRes['dL_dpsi2R'] = dL_dpsi2R self.midRes['v'] = v - + #====================================================================== # Compute log-likelihood #====================================================================== @@ -196,22 +195,22 @@ class VarDTC_minibatch(LatentFunctionInference): #====================================================================== # Compute dL_dKmm #====================================================================== - + dL_dKmm = dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2. #====================================================================== # Compute the Posterior distribution of inducing points p(u|Y) #====================================================================== - + if not self.Y_speedup or het_noise: post = Posterior(woodbury_inv=LmInv.T.dot(np.eye(input_dim)-LInv).dot(LmInv), woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm) else: post = None - + #====================================================================== # Compute dL_dthetaL for uncertian input and non-heter noise - #====================================================================== - + #====================================================================== + if not het_noise: dL_dthetaL = (YRY_full*beta + beta*output_dim*psi0_full - num_data*output_dim*beta)/2. - beta*(dL_dpsi2R*psi2_full).sum() - beta*(v.T*psi1Y_full).sum() self.midRes['dL_dthetaL'] = dL_dthetaL @@ -220,7 +219,7 @@ class VarDTC_minibatch(LatentFunctionInference): def inference_minibatch(self, kern, X, Z, likelihood, Y): """ - The second phase of inference: Computing the derivatives over a minibatch of Y + The second phase of inference: Computing the derivatives over a minibatch of Y Compute: dL_dpsi0, dL_dpsi1, dL_dpsi2, dL_dthetaL return a flag showing whether it reached the end of Y (isEnd) """ @@ -231,7 +230,7 @@ class VarDTC_minibatch(LatentFunctionInference): uncertain_inputs = True else: uncertain_inputs = False - + #see whether we've got a different noise variance for each datum beta = 1./np.fmax(likelihood.variance, 1e-6) het_noise = beta.size > 1 @@ -241,7 +240,7 @@ class VarDTC_minibatch(LatentFunctionInference): YYT_factor = self.get_YYTfactor(Y) else: YYT_factor = Y - + n_start = self.batch_pos n_end = min(self.batchsize+n_start, num_data) if n_end==num_data: @@ -250,10 +249,10 @@ class VarDTC_minibatch(LatentFunctionInference): else: isEnd = False self.batch_pos = n_end - + Y_slice = YYT_factor[n_start:n_end] X_slice = X[n_start:n_end] - + if not uncertain_inputs: psi0 = kern.Kdiag(X_slice) psi1 = kern.K(X_slice, Z) @@ -264,33 +263,33 @@ class VarDTC_minibatch(LatentFunctionInference): psi1 = kern.psi1(Z, X_slice) psi2 = kern.psi2(Z, X_slice) betapsi1 = np.einsum('n,nm->nm',beta,psi1) - + if het_noise: beta = beta[n_start] # assuming batchsize==1 betaY = beta*Y_slice - + #====================================================================== # Load Intermediate Results #====================================================================== - + dL_dpsi2R = self.midRes['dL_dpsi2R'] v = self.midRes['v'] - + #====================================================================== # Compute dL_dpsi #====================================================================== - + dL_dpsi0 = -output_dim * (beta * np.ones((n_end-n_start,)))/2. - + dL_dpsi1 = np.dot(betaY,v.T) - + if uncertain_inputs: dL_dpsi2 = beta* dL_dpsi2R else: dL_dpsi1 += np.dot(betapsi1,dL_dpsi2R)*2. dL_dpsi2 = None - + #====================================================================== # Compute dL_dthetaL #====================================================================== @@ -300,14 +299,14 @@ class VarDTC_minibatch(LatentFunctionInference): psiR = np.einsum('mo,mo->',dL_dpsi2R,psi2) else: psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R) - + dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1) else: if isEnd: dL_dthetaL = self.midRes['dL_dthetaL'] else: dL_dthetaL = 0. - + if uncertain_inputs: grad_dict = {'dL_dpsi0':dL_dpsi0, 'dL_dpsi1':dL_dpsi1, @@ -317,7 +316,7 @@ class VarDTC_minibatch(LatentFunctionInference): grad_dict = {'dL_dKdiag':dL_dpsi0, 'dL_dKnm':dL_dpsi1, 'dL_dthetaL':dL_dthetaL} - + return isEnd, (n_start,n_end), grad_dict @@ -330,18 +329,18 @@ def update_gradients(model, mpi_comm=None): X = model.X[model.N_range[0]:model.N_range[1]] model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y) - + het_noise = model.likelihood.variance.size > 1 - + if het_noise: dL_dthetaL = np.empty((model.Y.shape[0],)) else: dL_dthetaL = np.float64(0.) - + kern_grad = model.kern.gradient.copy() kern_grad[:] = 0. model.Z.gradient = 0. - + isEnd = False while not isEnd: isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, X, model.Z, model.likelihood, Y) @@ -352,24 +351,24 @@ def update_gradients(model, mpi_comm=None): X_slice = model.X[n_range[0]:n_range[1]] else: X_slice = model.X[model.N_range[0]+n_range[0]:model.N_range[0]+n_range[1]] - + #gradients w.r.t. kernel model.kern.update_gradients_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2']) kern_grad += model.kern.gradient - + #gradients w.r.t. Z model.Z.gradient += model.kern.gradients_Z_expectations( dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=X_slice) - + #gradients w.r.t. posterior parameters of X X_grad = model.kern.gradients_qX_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2']) model.set_X_gradients(X_slice, X_grad) - + if het_noise: dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL'] else: dL_dthetaL += grad_dict['dL_dthetaL'] - + # Gather the gradients from multiple MPI nodes if mpi_comm != None: if het_noise: @@ -380,14 +379,14 @@ def update_gradients(model, mpi_comm=None): mpi_comm.Allreduce([model.Z.gradient, MPI.DOUBLE], [Z_grad_all, MPI.DOUBLE]) kern_grad = kern_grad_all model.Z.gradient = Z_grad_all - + #gradients w.r.t. kernel model.kern.update_gradients_full(dL_dKmm, model.Z, None) model.kern.gradient += kern_grad #gradients w.r.t. Z model.Z.gradient += model.kern.gradients_X(dL_dKmm, model.Z) - + # Update Log-likelihood KL_div = model.variational_prior.KL_divergence(X) # update for the KL divergence diff --git a/GPy/kern/_src/coregionalize.py b/GPy/kern/_src/coregionalize.py index fc4a2f33..3fcf1c98 100644 --- a/GPy/kern/_src/coregionalize.py +++ b/GPy/kern/_src/coregionalize.py @@ -6,6 +6,7 @@ import numpy as np