516 lines
22 KiB
Python
516 lines
22 KiB
Python
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"""Gaussian processes regression. """
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# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
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# Modified by: Pete Green <p.l.green@liverpool.ac.uk>
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# License: BSD 3 clause
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import warnings
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from operator import itemgetter
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import numpy as np
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from scipy.linalg import cholesky, cho_solve, solve_triangular
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import scipy.optimize
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from ..base import BaseEstimator, RegressorMixin, clone
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from ..base import MultiOutputMixin
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from .kernels import RBF, ConstantKernel as C
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from ..utils import check_random_state
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from ..utils.validation import check_array
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from ..utils.optimize import _check_optimize_result
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from ..utils.validation import _deprecate_positional_args
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class GaussianProcessRegressor(MultiOutputMixin,
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RegressorMixin, BaseEstimator):
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"""Gaussian process regression (GPR).
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The implementation is based on Algorithm 2.1 of Gaussian Processes
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for Machine Learning (GPML) by Rasmussen and Williams.
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In addition to standard scikit-learn estimator API,
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GaussianProcessRegressor:
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* allows prediction without prior fitting (based on the GP prior)
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* provides an additional method sample_y(X), which evaluates samples
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drawn from the GPR (prior or posterior) at given inputs
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* exposes a method log_marginal_likelihood(theta), which can be used
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externally for other ways of selecting hyperparameters, e.g., via
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Markov chain Monte Carlo.
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Read more in the :ref:`User Guide <gaussian_process>`.
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.. versionadded:: 0.18
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Parameters
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----------
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kernel : kernel instance, default=None
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The kernel specifying the covariance function of the GP. If None is
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passed, the kernel "1.0 * RBF(1.0)" is used as default. Note that
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the kernel's hyperparameters are optimized during fitting.
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alpha : float or array-like of shape (n_samples), default=1e-10
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Value added to the diagonal of the kernel matrix during fitting.
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Larger values correspond to increased noise level in the observations.
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This can also prevent a potential numerical issue during fitting, by
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ensuring that the calculated values form a positive definite matrix.
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If an array is passed, it must have the same number of entries as the
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data used for fitting and is used as datapoint-dependent noise level.
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Note that this is equivalent to adding a WhiteKernel with c=alpha.
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Allowing to specify the noise level directly as a parameter is mainly
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for convenience and for consistency with Ridge.
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optimizer : "fmin_l_bfgs_b" or callable, default="fmin_l_bfgs_b"
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Can either be one of the internally supported optimizers for optimizing
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the kernel's parameters, specified by a string, or an externally
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defined optimizer passed as a callable. If a callable is passed, it
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must have the signature::
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def optimizer(obj_func, initial_theta, bounds):
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# * 'obj_func' is the objective function to be minimized, which
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# takes the hyperparameters theta as parameter and an
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# optional flag eval_gradient, which determines if the
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# gradient is returned additionally to the function value
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# * 'initial_theta': the initial value for theta, which can be
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# used by local optimizers
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# * 'bounds': the bounds on the values of theta
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....
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# Returned are the best found hyperparameters theta and
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# the corresponding value of the target function.
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return theta_opt, func_min
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Per default, the 'L-BGFS-B' algorithm from scipy.optimize.minimize
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is used. If None is passed, the kernel's parameters are kept fixed.
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Available internal optimizers are::
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'fmin_l_bfgs_b'
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n_restarts_optimizer : int, default=0
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The number of restarts of the optimizer for finding the kernel's
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parameters which maximize the log-marginal likelihood. The first run
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of the optimizer is performed from the kernel's initial parameters,
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the remaining ones (if any) from thetas sampled log-uniform randomly
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from the space of allowed theta-values. If greater than 0, all bounds
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must be finite. Note that n_restarts_optimizer == 0 implies that one
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run is performed.
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normalize_y : boolean, optional (default: False)
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Whether the target values y are normalized, the mean and variance of
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the target values are set equal to 0 and 1 respectively. This is
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recommended for cases where zero-mean, unit-variance priors are used.
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Note that, in this implementation, the normalisation is reversed
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before the GP predictions are reported.
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.. versionchanged:: 0.23
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copy_X_train : bool, default=True
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If True, a persistent copy of the training data is stored in the
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object. Otherwise, just a reference to the training data is stored,
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which might cause predictions to change if the data is modified
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externally.
