Non-stationary GP package written from scratch in PyTorch

Related tags

Deep Learningnsgptorch
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

MANU S PILLAI 5 Oct 10, 2021
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

Sagnik Roy 13 Jan 26, 2022
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

AntonMu 616 Jan 8, 2023
ML From Scratch

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

Tanishq Gautam 66 Nov 2, 2022
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

null 22 Aug 23, 2022
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

Rishabh Bali 22 Aug 23, 2022
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

Abel 2 Apr 12, 2022
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

null 1 Dec 14, 2021
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

Dennis Núñez-Fernández 5 Oct 20, 2022
Releases(v0.1.2)
Owner
Zeel B Patel
Ph.D. student at sustainability lab
Zeel B Patel
GitHub Repository
Supervised forecasting of sequential data in Python.

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

The Alan Turing Institute 54 Nov 15, 2022
Keras Model Implementation Walkthrough

Keras Model Implementation Walkthrough

Luke Wood 17 Sep 27, 2022
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Hust Visual Learning Team 203 Dec 31, 2022
In Search of Probeable Generalization Measures

In Search of Probeable Generalization Measures Exciting News! In Search of Probeable Generalization Measures has been accepted to the International Co

Mahdi S. Hosseini 6 Sep 11, 2022
Robot Servers and Server Manager software for robo-gym

robo-gym-server-modules Robot Servers and Server Manager software for robo-gym. For info on how to use this package please visit the robo-gym website

JR ROBOTICS 4 Aug 16, 2021
[CVPR 2021] MiVOS - Scribble to Mask module

MiVOS (CVPR 2021) - Scribble To Mask Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] A simplistic network that turns scri

Rex Cheng 65 Dec 22, 2022
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

Chenhongyi Yang 21 Dec 13, 2022
Character Controllers using Motion VAEs

Character Controllers using Motion VAEs This repo is the codebase for the SIGGRAPH 2020 paper with the title above. Please find the paper and demo at

Electronic Arts 165 Jan 03, 2023
Finetune alexnet with tensorflow - Code for finetuning AlexNet in TensorFlow >= 1.2rc0

Finetune AlexNet with Tensorflow Update 15.06.2016 I revised the entire code base to work with the new input pipeline coming with TensorFlow = versio

Frederik Kratzert 766 Jan 04, 2023
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

INK Lab @ USC 17 Oct 27, 2022
Deep Learning with PyTorch made easy 🚀 !

Deep Learning with PyTorch made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. It also provides a c

381 Dec 22, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Raindrop strategy for Irregular time series

Graph-Guided Network For Irregularly Sampled Multivariate Time Series Overview This repository contains processed datasets and implementation code for

Zitnik Lab @ Harvard 74 Jan 03, 2023
A Keras implementation of YOLOv4 (Tensorflow backend)

keras-yolo4 请使用更完善的版本: https://github.com/miemie2013/Keras-YOLOv4 Please visit here for more complete model: https://github.com/miemie2013/Keras-YOLOv

384 Nov 29, 2022
Record radiologists' eye gaze when they are labeling images.

Record radiologists' eye gaze when they are labeling images. Read for installation, usage, and deep learning examples. Why use MicEye Versatile As a l

24 Nov 03, 2022
OMAMO: orthology-based model organism selection

OMAMO: orthology-based model organism selection OMAMO is a tool that suggests the best model organism to study a biological process based on orthologo

Dessimoz Lab 5 Apr 22, 2022
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

Jianquan Ye 298 Dec 21, 2022
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

Abel 2 Apr 12, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022