diff --git a/publications/umap_paper_benchmarks/README.md b/publications/umap_paper_benchmarks/README.md new file mode 100644 index 00000000..63355cc9 --- /dev/null +++ b/publications/umap_paper_benchmarks/README.md @@ -0,0 +1,34 @@ +Datasets are not included in this repository and need to be downloaded separately. + +The necessary dependencies for reproducing the benchmarks have been captured in `conda` environment yaml files. + +To install dependencies for cuml and UMAP-learn benchmarks: +``` +conda env create --name cuml_umap_benchmarks -f conda/umap_paper_cuml_cuda10.2.yml +``` + +To install dependencies for GPUMAP benchmarks: +``` +conda env create --name gpumap_benchmarks -f conda/umap_paper_gpumap_cuda10.0.yml +``` + +You can run the notebooks using jupyter lab: +``` +conda activate +python -m ipykernel install --user +jupyter lab +``` + +# Datasets + +- PEN Digits - uses sklearn.datasets.load_digits +- GoogleNews Word2Vec - Downloaded from https://code.google.com/archive/p/word2vec/ and loaded using Gensim library +- Fashion MNIST - Downloaded from https://github.com/zalandoresearch/fashion-mnist +- CIFAR-100 - Downloaded from https://www.cs.toronto.edu/~kriz/cifar.html +- Shuttle - Downloaded from https://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle) +- MNIST - Uses datasets submodule to download and load +- TASIC2018 - Data from : https://portal.brain-map.org/atlases-and-data/rnaseq (see dedicated notebook) +- scRNA - Dataset downloaded from https://cells.ucsc.edu/ +- COIL-20 - Uses datasets submodule to download and load + + diff --git a/publications/umap_paper_benchmarks/__init__.py b/publications/umap_paper_benchmarks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/publications/umap_paper_benchmarks/conda/umap_paper_cuml_cuda10.2.yml b/publications/umap_paper_benchmarks/conda/umap_paper_cuml_cuda10.2.yml new file mode 100644 index 00000000..edc3a4ba --- /dev/null +++ b/publications/umap_paper_benchmarks/conda/umap_paper_cuml_cuda10.2.yml @@ -0,0 +1,37 @@ +channels: + - rapidsai-nightly + - nvidia + - conda-forge + - facebook + - pytorch + - defaults +dependencies: + - python=3.7 + - cudatoolkit=10.2 + - cudf + - dask-cuda + - dask-cudf + - cuml + - cugraph + - opencv + - scikit-image + - chainer + - scipy + - gensim + - ucx-py + - joblib + - matplotlib + - umap-learn + - numba + - ucx-proc=*=gpu + - scikit-learn + - cupy + - ipykernel + - jupyterlab + - pip + - pip: + - jupyter-server-proxy + - git+https://github.com/dask/dask.git + - git+https://github.com/dask/distributed.git + - git+https://github.com/overshiki/datasets.git + - wget diff --git a/publications/umap_paper_benchmarks/conda/umap_paper_gpumap_cuda10.0.yml b/publications/umap_paper_benchmarks/conda/umap_paper_gpumap_cuda10.0.yml new file mode 100644 index 00000000..a8f67bb8 --- /dev/null +++ b/publications/umap_paper_benchmarks/conda/umap_paper_gpumap_cuda10.0.yml @@ -0,0 +1,39 @@ +channels: + - rapidsai-nightly + - nvidia + - conda-forge + - facebook + - pytorch + - defaults +dependencies: + - python=3.7 + - cudatoolkit=10.0 + - cudf + - dask-cuda + - dask-cudf + - cuml + - cugraph + - opencv + - scikit-image + - chainer + - scipy + - ucx-py + - joblib + - matplotlib + - umap-learn + - numba + - ucx-proc=*=gpu + - scikit-learn + - cupy + - ipykernel + - jupyterlab + - pip + - pip: + - jupyter-server-proxy + - faiss==1.5.3 + - faiss-gpu==1.5.3 + - git+https://github.com/dask/dask.git + - git+https://github.com/dask/distributed.git + - git+https://github.com/p3732/gpumap.git + - git+https://github.com/overshiki/datasets.git + - wget diff --git a/publications/umap_paper_benchmarks/notebooks/__init__.py b/publications/umap_paper_benchmarks/notebooks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/publications/umap_paper_benchmarks/notebooks/benchmarks_to_csv.ipynb b/publications/umap_paper_benchmarks/notebooks/benchmarks_to_csv.ipynb new file mode 100644 index 00000000..daf22d10 --- /dev/null +++ b/publications/umap_paper_benchmarks/notebooks/benchmarks_to_csv.ipynb @@ -0,0 +1,880 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle \n", + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "benchmarks = pickle.load(open(\"results/results.pickle\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "algos = [\"umapcuml\", \"umaplearn\", \"umapgpumap\"]\n", + "types = [\"unsupervised\", \"supervised\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "datasets = list(benchmarks.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(datasets)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['digits', 'fashion_mnist', 'cifar100', 'coil20', 'shuttle', 'mnist', 'scrna']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'digits': {'umapcuml': [{'unsupervised': {'time': 0.4452664852142334,\n", + " 'trust': 0.9876636433389232},\n", + " 'supervised': {'time': 0.47392821311950684, 'trust': 0.9879378905549919},\n", + " 'xform': {'time': 0.15683841705322266, 'trust': 0.9847805133487961}},\n", + " {'unsupervised': {'time': 0.18512916564941406, 'trust': 0.9874644094493787},\n", + " 'supervised': {'time': 0.2050032615661621, 'trust': 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0.9575792140876225},\n", + " 'supervised': {'time': 88.2114098072052, 'trust': 0.9563557643207674},\n", + " 'xform': {'time': 85.75131511688232, 'trust': 0.9566137627757307}},\n", + " {'unsupervised': {'time': 51.839635133743286, 'trust': 0.9594030397578331},\n", + " 'supervised': {'time': 92.17171335220337, 'trust': 0.9564347384460339},\n", + " 'xform': {'time': 94.09139847755432, 'trust': 0.9575536319478022}},\n", + " {'unsupervised': {'time': 54.40650486946106, 'trust': 0.9590946155285452},\n", + " 'supervised': {'time': 90.77351665496826, 'trust': 0.9569490322586621},\n", + " 'xform': {'time': 88.45775318145752, 'trust': 0.9581549449881714}},\n", + " {'unsupervised': {'time': 51.73101210594177, 'trust': 0.9590602315702501},\n", + " 'supervised': {'time': 85.51447701454163, 'trust': 0.9584785717302744},\n", + " 'xform': {'time': 101.88577651977539, 'trust': 0.9568694395073667}}],\n", + " 'umapgpumap': [{'unsupervised': {'time': 10.581817388534546,\n", + " 'trust': 0.9442834214597077},\n", + " 'supervised': {'time': 25.554595947265625, 'trust': 0.9450770215619692}},\n", + " {'unsupervised': {'time': 10.227915525436401, 'trust': 0.9419907738521989},\n", + " 'supervised': {'time': 25.033493280410767, 'trust': 0.9447635175706243}},\n", + " {'unsupervised': {'time': 9.917372941970825, 'trust': 0.9436392852630545},\n", + " 'supervised': {'time': 25.709108352661133, 'trust': 0.9469258037618123}},\n", + " {'unsupervised': {'time': 11.701467275619507, 'trust': 0.9397684622623858},\n", + " 'supervised': {'time': 19.324782371520996, 'trust': 0.9448813560198076}}]},\n", + " 'scrna': {'umapcuml': [{'unsupervised': {'time': 3.8234424591064453,\n", + " 'trust': 0.6178009579665475},\n", + " 'xform': {'time': 3.4701781272888184, 'trust': 0.5337737424809548}},\n", + " {'unsupervised': {'time': 3.8994381427764893, 'trust': 0.6201815623705682},\n", + " 'xform': {'time': 3.9311397075653076, 'trust': 0.9708745134888634}},\n", + " {'unsupervised': {'time': 4.300644397735596, 'trust': 0.6187572904540646},\n", + " 'xform': {'time': 3.5282957553863525, 'trust': 0.8702917658612157}},\n", + " {'unsupervised': {'time': 4.389519453048706, 'trust': 0.9781377849332045},\n", + " 'xform': {'time': 3.602308988571167, 'trust': 0.6201129287721822}}],\n", + " 'umapgpumap': [{'unsupervised': {'time': 13.082006216049194,\n", + " 'trust': 0.6168243354282248}},\n", + " {'unsupervised': {'time': 10.061619758605957, 'trust': 