The implementation of FOLD-R++ algorithm

Related tags

Deep LearningFOLD-R-PP
Overview

FOLD-R-PP

The implementation of FOLD-R++ algorithm. The target of FOLD-R++ algorithm is to learn an answer set program for a classification task.

Installation

Prerequisites

FOLD-R++ is developed with only python3. Numpy is the only dependency:

python3 -m pip install numpy

Instruction

Data preparation

The FOLD-R++ algorithm takes tabular data as input, the first line for the tabular data should be the feature names of each column. The FOLD-R++ does not need encoding for training. It can deal with numeric, categorical, and even mixed type features (one column contains categorical and numeric values) directly. But, the numeric features should be specified before loading data, otherwise they would be dealt like categorical features (only literals with = and != would be generated).

There are many UCI datasets can be found in the data directory, and the code pieces of data preparation should be added to datasets.py.

For example, the UCI breast-w dataset can be loaded with the following code:

columns = ['clump_thickness', 'cell_size_uniformity', 'cell_shape_uniformity', 'marginal_adhesion',
'single_epi_cell_size', 'bare_nuclei', 'bland_chromatin', 'normal_nucleoli', 'mitoses']
nums = columns
data, num_idx, columns = load_data('data/breastw/breastw.csv', attrs=columns, label=['label'], numerics=nums, pos='benign')

columns lists all the features needed, nums lists all the numeric features, label implies the feature name of the label, pos indicates the positive value of the label.

Training

The FOLD-R++ algorithm generates an explainable model that is represented with an answer set program for classification tasks. Here's an training example for breast-w dataset:

X_train, Y_train = split_xy(data_train)
X_pos, X_neg = split_X_by_Y(X_train, Y_train)
rules1 = foldrpp(X_pos, X_neg, [])

We have got a rule set rules1 in a nested intermediate representation. Flatten and decode the nested rules to answer set program:

fr1 = flatten(rules1)
rule_set = decode_rules(fr1, attrs)
for r in rule_set:
    print(r)

The training process can be started with: python3 main.py

An answer set program that is compatible with s(CASP) is generated as below.

% breastw dataset (699, 10).
% the answer set program generated by foldr++:

label(X,'benign'):- bare_nuclei(X,'?').
label(X,'benign'):- bland_chromatin(X,N6), N6=<4.0,
		    clump_thickness(X,N0), N0=<6.0,  
                    bare_nuclei(X,N5), N5=<1.0, not ab7(X).   
label(X,'benign'):- cell_size_uniformity(X,N1), N1=<2.0,
		    not ab3(X), not ab5(X), not ab6(X).  
label(X,'benign'):- cell_size_uniformity(X,N1), N1=<4.0,
		    bare_nuclei(X,N5), N5=<3.0,
		    clump_thickness(X,N0), N0=<3.0, not ab8(X).  
ab2(X):- clump_thickness(X,N0), N0=<1.0.  
ab3(X):- bare_nuclei(X,N5), N5>5.0, not ab2(X).  
ab4(X):- cell_shape_uniformity(X,N2), N2=<1.0.  
ab5(X):- clump_thickness(X,N0), N0>7.0, not ab4(X).  
ab6(X):- bare_nuclei(X,N5), N5>4.0, single_epi_cell_size(X,N4), N4=<1.0.  
ab7(X):- marginal_adhesion(X,N3), N3>4.0.  
ab8(X):- marginal_adhesion(X,N3), N3>6.0.  

% foldr++ costs:  0:00:00.027710  post: 0:00:00.000127
% acc 0.95 p 0.96 r 0.9697 f1 0.9648

Testing in Python

The testing data X_test, a set of testing data, can be predicted with the predict function in Python.

Y_test_hat = predict(rules1, X_test)

The classify function can also be used to classify a single data.

y_test_hat = classify(rules1, x_test)

Justification by using s(CASP)

Classification and justification can be conducted with s(CASP), but the data also need to be converted into predicate format. The decode_test_data function can be used for generating predicates for testing data.

data_pred = decode_test_data(data_test, attrs)
for p in data_pred:
    print(p)

Here is an example of generated testing data predicates along with the answer set program for acute dataset:

% acute dataset (120, 7) 
% the answer set program generated by foldr++:

ab2(X):- a5(X,'no'), a1(X,N0), N0>37.9.
label(X,'yes'):- not a4(X,'no'), not ab2(X).