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random_state : int or RandomState, default=None
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Determines random number generation used to initialize the centers.
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Pass an int for reproducible results across multiple function calls.
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See :term: `Glossary <random_state>`.
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Attributes
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----------
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X_train_ : array-like of shape (n_samples, n_features) or list of object
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Feature vectors or other representations of training data (also
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required for prediction).
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y_train_ : array-like of shape (n_samples,) or (n_samples, n_targets)
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Target values in training data (also required for prediction)
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kernel_ : kernel instance
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The kernel used for prediction. The structure of the kernel is the
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same as the one passed as parameter but with optimized hyperparameters
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L_ : array-like of shape (n_samples, n_samples)
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Lower-triangular Cholesky decomposition of the kernel in ``X_train_``
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alpha_ : array-like of shape (n_samples,)
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Dual coefficients of training data points in kernel space
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log_marginal_likelihood_value_ : float
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The log-marginal-likelihood of ``self.kernel_.theta``
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Examples
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--------
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>>> from sklearn.datasets import make_friedman2
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>>> from sklearn.gaussian_process import GaussianProcessRegressor
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>>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel
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>>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
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>>> kernel = DotProduct() + WhiteKernel()
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>>> gpr = GaussianProcessRegressor(kernel=kernel,
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... random_state=0).fit(X, y)
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>>> gpr.score(X, y)
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0.3680...
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>>> gpr.predict(X[:2,:], return_std=True)
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(array([653.0..., 592.1...]), array([316.6..., 316.6...]))
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"""
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@_deprecate_positional_args
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def __init__(self, kernel=None, *, alpha=1e-10,
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optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0,
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normalize_y=False, copy_X_train=True, random_state=None):
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self.kernel = kernel
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self.alpha = alpha
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self.optimizer = optimizer
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self.n_restarts_optimizer = n_restarts_optimizer
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self.normalize_y = normalize_y
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self.copy_X_train = copy_X_train
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self.random_state = random_state
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def fit(self, X, y):
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"""Fit Gaussian process regression model.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features) or list of object
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Feature vectors or other representations of training data.
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y : array-like of shape (n_samples,) or (n_samples, n_targets)
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Target values
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Returns
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-------
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self : returns an instance of self.
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"""
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if self.kernel is None: # Use an RBF kernel as default
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self.kernel_ = C(1.0, constant_value_bounds="fixed") \
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* RBF(1.0, length_scale_bounds="fixed")
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else:
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self.kernel_ = clone(self.kernel)
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self._rng = check_random_state(self.random_state)
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if self.kernel_.requires_vector_input:
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X, y = self._validate_data(X, y, multi_output=True, y_numeric=True,
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ensure_2d=True, dtype="numeric")
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else:
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X, y = self._validate_data(X, y, multi_output=True, y_numeric=True,
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ensure_2d=False, dtype=None)
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# Normalize target value
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if self.normalize_y:
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self._y_train_mean = np.mean(y, axis=0)
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self._y_train_std = np.std(y, axis=0)
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# Remove mean and make unit variance
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y = (y - self._y_train_mean) / self._y_train_std
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else:
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self._y_train_mean = np.zeros(1)
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self._y_train_std = 1
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if np.iterable(self.alpha) \
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and self.alpha.shape[0] != y.shape[0]:
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if self.alpha.shape[0] == 1:
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self.alpha = self.alpha[0]
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else:
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raise ValueError("alpha must be a scalar or an array"
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" with same number of entries as y.(%d != %d)"
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% (self.alpha.shape[0], y.shape[0]))
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self.X_train_ = np.copy(X) if self.copy_X_train else X
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self.y_train_ = np.copy(y) if self.copy_X_train else y
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if self.optimizer is not None and self.kernel_.n_dims > 0:
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# Choose hyperparameters based on maximizing the log-marginal
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# likelihood (potentially starting from several initial values)
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def obj_func(theta, eval_gradient=True):
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if eval_gradient:
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lml, grad = self.log_marginal_likelihood(
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theta, eval_gradient=True, clone_kernel=False)
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return -lml, -grad
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else:
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return -self.log_marginal_likelihood(theta,
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clone_kernel=False)
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# First optimize starting from theta specified in kernel
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optima = [(self._constrained_optimization(obj_func,
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self.kernel_.theta,
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self.kernel_.bounds))]
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# Additional runs are performed from log-uniform chosen initial
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# theta
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if self.n_restarts_optimizer > 0:
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if not np.isfinite(self.kernel_.bounds).all():
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raise ValueError(
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"Multiple optimizer restarts (n_restarts_optimizer>0) "
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"requires that all bounds are finite.")
