An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

Overview

PC-SOS-SDP: an Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

PC-SOS-SDP is an exact algorithm based on the branch-and-bound technique for solving the semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problem with pairwise constraints (i.e. must-link and cannot-link constraints) described in the paper "An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering". This repository contains the C++ source code, the MATLAB scripts, and the datasets used for the experiments.

Installation

PC-SOS-SDP calls the semidefinite programming solver SDPNAL+ by using the MATLAB Engine API for C++. It requires the MATLAB engine library libMatlabEngine and the Matlab Data Array library libMatlabDataArray. PC-SOS-SDP calls the integer programming solver Gurobi. PC-SOS-SDP uses the Armadillo library to handle matrices and linear algebra operations efficiently. Before installing Armadillo, first install OpenBLAS and LAPACK along with the corresponding development files. PC-SOS-SDP implements a configurable thread pool of POSIX threads to speed up the branch-and-bound search.

Ubuntu and Debian instructions:

  1. Install MATLAB (>= 2016b)
  2. Install Gurobi (>= 9.0)
  3. Install CMake, OpenBLAS, LAPACK and Armadillo:
sudo apt-get update
sudo apt-get install cmake libopenblas-dev liblapack-dev libarmadillo-dev
  1. Open the makefile clustering_c++/Makefile
    • Set the variable matlab_path with your MATLAB folder.
    • Set the variable gurobi_path with your Gurobi folder.
  2. Compile the code:
cd clustering_c++/
make
  1. Download SDPNAL+, move the folder clustering_matlab containing the MATLAB source code of PC-SOS-SDP in the SDPNAL+ main directory and set the parameter SDP_SOLVER_FOLDER of the configuration file accordingly. This folder and its subfolders will be automatically added to the MATLAB search path when PC-SOS-SDP starts.

The code has been tested on Ubuntu Server 20.04 with MATLAB R2020b, Gurobi 9.2 and Armadillo 10.2.

Configuration

Various parameters used in PC-SOS-SDP can be modified in the configuration file clustering_c++/config.txt:

  • BRANCH_AND_BOUND_TOL - optimality tolerance of the branch-and-bound
  • BRANCH_AND_BOUND_PARALLEL - thread pool size: single thread (1), multi-thread (> 1)
  • BRANCH_AND_BOUND_MAX_NODES - maximum number of nodes
  • BRANCH_AND_BOUND_VISITING_STRATEGY - best first (0), depth first (1), breadth first (2)
  • SDP_SOLVER_SESSION_THREADS_ROOT - number of threads for the MATLAB session at the root
  • SDP_SOLVER_SESSION_THREADS - number of threads for the MATLAB session for the ML and CL nodes
  • SDP_SOLVER_FOLDER - full path of the SDPNAL+ folder
  • SDP_SOLVER_TOL - accuracy of SDPNAL+
  • SDP_SOLVER_VERBOSE - do not display log (0), display log (1)
  • SDP_SOLVER_MAX_CP_ITER_ROOT - maximum number of cutting-plane iterations at the root
  • SDP_SOLVER_MAX_CP_ITER - maximum number of cutting-plane iterations for the ML and CL nodes
  • SDP_SOLVER_CP_TOL - cutting-plane tolerance between two consecutive cutting-plane iterations
  • SDP_SOLVER_MAX_INEQ - maximum number of valid inequalities to add
  • SDP_SOLVER_INHERIT_PERC - fraction of inequalities to inherit
  • SDP_SOLVER_EPS_INEQ - tolerance for checking the violation of the inequalities
  • SDP_SOLVER_EPS_ACTIVE - tolerance for detecting the active inequalities
  • SDP_SOLVER_MAX_PAIR_INEQ - maximum number of pair inequalities to separate
  • SDP_SOLVER_PAIR_PERC - fraction of the most violated pair inequalities to add
  • SDP_SOLVER_MAX_TRIANGLE_INEQ - maximum number of triangle inequalities to separate
  • SDP_SOLVER_TRIANGLE_PERC - fraction of the most violated triangle inequalities to add