from scipy import weave from ...core.parameterization import Param from ...core.parameterization.transformations import Logexp +from ...util.config import config # for assesing whether to use weave class Coregionalize(Kern): """ @@ -56,6 +57,27 @@ class Coregionalize(Kern): self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa) def K(self, X, X2=None): + if config.getboolean('weave', 'working'): + try: + return self._K_weave(X, X2) + except: + print "\n Weave compilation failed. Falling back to (slower) numpy implementation\n" + config.set('weave', 'working', 'False') + return self._K_numpy(X, X2) + else: + return self._K_numpy(X, X2) + + + def _K_numpy(self, X, X2=None): + index = np.asarray(X, dtype=np.int) + if X2 is None: + return self.B[index,index.T] + else: + index2 = np.asarray(X2, dtype=np.int) + return self.B[index,index2.T] + + def _K_weave(self, X, X2=None): + """compute the kernel function using scipy.weave""" index = np.asarray(X, dtype=np.int) if X2 is None: @@ -91,12 +113,33 @@ class Coregionalize(Kern): def update_gradients_full(self, dL_dK, X, X2=None): index = np.asarray(X, dtype=np.int) - dL_dK_small = np.zeros_like(self.B) if X2 is None: index2 = index else: index2 = np.asarray(X2, dtype=np.int) + #attempt to use weave for a nasty double indexing loop: fall back to numpy + if config.getboolean('weave', 'working'): + try: + dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2) + except: + print "\n Weave compilation failed. Falling back to (slower) numpy implementation\n" + config.set('weave', 'working', 'False') + dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2) + else: + dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2) + + + + dkappa = np.diag(dL_dK_small) + dL_dK_small += dL_dK_small.T + dW = (self.W[:, None, :]*dL_dK_small[:, :, None]).sum(0) + + self.W.gradient = dW + self.kappa.gradient = dkappa + + def _gradient_reduce_weave(self, dL_dK, index, index2): + dL_dK_small = np.zeros_like(self.B) code=""" for(int i=0; inm',np.square(mu[:,None,:]-Z[None,:,:]),1./(S+lengthscale2)))/(-2.) _psi1 = variance*np.exp(_psi1_log) - + return _psi1 def __psi2computations(variance, lengthscale, Z, mu, S): @@ -54,27 +54,27 @@ def __psi2computations(variance, lengthscale, Z, mu, S): # here are the "statistics" for psi2 # Produced intermediate results: # _psi2 MxM - + lengthscale2 = np.square(lengthscale) - + _psi2_logdenom = np.log(2.*S/lengthscale2+1.).sum(axis=-1)/(-2.) # N _psi2_exp1 = (np.square(Z[:,None,:]-Z[None,:,:])/lengthscale2).sum(axis=-1)/(-4.) #MxM Z_hat = (Z[:,None,:]+Z[None,:,:])/2. #MxMxQ denom = 1./(2.*S+lengthscale2) _psi2_exp2 = -(np.square(mu)*denom).sum(axis=-1)[:,None,None]+2.*np.einsum('nq,moq,nq->nmo',mu,Z_hat,denom)-np.einsum('moq,nq->nmo',np.square(Z_hat),denom) _psi2 = variance*variance*np.exp(_psi2_logdenom[:,None,None]+_psi2_exp1[None,:,:]+_psi2_exp2) - + return _psi2 def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior): ARD = (len(lengthscale)!=1) - + dvar_psi1, dl_psi1, dZ_psi1, dmu_psi1, dS_psi1 = _psi1compDer(dL_dpsi1, variance, lengthscale, Z, variational_posterior.mean, variational_posterior.variance) dvar_psi2, dl_psi2, dZ_psi2, dmu_psi2, dS_psi2 = _psi2compDer(dL_dpsi2, variance, lengthscale, Z, variational_posterior.mean, variational_posterior.variance) dL_dvar = np.sum(dL_dpsi0) + dvar_psi1 + dvar_psi2 - + dL_dlengscale = dl_psi1 + dl_psi2 if not ARD: dL_dlengscale = dL_dlengscale.sum() @@ -82,7 +82,7 @@ def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscal dL_dmu = dmu_psi1 + dmu_psi2 dL_dS = dS_psi1 + dS_psi2 dL_dZ = dZ_psi1 + dZ_psi2 - + return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S): @@ -101,9 +101,9 @@ def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S): # _dL_dgamma NxQ # _dL_dmu NxQ # _dL_dS NxQ - + lengthscale2 = np.square(lengthscale) - + _psi1 = _psi1computations(variance, lengthscale, Z, mu, S) Lpsi1 = dL_dpsi1*_psi1 Zmu = Z[None,:,:]-mu[:,None,:] # NxMxQ @@ -114,7 +114,7 @@ def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S): _dL_dS = np.einsum('nm,nmq,nq->nq',Lpsi1,(Zmu2_denom-1.),denom)/2. _dL_dZ = -np.einsum('nm,nmq,nq->mq',Lpsi1,Zmu,denom) _dL_dl = np.einsum('nm,nmq,nq->q',Lpsi1,(Zmu2_denom+(S/lengthscale2)[:,None,:]),denom*lengthscale) - + return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S): @@ -133,13 +133,12 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S): # _dL_dgamma NxQ # _dL_dmu NxQ # _dL_dS NxQ - + lengthscale2 = np.square(lengthscale) denom = 1./(2*S+lengthscale2) denom2 = np.square(denom) _psi2 = _psi2computations(variance, lengthscale, Z, mu, S) # NxMxM - Lpsi2 = dL_dpsi2[None,:,:]*_psi2 Lpsi2sum = np.einsum('nmo->n',Lpsi2) #N Lpsi2Z = np.einsum('nmo,oq->nq',Lpsi2,Z) #NxQ @@ -147,7 +146,7 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S): Lpsi2Z2p = np.einsum('nmo,mq,oq->nq',Lpsi2,Z,Z) #NxQ Lpsi2Zhat = Lpsi2Z Lpsi2Zhat2 = (Lpsi2Z2+Lpsi2Z2p)/2 - + _dL_dvar = Lpsi2sum.sum()*2/variance _dL_dmu = (-2*denom) * (mu*Lpsi2sum[:,None]-Lpsi2Zhat) _dL_dS = (2*np.square(denom))*(np.square(mu)*Lpsi2sum[:,None]-2*mu*Lpsi2Zhat+Lpsi2Zhat2) - denom*Lpsi2sum[:,None] @@ -157,6 +156,6 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S): (2*mu*denom2)*Lpsi2Zhat+denom2*Lpsi2Zhat2).sum(axis=0) return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS - + _psi1computations = Cacher(__psi1computations, limit=1) _psi2computations = Cacher(__psi2computations, limit=1) diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py index 5c5e7b5c..67cb6e62 100644 --- a/GPy/models/bayesian_gplvm.py +++ b/GPy/models/bayesian_gplvm.py @@ -25,12 +25,14 @@ class BayesianGPLVM(SparseGP_MPI): """ def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10, - Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', mpi_comm=None, normalizer=None): + Z=None, kernel=None, inference_method=None, likelihood=None, + name='bayesian gplvm', mpi_comm=None, normalizer=None, + missing_data=False): self.mpi_comm = mpi_comm self.__IN_OPTIMIZATION__ = False self.logger = logging.getLogger(self.__class__.