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bounds = self.kernel_.bounds
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for iteration in range(self.n_restarts_optimizer):
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theta_initial = \
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self._rng.uniform(bounds[:, 0], bounds[:, 1])
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optima.append(
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self._constrained_optimization(obj_func, theta_initial,
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bounds))
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# Select result from run with minimal (negative) log-marginal
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# likelihood
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lml_values = list(map(itemgetter(1), optima))
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self.kernel_.theta = optima[np.argmin(lml_values)][0]
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self.log_marginal_likelihood_value_ = -np.min(lml_values)
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else:
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self.log_marginal_likelihood_value_ = \
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self.log_marginal_likelihood(self.kernel_.theta,
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clone_kernel=False)
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# Precompute quantities required for predictions which are independent
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# of actual query points
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K = self.kernel_(self.X_train_)
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K[np.diag_indices_from(K)] += self.alpha
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try:
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self.L_ = cholesky(K, lower=True) # Line 2
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# self.L_ changed, self._K_inv needs to be recomputed
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self._K_inv = None
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except np.linalg.LinAlgError as exc:
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exc.args = ("The kernel, %s, is not returning a "
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"positive definite matrix. Try gradually "
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"increasing the 'alpha' parameter of your "
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"GaussianProcessRegressor estimator."
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% self.kernel_,) + exc.args
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raise
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self.alpha_ = cho_solve((self.L_, True), self.y_train_) # Line 3
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return self
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def predict(self, X, return_std=False, return_cov=False):
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"""Predict using the Gaussian process regression model
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We can also predict based on an unfitted model by using the GP prior.
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In addition to the mean of the predictive distribution, also its
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standard deviation (return_std=True) or covariance (return_cov=True).
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Note that at most one of the two can be requested.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features) or list of object
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Query points where the GP is evaluated.
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return_std : bool, default=False
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If True, the standard-deviation of the predictive distribution at
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the query points is returned along with the mean.
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return_cov : bool, default=False
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If True, the covariance of the joint predictive distribution at
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the query points is returned along with the mean
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Returns
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-------
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y_mean : ndarray of shape (n_samples, [n_output_dims])
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Mean of predictive distribution a query points
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y_std : ndarray of shape (n_samples,), optional
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Standard deviation of predictive distribution at query points.
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Only returned when `return_std` is True.
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y_cov : ndarray of shape (n_samples, n_samples), optional
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Covariance of joint predictive distribution a query points.
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Only returned when `return_cov` is True.
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"""
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if return_std and return_cov:
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raise RuntimeError(
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"Not returning standard deviation of predictions when "
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"returning full covariance.")
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if self.kernel is None or self.kernel.requires_vector_input:
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X = check_array(X, ensure_2d=True, dtype="numeric")
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else:
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X = check_array(X, ensure_2d=False, dtype=None)
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if not hasattr(self, "X_train_"): # Unfitted;predict based on GP prior
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if self.kernel is None:
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kernel = (C(1.0, constant_value_bounds="fixed") *
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RBF(1.0, length_scale_bounds="fixed"))
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else:
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kernel = self.kernel
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y_mean = np.zeros(X.shape[0])
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if return_cov:
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y_cov = kernel(X)
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return y_mean, y_cov
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elif return_std:
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y_var = kernel.diag(X)
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return y_mean, np.sqrt(y_var)
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else:
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return y_mean
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else: # Predict based on GP posterior
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K_trans = self.kernel_(X, self.X_train_)
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y_mean = K_trans.dot(self.alpha_) # Line 4 (y_mean = f_star)
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# undo normalisation
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y_mean = self._y_train_std * y_mean + self._y_train_mean
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if return_cov:
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v = cho_solve((self.L_, True), K_trans.T) # Line 5
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y_cov = self.kernel_(X) - K_trans.dot(v) # Line 6
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# undo normalisation
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y_cov = y_cov * self._y_train_std**2
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return y_mean, y_cov
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elif return_std:
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# cache result of K_inv computation
|
||
|
|
if self._K_inv is None:
|
||
|
|
# compute inverse K_inv of K based on its Cholesky
|
||
|
|
# decomposition L and its inverse L_inv
|
||
|
|
L_inv = solve_triangular(self.L_.T,
|
||
|
|
np.eye(self.L_.shape[0]))
|
||
|
|
self._K_inv = L_inv.dot(L_inv.T)
|
||
|
|
|
||
|
|
# Compute variance of predictive distribution
|
||
|
|
y_var = self.kernel_.diag(X)
|
||
|
|
y_var -= np.einsum("ij,ij->i",
|
||
|
|
np.dot(K_trans, self._K_inv), K_trans)
