Non-stationary GP package written from scratch in PyTorch

Overview

NSGP-Torch

Examples

gpytorch model with skgpytorch

# Import packages
import torch
from regdata import NonStat2D
from gpytorch.kernels import RBFKernel, ScaleKernel
from skgpytorch.models import ExactGPRegressor
from skgpytorch.metrics import mean_squared_error

# Hyperparameters
n_iters = 100

# Load data
datafunc = NonStat2D(backend="torch")
X_train, y_train, X_test = map(lambda x: x.to(torch.float32), datafunc.get_data())
y_test = datafunc.f(X_test[:, 0], X_test[:, 1]).to(torch.float32)

# Define a kernel
kernel = ScaleKernel(RBFKernel(ard_num_dims=X_train.shape[1]))

# Define a model 
model = ExactGPRegressor(X_train, y_train, kernel, device='cpu')

# Train the model
model.fit(n_iters=n_iters, random_state=seed)

# Predict the distribution
pred_dist = model.predict(X_train, y_train, X_test)

# Compute RMSE and/or NLPD
mse = mean_squared_error(pred_dist, y_test, squared=False)
nlpd = neg_log_posterior_density(pred_dist, y_test)

nsgptorch model with skgpytorch

# Import packages
import torch
from regdata import NonStat2D

from nsgptorch.kernels import rbf

from skgpytorch.models import ExactNSGPRegressor
from skgpytorch.metrics import mean_squared_error

# Hyperparameters
n_iters = 100

# Load data
datafunc = NonStat2D(backend="torch")
X_train, y_train, X_test = map(lambda x: x.to(torch.float32), datafunc.get_data())
y_test = datafunc.f(X_test[:, 0], X_test[:, 1]).to(torch.float32)

# Define a kernel list for each dimension
kernel_list = [rbf, rbf]

# Define inducing points for each dimension (must be none if not applicable)
inducing_points = [None, None]

# Define a model 
model = ExactNSGPRegressor(kernel_list, input_dim=2, inducing_points, device='cpu')

# Train the model
model.fit(X_train, y_train, n_iters=n_iters, random_state=seed)

# Predict the distribution
pred_dist = model.predict(X_train, y_train, X_test)

# Compute RMSE and/or NLPD
mse = mean_squared_error(pred_dist, y_test, squared=False)
nlpd = neg_log_posterior_density(pred_dist, y_test)

Plan

  • Each kernel is 1D
  • Multiply kernels to each other

Ideas

  • Compute distance once and save it
  • Update skgpytorch to use 1 std instead of 0.1
  • Do something about mean learning of gpytorch for comparison
You might also like...
Keras like implementation of Deep Learning architectures from scratch using numpy.

Mini-Keras Keras like implementation of Deep Learning architectures from scratch using numpy. How to contribute? The project contains implementations

This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.
This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.

GANs for Fun Created because I can! GOAL The goal of this repo is to be freely used by ML devs to check the GAN performances without coding from scrat

Train a state-of-the-art yolov3 object detector from scratch!
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

ML From Scratch

ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision

In this project, we create and implement a deep learning library from scratch.
In this project, we create and implement a deep learning library from scratch.

ARA In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The

Create and implement a deep learning library from scratch.
Create and implement a deep learning library from scratch.

In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The Proj

AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

Controlling the MicriSpotAI robot from scratch
Controlling the MicriSpotAI robot from scratch

Project-MicroSpot-AI Controlling the MicriSpotAI robot from scratch Colaborators Alexander Dennis Components from MicroSpot The MicriSpotAI has the fo

Releases(v0.1.2)
Owner
Zeel B Patel
Ph.D. student at sustainability lab
Zeel B Patel
Experiments for Operating Systems Lab (ETCS-352)

Operating Systems Lab (ETCS-352) Experiments for Operating Systems Lab (ETCS-352) performed by me in 2021 at uni. All codes are written by me except t

Deekshant Wadhwa 0 Sep 06, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
The Empirical Investigation of Representation Learning for Imitation (EIRLI)

The Empirical Investigation of Representation Learning for Imitation (EIRLI)

Center for Human-Compatible AI 31 Nov 06, 2022
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model This repository is the official PyTorch implementation of GraphRNN, a graph gene

Jiaxuan 568 Dec 29, 2022
Tensorflow-seq2seq-tutorials - Dynamic seq2seq in TensorFlow, step by step

seq2seq with TensorFlow Collection of unfinished tutorials. May be good for educational purposes. 1 - simple sequence-to-sequence model with dynamic u

Matvey Ezhov 1k Dec 17, 2022
Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs Hi this is the source code of our paper "ATP: AMRize Then Parse! Enhancing AMR Parsing w

Chen Liang 13 Nov 23, 2022
A framework for the elicitation, specification, formalization and understanding of requirements.

A framework for the elicitation, specification, formalization and understanding of requirements.

NASA - Software V&V 161 Jan 03, 2023
PyTorch Live is an easy to use library of tools for creating on-device ML demos on Android and iOS.

PyTorch Live is an easy to use library of tools for creating on-device ML demos on Android and iOS. With Live, you can build a working mobile app ML demo in minutes.

559 Jan 01, 2023
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
DAT4 - General Assembly's Data Science course in Washington, DC

DAT4 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (12/15/14 - 3/16/15). Instructors: Sinan Ozdemir

Kevin Markham 779 Dec 25, 2022
Utilities and information for the signals.numer.ai tournament

dsignals Utilities and information for the signals.numer.ai tournament using eodhistoricaldata.com eodhistoricaldata.com provides excellent historical

Degerhan Usluel 23 Dec 18, 2022
Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

CMSF Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning" Requirements Python = 3.7.6 PyTorch

4 Nov 25, 2022
Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation Exploring Cross-Image Pixel Contrast for Semantic Segmentation, Wenguan Wang, Tianfei Z

Tianfei Zhou 510 Jan 02, 2023
Implementation of the SUMO (Slim U-Net trained on MODA) model

SUMO - Slim U-Net trained on MODA Implementation of the SUMO (Slim U-Net trained on MODA) model as described in: TODO: add reference to paper once ava

6 Nov 19, 2022
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image This repository is an implementation of the method described in the following pap

21 Dec 15, 2022
(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

yuta-saito 19 Dec 01, 2022
Instance-based label smoothing for improving deep neural networks generalization and calibration

Instance-based Label Smoothing for Neural Networks Pytorch Implementation of the algorithm. This repository includes a new proposed method for instanc

Mohamed Maher 1 Aug 13, 2022
Functional TensorFlow Implementation of Singular Value Decomposition for paper Fast Graph Learning

tf-fsvd TensorFlow Implementation of Functional Singular Value Decomposition for paper Fast Graph Learning with Unique Optimal Solutions Cite If you f

Sami Abu-El-Haija 14 Nov 25, 2021
Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting #Dataset The folder "Dataset" contains the dataset use in this work and m

0 Jan 08, 2022