0.9434946335833367}},\n", + " {'unsupervised': {'time': 10.218225479125977, 'trust': 0.6203727995014007}},\n", + " {'unsupervised': {'time': 10.199477195739746,\n", + " 'trust': 0.9425912295012873}}],\n", + " 'umaplearn': [{'unsupervised': {'time': 222.38141894340515,\n", + " 'trust': 0.6233079229256522},\n", + " 'xform': {'time': 2.1147098541259766, 'trust': 0.9412946245569216}},\n", + " {'unsupervised': {'time': 225.683984041214, 'trust': 0.6231010898670823},\n", + " 'xform': {'time': 2.2269630432128906, 'trust': 0.9502004104337913}},\n", + " {'unsupervised': {'time': 227.73396110534668, 'trust': 0.6238778233428524},\n", + " 'xform': {'time': 2.115278482437134, 'trust': 0.9481981978106048}},\n", + " {'unsupervised': {'time': 219.88730216026306, 'trust': 0.623651432003607},\n", + " 'xform': {'time': 2.3570568561553955, 'trust': 0.9415185740893797}}]}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "final_results = {}\n", + "\n", + "for dataset in datasets:\n", + " d = benchmarks[dataset]\n", + " final_results[dataset] = {}\n", + " for algo in algos:\n", + " if algo in d:\n", + " a = d[algo]\n", + " summary = {}\n", + " for bench in a:\n", + " for t in types:\n", + " if t not in summary:\n", + " summary[t] = { \"sum\": 0, \"count\": 0, \"sum_squared\": 0, \"trust\": 0}\n", + " if t in bench:\n", + " time = bench[t][\"time\"]\n", + " summary[t][\"sum\"] += time\n", + " summary[t][\"count\"] += 1\n", + " summary[t][\"sum_squared\"] += time**2\n", + " summary[t][\"trust\"] = max(summary[t][\"trust\"], bench[t][\"trust\"])\n", + "\n", + " final_results[dataset][algo] = {}\n", + " for t in types:\n", + " if summary[t][\"count\"] > 0:\n", + " mean = (summary[t][\"sum\"]) / summary[t][\"count\"]\n", + " var = ((summary[t][\"sum_squared\"]) / summary[t][\"count\"]) - (mean**2)\n", + " trust = summary[t][\"trust\"]\n", + " else:\n", + " mean = 0\n", + " var = 0\n", + " trust = 0\n", + " final_results[dataset][algo][t] = {\"mean\": mean, \"var\": var, \"trust\": trust}\n", + " \n", + " else:\n", + " print(algo + \" not in \" + dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'digits': {'umapcuml': {'unsupervised': {'mean': 0.35827815532684326,\n", + " 'var': 0.011162857575371277,\n", + " 'trust': 0.9876636433389232},\n", + " 'supervised': {'mean': 0.40629714727401733,\n", + " 'var': 0.013507401316314116,\n", + " 'trust': 0.9879864913274599}},\n", + " 'umaplearn': {'unsupervised': {'mean': 6.328544795513153,\n", + " 'var': 2.8975128262992627,\n", + " 'trust': 0.9879530939808027},\n", + " 'supervised': {'mean': 6.755777180194855,\n", + " 'var': 0.1106131444620786,\n", + " 'trust': 0.9876400330669284}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 2.482882022857666,\n", + " 'var': 1.0582028882037378,\n", + " 'trust': 0.9558078864163978},\n", + " 'supervised': {'mean': 2.5530967116355896,\n", + " 'var': 0.10950217290617204,\n", + " 'trust': 0.9548070534694238}}},\n", + " 'fashion_mnist': {'umapcuml': {'unsupervised': {'mean': 0.45551514625549316,\n", + " 'var': 0.006011408943862762,\n", + " 'trust': 0.9773246413616931},\n", + " 'supervised': {'mean': 1.036961555480957,\n", + " 'var': 0.0002095378332569453,\n", + " 'trust': 0.9762932845375539}},\n", + " 'umaplearn': {'unsupervised': {'mean': 45.870298981666565,\n", + " 'var': 10.23240442609631,\n", + " 'trust': 0.9783708581947695},\n", + " 'supervised': {'mean': 53.09064221382141,\n", + " 'var': 6.182976974198482,\n", + " 'trust': 0.9781969233047202}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 4.158320248126984,\n", + " 'var': 1.800035649938632,\n", + " 'trust': 0.9750927790516971},\n", + " 'supervised': {'mean': 6.476689338684082,\n", + " 'var': 0.06324447576008652,\n", + " 'trust': 0.9696567894031254}}},\n", + " 'cifar100': {'umapcuml': {'unsupervised': {'mean': 1.008723258972168,\n", + " 'var': 0.018844815007184934,\n", + " 'trust': 0.8342801452408608},\n", + " 'supervised': {'mean': 1.081564486026764,\n", + " 'var': 0.0003376463217925618,\n", + " 'trust': 0.8382352637585149}},\n", + " 'umaplearn': {'unsupervised': {'mean': 105.85025483369827,\n", + " 'var': 2.482921346083458,\n", + " 'trust': 0.8472284890319066},\n", + " 'supervised': {'mean': 98.42393732070923,\n", + " 'var': 2.272873735988469,\n", + " 'trust': 0.8490670250262601}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 6.186033010482788,\n", + " 'var': 1.7708737036427635,\n", + " 'trust': 0.8401050404506913},\n", + " 'supervised': {'mean': 5.953894853591919,\n", + " 'var': 0.023564399280502357,\n", + " 'trust': 0.8309824029952222}}},\n", + " 'coil20': {'umapcuml': {'unsupervised': {'mean': 0.7574235200881958,\n", + " 'var': 0.5752119048886044,\n", + " 'trust': 0.9927721908570533},\n", + " 'supervised': {'mean': 0.3695611357688904,\n", + " 'var': 0.006603219726240894,\n", + " 'trust': 0.986986493374108}},\n", + " 'umaplearn': {'unsupervised': {'mean': 11.210062146186829,\n", + " 'var': 2.571288658299423,\n", + " 'trust': 0.9936500235238768},\n", + " 'supervised': {'mean': 12.338415145874023,\n", + " 'var': 0.02176407274296821,\n", + " 'trust': 0.9868479965498315}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 2.5822073221206665,\n", + " 'var': 0.005004079547958895,\n", + " 'trust': 0.9565478057450535},\n", + " 'supervised': {'mean': 8.210693836212158,\n", + " 'var': 0.02740621988587577,\n", + " 'trust': 0.9332845866332105}}},\n", + " 'shuttle': {'umapcuml': {'unsupervised': {'mean': 0.5825626254081726,\n", + " 'var': 0.025230869766499353,\n", + " 'trust': 0.9999991344960172},\n", + " 'supervised': {'mean': 0.5559555292129517,\n", + " 'var': 0.011077821222144735,\n", + " 'trust': 0.9999999959879131}},\n", + " 'umaplearn': {'unsupervised': {'mean': 38.87588673830032,\n", + " 'var': 8.039266967871527,\n", + " 'trust': 1.0},\n", + " 'supervised': {'mean': 50.173097372055054,\n", + " 'var': 17.54718326385637,\n", + " 'trust': 1.0}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 9.064357042312622,\n", + " 'var': 3.431728740054808,\n", + " 'trust': 0.9778424900551579},\n", + " 'supervised': {'mean': 17.151020526885986,\n", + " 'var': 23.923241572835195,\n", + " 'trust': 0.9668076059384338}}},\n", + " 'mnist': {'umapcuml': {'unsupervised': {'mean': 0.7078064680099487,\n", + " 'var': 0.008856602310757467,\n", + " 'trust': 0.9574214731202516},\n", + " 'supervised': {'mean': 0.9175034761428833,\n", + " 'var': 0.0029794700764256277,\n", + " 'trust': 0.9574419861872977}},\n", + " 'umaplearn': {'unsupervised': {'mean': 52.57512265443802,\n", + " 'var': 1.1677051461933843,\n", + " 'trust': 0.9594030397578331},\n", + " 'supervised': {'mean': 89.16777920722961,\n", + " 'var': 6.465818109702013,\n", + " 'trust': 0.9584785717302744}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 10.60714328289032,\n", + " 'var': 0.45444580430584836,\n", + " 'trust': 0.9442834214597077},\n", + " 'supervised': {'mean': 23.90549498796463,\n", + " 'var': 7.056965841371607,\n", + " 'trust': 0.9469258037618123}}},\n", + " 'scrna': {'umapcuml': {'unsupervised': {'mean': 4.103261113166809,\n", + " 'var': 0.06018657014949724,\n", + " 'trust': 0.9781377849332045},\n", + " 'supervised': {'mean': 0, 'var': 0, 'trust': 0}},\n", + " 'umaplearn': {'unsupervised': {'mean': 223.92166656255722,\n", + " 'var': 9.071952858837903,\n", + " 'trust': 0.6238778233428524},\n", + " 'supervised': {'mean': 0, 'var': 0, 'trust': 0}},\n", + " 'umapgpumap': {'unsupervised': {'mean': 10.890332162380219,\n", + " 'var': 1.604801846075489,\n", + " 'trust': 0.9434946335833367},\n", + " 'supervised': {'mean': 0, 'var': 0, 'trust': 0}}}}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "for dataset in datasets:\n", + " for algo