__name__) - if X == None: + if X is None: from ..util.initialization import initialize_latent self.logger.info("initializing latent space X with method {}".format(init)) X, fracs = initialize_latent(init, input_dim, Y) @@ -59,24 +61,23 @@ class BayesianGPLVM(SparseGP_MPI): X = NormalPosterior(X, X_variance) if inference_method is None: - inan = np.isnan(Y) - if np.any(inan): - from ..inference.latent_function_inference.var_dtc import VarDTCMissingData - self.logger.debug("creating inference_method with var_dtc missing data") - inference_method = VarDTCMissingData(inan=inan) - elif mpi_comm is not None: + if mpi_comm is not None: inference_method = VarDTC_minibatch(mpi_comm=mpi_comm) else: from ..inference.latent_function_inference.var_dtc import VarDTC self.logger.debug("creating inference_method var_dtc") - inference_method = VarDTC() + inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1]) if isinstance(inference_method,VarDTC_minibatch): inference_method.mpi_comm = mpi_comm if kernel.useGPU and isinstance(inference_method, VarDTC_GPU): kernel.psicomp.GPU_direct = True - super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood, name=name, inference_method=inference_method, normalizer=normalizer, mpi_comm=mpi_comm, variational_prior=self.variational_prior) + super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood, + name=name, inference_method=inference_method, + normalizer=normalizer, mpi_comm=mpi_comm, + variational_prior=self.variational_prior, + missing_data=missing_data) def set_X_gradients(self, X, X_grad): """Set the gradients of the posterior distribution of X in its specific form.""" @@ -86,15 +87,53 @@ class BayesianGPLVM(SparseGP_MPI): """Get the gradients of the posterior distribution of X in its specific form.""" return X.mean.gradient, X.variance.gradient + def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None): + posterior, log_marginal_likelihood, grad_dict, current_values, value_indices = super(BayesianGPLVM, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices) + + log_marginal_likelihood -= self.variational_prior.KL_divergence(X) + + current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations( + variational_posterior=X, + Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'], + dL_dpsi1=grad_dict['dL_dpsi1'], + dL_dpsi2=grad_dict['dL_dpsi2']) + + # Subsetting Variational Posterior objects, makes the gradients + # empty. We need them to be 0 though: + X.mean.gradient[:] = 0 + X.variance.gradient[:] = 0 + + self.variational_prior.update_gradients_KL(X) + current_values['meangrad'] += X.mean.gradient + current_values['vargrad'] += X.variance.gradient + + if subset_indices is not None: + value_indices['meangrad'] = subset_indices['samples'] + value_indices['vargrad'] = subset_indices['samples'] + return posterior, log_marginal_likelihood, grad_dict, current_values, value_indices + + def _outer_values_update(self, full_values): + """ + Here you put the values, which were collected before in the right places. + E.g. set the gradients of parameters, etc. + """ + super(BayesianGPLVM, self)._outer_values_update(full_values) + self.X.mean.gradient = full_values['meangrad'] + self.X.variance.gradient = full_values['vargrad'] + + def _outer_init_full_values(self): + return dict(meangrad=np.zeros(self.X.mean.shape), + vargrad=np.zeros(self.X.variance.shape)) + def parameters_changed(self): super(BayesianGPLVM,self).parameters_changed() if isinstance(self.inference_method, VarDTC_minibatch): return - super(BayesianGPLVM, self).parameters_changed() - self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) + #super(BayesianGPLVM, self).parameters_changed() + #self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) - self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2']) + #self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2']) # This is testing code ------------------------- # i = np.random.randint(self.X.shape[0]) @@ -113,7 +152,7 @@ class BayesianGPLVM(SparseGP_MPI): # ----------------------------------------------- # update for the KL divergence - self.variational_prior.update_gradients_KL(self.X) + #self.variational_prior.update_gradients_KL(self.X) def plot_latent(self, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, @@ -178,7 +217,7 @@ class BayesianGPLVM(SparseGP_MPI): """ dmu_dX = np.zeros_like(Xnew) for i in range(self.Z.shape[0]): - dmu_dX += self.kern.gradients_X(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :]) + dmu_dX += self.kern.gradients_X(self.grad_dict['dL_dpsi1'][i:i + 1, :], Xnew, self.Z[i:i + 1, :]) return dmu_dX def dmu_dXnew(self, Xnew): @@ -189,7 +228,7 @@ class BayesianGPLVM(SparseGP_MPI): ones = np.ones((1, 1)) for i in range(self.Z.shape[0]): gradients_X[:, i] = self.kern.gradients_X(ones, Xnew, self.Z[i:i + 1, :]).sum(-1) - return np.dot(gradients_X, self.Cpsi1Vf) + return np.dot(gradients_X, self.grad_dict['dL_dpsi1']) def plot_steepest_gradient_map(self, *args, ** kwargs): """ diff --git a/GPy/models/sparse_gp_regression.py b/GPy/models/sparse_gp_regression.py index 744de6e7..3be2aed2 100644 --- a/GPy/models/sparse_gp_regression.py +++ b/GPy/models/sparse_gp_regression.py @@ -7,7 +7,6 @@ from ..core import SparseGP from .. import likelihoods from .. import kern from ..inference.latent_function_inference import VarDTC -from ..util.misc import param_to_array from ..core.parameterization.variational import NormalPosterior class SparseGPRegression(SparseGP): @@ -40,7 +39,7 @@ class SparseGPRegression(SparseGP): # Z defaults to a subset of the data if Z is None: i = np.random.permutation(num_data)[:min(num_inducing, num_data)] - Z = param_to_array(X)[i].copy() + Z = X.view(np.ndarray)[i].copy() else: assert Z.shape[1] == input_dim diff --git a/GPy/plotting/matplot_dep/dim_reduction_plots.py b/GPy/plotting/matplot_dep/dim_reduction_plots.py index 20e8e962..b4fed8fd 100644 --- a/GPy/plotting/matplot_dep/dim_reduction_plots.py +++ b/GPy/plotting/matplot_dep/dim_reduction_plots.py @@ -1,7 +1,6 @@ import numpy as np from latent_space_visualizations.controllers.imshow_controller import ImshowController,ImAnnotateController -from ...util.misc import param_to_array from ...core.parameterization.variational import VariationalPosterior from .base_plots import x_frame2D import itertools @@ -55,9 +54,9 @@ def plot_latent(model, labels=None, which_indices=None, #fethch the data points X that we'd like to plot X = model.X if isinstance(X, VariationalPosterior): - X = param_to_array(X.mean) + X = X.mean else: - X = param_to_array(X) + X = X if X.shape[0] > 1000: @@ -175,7 +174,7 @@ def plot_latent(model, labels=None, which_indices=None, ax.set_aspect('auto') # set