Source code for GPy.models.sparse_gp_classification

# Copyright (c) 2013, Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)


import numpy as np
from ..core import SparseGP
from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference import EPDTC
from copy import deepcopy

[docs]class SparseGPClassification(SparseGP): """ Sparse Gaussian Process model for classification This is a thin wrapper around the sparse_GP class, with a set of sensible defaults :param X: input observations :param Y: observed values :param likelihood: a GPy likelihood, defaults to Bernoulli :param kernel: a GPy kernel, defaults to rbf+white :param inference_method: Latent function inference to use, defaults to EPDTC :type inference_method: :class:`GPy.inference.latent_function_inference.LatentFunctionInference` :param normalize_X: whether to normalize the input data before computing (predictions will be in original scales) :type normalize_X: False|True :param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales) :type normalize_Y: False|True :rtype: model object """ def __init__(self, X, Y=None, likelihood=None, kernel=None, Z=None, num_inducing=10, Y_metadata=None, mean_function=None, inference_method=None, normalizer=False): if kernel is None: kernel = kern.RBF(X.shape[1]) if likelihood is None: likelihood = likelihoods.Bernoulli() if Z is None: i = np.random.permutation(X.shape[0])[:num_inducing] Z = X[i].copy() else: assert Z.shape[1] == X.shape[1] if inference_method is None: inference_method = EPDTC() super(SparseGPClassification, self).__init__(X, Y, Z, kernel, likelihood, mean_function=mean_function, inference_method=inference_method, normalizer=normalizer, name='SparseGPClassification', Y_metadata=Y_metadata)
[docs] @staticmethod def from_sparse_gp(sparse_gp): from copy import deepcopy sparse_gp = deepcopy(sparse_gp) SparseGPClassification(sparse_gp.X, sparse_gp.Y, sparse_gp.Z, sparse_gp.kern, sparse_gp.likelihood, sparse_gp.inference_method, sparse_gp.mean_function, name='sparse_gp_classification')
[docs] def to_dict(self, save_data=True): """ Store the object into a json serializable dictionary :param boolean save_data: if true, it adds the data self.X and self.Y to the dictionary :return dict: json serializable dictionary containing the needed information to instantiate the object """ model_dict = super(SparseGPClassification,self).to_dict(save_data) model_dict["class"] = "GPy.models.SparseGPClassification" return model_dict
@staticmethod def _build_from_input_dict(input_dict, data=None): input_dict = SparseGPClassification._format_input_dict(input_dict, data) input_dict.pop('name', None) # Name parameter not required by SparseGPClassification return SparseGPClassification(**input_dict)
[docs] @staticmethod def from_dict(input_dict, data=None): """ Instantiate an SparseGPClassification object using the information in input_dict (built by the to_dict method). :param data: It is used to provide X and Y for the case when the model was saved using save_data=False in to_dict method. :type data: tuple(:class:`np.ndarray`, :class:`np.ndarray`) """ import GPy m = GPy.core.model.Model.from_dict(input_dict, data) from copy import deepcopy sparse_gp = deepcopy(m) return SparseGPClassification(sparse_gp.X, sparse_gp.Y, sparse_gp.Z, sparse_gp.kern, sparse_gp.likelihood, sparse_gp.inference_method, sparse_gp.mean_function, name='sparse_gp_classification')
[docs] def save_model(self, output_filename, compress=True, save_data=True): """ Method to serialize the model. :param string output_filename: Output file :param boolean compress: If true compress the file using zip :param boolean save_data: if true, it serializes the training data (self.X and self.Y) """ self._save_model(output_filename, compress=True, save_data=True)
[docs]class SparseGPClassificationUncertainInput(SparseGP): """ Sparse Gaussian Process model for classification with uncertain inputs. This is a thin wrapper around the sparse_GP class, with a set of sensible defaults :param X: input observations :type X: np.ndarray (num_data x input_dim) :param X_variance: The uncertainty in the measurements of X (Gaussian variance, optional) :type X_variance: np.ndarray (num_data x input_dim) :param Y: observed values :param kernel: a GPy kernel, defaults to rbf+white :param Z: inducing inputs (optional, see note) :type Z: np.ndarray (num_inducing x input_dim) | None :param num_inducing: number of inducing points (ignored if Z is passed, see note) :type num_inducing: int :rtype: model object .. Note:: If no Z array is passed, num_inducing (default 10) points are selected from the data. Other wise num_inducing is ignored .. Note:: Multiple independent outputs are allowed using columns of Y """ def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, Y_metadata=None, normalizer=None): from GPy.core.parameterization.variational import NormalPosterior if kernel is None: kernel = kern.RBF(X.shape[1]) likelihood = likelihoods.Bernoulli() if Z is None: i = np.random.permutation(X.shape[0])[:num_inducing] Z = X[i].copy() else: assert Z.shape[1] == X.shape[1] X = NormalPosterior(X, X_variance) super(SparseGPClassificationUncertainInput, self).__init__(X, Y, Z, kernel, likelihood, inference_method=EPDTC(), name='SparseGPClassification', Y_metadata=Y_metadata, normalizer=normalizer)
[docs] def parameters_changed(self): #Compute the psi statistics for N once, but don't sum out N in psi2 self.psi0 = self.kern.psi0(self.Z, self.X) self.psi1 = self.kern.psi1(self.Z, self.X) self.psi2 = self.kern.psi2n(self.Z, self.X) self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata, psi0=self.psi0, psi1=self.psi1, psi2=self.psi2) self._update_gradients()