in algos:\n", + " for t in types:\n", + " if algo in final_results[dataset]:\n", + " b = final_results[dataset][algo][t]\n", + " data.append((dataset, algo, t, b[\"mean\"], b[\"var\"], b[\"trust\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "with open('benchmark_results.csv','w') as out:\n", + " csv_out=csv.writer(out)\n", + " csv_out.writerow(['dataset','impl', 'bench', 'mean', 'var', 'max_trust'])\n", + " for row in data:\n", + " csv_out.writerow(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('digits',\n", + " 'umapcuml',\n", + " 'unsupervised',\n", + " 0.35827815532684326,\n", + " 0.011162857575371277,\n", + " 0.9876636433389232),\n", + " ('digits',\n", + " 'umapcuml',\n", + " 'supervised',\n", + " 0.40629714727401733,\n", + " 0.013507401316314116,\n", + " 0.9879864913274599),\n", + " ('digits',\n", + " 'umaplearn',\n", + " 'unsupervised',\n", + " 6.328544795513153,\n", + " 2.8975128262992627,\n", + " 0.9879530939808027),\n", + " ('digits',\n", + " 'umaplearn',\n", + " 'supervised',\n", + " 6.755777180194855,\n", + " 0.1106131444620786,\n", + " 0.9876400330669284),\n", + " ('digits',\n", + " 'umapgpumap',\n", + " 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'umapcuml',\n", + " 'unsupervised',\n", + " 0.5825626254081726,\n", + " 0.025230869766499353,\n", + " 0.9999991344960172),\n", + " ('shuttle',\n", + " 'umapcuml',\n", + " 'supervised',\n", + " 0.5559555292129517,\n", + " 0.011077821222144735,\n", + " 0.9999999959879131),\n", + " ('shuttle',\n", + " 'umaplearn',\n", + " 'unsupervised',\n", + " 38.87588673830032,\n", + " 8.039266967871527,\n", + " 1.0),\n", + " ('shuttle',\n", + " 'umaplearn',\n", + " 'supervised',\n", + " 50.173097372055054,\n", + " 17.54718326385637,\n", + " 1.0),\n", + " ('shuttle',\n", + " 'umapgpumap',\n", + " 'unsupervised',\n", + " 9.064357042312622,\n", + " 3.431728740054808,\n", + " 0.9778424900551579),\n", + " ('shuttle',\n", + " 'umapgpumap',\n", + " 'supervised',\n", + " 17.151020526885986,\n", + " 23.923241572835195,\n", + " 0.9668076059384338),\n", + " ('mnist',\n", + " 'umapcuml',\n", + " 'unsupervised',\n", + " 0.7078064680099487,\n", + " 0.008856602310757467,\n", + " 0.9574214731202516),\n", + " ('mnist',\n", + " 'umapcuml',\n", + " 'supervised',\n", + " 0.9175034761428833,\n", + " 0.0029794700764256277,\n", + " 0.9574419861872977),\n", + " ('mnist',\n", + " 'umaplearn',\n", + " 'unsupervised',\n", + " 52.57512265443802,\n", + " 1.1677051461933843,\n", + " 0.9594030397578331),\n", + " ('mnist',\n", + " 'umaplearn',\n", + " 'supervised',\n", + " 89.16777920722961,\n", + " 6.465818109702013,\n", + " 0.9584785717302744),\n", + " ('mnist',\n", + " 'umapgpumap',\n", + " 'unsupervised',\n", + " 10.60714328289032,\n", + " 0.45444580430584836,\n", + " 0.9442834214597077),\n", + " ('mnist',\n", + " 'umapgpumap',\n", + " 'supervised',\n", + " 23.90549498796463,\n", + " 7.056965841371607,\n", + " 0.9469258037618123),\n", + " ('scrna',\n", + " 'umapcuml',\n", + " 'unsupervised',\n", + " 4.103261113166809,\n", + " 0.06018657014949724,\n", + " 0.9781377849332045),\n", + " ('scrna', 'umapcuml', 'supervised', 0, 0, 0),\n", + " ('scrna',\n", + " 'umaplearn',\n", + " 'unsupervised',\n", + " 223.92166656255722,\n", + " 9.071952858837903,\n", + " 0.6238778233428524),\n", + " ('scrna', 'umaplearn', 'supervised', 0, 0, 0),\n", + " ('scrna',\n", + " 'umapgpumap',\n", + " 'unsupervised',\n", + " 10.890332162380219,\n", + " 1.604801846075489,\n", + " 0.9434946335833367),\n", + " ('scrna', 'umapgpumap', 'supervised', 0, 0, 0)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "scale_results_noknn = pickle.load(open(\"results/scale_results_precompute_knn.pickle\", \"rb\"))[\"umapcuml\"]\n", + "scale_results_knn = pickle.load(open(\"results/scale_results_with_knn.pickle\", \"rb\"))[\"umapcuml\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "xaxis = sorted([int(a) for a in scale_results_knn.keys()])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "knn_with = { int(k): v[0][\"unsupervised\"][\"time\"] for k,v in scale_results_knn.items() }" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "knn_without = { int(k): v[0][\"unsupervised\"][\"time\"] for k,v in scale_results_noknn.items() }" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "with open('googlenews_knn_and_without.csv','w') as out:\n", + " csv_out=csv.writer(out)\n", + " csv_out.writerow(['n_samples', 'with_knn', 'without_knn'])\n", + " for n_samples in xaxis:\n", + " csv_out.writerow((n_samples, knn_with[n_samples], knn_without[n_samples]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/publications/umap_paper_benchmarks/notebooks/umap_benchmark.ipynb b/publications/umap_paper_benchmarks/notebooks/umap_benchmark.ipynb new file mode 100644 index 00000000..28d2b410 --- /dev/null +++ b/publications/umap_paper_benchmarks/notebooks/umap_benchmark.ipynb @@ -0,0 +1,853 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# UMAP Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "import datasets\n", + "\n", + "from umap_bench.funcs import build_and_train\n", + "from umap_bench.funcs import draw_chart\n", + "from umap_bench.funcs import _run_build_and_train_once\n", + "from umap_bench.funcs import store_results\n", + "from umap_bench.funcs import maybe_load_results\n", + "from umap_bench.funcs import maybe_get_results\n", + "\n", + "from umap_bench.funcs import perform_n_samples_test\n", + "from umap_bench.funcs import perform_n_components_test\n", + "\n", + "from umap_bench import loaders\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import pickle\n", + "import rmm\n", + "import time\n", + "import numpy as np\n", + "\n", + "from cuml.metrics import trustworthiness\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from umap import UMAP as UMAP_LEARN\n", + "from cuml.manifold import UMAP as UMAP_CUML\n", + "\n", + "import os\n", + "os.getcwd()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the number of cores for the multi-core CPU UMAP implementation to use" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"NUMBA_NUM_THREADS\"] = \"80\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the GPUMAP project is no longer being maintained, we make a best effort to provide reproducibility of benchmarks. We make it optional so the other implementations may still be evaluated if GPUMAP is not installed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "has_gpumap = True\n", + "try:\n", + " from gpumap import GPUMAP as UMAP_GPUMAP\n", + "except ImportError:\n", + " has_gpumap = False\n", + " \n", + "has_gpumap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RESULTS_FILE=\"results/results.pickle\"\n", + "SCALE_RESULTS_FILE=\"results/scale_results.pickle\"\n", + "\n", + "POOL_SIZE_GB=15 # Number of GB to use for device memory pool\n", + "\n", + "TRUST_BATCH_SIZE=5000 # Number of rows to use per batch for computing trustworthiness\n", + "\n", + "KEY_UMAPCUML = \"umapcuml\"\n", + "KEY_UMAPLEARN = \"umaplearn\"\n", + "KEY_UMAPGPUMAP = \"umapgpumap\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rmm.reinitialize(\n", + " pool_allocator=True, # default is False\n", + " managed_memory=False, # default is False\n", + " initial_pool_size=int(1024*1024*1024*POOL_SIZE_GB), # set to 2GiB. Default is 1/2 total GPU memory\n", + " devices=0, # GPU device IDs to register. By default registers only GPU 0.