a nice aspect ratio if plot_inducing: - Z = param_to_array(model.Z) + Z = model.Z ax.plot(Z[:, input_1], Z[:, input_2], '^w') ax.set_xlim((xmin, xmax)) diff --git a/GPy/plotting/matplot_dep/kernel_plots.py b/GPy/plotting/matplot_dep/kernel_plots.py index c0bd1599..c2bd7d38 100644 --- a/GPy/plotting/matplot_dep/kernel_plots.py +++ b/GPy/plotting/matplot_dep/kernel_plots.py @@ -35,8 +35,7 @@ def add_bar_labels(fig, ax, bars, bottom=0): def plot_bars(fig, ax, x, ard_params, color, name, bottom=0): - from ...util.misc import param_to_array - return ax.bar(left=x, height=param_to_array(ard_params), width=.8, + return ax.bar(left=x, height=ard_params.view(np.ndarray), width=.8, bottom=bottom, align='center', color=color, edgecolor='k', linewidth=1.2, label=name.replace("_"," ")) diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py index 509c9485..ed024c0a 100644 --- a/GPy/plotting/matplot_dep/models_plots.py +++ b/GPy/plotting/matplot_dep/models_plots.py @@ -8,7 +8,6 @@ except: pass import numpy as np from base_plots import gpplot, x_frame1D, x_frame2D -from ...util.misc import param_to_array from ...models.gp_coregionalized_regression import GPCoregionalizedRegression from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression from scipy import sparse @@ -67,7 +66,6 @@ def plot_fit(model, plot_limits=None, which_data_rows='all', X_variance = model.X.variance else: X = model.X - #X, Y = param_to_array(X, model.Y) Y = model.Y if sparse.issparse(Y): Y = Y.todense().view(np.ndarray) diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py index e97f001b..5cced10d 100644 --- a/GPy/plotting/matplot_dep/variational_plots.py +++ b/GPy/plotting/matplot_dep/variational_plots.py @@ -1,5 +1,4 @@ import pylab as pb, numpy as np -from ...util.misc import param_to_array def plot(parameterized, fignum=None, ax=None, colors=None): """ @@ -21,7 +20,7 @@ def plot(parameterized, fignum=None, ax=None, colors=None): else: colors = iter(colors) plots = [] - means, variances = param_to_array(parameterized.mean, parameterized.variance) + means, variances = parameterized.mean, parameterized.variance x = np.arange(means.shape[0]) for i in range(means.shape[1]): if ax is None: @@ -68,7 +67,7 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid else: colors = iter(colors) plots = [] - means, variances, gamma = param_to_array(parameterized.mean, parameterized.variance, parameterized.binary_prob) + means, variances, gamma = parameterized.mean, parameterized.variance, parameterized.binary_prob x = np.arange(means.shape[0]) for i in range(means.shape[1]): if side_by_side: @@ -77,7 +76,7 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid else: sub1 = (means.shape[1]*2,1,2*i+1) sub2 = (means.shape[1]*2,1,2*i+2) - + # mean and variance plot a = fig.add_subplot(*sub1) a.plot(means, c='k', alpha=.3) diff --git a/GPy/plotting/matplot_dep/visualize.py b/GPy/plotting/matplot_dep/visualize.py index 957d5a78..9ff41730 100644 --- a/GPy/plotting/matplot_dep/visualize.py +++ b/GPy/plotting/matplot_dep/visualize.py @@ -4,7 +4,6 @@ import GPy import numpy as np import matplotlib as mpl import time -from ...util.misc import param_to_array from GPy.core.parameterization.variational import VariationalPosterior try: import visual @@ -127,7 +126,7 @@ class lvm(matplotlib_show): self.latent_index = latent_index self.latent_dim = model.input_dim self.disable_drag = disable_drag - + # The red cross which shows current latent point. self.latent_values = vals self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0] @@ -474,7 +473,7 @@ class mocap_data_show(matplotlib_show): self.axes.set_ylim(self.y_lim) self.axes.set_zlim(self.z_lim) self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1., 1.]) - + # self.axes.set_aspect('equal') # self.axes.autoscale(enable=False) @@ -500,7 +499,7 @@ class skeleton_show(mocap_data_show): :param vals: set of modeled angles to use for printing in the axis when it's first created. :type vals: np.array :param skel: skeleton object that has the parameters of the motion capture skeleton associated with it. - :type skel: mocap.skeleton object + :type skel: mocap.skeleton object :param padding: :type int """ @@ -512,7 +511,7 @@ class skeleton_show(mocap_data_show): """Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting. :param vals: the values that are being modelled.""" - + if self.padding>0: channels = np.zeros((self.vals.shape[0], self.vals.shape[1]+self.padding)) channels[:, 0:self.vals.shape[0]] = self.vals @@ -524,7 +523,7 @@ class skeleton_show(mocap_data_show): self.vals[:, 0] = vals_mat[:, 0].copy() self.vals[:, 1] = vals_mat[:, 2].copy() self.vals[:, 2] = vals_mat[:, 1].copy() - + def wrap_around(self, lim, connect): quot = lim[1] - lim[0] self.vals = rem(self.vals, quot)+lim[0] @@ -546,7 +545,7 @@ def data_play(Y, visualizer, frame_rate=30): Example usage: This example loads in the CMU mocap database (http://mocap.cs.cmu.edu) subject number 35 motion number 01. It then plays it using the mocap_show visualize object. - + .. code-block:: python data = GPy.util.datasets.cmu_mocap(subject='35', train_motions=['01']) @@ -556,7 +555,7 @@ def data_play(Y, visualizer, frame_rate=30): GPy.util.visualize.data_play(Y, visualize) """ - + for y in Y: visualizer.modify(y[None, :]) diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py index 95ad7961..dcfb9de4 100644 --- a/GPy/testing/kernel_tests.py +++ b/GPy/testing/kernel_tests.py @@ -356,6 +356,51 @@ class KernelTestsNonContinuous(unittest.TestCase): X2 = self.X2[self.X2[:,-1]!