|
||
|
|
|
||
|
|
# Check if any of the variances is negative because of
|
||
|
|
# numerical issues. If yes: set the variance to 0.
|
||
|
|
y_var_negative = y_var < 0
|
||
|
|
if np.any(y_var_negative):
|
||
|
|
warnings.warn("Predicted variances smaller than 0. "
|
||
|
|
"Setting those variances to 0.")
|
||
|
|
y_var[y_var_negative] = 0.0
|
||
|
|
|
||
|
|
# undo normalisation
|
||
|
|
y_var = y_var * self._y_train_std**2
|
||
|
|
|
||
|
|
return y_mean, np.sqrt(y_var)
|
||
|
|
else:
|
||
|
|
return y_mean
|
||
|
|
|
||
|
|
def sample_y(self, X, n_samples=1, random_state=0):
|
||
|
|
"""Draw samples from Gaussian process and evaluate at X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features) or list of object
|
||
|
|
Query points where the GP is evaluated.
|
||
|
|
|
||
|
|
n_samples : int, default=1
|
||
|
|
The number of samples drawn from the Gaussian process
|
||
|
|
|
||
|
|
random_state : int, RandomState, default=0
|
||
|
|
Determines random number generation to randomly draw samples.
|
||
|
|
Pass an int for reproducible results across multiple function
|
||
|
|
calls.
|
||
|
|
See :term: `Glossary <random_state>`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_samples : ndarray of shape (n_samples_X, [n_output_dims], n_samples)
|
||
|
|
Values of n_samples samples drawn from Gaussian process and
|
||
|
|
evaluated at query points.
|
||
|
|
"""
|
||
|
|
rng = check_random_state(random_state)
|
||
|
|
|
||
|
|
y_mean, y_cov = self.predict(X, return_cov=True)
|
||
|
|
if y_mean.ndim == 1:
|
||
|
|
y_samples = rng.multivariate_normal(y_mean, y_cov, n_samples).T
|
||
|
|
else:
|
||
|
|
y_samples = \
|
||
|
|
[rng.multivariate_normal(y_mean[:, i], y_cov,
|
||
|
|
n_samples).T[:, np.newaxis]
|
||
|
|
for i in range(y_mean.shape[1])]
|
||
|
|
y_samples = np.hstack(y_samples)
|
||
|
|
return y_samples
|
||
|
|
|
||
|
|
def log_marginal_likelihood(self, theta=None, eval_gradient=False,
|
||
|
|
clone_kernel=True):
|
||
|
|
"""Returns log-marginal likelihood of theta for training data.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
theta : array-like of shape (n_kernel_params,) default=None
|
||
|
|
Kernel hyperparameters for which the log-marginal likelihood is
|
||
|
|
evaluated. If None, the precomputed log_marginal_likelihood
|
||
|
|
of ``self.kernel_.theta`` is returned.
|
||
|
|
|
||
|
|
eval_gradient : bool, default=False
|
||
|
|
If True, the gradient of the log-marginal likelihood with respect
|
||
|
|
to the kernel hyperparameters at position theta is returned
|
||
|
|
additionally. If True, theta must not be None.
|
||
|
|
|
||
|
|
clone_kernel : bool, default=True
|
||
|
|
If True, the kernel attribute is copied. If False, the kernel
|
||
|
|
attribute is modified, but may result in a performance improvement.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
log_likelihood : float
|
||
|
|
Log-marginal likelihood of theta for training data.