Usage

cd clustering_c++/
./bb <DATASET> <K> <CONSTRAINTS> <LOG> <RESULT>
  • DATASET - path of the dataset
  • K - number of clusters
  • CONSTRAINTS - path of the constraints
  • LOG - path of the log file
  • RESULT - path of the optimal cluster assignment matrix

File DATASET contains the data points x_ij and the must include an header line with the problem size n and the dimension d:

n d
x_11 x_12 ... x_1d
x_21 x_22 ... x_2d
...
...
x_n1 x_n2 ... x_nd

File CONSTRAINTS should include indices (i, j) of the data points involved in must-link (ML) and/or cannot-link (CL) constraints:

CL i1 j1
CL i2 j2
...
...
ML i3 j3
ML i4 j4

If it does not contain any constraint (empty file), PC-SOS-SDP becomes SOS-SDP (the exact solver for unsupervised MSSC).

Log

The log file reports the progress of the algorithm:

  • N - size of the current node
  • NODE_PAR - id of the parent node
  • NODE - id of the current node
  • LB_PAR - lower bound of the parent node
  • LB - lower bound of the current node
  • FLAG - termination flag of SDPNAL+
    • 0 - SDP is solved to the required accuracy
    • 1 - SDP is not solved successfully
    • -1, -2, -3 - SDP is partially solved successfully
  • TIME (s) - running time in seconds of the current node
  • CP_ITER - number of cutting-plane iterations
  • CP_FLAG - termination flag of the cutting-plane procedure
    • -3 - current bound is worse than the previous one
    • -2 - SDP is not solved successfully
    • -1 - maximum number of iterations
    • 0 - no violated inequalities
    • 1 - maximum number of inequalities
    • 2 - node must be pruned
    • 3 - cutting-plane tolerance
  • CP_INEQ - number of inequalities added in the last cutting-plane iteration
  • PAIR TRIANGLE CLIQUE - average number of added cuts for each class of inequalities
  • UB - current upper bound
  • GUB - global upper bound
  • I J - current branching decision
  • NODE_GAP - gap at the current node
  • GAP - overall gap
  • OPEN - number of open nodes

Log file example:

DATA_PATH, n, d, k: /home/ubuntu/PC-SOS-SDP/instances/glass.txt 214 9 6
CONSTRAINTS_PATH: /home/ubuntu/PC-SOS-SDP/instances/constraints/glass/ml_50_cl_50_3.txt
LOG_PATH: /home/ubuntu/PC-SOS_SDP/logs/glass/log_ml_50_cl_50_3.txt

BRANCH_AND_BOUND_TOL: 1e-4
BRANCH_AND_BOUND_PARALLEL: 16
BRANCH_AND_BOUND_MAX_NODES: 200
BRANCH_AND_BOUND_VISITING_STRATEGY: 0

SDP_SOLVER_SESSION_THREADS_ROOT: 16
SDP_SOLVER_SESSION_THREADS: 1
SDP_SOLVER_FOLDER: /home/ubuntu/PC-SOS-SDP/SDPNAL+/
SDP_SOLVER_TOL: 1e-05
SDP_SOLVER_VERBOSE: 0
SDP_SOLVER_MAX_CP_ITER_ROOT: 80
SDP_SOLVER_MAX_CP_ITER: 40
SDP_SOLVER_CP_TOL: 1e-06
SDP_SOLVER_MAX_INEQ: 100000
SDP_SOLVER_INHERIT_PERC: 1
SDP_SOLVER_EPS_INEQ: 0.0001
SDP_SOLVER_EPS_ACTIVE: 1e-06
SDP_SOLVER_MAX_PAIR_INEQ: 100000
SDP_SOLVER_PAIR_PERC: 0.05
SDP_SOLVER_MAX_TRIANGLE_INEQ: 100000
SDP_SOLVER_TRIANGLE_PERC: 0.05