=2] self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1)) +class Coregionalize_weave_test(unittest.TestCase): + """ + Make sure that the coregionalize kernel work with and without weave enabled + """ + def setUp(self): + self.k = GPy.kern.Coregionalize(1, output_dim=12) + self.N1, self.N2 = 100, 200 + self.X = np.random.randint(0,12,(self.N1,1)) + self.X2 = np.random.randint(0,12,(self.N2,1)) + + def test_sym(self): + dL_dK = np.random.randn(self.N1, self.N1) + GPy.util.config.config.set('weave', 'working', 'True') + K_weave = self.k.K(self.X) + self.k.update_gradients_full(dL_dK, self.X) + grads_weave = self.k.gradient.copy() + + GPy.util.config.config.set('weave', 'working', 'False') + K_numpy = self.k.K(self.X) + self.k.update_gradients_full(dL_dK, self.X) + grads_numpy = self.k.gradient.copy() + + self.assertTrue(np.allclose(K_numpy, K_weave)) + self.assertTrue(np.allclose(grads_numpy, grads_weave)) + + def test_nonsym(self): + dL_dK = np.random.randn(self.N1, self.N2) + GPy.util.config.config.set('weave', 'working', 'True') + K_weave = self.k.K(self.X, self.X2) + self.k.update_gradients_full(dL_dK, self.X, self.X2) + grads_weave = self.k.gradient.copy() + + GPy.util.config.config.set('weave', 'working', 'False') + K_numpy = self.k.K(self.X, self.X2) + self.k.update_gradients_full(dL_dK, self.X, self.X2) + grads_numpy = self.k.gradient.copy() + + self.assertTrue(np.allclose(K_numpy, K_weave)) + self.assertTrue(np.allclose(grads_numpy, grads_weave)) + + #reset the weave state for any other tests + GPy.util.config.config.set('weave', 'working', 'False') + + + if __name__ == "__main__": print "Running unit tests, please be (very) patient..." diff --git a/GPy/testing/model_tests.py b/GPy/testing/model_tests.py index 42f82121..c87a44b1 100644 --- a/GPy/testing/model_tests.py +++ b/GPy/testing/model_tests.py @@ -20,7 +20,7 @@ class MiscTests(unittest.TestCase): m = GPy.models.GPRegression(self.X, self.Y, kernel=k) m.randomize() m.likelihood.variance = .5 - Kinv = np.linalg.pinv(k.K(self.X) + np.eye(self.N)*m.likelihood.variance) + Kinv = np.linalg.pinv(k.K(self.X) + np.eye(self.N) * m.likelihood.variance) K_hat = k.K(self.X_new) - k.K(self.X_new, self.X).dot(Kinv).dot(k.K(self.X, self.X_new)) mu_hat = k.K(self.X_new, self.X).dot(Kinv).dot(m.Y_normalized) @@ -43,7 +43,7 @@ class MiscTests(unittest.TestCase): Z = m.Z[:] X = self.X[:] - #Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression + # Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression Kinv = m.posterior.woodbury_inv K_hat = k.K(self.X_new) - k.K(self.X_new, Z).dot(Kinv).dot(k.K(Z, self.X_new)) @@ -51,13 +51,13 @@ class MiscTests(unittest.TestCase): self.assertEquals(mu.shape, (self.N_new, self.D)) self.assertEquals(covar.shape, (self.N_new, self.N_new)) np.testing.assert_almost_equal(K_hat, covar) - #np.testing.assert_almost_equal(mu_hat, mu) + # np.testing.assert_almost_equal(mu_hat, mu) mu, var = m._raw_predict(self.X_new) self.assertEquals(mu.shape, (self.N_new, self.D)) self.assertEquals(var.shape, (self.N_new, 1)) np.testing.assert_almost_equal(np.diag(K_hat)[:, None], var) - #np.testing.assert_almost_equal(mu_hat, mu) + # np.testing.assert_almost_equal(mu_hat, mu) def test_likelihood_replicate(self): m = GPy.models.GPRegression(self.X, self.Y) @@ -110,6 +110,29 @@ class MiscTests(unittest.TestCase): m2.kern.lengthscale = m.kern['.*lengthscale'] np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood()) + def test_missing_data(self): + from GPy import kern + from GPy.models import BayesianGPLVM + from GPy.examples.dimensionality_reduction import _simulate_matern + + D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4 + _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False) + Y = Ylist[0] + + inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data + Ymissing = Y.copy() + Ymissing[inan] = np.nan + + k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q) + m = BayesianGPLVM(Ymissing, Q, init="random", num_inducing=num_inducing, + kernel=k, missing_data=True) + assert(m.checkgrad()) + + k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q) + m = BayesianGPLVM(Ymissing, Q, init="random", num_inducing=num_inducing, + kernel=k, missing_data=True) + assert(m.checkgrad()) + def test_likelihood_replicate_kern(self): m = GPy.models.GPRegression(self.X, self.Y) m2 = GPy.models.GPRegression(self.X, self.Y) @@ -158,7 +181,7 @@ class MiscTests(unittest.TestCase): def test_model_optimize(self): X = np.random.uniform(-3., 3., (20, 1)) Y = np.sin(X) + np.random.randn(20, 1) * 0.05 - m = GPy.models.GPRegression(X,Y) + m = GPy.models.GPRegression(X, Y) m.optimize() print m @@ -219,8 +242,8 @@ class GradientTests(np.testing.TestCase): mlp = GPy.kern.MLP(1) self.check_model(mlp, model_type='GPRegression', dimension=1) - #TODO: - #def test_GPRegression_poly_1d(self): + # TODO: + # def test_GPRegression_poly_1d(self): # ''' Testing the GP regression with polynomial kernel with white kernel on 1d data ''' # mlp = GPy.kern.Poly(1, degree=5) # self.check_model(mlp, model_type='GPRegression', dimension=1) @@ -417,11 +440,11 @@ class GradientTests(np.testing.TestCase): def test_gp_heteroscedastic_regression(self): num_obs = 25 - X = np.random.randint(0,140,num_obs) - X = X[:,None] - Y = 25. + np.sin(X/20.) * 2. + np.random.rand(num_obs)[:,None] + X = np.random.randint(0, 140, num_obs) + X = X[:, None] + Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None] kern = GPy.kern.Bias(1) + GPy.kern.RBF(1) - m = GPy.models.GPHeteroscedasticRegression(X,Y,kern) + m = GPy.models.GPHeteroscedasticRegression(X, Y, kern) self.assertTrue(m.checkgrad()) def test_gp_kronecker_gaussian(self): @@ -432,12 +455,12 @@ class GradientTests(np.testing.TestCase): k1 = GPy.kern.RBF(1) # + GPy.kern.White(1) k2 = GPy.kern.RBF(1) # + GPy.kern.White(1) Y = np.random.randn(N1, N2) - Y = Y-Y.mean(0) - Y = Y/Y.std(0) + Y = Y - Y.mean(0) + Y = Y / Y.std(0) m = GPy.models.GPKroneckerGaussianRegression(X1, X2, Y, k1, k2) # build the model the dumb way - assert (N1*N2<1000), "too much data for standard GPs!" + assert (N1 * N2 < 1000), "too much data for standard GPs!" yy, xx = np.meshgrid(X2, X1) Xgrid = np.vstack((xx.flatten(order='F'), yy.flatten(order='F'))).T kg = GPy.kern.RBF(1, active_dims=[0]) * GPy.kern.RBF(1, active_dims=[1]) @@ -458,11 +481,11 @@ class GradientTests(np.testing.TestCase): def test_gp_VGPC(self): num_obs = 25 - X = np.random.randint(0,140,num_obs) - X = X[:,None] - Y = 25. + np.sin(X/20.) * 2. + np.random.rand(num_obs)[:,None] + X = np.random.randint(0, 140, num_obs) + X = X[:, None] + Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None] kern = GPy.kern.Bias(1) + GPy.kern.RBF(1) - m = GPy.models.GPVariationalGaussianApproximation(X,Y,kern) + m = GPy.models.GPVariationalGaussianApproximation(X, Y, kern) self.assertTrue(m.checkgrad()) diff --git a/GPy/util/data_resources.json b/GPy/util/data_resources.json index f8b00ce8..1ed735c3 100644 --- a/GPy/util/data_resources.json +++ b/GPy/util/data_resources.json @@ -228,7 +228,7 @@ "fruitfly_tomancak_cel_files": { "citation": "'Systematic determination of patterns of gene expression during Drosophila embryogenesis' Pavel Tomancak, Amy Beaton, Richard Weiszmann, Elaine Kwan, ShengQiang Shu, Suzanna E Lewis, Stephen Richards, Michael Ashburner, Volker Hartenstein, Susan E Celniker, and Gerald M Rubin", "details": "Gene expression results from blastoderm development in Drosophila Melanogaster.", - "files": [ + "files": [ [ "embryo_tc_4_1.CEL", "embryo_tc_4_2.CEL", @@ -284,7 +284,7 @@ "details": "Google trends results.", "files": [ [ - + ] ], "license": null, @@ -293,7 +293,7 @@ "http://www.google.com/trends/" ] }, - + "hapmap3": { "citation": "Gibbs, Richard A., et al. 