\n", + " logging=False, # default is False -- has perf overhead\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results = maybe_load_results(RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pen Digits Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_DIGITS = \"digits\"\n", + "\n", + "X, y = loaders.load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_digits = maybe_get_results(final_results, KEY_DIGITS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_digits[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_digits[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_digits[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_DIGITS] = results_digits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_DIGITS]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fashion MNIST Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# https://github.com/zalandoresearch/fashion-mnist/blob/master/utils/mnist_reader.py\n", + "KEY_FASHION_MNIST = \"fashion_mnist\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train, train_labels = loaders.load_fashion_mnist('data/fashion', kind='train')\n", + "test, test_labels = loaders.load_fashion_mnist('data/fashion', kind='t10k')\n", + "X = (np.array(np.vstack([train, test]), dtype=np.float64) [:50000]/ 255.0).astype(np.float32)\n", + "y = np.array(np.hstack([train_labels, test_labels]))[:50000].astype(np.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_fashion = maybe_get_results(final_results, KEY_FASHION_MNIST)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_fashion[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_fashion[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_fashion[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_FASHION_MNIST] = results_fashion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_FASHION_MNIST]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classes = [\n", + " 'T-shirt/top',\n", + " 'Trouser',\n", + " 'Pullover',\n", + " 'Dress',\n", + " 'Coat',\n", + " 'Sandal',\n", + " 'Shirt',\n", + " 'Sneaker',\n", + " 'Bag',\n", + " 'Ankle boot']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_chart(UMAP_LEARN(n_neighbors=10, min_dist=0.01), X, y, \"Fashion MNIST\", \"UMAP-learn\", classes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_chart(UMAP_CUML(n_neighbors=10, min_dist=0.01), X, y, \"Fashion MNIST\", \"cuML UMAP\", classes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_chart(UMAP_GPUMAP(n_neighbors=10, min_dist=0.01), X, y, \"Fashion MNIST\", \"GPUUMAP\", classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CIFAR-100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_CIFAR100 = \"cifar100\"\n", + "\n", + "train, test = loaders.load_cifar100(\"data/cifar100/cifar-100-python\")\n", + "\n", + "train, train_labels = (train[b\"data\"], train[b\"fine_labels\"])\n", + "test, test_labels = (test[b\"data\"], test[b\"fine_labels\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = (np.array(np.vstack([train, test]), dtype=np.float64) [:60000]/ 255.0).astype(np.float32)\n", + "y = np.array(np.hstack([train_labels, test_labels]))[:60000].astype(np.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_cifar100 = maybe_get_results(final_results, KEY_CIFAR100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_cifar100[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_cifar100[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_cifar100[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_CIFAR100] = results_cifar100\n", + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_cifar100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shuttle Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_SHUTTLE = \"shuttle\"\n", + "\n", + "X, y = loaders.load_shuttle(\"data/shuttle.mat\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_shuttle = maybe_get_results(final_results, KEY_SHUTTLE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_shuttle[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_shuttle[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_shuttle[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_SHUTTLE] = results_shuttle\n", + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_shuttle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## COIL-20 Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_COIL20 = \"coil20\"\n", + "\n", + "X, y = loaders.load_coil20(\"data/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_coil20 = maybe_get_results(final_results, KEY_COIL20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_coil20[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_coil20[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_coil20[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_COIL20] = results_coil20\n", + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_COIL20]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_MNIST = \"mnist\"\n", + "\n", + "X, y = loaders.load_mnist(\"data/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_mnist = maybe_get_results(final_results, KEY_MNIST)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_mnist[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_mnist[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_mnist[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, y, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_MNIST] = results_mnist\n", + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_mnist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## scRNA\n", + "\n", + "This benchmark requires a pickle file to be output from the GPU notebook [here](https://github.com/clara-parabricks/rapids-single-cell-examples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KEY_SCRNA = \"scrna\"\n", + "\n", + "X = pickle.load( open( \"data/scrna.pickle\", \"rb\" ) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_scrna = maybe_get_results(final_results, KEY_SCRNA)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_scrna[KEY_UMAPCUML] = build_and_train(UMAP_CUML, X, None, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_scrna[KEY_UMAPLEARN] = build_and_train(UMAP_LEARN, X, None, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_scrna[KEY_UMAPGPUMAP] = build_and_train(UMAP_GPUMAP, X, None, {})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results[KEY_SCRNA] = results_scrna\n", + "store_results(final_results, RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_scrna" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scale Benchmark\n", + "\n", + "Test UMAP variants at different `n_samples` and `n_components`. Need to download the \"GoogleNews-vectors-negative300.bin.gz\" dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = load_word2vec(\"data/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scale_results = maybe_load_results(SCALE_RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scale_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "perform_n_components_test(UMAP_CUML, X, KEY_UMAPCUML)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "store_results(scale_results, SCALE_RESULTS_FILE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "scale_results[KEY_UMAPCUML] = perform_n_samples_test(UMAP_CUML, X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "store_results(scale_results, SCALE_RESULTS_FILE)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/publications/umap_paper_benchmarks/notebooks/umap_mnmg_scaling.ipynb b/publications/umap_paper_benchmarks/notebooks/umap_mnmg_scaling.ipynb new file mode 100644 index 00000000..8e90b76d --- /dev/null +++ b/publications/umap_paper_benchmarks/notebooks/umap_mnmg_scaling.