|    N| NODE_PAR|    NODE|      LB_PAR|          LB|  FLAG|  TIME (s)| CP_ITER| CP_FLAG|   CP_INEQ|     PAIR  TRIANGLE    CLIQUE|          UB|         GUB|     I      J|     NODE_GAP|          GAP|  OPEN|
|  164|       -1|       0|        -inf|     93.3876|     0|       110|       7|      -3|      6456|  242.571      4802   8.14286|     93.5225|    93.5225*|    -1     -1|   0.00144229|   0.00144229|     0|
|  163|        0|       1|     93.3876|     93.4388|     0|        35|       2|      -3|      5958|        1      3675         0|     93.4777|    93.4777*|    79    142|  0.000416211|  0.000416211|     0|
|  164|        0|       2|     93.3876|     93.4494|     0|        47|       2|      -3|      6888|        0      4635         0|     93.5225|     93.4777|    79    142|  0.000302427|  0.000302427|     0|
|  162|        1|       3|     93.4388|      93.506|     0|        27|       1|       2|      6258|        9      3759         0|         inf|     93.4777|   119    152| -0.000302724| -0.000302724|     0|
|  163|        1|       4|     93.4388|     93.4536|     0|        47|       4|      -3|      3336|        0      1789         0|     93.4777|     93.4777|   119    152|   0.00025747|   0.00025747|     0|
|  164|        2|       5|     93.4494|     93.4549|     0|        37|       1|      -3|      6888|        0      5000         0|     93.5225|     93.4777|    47     54|  0.000243844|  0.000243844|     0|
|  163|        2|       6|     93.4494|     93.4708|     0|        51|       2|       2|      7292|       11      4693         0|     93.5559|     93.4777|    47     54|  7.36443e-05|  7.36443e-05|     0|
|  164|        5|       7|     93.4549|      93.475|     0|        22|       0|       2|      6888|        0         0         0|     93.5225|     93.4777|   122    153|  2.82805e-05|  2.82805e-05|     0|
|  163|        4|       8|     93.4536|     93.4536|     0|        38|       2|      -3|      3257|        0     668.5         0|     93.4704|    93.4704*|    47     54|  0.000180057|  0.000180057|     0|
|  163|        5|       9|     93.4549|     93.5216|     0|        41|       1|       2|      6893|        8      5000         0|         inf|     93.4704|   122    153| -0.000547847| -0.000547847|     0|
|  163|        8|      10|     93.4536|     93.4536|     0|        27|       1|      -3|      3257|        0       879         0|     93.4704|     93.4704|    37     45|  0.000180057|  0.000180057|     0|
|  162|        8|      11|     93.4536|     93.4838|     0|        33|       1|       2|      6158|       24      4233         0|         inf|     93.4704|    37     45| -0.000143677| -0.000143677|     0|
|  162|        4|      12|     93.4536|     93.4658|     0|        75|       5|      -3|      2793|      4.6      2379         0|     93.5111|     93.4704|    47     54|  4.89954e-05|  4.89954e-05|     0|
|  162|       10|      13|     93.4536|     93.5053|     0|        19|       0|       2|      3122|        0         0         0|         inf|     93.4704|    37     99|  -0.00037365|  -0.00037365|     0|
|  163|       10|      14|     93.4536|     93.4701|     0|        31|       0|       2|      3257|        0         0         0|     93.4704|     93.4704|    37     99|  3.13989e-06|  3.13989e-06|     0|

WALL_TIME: 304 sec
N_NODES: 15
AVG_INEQ: 2788.05
AVG_CP_ITER: 1.93333
ROOT_GAP: 0.00144229
GAP: 0
BEST: 93.4704
Owner
Antonio M. Sudoso
Antonio M. Sudoso
PyTorch implementation of Densely Connected Time Delay Neural Network

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

Ya-Qi Yu 64 Oct 11, 2022
Projects for AI/ML and IoT integration for games and other presented at re:Invent 2021.

Playground4AWS Projects for AI/ML and IoT integration for games and other presented at re:Invent 2021. Architecture Minecraft and Lamps This project i

Vinicius Senger 5 Nov 30, 2022
This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

CLGo This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints An earlier

ๅˆ˜่Šฎ้‡‘ 32 Dec 20, 2022
๐Ÿ”ฅ๐Ÿ”ฅHigh-Performance Face Recognition Library on PaddlePaddle & PyTorch๐Ÿ”ฅ๐Ÿ”ฅ

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 02, 2023
The repository is for safe reinforcement learning baselines.