'The international HapMap project.' Nature 426.6968 (2003): 789-796.", "details": "HapMap Project: Single Nucleotide Polymorphism sequenced in all human populations. \n The HapMap phase three SNP dataset - 1184 samples out of 11 populations.\n See http://www.nature.com/nature/journal/v426/n6968/abs/nature02168.html for details.\n\n SNP_matrix (A) encoding [see Paschou et all. 2007 (PCA-Correlated SNPs...)]:\n Let (B1,B2) be the alphabetically sorted bases, which occur in the j-th SNP, then\n\n / 1, iff SNPij==(B1,B1)\n Aij = | 0, iff SNPij==(B1,B2)\n \\\\ -1, iff SNPij==(B2,B2)\n\n The SNP data and the meta information (such as iid, sex and phenotype) are\n stored in the dataframe datadf, index is the Individual ID, \n with following columns for metainfo:\n\n * family_id -> Family ID\n * paternal_id -> Paternal ID\n * maternal_id -> Maternal ID\n * sex -> Sex (1=male; 2=female; other=unknown)\n * phenotype -> Phenotype (-9, or 0 for unknown)\n * population -> Population string (e.g. 'ASW' - 'YRI')\n * rest are SNP rs (ids)\n\n More information is given in infodf:\n\n * Chromosome:\n - autosomal chromosemes -> 1-22\n - X X chromosome -> 23\n - Y Y chromosome -> 24\n - XY Pseudo-autosomal region of X -> 25\n - MT Mitochondrial -> 26\n * Relative Positon (to Chromosome) [base pairs]\n\n ", @@ -523,6 +523,23 @@ "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/singlecell/" ] }, + "singlecell_islam": { + "citation": "Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells Qiaolin Deng, Daniel Ramskoeld, Bjoern Reinius, and Rickard Sandberg Science 10 January 2014: 343 (6167), 193-196. [DOI:10.1126/science.1245316]", + "details" : "92 single cells (48 mouse ES cells, 44 mouse embryonic fibroblasts and 4 negative controls) were analyzed by single-cell tagged reverse transcription (STRT)", + "files" : [["GSE29087_L139_expression_tab.txt.gz"], ["GSE29087_family.soft.gz"]], + "license" : "Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html", + "size" : 1159449, + "urls" : ["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29087/suppl/", "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29087/soft/"] + }, + "singlecell_deng": { + "citation": "Deng Q, Ramsköld D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014 Jan 10;343(6167):193-6. PMID: 24408435", + "details" : "First generation mouse strain crosses were used to study monoallelic expression on the single cell level", + "files" : [["?acc=GSE45719&format=file"], ["GSE45719_series_matrix.txt.gz"]], + "license" : "Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html", + "size" : 1159449, + "save_names": [["GSE45719_Raw.tar"], [null]], + "urls" : ["http://www.ncbi.nlm.nih.gov/geo/download/", "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45719/matrix/"] + }, "sod1_mouse": { "citation": "Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis' Nardo G1, Iennaco R, Fusi N, Heath PR, Marino M, Trolese MC, Ferraiuolo L, Lawrence N, Shaw PJ, Bendotti C Brain. 2013 Nov;136(Pt 11):3305-32. doi: 10.1093/brain/awt250. Epub 2013 Sep 24.", "details": "Gene expression data from two separate strains of mice: C57 and 129Sv in wild type and SOD1 mutant strains.", diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py index 93a5dceb..c14ce66f 100644 --- a/GPy/util/datasets.py +++ b/GPy/util/datasets.py @@ -82,20 +82,32 @@ def prompt_user(prompt): def data_available(dataset_name=None): """Check if the data set is available on the local machine already.""" - for file_list in data_resources[dataset_name]['files']: - for file in file_list: - if not os.path.exists(os.path.join(data_path, dataset_name, file)): + from itertools import izip_longest + dr = data_resources[dataset_name] + zip_urls = (dr['files'], ) + if dr.has_key('save_names'): zip_urls += (dr['save_names'], ) + else: zip_urls += ([],) + + for file_list, save_list in izip_longest(*zip_urls, fillvalue=[]): + for f, s in izip_longest(file_list, save_list, fillvalue=None): + if s is not None: f=s # If there is a save_name given, use that one + if not os.path.exists(os.path.join(data_path, dataset_name, f)): return False return True -def download_url(url, store_directory, save_name = None, messages = True, suffix=''): +def download_url(url, store_directory, save_name=None, messages=True, suffix=''): """Download a file from a url and save it to disk.""" i = url.rfind('/') file = url[i+1:] print file dir_name = os.path.join(data_path, store_directory) - save_name = os.path.join(dir_name, file) - print "Downloading ", url, "->", os.path.join(store_directory, file) + + if save_name is None: save_name = os.path.join(dir_name, file) + else: save_name = os.path.join(dir_name, save_name) + + if suffix is None: suffix='' + + print "Downloading ", url, "->", save_name if not os.path.exists(dir_name): os.makedirs(dir_name) try: @@ -178,19 +190,24 @@ def authorize_download(dataset_name=None): def download_data(dataset_name=None): """Check with the user that the are happy with terms and conditions for the data set, then download it.""" + import itertools dr = data_resources[dataset_name] if not authorize_download(dataset_name): raise Exception("Permission to download data set denied.") - if dr.has_key('suffices'): - for url, files, suffices in zip(dr['urls'], dr['files'], dr['suffices']): - for file, suffix in zip(files, suffices): - download_url(os.path.join(url,file), dataset_name, dataset_name, suffix=suffix) - else: - for url, files in zip(dr['urls'], dr['files']): - for file in files: - download_url(os.path.join(url,file), dataset_name, dataset_name) + zip_urls = (dr['urls'], dr['files']) + + if dr.has_key('save_names'): zip_urls += (dr['save_names'], ) + else: zip_urls += ([],) + + if dr.has_key('suffices'): zip_urls += (dr['suffices'], ) + else: zip_urls += ([],) + + for url, files, save_names, suffices in itertools.izip_longest(*zip_urls, fillvalue=[]): + for f, save_name, suffix in itertools.izip_longest(files, save_names, suffices, fillvalue=None): + download_url(os.path.join(url,f), dataset_name, save_name, suffix=suffix) + return True def data_details_return(data, data_set): @@ -895,6 +912,129 @@ def singlecell(data_set='singlecell'): 'genes': genes, 'labels':labels, }, data_set) +def singlecell_rna_seq_islam(dataset='singlecell_islam'): + if not data_available(dataset): + download_data(dataset) + + from pandas import read_csv, DataFrame, concat + dir_path = os.path.join(data_path, dataset) + filename = os.path.join(dir_path, 'GSE29087_L139_expression_tab.txt.gz') + data = read_csv(filename, sep='\t', skiprows=6, compression='gzip', header=None) + header1 = read_csv(filename, sep='\t', header=None, skiprows=5, nrows=1, compression='gzip') + header2 = read_csv(filename, sep='\t', header=None, skiprows=3, nrows=1, compression='gzip') + data.columns = np.concatenate((header1.ix[0, :], header2.ix[0, 7:])) + Y = data.set_index("Feature").ix[8:, 6:-4].T.astype(float) + + # read the info .soft + filename = os.path.join(dir_path, 'GSE29087_family.soft.gz') + info = read_csv(filename, sep='\t', skiprows=0, compression='gzip', header=None) + # split at ' = ' + info = DataFrame(info.ix[:,0].str.split(' = ').tolist()) + # only take samples: + info = info[info[0].str.contains("!Sample")] + info[0] = info[0].apply(lambda row: row[len("!Sample_"):]) + + groups = info.groupby(0).groups + # remove 'GGG' from barcodes + barcode = info[1][groups['barcode']].apply(lambda row: row[:-3]) + + title = info[1][groups['title']] + title.index = barcode + title.name = 'title' + geo_accession = info[1][groups['geo_accession']] + geo_accession.index = barcode + geo_accession.name = 'geo_accession' + case_id = info[1][groups['source_name_ch1']] + case_id.index = barcode + case_id.name = 'source_name_ch1' + + info = concat([title, geo_accession, case_id], axis=1) + labels = info.join(Y).source_name_ch1[:-4] + labels[labels=='Embryonic stem cell'] = "ES" + labels[labels=='Embryonic fibroblast'] = "MEF" + + return data_details_return({'Y': Y, + 'info': '92 single cells (48 mouse ES cells, 44 mouse embryonic fibroblasts and 4 negative controls) were analyzed by single-cell tagged reverse transcription (STRT)', + 'genes': Y.columns, + 'labels': labels, + 'datadf': data, + 'infodf': info}, dataset) + +def singlecell_rna_seq_deng(dataset='singlecell_deng'): + if not data_available(dataset): + download_data(dataset) + + from pandas import read_csv + dir_path = os.path.join(data_path, dataset) + + # read the info .soft + filename = os.path.join(dir_path, 'GSE45719_series_matrix.txt.gz') + info = read_csv(filename, sep='\t', skiprows=0, compression='gzip', header=None, nrows=29, index_col=0) + summary = info.loc['!Series_summary'][1] + design = info.loc['!Series_overall_design'] + + # only take samples: + sample_info = read_csv(filename, sep='\t', skiprows=30, compression='gzip', header=0, index_col=0).T + sample_info.columns = sample_info.columns.to_series().apply(lambda row: row[len("!Sample_"):]) + sample_info.columns.name = sample_info.columns.name[len("!Sample_"):] + sample_info = sample_info[['geo_accession', 'characteristics_ch1', 'description']] + sample_info = sample_info.iloc[:, np.r_[0:4, 5:sample_info.shape[1]]] + c = sample_info.columns.to_series() + c[1:4] = ['strain', 'cross', 'developmental_stage'] + sample_info.columns = c + + # Extract the tar file + filename = os.path.join(dir_path, 'GSE45719_Raw.tar') + with tarfile.open(filename, 'r') as files: + print "Extracting Archive {}...".format(files.name) + data = None + gene_info = None + message = '' + members = files.getmembers() + overall = len(members) + for i, file_info in enumerate(members): + f = files.extractfile(file_info) + inner = read_csv(f, sep='\t', header=0, compression='gzip', index_col=0) + print ' '*(len(message)+1) + '\r', + message = "{: >7.2%}: Extracting: {}".format(float(i+1)/overall, file_info.name[:20]+"...txt.gz") + print message, + if data is None: + data = inner.RPKM.to_frame() + data.columns = [file_info.name[:-18]] + gene_info = inner.Refseq_IDs.to_frame() + gene_info.columns = [file_info.name[:-18]] + else: + data[file_info.name[:-18]] = inner.RPKM + gene_info[file_info.name[:-18]] = inner.Refseq_IDs + + # Strip GSM number off data index + rep = re.compile('GSM\d+_') + data.columns = data.columns.to_series().apply(lambda row: row[rep.match(row).end():]) + data = data.T + + # make sure the same index gets used + sample_info.index = data.index + + # get the labels from the description + rep = re.compile('fibroblast|\d+-cell|embryo|liver|blastocyst|blastomere|zygote', re.IGNORECASE) + labels = sample_info.developmental_stage.apply(lambda row: " ".join(rep.findall(row))) + + sys.stdout.write(' '*len(message) + '\r') + sys.stdout.flush() + print + print "Read Archive {}".format(files.name) + + return data_details_return({'Y': data, + 'series_info': info, + 'sample_info': sample_info, + 'gene_info': gene_info, + 'summary': summary, + 'design': design, + 'genes': data.columns, + 'labels': labels, + }, dataset) + + def swiss_roll_1000(): return swiss_roll(num_samples=1000) diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py index 517c0b52..cef699cf 100644 --- a/GPy/util/linalg.py +++ b/GPy/util/linalg.py @@ -10,11 +10,10 @@ from scipy import linalg, weave import types import ctypes from ctypes import byref, c_char, c_int, c_double # TODO -# import scipy.lib.lapack import scipy import warnings import os -from config import * +from config import config import logging _scipyversion = np.float64((scipy.__version__).split('.')