|
||
|
|
|
||
|
|
log_likelihood_gradient : ndarray of shape (n_kernel_params,), optional
|
||
|
|
Gradient of the log-marginal likelihood with respect to the kernel
|
||
|
|
hyperparameters at position theta.
|
||
|
|
Only returned when eval_gradient is True.
|
||
|
|
"""
|
||
|
|
if theta is None:
|
||
|
|
if eval_gradient:
|
||
|
|
raise ValueError(
|
||
|
|
"Gradient can only be evaluated for theta!=None")
|
||
|
|
return self.log_marginal_likelihood_value_
|
||
|
|
|
||
|
|
if clone_kernel:
|
||
|
|
kernel = self.kernel_.clone_with_theta(theta)
|
||
|
|
else:
|
||
|
|
kernel = self.kernel_
|
||
|
|
kernel.theta = theta
|
||
|
|
|
||
|
|
if eval_gradient:
|
||
|
|
K, K_gradient = kernel(self.X_train_, eval_gradient=True)
|
||
|
|
else:
|
||
|
|
K = kernel(self.X_train_)
|
||
|
|
|
||
|
|
K[np.diag_indices_from(K)] += self.alpha
|
||
|
|
try:
|
||
|
|
L = cholesky(K, lower=True) # Line 2
|
||
|
|
except np.linalg.LinAlgError:
|
||
|
|
return (-np.inf, np.zeros_like(theta)) \
|
||
|
|
if eval_gradient else -np.inf
|
||
|
|
|
||
|
|
# Support multi-dimensional output of self.y_train_
|
||
|
|
y_train = self.y_train_
|
||
|
|
if y_train.ndim == 1:
|
||
|
|
y_train = y_train[:, np.newaxis]
|
||
|
|
|
||
|
|
alpha = cho_solve((L, True), y_train) # Line 3
|
||
|
|
|
||
|
|
# Compute log-likelihood (compare line 7)
|
||
|
|
log_likelihood_dims = -0.5 * np.einsum("ik,ik->k", y_train, alpha)
|
||
|
|
log_likelihood_dims -= np.log(np.diag(L)).sum()
|
||
|
|
log_likelihood_dims -= K.shape[0] / 2 * np.log(2 * np.pi)
|
||
|
|
log_likelihood = log_likelihood_dims.sum(-1) # sum over dimensions
|
||
|
|
|
||
|
|
if eval_gradient: # compare Equation 5.9 from GPML
|
||
|
|
tmp = np.einsum("ik,jk->ijk", alpha, alpha) # k: output-dimension
|
||
|
|
tmp -= cho_solve((L, True), np.eye(K.shape[0]))[:, :, np.newaxis]
|
||
|
|
# Compute "0.5 * trace(tmp.dot(K_gradient))" without
|
||
|
|
# constructing the full matrix tmp.dot(K_gradient) since only
|
||
|
|
# its diagonal is required
|
||
|
|
log_likelihood_gradient_dims = \
|
||
|
|
0.5 * np.einsum("ijl,jik->kl", tmp, K_gradient)
|
||
|
|
log_likelihood_gradient = log_likelihood_gradient_dims.sum(-1)
|
||
|
|
|
||
|
|
if eval_gradient:
|
||
|
|
return log_likelihood, log_likelihood_gradient
|
||
|
|
else:
|
||
|
|
return log_likelihood
|
||
|
|
|
||
|
|
def _constrained_optimization(self, obj_func, initial_theta, bounds):
|
||
|
|
if self.optimizer == "fmin_l_bfgs_b":
|
||
|
|
opt_res = scipy.optimize.minimize(
|
||
|
|
obj_func, initial_theta, method="L-BFGS-B", jac=True,
|
||
|
|
bounds=bounds)
|
||
|
|
_check_optimize_result("lbfgs", opt_res)
|
||
|
|
theta_opt, func_min = opt_res.x, opt_res.fun
|
||
|
|
elif callable(self.optimizer):
|
||
|
|
theta_opt, func_min = \
|
||
|
|
self.optimizer(obj_func, initial_theta, bounds=bounds)
|
||
|
|
else:
|
||
|
|
raise ValueError("Unknown optimizer %s." % self.optimizer)
|
||
|
|
|
||
|
|
return theta_opt, func_min
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {'requires_fit': False}
|