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UMAP MNMG runtime on large dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cuml.dask.manifold import UMAP as UMAP_MNMG\n", + "from cuml.manifold import UMAP\n", + "from cuml.dask.datasets import make_blobs\n", + "\n", + "from dask_cuda import LocalCUDACluster\n", + "from dask.distributed import Client, wait\n", + "\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(args, client):\n", + " dX, _ = make_blobs(n_samples=args['n_samples'],\n", + " n_features=args['n_features'],\n", + " cluster_std=1.0,\n", + " dtype=\"float32\",\n", + " n_parts=args['n_parts'],\n", + " client=client)\n", + " n_to_sample = int(args['n_samples'] * args['sampling_ratio'])\n", + " dX = client.persist(dX)\n", + " wait(dX)\n", + " lX = dX[:n_to_sample].compute()\n", + " return lX, dX" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark(args):\n", + " # Start Dask-CUDA cluster & Dask client\n", + " cluster = LocalCUDACluster(n_workers=args['n_parts'], threads_per_worker=1)\n", + " client = Client(cluster)\n", + "\n", + " lX, dX = generate_data(args, client)\n", + " \n", + " # Measure runtime accross n_iter runs (+1 \"warm-up test\")\n", + " durations = []\n", + " for i in range(args['n_iter'] + 1):\n", + " \n", + " # Train local model\n", + " local_model = UMAP(n_components=args['n_components'], n_neighbors=args['n_neighbors'],\n", + " n_epochs=args['n_epochs'])\n", + " local_model.fit(lX)\n", + " \n", + " # Pass trained model and order distributed inference\n", + " model = UMAP_MNMG(local_model)\n", + " lazy_transformed = model.transform(dX)\n", + " \n", + " # Perform distributed inference and measure time\n", + " start = time.time()\n", + " lazy_transformed.compute()\n", + " durations.append(time.time()-start)\n", + " \n", + " # Remove \"warm-up\" test\n", + " durations = np.array(durations[1:])\n", + " \n", + " # Stop Dask-CUDA cluster & Dask client\n", + " client.close()\n", + " cluster.close()\n", + " \n", + " # Return runtime average\n", + " return durations.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def runtime_barchart(args, mean_durations):\n", + " labels = ['1 GPU', '2 GPUs', '4 GPUs', '8 GPUs']\n", + " runtimes = list(map(lambda x: round(x, 2), mean_durations))\n", + " x = np.arange(len(labels))\n", + " fig, ax = plt.subplots()\n", + " rects = ax.bar(x, runtimes, 0.35)\n", + "\n", + " ax.set_ylabel('Runtime (s)')\n", + " ax.set_title('Scale of random dataset transform: {}x{}'.format(args['n_samples'], args['n_features']))\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(labels)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.annotate('{}'.format(height),\n", + " xy=(rect.get_x() + rect.get_width() / 2, height),\n", + " xytext=(0, 3),\n", + " textcoords=\"offset points\",\n", + " ha='center', va='bottom')\n", + "\n", + " autolabel(rects)\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of GPUs: 1, mean runtime: 25.30\n", + "Number of GPUs: 2, mean runtime: 13.38\n", + "Number of GPUs: 4, mean runtime: 7.34\n", + "Number of GPUs: 8, mean runtime: 4.50\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "args = {'n_samples': 100000, 'n_features':300,\n", + " 'n_components': 64, 'n_neighbors':15, 'n_epochs':5000,\n", + " 'sampling_ratio': 0.001, 'n_iter': 3}\n", + "\n", + "mean_runtimes = []\n", + "for n_gpus in [1, 2, 4, 8]:\n", + " args['n_parts'] = n_gpus\n", + " mean_runtime = benchmark(args)\n", + " mean_runtimes.append(mean_runtime)\n", + " print(\"Number of GPUs: {}, mean runtime: {:.2f}\".format(n_gpus, mean_runtime))\n", + "runtime_barchart(args, mean_runtimes)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "with open('../results/mnmg_scaling.csv','w') as out:\n", + " csv_out=csv.writer(out)\n", + " csv_out.writerow(['number of GPUs', 'transformation runtime (s)'])\n", + " for i, n_gpus in enumerate([1, 2, 4, 8]):\n", + " csv_out.writerow([n_gpus, mean_runtimes[i]])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/publications/umap_paper_benchmarks/notebooks/umap_mnmg_visualization.ipynb b/publications/umap_paper_benchmarks/notebooks/umap_mnmg_visualization.ipynb new file mode 100644 index 00000000..fc0a8ea3 --- /dev/null +++ b/publications/umap_paper_benchmarks/notebooks/umap_mnmg_visualization.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UMAP MNMG Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cuml.dask.manifold import UMAP as UMAP_MNMG\n", + "from cuml.manifold import UMAP\n", + "from cuml.metrics import trustworthiness\n", + "\n", + "from dask_cuda import LocalCUDACluster\n", + "from dask.distributed import Client\n", + "import dask.array as da\n", + "\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def distribute_data(data, n_parts, sampling_ratio):\n", + " n_samples = data.shape[0]\n", + " \n", + " # Number of samples for local train\n", + " n_to_sample = int(n_samples * sampling_ratio)\n", + "\n", + " # Generate local train data\n", + " selection = np.random.choice(n_samples, n_to_sample)\n", + " lX = data[selection]\n", + "\n", + " # Number of samples per partition\n", + " n_samples_per_part = int(n_samples / n_parts)\n", + "\n", + " # Generate partitioning of distributed data for inference\n", + " chunks = [n_samples_per_part] * n_parts\n", + " chunks[-1] += n_samples % n_samples_per_part\n", + " chunks = tuple(chunks)\n", + " dX = da.from_array(data, chunks=(chunks, -1))\n", + " \n", + " return lX, dX" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_umap_mnmg(args, X, y):\n", + " fig, ax = plt.subplots(len(args['sampling_ratio']), len(args['n_parts']),\n", + " sharex='col', sharey='row', figsize=(20,27))\n", + " fig.subplots_adjust(wspace=0.1, hspace=0.15)\n", + " fig.suptitle('Comparison of Sampling Ratios and Partitioning in Distributed UMAP', y=0.9, size=20)\n", + " \n", + " for a, n_parts in zip(ax[0], args['n_parts']):\n", + " a.set_title(\"Partitions: {}\".format(n_parts))\n", + "\n", + " for a, sampling_ratio in zip(ax[:,0], args['sampling_ratio']):\n", + " a.set_ylabel(\"Sampling: {:.2f}%\".format(sampling_ratio * 100), rotation=0, size='large', labelpad=45)\n", + " \n", + " if n_parts > 1:\n", + " cluster = LocalCUDACluster(n_workers=8, threads_per_worker=1)\n", + " client = Client(cluster)\n", + " \n", + " for i, n_parts in enumerate(args['n_parts']):\n", + " for j, sampling_ratio in enumerate(args['sampling_ratio']): \n", + " if n_parts == 1: # Local transformation\n", + " local_model = UMAP(n_components=2, n_neighbors=args['n_neighbors'],\n", + " n_epochs=args['n_epochs'], random_state=args['random_state'])\n", + " \n", + " # Generate subsample for training data\n", + " n_samples = X.shape[0]\n", + " n_to_sample = int(n_samples * sampling_ratio)\n", + " selection = np.random.choice(n_samples, n_to_sample)\n", + " train_data = X[selection]\n", + " \n", + " # Run fit with subsample and transform with full data\n", + " local_model.fit(train_data)\n", + " transformed = local_model.transform(X)\n", + " else: # Distributed transformation\n", + " \n", + " # Distribute data\n", + " lX, dX = distribute_data(X, n_parts, sampling_ratio)\n", + "\n", + " # Train local model\n", + " local_model = UMAP(n_components=2, n_neighbors=args['n_neighbors'],\n", + " n_epochs=args['n_epochs'], random_state=args['random_state'])\n", + " local_model.fit(lX)\n", + " \n", + " # Pass trained model and perform distributed inference\n", + " model = UMAP_MNMG(local_model, random_state=args['random_state'])\n", + " transformed = model.transform(dX).compute()\n", + " \n", + " trust_score = trustworthiness(X, transformed, n_neighbors=args['n_neighbors'])\n", + " \n", + " # Plot transformed data\n", + " subplot = ax[j, i]\n", + " subplot.scatter(transformed[:,0], transformed[:,1], c=y,\n", + " s=0.2, alpha=0.3, edgecolors='none')\n", + " trust_score_text = \"Trust: \" + str(round(trust_score, 4))\n", + " subplot.text(0.5,-0.1, trust_score_text, size=14, ha=\"center\", transform=subplot.transAxes)\n", + " \n", + " if n_parts > 1:\n", + " client.close()\n", + " cluster.