[:2]) @@ -521,6 +520,27 @@ def symmetrify(A, upper=False): Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper works IN PLACE. + + note: tries to use weave, falls back to a slower numpy version + """ + if config.getboolean('weave', 'working'): + try: + symmetrify_weave(A, upper) + except: + print "\n Weave compilation failed. Falling back to (slower) numpy implementation\n" + config.set('weave', 'working', 'False') + symmetrify_numpy(A, upper) + else: + symmetrify_numpy(A, upper) + + +def symmetrify_weave(A, upper=False): + """ + Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper + + works IN PLACE. + + """ N, M = A.shape assert N == M @@ -563,10 +583,15 @@ def symmetrify(A, upper=False): A += np.tril(tmp, -1).T -def symmetrify_murray(A): - A += A.T - nn = A.shape[0] - A[[range(nn), range(nn)]] /= 2.0 +def symmetrify_numpy(A, upper=False): + """ + Force a matrix to be symmetric + """ + triu = np.triu_indices_from(A,k=1) + if upper: + A.T[triu] = A[triu] + else: + A[triu] = A.T[triu] def cholupdate(L, x): """ diff --git a/GPy/util/misc.py b/GPy/util/misc.py index fa9bb24c..bf37159d 100644 --- a/GPy/util/misc.py +++ b/GPy/util/misc.py @@ -2,7 +2,6 @@ # Licensed under the BSD 3-clause license (see LICENSE.txt) import numpy as np -from scipy import weave from config import * def chain_1(df_dg, dg_dx): @@ -84,96 +83,6 @@ def kmm_init(X, m = 10): inducing = np.array(inducing) return X[inducing] -def fast_array_equal(A, B): - - - if config.getboolean('parallel', 'openmp'): - pragma_string = '#pragma omp parallel for private(i, j)' - else: - pragma_string = '' - - code2=""" - int i, j; - return_val = 1; - - %s - for(i=0;i - """ % header_string - - - weave_options_openmp = {'headers' : [''], - 'extra_compile_args': ['-fopenmp -O3'], - 'extra_link_args' : ['-lgomp'], - 'libraries': ['gomp']} - weave_options_noopenmp = {'extra_compile_args': ['-O3']} - - if config.getboolean('parallel', 'openmp'): - weave_options = weave_options_openmp - else: - weave_options = weave_options_noopenmp - - value = False - - - if (A == None) and (B == None): - return True - elif ((A == None) and (B != None)) or ((A != None) and (B == None)): - return False - elif A.shape == B.shape: - if A.ndim == 2: - N, D = [int(i) for i in A.shape] - value = weave.inline(code2, support_code=support_code, - arg_names=['A', 'B', 'N', 'D'], - type_converters=weave.converters.blitz, **weave_options) - elif A.ndim == 3: - N, D, Q = [int(i) for i in A.shape] - value = weave.inline(code3, support_code=support_code, - arg_names=['A', 'B', 'N', 'D', 'Q'], - type_converters=weave.converters.blitz, **weave_options) - else: - value = np.array_equal(A,B) - - return value - ### make a parameter to its corresponding array: def param_to_array(*param): """ @@ -181,6 +90,8 @@ Convert an arbitrary number of parameters to :class:ndarray class objects. This converting parameter objects to numpy arrays, when using scipy.weave.inline routine. In scipy.weave.blitz there is no automatic array detection (even when the array inherits from :class:ndarray)""" + import warnings + warnings.warn("Please use param.values, as this function will be deprecated in the next release.", DeprecationWarning) assert len(param) > 0, "At least one parameter needed" if len(param) == 1: return param[0].view(np.ndarray) diff --git a/GPy/util/pca.py b/GPy/util/pca.py index 046f47d7..996e6f48 100644 --- a/GPy/util/pca.py +++ b/GPy/util/pca.py @@ -11,14 +11,16 @@ try: except: pass from numpy.linalg.linalg import LinAlgError +from operator import setitem +import itertools class pca(object): """ pca module with automatic primal/dual determination. """ def __init__(self, X): - self.mu = X.mean(0) - self.sigma = X.std(0) + self.mu = None + self.sigma = None X = self.center(X) @@ -39,6 +41,13 @@ class pca(object): """ Center `X` in pca space. """ + X = X.copy() + inan = numpy.isnan(X) + if self.mu is None: + X_ = numpy.ma.masked_array(X, inan) + self.mu = X_.mean(0).base + self.sigma = X_.std(0).base + reduce(lambda y,x: setitem(x[0], x[1], x[2]), itertools.izip(X.T, inan.T, self.mu), None) X = X - self.mu X = X / numpy.where(self.sigma == 0, 1e-30, self.sigma) return X @@ -94,7 +103,7 @@ class pca(object): fignum=None, cmap=None, # @UndefinedVariable ** kwargs): """ - Plot dimensions `dimensions` with given labels against each other in + Plot dimensions `dimensions` with given labels against each other in PC space. Labels can be any sequence of labels of dimensions X.shape[0]. Labels can be drawn with a subsequent call to legend() """ diff --git a/GPy/util/univariate_Gaussian.py b/GPy/util/univariate_Gaussian.py index b5472e0a..09b2e99c 100644 --- a/GPy/util/univariate_Gaussian.py +++ b/GPy/util/univariate_Gaussian.py @@ -12,6 +12,39 @@ def std_norm_cdf(x): """ Cumulative standard Gaussian distribution Based on Abramowitz, M. and Stegun, I. (1970) + """ + x_shape = np.asarray(x).shape + + if len(x_shape) == 0 or x_shape[0] == 1: + sign = np.sign(x) + x *= sign + x /= np.sqrt(2.) + t = 1.0/(1.0 + 0.3275911*x) + erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429))))) + cdf_x = 0.5*(1.0 + sign*erf) + return cdf_x + else: + x = np.atleast_1d(x).copy() + cdf_x = np.zeros_like(x) + sign = np.ones_like(x) + neg_x_ind = x<0 + sign[neg_x_ind] = -1.0 + x[neg_x_ind] = -x[neg_x_ind] + x /= np.sqrt(2.) + t = 1.0/(1.0 + 0.3275911*x) + erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429))))) + cdf_x = 0.5*(1.0 + sign*erf) + cdf_x = cdf_x.reshape(x_shape) + return cdf_x + +def std_norm_cdf_weave(x): + """ + Cumulative standard Gaussian distribution + Based on Abramowitz, M. and Stegun, I. (1970) + + A weave implementation of std_norm_cdf, which is faster. this is unused, + because of the difficulties of a weave dependency. (see github issue #94) + """ #Generalize for many x x = np.asarray(x).copy() @@ -40,37 +73,6 @@ def std_norm_cdf(x): weave.inline(code, arg_names=['x', 'cdf_x', 'N'], support_code=support_code) return cdf_x -def std_norm_cdf_np(x): - """ - Cumulative standard Gaussian distribution - Based on Abramowitz, M. and Stegun, I. (1970) - Around 3 times slower when x is a scalar otherwise quite a lot slower - """ - x_shape = np.asarray(x).shape - - if len(x_shape) == 0 or x_shape[0] == 1: - sign = np.sign(x) - x *= sign - x /= np.sqrt(2.) - t = 1.0/(1.0 + 0.3275911*x) - erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429))))) - cdf_x = 0.5*(1.0 + sign*erf) - return cdf_x - else: - x = np.atleast_1d(x).copy() - cdf_x = np.zeros_like(x) - sign = np.ones_like(x) - neg_x_ind = x<0 - sign[neg_x_ind] = -1.0 - x[neg_x_ind] = -x[neg_x_ind] - x /= np.sqrt(2.) - t = 1.0/(1.0 + 0.3275911*x) - erf = 1. - np.exp(-x**2)*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429))))) - cdf_x = 0.5*(1.0 + sign*erf) - cdf_x = cdf_x.reshape(x_shape) - return cdf_x - - def inv_std_norm_cdf(x): """ Inverse cumulative standard Gaussian distribution