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Added kernel functions to Gaussian Processes model #22

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Added kernel functions to Gaussian Processes model #22

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78 changes: 63 additions & 15 deletions autompc/sysid/largegp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,12 @@
import numpy.linalg as la
import tqdm
from ConfigSpace import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter
import ConfigSpace.conditions as CSC
from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter, CategoricalHyperparameter

# Internal library includes
from ..utils.cs_utils import (get_hyper_bool, get_hyper_str,
get_hyper_float, get_hyper_int)

try:
import torch
Expand Down Expand Up @@ -35,7 +40,7 @@ def transform_output(xu_means, xu_std, XU):
return np.vstack(XUt).T

class ApproximateGPytorchModel(gpytorch.models.ApproximateGP):
def __init__(self, inducing_points, num_task):
def __init__(self, inducing_points, num_task, kernel_function, matern_nu, pp_q):
# We have to mark the CholeskyVariationalDistribution as batch
# so that we learn a variational distribution for each task
size = torch.Size([num_task])
Expand All @@ -53,14 +58,29 @@ def __init__(self, inducing_points, num_task):

super().__init__(variational_strategy)

# The mean and covariance modules should be marked as batch
# The mean and covariance modules should be marked as batch
# so we learn a different set of hyperparameters
self.mean_module = gpytorch.means.ConstantMean(batch_shape=size)
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel(batch_shape=size),
batch_shape=size
)

self.mean_module = gpytorch.means.ConstantMean(batch_shape=size)
if kernel_function == "periodic":
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.PeriodicKernel(batch_shape=size), batch_shape=size)
elif kernel_function == "piecewise_polynomial":
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.PiecewisePolynomialKernel(q=pp_q, batch_shape=size), batch_shape=size)
elif kernel_function == "rq":
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RQKernel(batch_shape=size), batch_shape=size)
elif kernel_function == "matern":
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.MaternKernel(nu=matern_nu, batch_shape=size), batch_shape=size)
elif kernel_function == "linear":
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.LinearKernel(batch_shape=size), batch_shape=size)
else:
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel(batch_shape=size), batch_shape=size
)

def forward(self, x):
# The forward function should be written as if we were dealing with each output
# dimension in batch
Expand Down Expand Up @@ -88,6 +108,14 @@ class ApproximateGPModel(FullyObservableModel):
- **induce_count** *(Type: int, Lower: 50, Upper: 200, Default: 100)*: Number of inducing points
to include in the gaussian process.
- **learning_rate** *(Type: float, Lower: 1e-5, Upper: 10, Default: 0.1)*: Learning rate for training.
- **kernel_function** *(Type: string, Choices: ["rbf", "periodic", "piecewise_polynomial", "rq",
"matern", "linear"], Default: "rbf")*: Kernel function defining the gaussian process.
- **matern_nu** *(Type: float, Choices: [1/2, 3/2, 5/2], Default: 3/2)*: Smoothing parameter nu
for Matern kernel function.
(Conditioned on kernel_function="matern")
- **pp_q** *(Type: int, Choices: [0, 1, 2, 3], Default: 2)*: Smoothing parameter q
for piecewise polynomial kernel function.
(Conditioned on kernel_function="piecewise_polynomial")
"""
def __init__(self, system, n_train_iters=20, batch_size=1024, use_cuda=True):
super().__init__(system, "ApproximateGPModel")
Expand All @@ -106,13 +134,34 @@ def get_default_config_space(self):
upper=200, default_value=100)
lr = UniformFloatHyperparameter("learning_rate", lower=1e-5, upper=10,
default_value=1e-1)
cs.add_hyperparameters([induce_count,lr])

kf = CategoricalHyperparameter("kernel_function", choices=["rbf",
"periodic", "piecewise_polynomial", "rq", "matern", "linear"],
default_value="rbf")

# Matern smoothing parameter (smaller values are less smooth)
matern_nu = CategoricalHyperparameter('matern_nu', choices=[1/2, 3/2, 5/2],
default_value=3/2)
use_matern_nu = CSC.InCondition(child=matern_nu, parent=kf,
values=["matern"])

# Piecewise Polynomial smoothing parameter
pp_q = CategoricalHyperparameter('pp_q', choices=[0, 1, 2, 3],
default_value=2)
use_pp_q = CSC.InCondition(child=pp_q, parent=kf,
values=["piecewise_polynomial"])

cs.add_hyperparameters([induce_count,lr, kf, matern_nu, pp_q])
cs.add_conditions([use_matern_nu, use_pp_q])
return cs

def set_config(self, config):
self.induce_count = config["induce_count"]
self.lr = config["learning_rate"]

self.kf = config["kernel_function"]
self.pp_q = get_hyper_int(config, "pp_q")
self.matern_nu = get_hyper_float(config, "matern_nu")

def clear(self):
self.gpmodel = None
self.is_trained = False
Expand Down Expand Up @@ -143,7 +192,7 @@ def _prepare_data(self):

def _init_train(self, seed):
torch.manual_seed(seed)
self.gpmodel = ApproximateGPytorchModel(self.induce, self.num_task).double()
self.gpmodel = ApproximateGPytorchModel(self.induce, self.num_task, self.kf, self.matern_nu, self.pp_q).double()
self.gpmodel = self.gpmodel.to(self.device)

# construct the approximate GP instance
Expand Down Expand Up @@ -219,7 +268,7 @@ def set_parameters(self, params):
self.dy_std = params["dy_std"]
self.induce = params["induce"]
self.num_task = params["num_task"]
self.gpmodel = ApproximateGPytorchModel(self.induce, self.num_task).double()
self.gpmodel = ApproximateGPytorchModel(self.induce, self.num_task, self.kf, self.matern_nu, self.pp_q).double()
self.gpmodel = self.gpmodel.to(self.device)
likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(
num_tasks=self.num_task)
Expand Down Expand Up @@ -343,5 +392,4 @@ def pred_diff_parallel(self, state, ctrl):
n = self.system.obs_dim
state_jacs = jac[:, :, :n] + np.tile(np.eye(n), (m,1,1))
ctrl_jacs = jac[:, :, n:]
return state + dy, state_jacs, ctrl_jacs

return state + dy, state_jacs, ctrl_jacs