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocess tasic2018 dataset\n", + "Generate the preprocessed dataset with the **tasic2018_dataset_preprocessing.ipynb** notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = pickle.load(open(\"tasic2018_X.p\", \"rb\"))\n", + "y = pickle.load(open(\"tasic2018_y.p\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "args = {'n_neighbors': 15, 'n_epochs': 500, 'random_state': 42,\n", + " 'n_parts':[1,4,8,16], 'sampling_ratio': [0.008,0.016,0.032,0.5,1.0]}\n", + "\n", + "plot_umap_mnmg(args, X, y)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/publications/umap_paper_benchmarks/notebooks/umap_reproducibility_benchmark.ipynb b/publications/umap_paper_benchmarks/notebooks/umap_reproducibility_benchmark.ipynb new file mode 100644 index 00000000..41463447 --- /dev/null +++ b/publications/umap_paper_benchmarks/notebooks/umap_reproducibility_benchmark.ipynb @@ -0,0 +1,232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UMAP reproducibility benchmark (runtime & trustworthiness)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from umap import UMAP as umap_learn\n", + "from cuml.manifold import UMAP as umap_cuml\n", + "from cuml.metrics import trustworthiness\n", + "\n", + "from sklearn.datasets import make_blobs\n", + "import time\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(n_samples):\n", + " X, y = make_blobs(n_samples=n_samples, n_features=args['n_features'],\n", + " centers=int(n_samples/20), cluster_std=8.0)\n", + " return X" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark_model(model_constr, args, data, n_components):\n", + " durations = []\n", + " trust_scores = []\n", + " for i in range(args['n_iter'] + 1):\n", + " # Instantiate model\n", + " model = model_constr(n_components=n_components, n_neighbors=args['n_neighbors'],\n", + " n_epochs=args['n_epochs'], random_state=args['random_state'])\n", + " \n", + " # Perform transformation and measure time\n", + " start = time.time()\n", + " transformed = model.fit_transform(data)\n", + " durations.append(time.time()-start)\n", + " \n", + " # Compute trustworthiness score\n", + " trust_scores.append(trustworthiness(data, transformed, n_neighbors=args['n_neighbors']))\n", + " \n", + " durations = np.array(durations[1:])\n", + " trust_scores = np.array(trust_scores)\n", + " \n", + " # Compute runtime average and variance as well as trustworthiness score average\n", + " return durations.mean(), durations.var(), trust_scores.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark(args):\n", + " for n_samples in args['n_samples']:\n", + " for n_components in args['n_components']:\n", + " # Generate dataset\n", + " X = generate_data(n_samples)\n", + "\n", + " # Benchmarks the two models\n", + " print(\"For dataset of shape ({}, {}) and n_components = {}:\".format(n_samples, args['n_features'], n_components))\n", + "\n", + " n_elements = n_samples * args['n_features']\n", + " if n_elements <= 10000000:\n", + " print(\"\\tUMAP-LEARN:\")\n", + " args['random_state'] = None\n", + " ul_inconsistent = benchmark_model(umap_learn, args, X, n_components)\n", + " args['random_state'] = 42\n", + " ul_consistent = benchmark_model(umap_learn, args, X, n_components)\n", + " print_results(ul_inconsistent, ul_consistent)\n", + "\n", + " print(\"\\tCUML UMAP:\")\n", + " args['random_state'] = None\n", + " cuml_inconsistent = benchmark_model(umap_cuml, args, X, n_components)\n", + " args['random_state'] = 42\n", + " cuml_consistent = benchmark_model(umap_cuml, args, X, n_components)\n", + " print_results(cuml_inconsistent, cuml_consistent)\n", + "\n", + " a = cuml_consistent[0]\n", + " b = cuml_inconsistent[0]\n", + " slowdown = ((a - b) / a) * 100\n", + " print('\\t\\tcuML consistent pathway is {:.2f}% slower\\n'.format(slowdown))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def print_results(inconsistent, consistent):\n", + " ic_dur_mean, ic_dur_var, ic_trust = inconsistent\n", + " print(\"\\t\\tWithout random seed: runtime avg - var: {:.2f} - {:.2f}, tustworthiness: {:.2f}\".format(ic_dur_mean, ic_dur_var, ic_trust))\n", + " c_dur_mean, c_dur_var, c_trust = consistent\n", + " print(\"\\t\\tWith random seed: runtime avg - var: {:.2f} - {:.2f}, tustworthiness: {:.2f}\".format(c_dur_mean, c_dur_var, c_trust))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For dataset of shape (1000, 1000) and n_components = 2:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 2.56 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 2.54 - 0.00, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.24 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.24 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is -3.18% slower\n", + "\n", + "For dataset of shape (1000, 1000) and n_components = 8:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 2.75 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 2.71 - 0.00, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.25 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.26 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 5.35% slower\n", + "\n", + "For dataset of shape (1000, 1000) and n_components = 16:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 2.85 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 2.88 - 0.00, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.27 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.31 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 11.07% slower\n", + "\n", + "For dataset of shape (10000, 1000) and n_components = 2:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 31.17 - 0.10, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 31.08 - 0.02, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.29 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.32 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 9.54% slower\n", + "\n", + "For dataset of shape (10000, 1000) and n_components = 8:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 37.21 - 0.37, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 35.90 - 0.03, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.35 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.46 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 24.26% slower\n", + "\n", + "For dataset of shape (10000, 1000) and n_components = 16:\n", + "\tUMAP-LEARN:\n", + "\t\tWithout random seed: runtime avg - var: 42.85 - 0.02, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 45.47 - 0.01, tustworthiness: 1.00\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 0.43 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 0.84 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 49.20% slower\n", + "\n", + "For dataset of shape (100000, 1000) and n_components = 2:\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 1.26 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 1.59 - 0.00, tustworthiness: 1.00\n", + "\t\tcuML consistent pathway is 20.60% slower\n", + "\n", + "For dataset of shape (100000, 1000) and n_components = 8:\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 1.80 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 3.20 - 0.00, tustworthiness: 0.80\n", + "\t\tcuML consistent pathway is 43.67% slower\n", + "\n", + "For dataset of shape (100000, 1000) and n_components = 16:\n", + "\tCUML UMAP:\n", + "\t\tWithout random seed: runtime avg - var: 2.58 - 0.00, tustworthiness: 1.00\n", + "\t\tWith random seed: runtime avg - var: 7.09 - 0.00, tustworthiness: 0.76\n", + "\t\tcuML