AtSNE

AtSNE is a solution of high-dimensional data visualization problem. It can project large-scale high-dimension vectors into low-dimension space while keeping the pair-wise similarity amount point. AtSNE is efficient and scalable and can visualize 20M points in less than 5 hours using GPU. The spatial structure of its result is also robust to random initializations. It implements the algorithm of our KDD'19 paper - AtSNE: Efficient and Robust Visulization on GPU through Hierarchical Optimization

Benchmark datasets

Dataset	Dimensions	Number of Points	Number of Categories	Data	Label
CIFAR10	1024	60,000	10	.txt .fvces	.txt .fvces
CIFAR100	1024	60,000	100	.txt .fvces	.txt .fvces
MNIST	784	70,000	10	.txt .fvces	.txt .fvces
Fashion-MNIST	784	70,000	10	.txt .fvces	.txt .fvces
AG’s News	100	120,000	4	.txt .fvces	.txt .fvces
DBPedia	100	560,000	14	.txt .fvces	.txt .fvces
ImageNet	128	1,281,167	1000	.txt .fvces	.txt .fvces
Yahoo	100	1,400,000	10	.txt .fvces	.txt .fvces
Crawl	300	2,000,000	10	.txt .fvces	.txt .fvces
Amazon3M	100	3,000,000	5	.txt .fvces	.txt .fvces
Amazon20M	96	19,531,329	5	.txt .fvces	.txt .fvces

• Details of dataset pre-processing are provided in our papers

Visualization Examples

Performance

Compared Algorithms:

• LargeVis
• BH-t-SNE
• TSNE-CUDA

Dataset	method	10-NN accuracy	time	Memory (GB)	speedup
CIFAR10	BH-t-SNE	0.966	5m12s	2.61	1.6
	LargeVis	0.965	8m23s	7.90	1.0
	tsne-cuda	0.963	27.7s	2.17	18.1
	AtSNE	0.957	19.6s	0.93	25.7
CIFAR100	BH-t-SNE	0.636	9m51s	2.62	0.9
	LargeVis	0.607	8m50s	7.90	1.0
	tsne-cuda	0.646	28.3s	2.33	18.7
	AtSNE	0.600	19s	0.93	27.9
MNIST	BH-t-SNE	0.970	5m20s	2.35	1.7
	LargeVis	0.966	8m59s	7.15	1.0
	tsne-cuda	0.968	31.3s	2.33	14.7
	AtSNE	0.967	19.6s	0.93	27.5
Fashion-MNIST	BH-t-SNE	0.821	3m46s	2.28	2.3
	LargeVis	0.797	8m30s	7.18	1.0
	tsne-cuda	0.827	31.1s	2.17	16.4
	AtSNE	0.822	19.9s	0.93	25.6
AG’s News	BH-t-SNE	0.993	5m30s	0.95	1.9
	LargeVis	0.994	10m37s	2.65	1.0
	tsne-cuda	0.993	39.3s	2.17	16.2
	AtSNE	0.995	23s	0.88	27.7
DBPedia	BH-t-SNE	0.993	36m8s	4.22	0.93
	LargeVis	0.999	33m43s	12.71	1.0
	tsne-cuda	-	-	-
	AtSNE	0.999	3m	2.03	11.2
ImageNet	BH-t-SNE	0.412	4h7m53s	10.8	0.3
	LargeVis	0.608	1h18m45s	53.09	1.0
	tsne-cuda	-	-	-
	AtSNE	0.649	11m53s	4.01	6.6
Yahoo	BH-t-SNE	0.537	2h17m17s	10.47	0.62
	LargeVis	0.775	1h25m17s	49.99	1.0
	tsne-cuda	–	–	–
	AtSNE	0.780	12m52s	4.27	6.6
Crawl	BH-t-SNE	-	>24h	–
	LargeVis	0.688	2h34m14s	139.05	1.0
	tsne-cuda	-	–	–
	AtSNE	0.692	30m1s	7.19	5.1
Amazon3M	BH-t-SNE	-	> 24h	-
	LargeVis	0.606	2h53m25s	104	1.0
	tsne-cuda	-	-	–
	AtSNE	0.603	34m4s	7.98	5.1
Amazon20M	BH-t-SNE	-	-	-
	LargeVis	-	-	-
	tsne-cuda	-	-	-
	AtSNE	0.755	4h54m	19.70

• Tested on i9-7980XE (18Cores, 36 Threads) with 128GB Memory
• AtSNE and TSNE-CUDA use one GTX 1080Ti GPU
• BH-t-SNE and LargeVis use 32 threads in the table above
• - means this method crashed in testing progress, mostly because of memory issues
• Tested version of LargeVis, BH-t-SNE and TSNE-CUDA are feb8121, 62dedde and efa2098 respectively
• For Amazon20M dataset which is too large to fit in memory, we use Product Quantization to build KNN graph. AtSNE use extra parameters -k 50 --ivfpq 1 --subQuantizers 24 --bitsPerCode 8.
• AtSNE just use the default parameters in the test above, except --n_negative 400. Exact parameters of aforementioned result are provided below in case you need it.

--lr 0.05 --vis_iter 2000 --save_interval 0 -k 100 --clusters 1000 --n_negative 400 --center_number 5 --nprobe 50 --knn_negative_rate 0 -p 50 --early_pull_rate 20 --center_pull_iter 500 --early_pull_iter 1000 --scale 10 --center_perplexity 70 --center_grad_coeff 1

How to use

Requirement

• CUDA (8 or later), nvcc and cublas included
• gcc
• faiss

Compile

1. Clone this project
2. init submodule (cmdline and faiss)
   • enter the project root directory
   • run git submodule init; git submodule update
3. Compile faiss, enter directory of faiss (vendor/faiss), and follow Step1 and Step3, confirm that vendor/faiss/libfaiss.a and vendor/faiss/gpu/libgpufaiss.a is generated. Simplified instructions are shown below:
   • install required BLAS library (MKL, openblas): sudo apt install libopenblas-dev
   • cd vender/faiss
   • build faiss cpu library: ./configure && make -j8
   • build faiss gpu library: cd gpu; make -j
4. enter project root directory, run make -j

Run

./qvis_gpu -b mnist_vec784D_data.txt.fvecs -o mnist_result.txt

We choose good default parameters for you. And there are many other parameters you can change. If you want to reproduce the test in our KDD paper, please add --n_negative 400.

./qvis_gpu -b mnist_vec784D_data.txt.fvecs --n_negative 400 -o mnist_result.txt

ivecs/fvecs vector file formats are defined here

Supplementary tools

There are some supplementary tools we use during developing/debugging/experimentation

• tools/view.py Draw the result in 2D space and save images for you.
• Label file is optional.
• Use multi-process to draw images for results with the same filename-prefix
• tools/txt_to_fvecs.py covert txt file, like result of largevVis or label file, to ivecs/fvecs
• tools/largevis_convert.py convert dataset of fvecs/ivecs to largeVis input format
• tools/imagenet_infer.py generate 128D imagenet feature vectors from ImageNet dataset
• tools/box_filter.py Give a bounding-box, print the points and corresponding labels. Used for case-study in our paper
• test_knn_accuracy (Build required) Test knn classifier accuracy(label needed) of visualization result
• test_top1_error (Build required) Test top-1 error of visualization result. The top-1 error is the ratio that the nearest neighbor of one point in low-dimension is not the nearest neighbor in high-dimension