% foldr++ costs:  0:00:00.001990  post: 0:00:00.000040
% acc 1.0 p 1.0 r 1.0 f1 1.0 

id(1).
a1(1,37.2).
a2(1,'no').
a3(1,'yes').
a4(1,'no').
a5(1,'no').
a6(1,'no').

id(2).
a1(2,38.1).
a2(2,'no').
a3(2,'yes').
a4(2,'yes').
a5(2,'no').
a6(2,'yes').

id(3).
a1(3,37.5).
a2(3,'no').
a3(3,'no').
a4(3,'yes').
a5(3,'yes').
a6(3,'yes').

s(CASP)

All the resources of s(CASP) can be found at https://gitlab.software.imdea.org/ciao-lang/sCASP.

Citation

@misc{wang2021foldr,
      title={FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data}, 
      author={Huaduo Wang and Gopal Gupta},
      year={2021},
      eprint={2110.07843},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Owner
GitHub Repository
Node Dependent Local Smoothing for Scalable Graph Learning

Node Dependent Local Smoothing for Scalable Graph Learning Requirements Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04

Wentao Zhang 15 Nov 28, 2022
Pseudo-Visual Speech Denoising

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

Sindhu 94 Oct 22, 2022
Progressive Coordinate Transforms for Monocular 3D Object Detection

Progressive Coordinate Transforms for Monocular 3D Object Detection This repository is the official implementation of PCT. Introduction In this paper,

58 Nov 06, 2022
Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Implementation for Iso-Points (CVPR 2021) Official code for paper Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations paper |

Yifan Wang 66 Nov 08, 2022
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning"

A Unified Framework for Parameter-Efficient Transfer Learning This is the official implementation of the paper: Towards a Unified View of Parameter-Ef

Junxian He 216 Dec 29, 2022
Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Cross Transformers - Pytorch (wip) Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch Install $ pip install cross-t

Phil Wang 40 Dec 22, 2022
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search Pytorch implementation for "Breaking the Curse of Space Explosion:

guoyong 17 Jan 03, 2023
DP-CL(Continual Learning with Differential Privacy)

DP-CL(Continual Learning with Differential Privacy) This is the official implementation of the Continual Learning with Differential Privacy. If you us

Phung Lai 3 Nov 04, 2022
Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies

SIGGRAPH 2021: Discovering Diverse Athletic Jumping Strategies project page paper demo video Prerequisites Important Notes We suspect there are bugs i

54 Dec 06, 2022
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

Cognitive Modeling 20 Dec 18, 2022
Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

hongbin_xu 127 Jan 04, 2023
Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

recommendationsystem matrixfactorization colloborative filtering recommendation

kunal jagdish madavi 1 Jan 01, 2022
NLMpy - A Python package to create neutral landscape models

NLMpy is a Python package for the creation of neutral landscape models that are widely used by landscape ecologists to model ecological patterns

Manaaki Whenua – Landcare Research 1 Oct 08, 2022
Project page for End-to-end Recovery of Human Shape and Pose

End-to-end Recovery of Human Shape and Pose Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018 Project Page Requirements Pyt

1.4k Dec 29, 2022
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation This repo is official implementation CVPR 2021 paper: Equalization

Jingru Tan 129 Dec 16, 2022
RodoSol-ALPR Dataset

RodoSol-ALPR Dataset This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Ro

Rayson Laroca 45 Dec 15, 2022
A spherical CNN for weather forecasting

DeepSphere-Weather - Deep Learning on the sphere for weather/climate applications. The code in this repository provides a scalable and flexible framew

DeepSphere 47 Dec 25, 2022
Framework for training options with different attention mechanism and using them to solve downstream tasks.

Using Attention in HRL Framework for training options with different attention mechanism and using them to solve downstream tasks. Requirements GPU re

5 Nov 03, 2022
Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Sup

Victor Chen 40 Nov 23, 2022
Unsupervised Representation Learning by Invariance Propagation

Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre

FengWang 15 Jul 06, 2022