consistent pathway is 63.67% slower\n", + "\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "args = {'n_samples':[1000, 10000, 100000], 'n_features':1000, 'centers':500,\n", + " 'n_components':[2, 8, 16], 'n_neighbors':15, 'n_epochs':500, 'n_iter':3}\n", + "\n", + "benchmark(args)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/publications/umap_paper_benchmarks/results/benchmark_results.csv b/publications/umap_paper_benchmarks/results/benchmark_results.csv new file mode 100644 index 00000000..4e2a9aa9 --- /dev/null +++ b/publications/umap_paper_benchmarks/results/benchmark_results.csv @@ -0,0 +1,43 @@ +dataset,impl,bench,mean,var,max_trust +digits,umapcuml,unsupervised,0.35827815532684326,0.011162857575371277,0.9876636433389232 +digits,umapcuml,supervised,0.40629714727401733,0.013507401316314116,0.9879864913274599 +digits,umaplearn,unsupervised,6.328544795513153,2.8975128262992627,0.9879530939808027 +digits,umaplearn,supervised,6.755777180194855,0.1106131444620786,0.9876400330669284 +digits,umapgpumap,unsupervised,2.482882022857666,1.0582028882037378,0.9558078864163978 +digits,umapgpumap,supervised,2.5530967116355896,0.10950217290617204,0.9548070534694238 +fashion_mnist,umapcuml,unsupervised,0.45551514625549316,0.006011408943862762,0.9773246413616931 +fashion_mnist,umapcuml,supervised,1.036961555480957,0.0002095378332569453,0.9762932845375539 +fashion_mnist,umaplearn,unsupervised,45.870298981666565,10.23240442609631,0.9783708581947695 +fashion_mnist,umaplearn,supervised,53.09064221382141,6.182976974198482,0.9781969233047202 +fashion_mnist,umapgpumap,unsupervised,4.158320248126984,1.800035649938632,0.9750927790516971 +fashion_mnist,umapgpumap,supervised,6.476689338684082,0.06324447576008652,0.9696567894031254 +cifar100,umapcuml,unsupervised,1.008723258972168,0.018844815007184934,0.8342801452408608 +cifar100,umapcuml,supervised,1.081564486026764,0.0003376463217925618,0.8382352637585149 +cifar100,umaplearn,unsupervised,105.85025483369827,2.482921346083458,0.8472284890319066 +cifar100,umaplearn,supervised,98.42393732070923,2.272873735988469,0.8490670250262601 +cifar100,umapgpumap,unsupervised,6.186033010482788,1.7708737036427635,0.8401050404506913 +cifar100,umapgpumap,supervised,5.953894853591919,0.023564399280502357,0.8309824029952222 +coil20,umapcuml,unsupervised,0.7574235200881958,0.5752119048886044,0.9927721908570533 +coil20,umapcuml,supervised,0.3695611357688904,0.006603219726240894,0.986986493374108 +coil20,umaplearn,unsupervised,11.210062146186829,2.571288658299423,0.9936500235238768 +coil20,umaplearn,supervised,12.338415145874023,0.02176407274296821,0.9868479965498315 +coil20,umapgpumap,unsupervised,2.5822073221206665,0.005004079547958895,0.9565478057450535 +coil20,umapgpumap,supervised,8.210693836212158,0.02740621988587577,0.9332845866332105 +shuttle,umapcuml,unsupervised,0.5825626254081726,0.025230869766499353,0.9999991344960172 +shuttle,umapcuml,supervised,0.5559555292129517,0.011077821222144735,0.9999999959879131 +shuttle,umaplearn,unsupervised,38.87588673830032,8.039266967871527,1.0 +shuttle,umaplearn,supervised,50.173097372055054,17.54718326385637,1.0 +shuttle,umapgpumap,unsupervised,9.064357042312622,3.431728740054808,0.9778424900551579 +shuttle,umapgpumap,supervised,17.151020526885986,23.923241572835195,0.9668076059384338 +mnist,umapcuml,unsupervised,0.7078064680099487,0.008856602310757467,0.9574214731202516 +mnist,umapcuml,supervised,0.9175034761428833,0.0029794700764256277,0.9574419861872977 +mnist,umaplearn,unsupervised,52.57512265443802,1.1677051461933843,0.9594030397578331 +mnist,umaplearn,supervised,89.16777920722961,6.465818109702013,0.9584785717302744 +mnist,umapgpumap,unsupervised,10.60714328289032,0.45444580430584836,0.9442834214597077 +mnist,umapgpumap,supervised,23.90549498796463,7.056965841371607,0.9469258037618123 +scrna,umapcuml,unsupervised,4.103261113166809,0.06018657014949724,0.9781377849332045 +scrna,umapcuml,supervised,0,0,0 +scrna,umaplearn,unsupervised,223.92166656255722,9.071952858837903,0.6238778233428524 +scrna,umaplearn,supervised,0,0,0 +scrna,umapgpumap,unsupervised,10.890332162380219,1.604801846075489,0.9434946335833367 +scrna,umapgpumap,supervised,0,0,0 diff --git a/publications/umap_paper_benchmarks/results/googlenews_knn_and_without.csv b/publications/umap_paper_benchmarks/results/googlenews_knn_and_without.csv new file mode 100644 index 00000000..48441815 --- /dev/null +++ b/publications/umap_paper_benchmarks/results/googlenews_knn_and_without.csv @@ -0,0 +1,11 @@ +n_samples,with_knn,without_knn +1024,0.25798463821411133,1.2285232543945312 +334243,7.277303218841553,0.767493486404419 +667463,28.77500081062317,1.6629040241241455 +1000682,64.54534125328064,2.7732017040252686 +1333902,114.33410835266113,3.8045833110809326 +1667121,176.93605589866638,5.041834831237793 +2000341,255.18308854103088,5.964860916137695 +2333560,345.6909635066986,7.245739221572876 +2666780,451.7911911010742,8.139975309371948 +3000000,570.7973875999451,9.288048505783081 diff --git a/publications/umap_paper_benchmarks/results/mnmg_scaling.csv b/publications/umap_paper_benchmarks/results/mnmg_scaling.csv new file mode 100644 index 00000000..ac49254b --- /dev/null +++ b/publications/umap_paper_benchmarks/results/mnmg_scaling.csv @@ -0,0 +1,5 @@ +number of GPUs,transformation runtime (s) +1,25.3 +2,13.38 +4,7.34 +8,4.5 diff --git a/publications/umap_paper_benchmarks/results/results.pickle b/publications/umap_paper_benchmarks/results/results.pickle new file mode 100644 index 00000000..b83794cd Binary files /dev/null and b/publications/umap_paper_benchmarks/results/results.pickle differ diff --git a/publications/umap_paper_benchmarks/results/scale_results_precompute_knn.pickle b/publications/umap_paper_benchmarks/results/scale_results_precompute_knn.pickle new file mode 100644 index 00000000..0cc7fa84 Binary files /dev/null and b/publications/umap_paper_benchmarks/results/scale_results_precompute_knn.pickle differ diff --git a/publications/umap_paper_benchmarks/results/scale_results_with_knn.pickle b/publications/umap_paper_benchmarks/results/scale_results_with_knn.pickle new file mode 100644 index 00000000..d31e4fc2 Binary files /dev/null and b/publications/umap_paper_benchmarks/results/scale_results_with_knn.pickle differ diff --git a/publications/umap_paper_benchmarks/umap_bench/__init__.py b/publications/umap_paper_benchmarks/umap_bench/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/publications/umap_paper_benchmarks/umap_bench/funcs.py b/publications/umap_paper_benchmarks/umap_bench/funcs.py new file mode 100644 index 00000000..51033ac8 --- /dev/null +++ b/publications/umap_paper_benchmarks/umap_bench/funcs.py @@ -0,0 +1,203 @@ +import matplotlib.pyplot as plt +import numpy as np + +import os + +import time +import numpy as np + +import pickle + +from cuml.metrics import trustworthiness + +TRUST_BATCH_SIZE = 5000 + +def maybe_get_results(results, key): + return results[key] if key in results else {} + +def draw_chart(model, X, y, dataset, model_name, classes=None): + + embedding = model.fit_transform(X, y) + + fig, ax = plt.subplots(1, figsize=(14, 10)) + plt.scatter(embedding[:,1], embedding[:,0], s=0.3, c=y, cmap='Spectral', alpha=1.0) + plt.setp(ax, xticks=[], yticks=[]) + cbar = plt.colorbar(boundaries=np.arange(11)-0.5) + cbar.set_ticks(np.arange(10)) + if classes is not None: + cbar.set_ticklabels(classes) + plt.title("%s Embedded via %s" % (dataset, model_name)); + + +def _run_build_and_train_once(model_class, X, y=None, kwargs={}, knn_graph=None, verbose=False, eval_trust=True): + + results = {} + extra_args = {} + if knn_graph is not