Safe-Reinforcement-Learning-Baseline The repository is for Safe Reinforcement Learning (RL) research, in which we investigate various safe RL baseline

172 Dec 19, 2022
Hierarchical probabilistic 3D U-Net, with attention mechanisms (โ€”๐˜ˆ๐˜ต๐˜ต๐˜ฆ๐˜ฏ๐˜ต๐˜ช๐˜ฐ๐˜ฏ ๐˜œ-๐˜•๐˜ฆ๐˜ต, ๐˜š๐˜Œ๐˜™๐˜ฆ๐˜ด๐˜•๐˜ฆ๐˜ต) and a nested decoder structure with deep supervision (โ€”๐˜œ๐˜•๐˜ฆ๐˜ต++).

Hierarchical probabilistic 3D U-Net, with attention mechanisms (โ€”๐˜ˆ๐˜ต๐˜ต๐˜ฆ๐˜ฏ๐˜ต๐˜ช๐˜ฐ๐˜ฏ ๐˜œ-๐˜•๐˜ฆ๐˜ต, ๐˜š๐˜Œ๐˜™๐˜ฆ๐˜ด๐˜•๐˜ฆ๐˜ต) and a nested decoder structure with deep supervision (โ€”๐˜œ๐˜•๐˜ฆ๐˜ต++). Built in TensorFlow 2.5. Configured for vox

Diagnostic Image Analysis Group 32 Dec 08, 2022
Repository for MuSiQue: Multi-hop Questions via Single-hop Question Composition

๐ŸŽต MuSiQue: Multi-hop Questions via Single-hop Question Composition This is the repository for our paper "MuSiQue: Multi-hop Questions via Single-hop

21 Jan 02, 2023
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

PGDF This repo is the official implementation of our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ". Citation If you use

CVSM Group - email: <a href=[email protected]"> 22 Dec 23, 2022
Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

Memformer - Pytorch Implementation of Memformer, a Memory-augmented Transformer, in Pytorch. It includes memory slots, which are updated with attentio

Phil Wang 60 Nov 06, 2022
efficient neural audio synthesis in the waveform domain

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper โ€ข website โ€ข colab โ€ข audio by Ben Hayes, Charalampos Saitis,

Ben Hayes 169 Dec 23, 2022
Keywords : Streamlit, BertTokenizer, BertForMaskedLM, Pytorch

Next Word Prediction Keywords : Streamlit, BertTokenizer, BertForMaskedLM, Pytorch ๐ŸŽฌ Project Demo โœ” Application is hosted on Streamlit. You can see t

Vivek7 3 Aug 26, 2022
DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures Dataset: https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polar

Ashish Salunkhe 37 Dec 17, 2022
Source code for Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning Official implementation of ACC, described in the paper "Adaptively Calibrated C

3 Sep 16, 2022
SPTAG: A library for fast approximate nearest neighbor search

SPTAG: A library for fast approximate nearest neighbor search SPTAG SPTAG (Space Partition Tree And Graph) is a library for large scale vector approxi

Microsoft 4.3k Jan 01, 2023
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Dominik Klein 189 Dec 21, 2022
Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

STAM - Pytorch Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in

Phil Wang 109 Dec 28, 2022
Localizing Visual Sounds the Hard Way

Localizing-Visual-Sounds-the-Hard-Way Code and Dataset for "Localizing Visual Sounds the Hard Way". The repo contains code and our pre-trained model.

Honglie Chen 58 Dec 07, 2022
An essential implementation of BYOL in PyTorch + PyTorch Lightning

Essential BYOL A simple and complete implementation of Bootstrap your own latent: A new approach to self-supervised Learning in PyTorch + PyTorch Ligh

Enrico Fini 48 Sep 27, 2022
A faster pytorch implementation of faster r-cnn

A Faster Pytorch Implementation of Faster R-CNN Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the firs

Jianwei Yang 7.1k Jan 01, 2023