None: + extra_args["knn_graph"] = knn_graph + + if verbose: + print("Unsupervised") + model = model_class(**kwargs) + + try: + start = time.time() + embeddings = model.fit_transform(X, **extra_args) + end = time.time() - start + + if verbose: + print("Time: "+ str(end)) + + n_neighbors = model.n_neighbors + del model + + if eval_trust: + if verbose: + print("Done. Evaluating trustworthiness") + trust = trustworthiness(X, embeddings, n_neighbors=n_neighbors, batch_size=TRUST_BATCH_SIZE) + else: + trust = None + + if verbose: + print(str(trust)) + results["unsupervised"] = {"time": end, "trust": trust} + except: + import traceback + traceback.print_exc() + + # Supervised + + if y is not None: + if verbose: + print("Supervised") + kwargs["target_metric"] = "categorical" + model = model_class(**kwargs) + + try: + start = time.time() + embeddings = model.fit_transform(X, y, **extra_args) + end = time.time() - start + + + n_neighbors = model.n_neighbors + del model + + if eval_trust: + if verbose: + print("Done. Evaluating trustworthiness") + trust = trustworthiness(X, embeddings, n_neighbors=n_neighbors, batch_size=TRUST_BATCH_SIZE) + else: + trust = None + + if verbose: + print(str(trust)) + print("Time: "+ str(end)) + + results["supervised"] = {"time": end, "trust": trust} + except: + import traceback + traceback.print_exc() + + # Transform + + + if verbose: + print("Transform") + model = model_class(**kwargs) + + try: + + if knn_graph is not None: + model.fit(X) + start = time.time() + embeddings = model.transform(X, **extra_args) + end = time.time() - start + else: + model.fit(X, knn_graph=knn_graph) + start = time.time() + embeddings = model.transform(X, knn_graph=knn_graph) + end = time.time() - start + + + n_neighbors = model.n_neighbors + del model + + if eval_trust: + if verbose: + print("Done. Evaluating trustworthiness") + trust = trustworthiness(X, embeddings, n_neighbors=n_neighbors, batch_size=TRUST_BATCH_SIZE) + else: + trust = None + + if verbose: + print(str(trust)) + print("Time: "+ str(end)) + results["xform"] = {"time": end, "trust": trust} + except: + import traceback + traceback.print_exc() + + return results + + +def build_and_train(model_class, X, y=None, kwargs={}, n_trials=4, knn_graph=None, verbose=False, eval_trust=True): + + results = [] + + for trial in range(n_trials): + results.append(_run_build_and_train_once(model_class, X, y=y, kwargs=kwargs, + knn_graph=knn_graph, verbose=verbose, + eval_trust=eval_trust)) + return results + +def store_results(results, filename): + with open(filename, 'wb') as handle: + pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL) + + +def maybe_load_results(filename): + # Load a results file if it exists, otherwise load empty dictionary" + return pickle.load( open(filename, "rb" ) ) if os.path.exists(filename) else {} + + +def perform_n_samples_test(model, X, precompute_knn=True, start_samples=1024, n_indep=10, n_trials=1, n_components=2): + import math + results = {} + s = np.linspace(start_samples, X.shape[0], n_indep) + for n_samples in s: + print("Testing " + str(n_samples)) + samples = np.random.choice(np.arange(X.shape[0]), math.floor(n_samples)) + X_sampled = X[samples] + + if precopute_knn: + from cuml.neighbors import NearestNeighbors + import cupy as cp + d, i = NearestNeighbors(n_neighbors=15).fit(X_sampled).kneighbors(X_sampled) + knn_graph = cp.sparse.coo_matrix((cp.asarray(d.ravel()), (cp.repeat(cp.arange(d.shape[0]), 15), cp.asarray(i.ravel())))) + else: + knn_graph = None + + results[n_samples] = build_and_train(model, + X_sampled, + y=None, + kwargs={"n_components": n_components}, + knn_graph=knn_graph, + verbose=True, + n_trials=n_trials, + eval_trust=False) + + return results + +def perform_n_components_test(model, X, model_name, start_components=2, stop_components=1024): + + import math + + n_components = np.linspace(start_components, stop_components, 3) + + print(n_components) + + for components in n_components: + print("Testing " + str(math.floor(components)) + " components") + scale_results[model_name + "_" + str(math.floor(components)) + "_components"] = \ + perform_n_samples_test(model, X, n_components=math.floor(components)) + store_results(scale_results, "results/scale_results.pickle") \ No newline at end of file diff --git a/publications/umap_paper_benchmarks/umap_bench/loaders.py b/publications/umap_paper_benchmarks/umap_bench/loaders.py new file mode 100644 index 00000000..e2585ac4 --- /dev/null +++ b/publications/umap_paper_benchmarks/umap_bench/loaders.py @@ -0,0 +1,124 @@ +import os +import gzip +import numpy as np +import pickle +import wget + +from datasets.coil20.feed import feed + +import scipy.io + + +def load_digits(): + from sklearn import datasets + data = datasets.load_digits() + + return data.data, data.target + + +def load_fashion_mnist(path, kind='train'): + + """Load MNIST data from `path`""" + labels_path = os.path.join(path, + '%s-labels-idx1-ubyte.gz' + % kind) + images_path = os.path.join(path, + '%s-images-idx3-ubyte.gz' + % kind) + + with gzip.open(labels_path, 'rb') as lbpath: + labels = np.frombuffer(lbpath.read(), dtype=np.uint8, + offset=8) + + with gzip.open(images_path, 'rb') as imgpath: + images = np.frombuffer(imgpath.read(), dtype=np.uint8, + offset=16).reshape(len(labels), 784) + + return images, labels + + +def unpickle_cifar100(file): + import pickle + with open(file, 'rb') as fo: + dict = pickle.load(fo, encoding='bytes') + return dict + +def load_cifar100(path="data/cifar100/cifar-100-python"): + + train_path = os.path.join(path, "train") + test_path = os.path.join(path, "test") + + if not os.path.exists(train_path): + raise ValueError("Path %s not found. Please provide path to " + "untarred cifar100 dataset." % train_path) + + if not os.path.exists(test_path): + raise ValueError("Path %s not found. Please provide path to " + "untarred cifar100 dataset." % test_path) + + train = unpickle_cifar100(train_path) + test = unpickle_cifar100(test_path) + + return train, test + + +def load_shuttle(filepath="data/shuttle.mat"): + if not os.path.exists(filepath): + raise ValueError("File shuttle.mat not found. Please download " + "from 'https://www.dropbox.com/s/mk8ozgisimfn3dw/shuttle.mat'") + + mat = scipy.io.loadmat(filepath) + + X = mat["X"].astype(np.float32) + y = mat["y"].astype(np.int32).ravel() + return X, y + + +def load_coil20(path="data/coil20"): + feed(feed_path=path, dataset_type='processed') + + from datasets import pa2np + X, Y = pa2np(os.path.join(path, "X_processed.pa")), pa2np(os.path.join(path, "Y_processed.pa")) + + features = X.shape[2]*X.shape[3] + new_X = np.zeros((X.shape[0], features)) + + from skimage import color + for i in range(X.shape[0]): + img = X[i, :, :, :] + shape = features + gray = color.rgb2gray(np.moveaxis(img, 0, 2)).reshape(shape) + new_X[i] = gray + + X = new_X.astype(np.float32) + y = Y.astype(np.float32) + + return X, y + +def load_mnist(path="data/mnist/"): + from datasets.mnist.feed import feed + feed(feed_path=path) + + from datasets import pa2np + X, Y = pa2np(os.path.join(path, "X.pa")), pa2np(os.path.join(path, "Y.pa")) + + X = X.reshape(X.shape[0], X.shape[1] * X.shape[2]) + y = Y + + return X, y + + +def load_word2vec(path): + + from gensim.models import KeyedVectors + + bin_file = os.path.join(path, "/GoogleNews-vectors-negative300.bin") + + if not os.path.exists(bin_file): + raise ValueError("GoogleNews-vectors-negative300.bin was not found in " + path + + ". You will need to download this file and place in 'path'") + + vecs = KeyedVectors.load_word2vec_format(bin_file, binary=True) + + return vecs.vectors +