sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a09dbc3e-3d62-42ec-9ae3-cb4bdb156b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m environment at `~/Desktop/SelectivelyAdaptiveLasso/Project.toml`\n"
     ]
    }
   ],
   "source": [
    "using Pkg, Revise\n",
    "Pkg.activate(\".\")\n",
    "using CSV, DataFrames, VegaLite\n",
    "import Statistics: mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db3d4db5-5fdf-4a8a-87cf-77a3ef4a04c1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m     Testing\u001b[22m\u001b[39m SelectivelyAdaptiveLasso\n",
      "\u001b[32m\u001b[1m      Status\u001b[22m\u001b[39m `/private/var/folders/1j/vn8fvqrs7pn5ln731w20k34c0000gn/T/jl_URoMgd/Project.toml`\n",
      " \u001b[90m [6e4b80f9] \u001b[39mBenchmarkTools v1.2.2\n",
      " \u001b[90m [336ed68f] \u001b[39mCSV v0.9.11\n",
      " \u001b[90m [861a8166] \u001b[39mCombinatorics v1.0.2\n",
      " \u001b[90m [a93c6f00] \u001b[39mDataFrames v1.3.0\n",
      " \u001b[90m [da5c29d0] \u001b[39mEllipsisNotation v1.1.3\n",
      " \u001b[90m [41ab1584] \u001b[39mInvertedIndices v1.1.0\n",
      " \u001b[90m [add582a8] \u001b[39mMLJ v0.16.11\n",
      " \u001b[90m [4391764f] \u001b[39mProfileVega v1.1.1\n",
      " \u001b[90m [52f33b2b] \u001b[39mSelectivelyAdaptiveLasso v0.1.0 `~/Desktop/SelectivelyAdaptiveLasso`\n",
      " \u001b[90m [2913bbd2] \u001b[39mStatsBase v0.33.13\n",
      " \u001b[90m [2dacb708] \u001b[39mUCIData v0.1.0 `https://github.com/JackDunnNZ/UCIData.jl#master`\n",
      " \u001b[90m [112f6efa] \u001b[39mVegaLite v2.6.0\n",
      " \u001b[90m [009559a3] \u001b[39mXGBoost v1.1.1\n",
      " \u001b[90m [fdbf4ff8] \u001b[39mXLSX v0.7.8\n",
      " \u001b[90m [9a3f8284] \u001b[39mRandom `@stdlib/Random`\n",
      " \u001b[90m [10745b16] \u001b[39mStatistics `@stdlib/Statistics`\n",
      " \u001b[90m [8dfed614] \u001b[39mTest `@stdlib/Test`\n",
      "\u001b[32m\u001b[1m      Status\u001b[22m\u001b[39m `/private/var/folders/1j/vn8fvqrs7pn5ln731w20k34c0000gn/T/jl_URoMgd/Manifest.toml`\n",
      " \u001b[90m [da404889] \u001b[39mARFFFiles v1.4.1\n",
      " \u001b[90m [1520ce14] \u001b[39mAbstractTrees v0.3.4\n",
      " \u001b[90m [4fba245c] \u001b[39mArrayInterface v3.2.2\n",
      " \u001b[90m [fbb218c0] \u001b[39mBSON v0.3.4\n",
      " \u001b[90m [6e4b80f9] \u001b[39mBenchmarkTools v1.2.2\n",
      " \u001b[90m [b99e7846] \u001b[39mBinaryProvider v0.5.10\n",
      " \u001b[90m [336ed68f] \u001b[39mCSV v0.9.11\n",
      " \u001b[90m [324d7699] \u001b[39mCategoricalArrays v0.10.2\n",
      " \u001b[90m [d360d2e6] \u001b[39mChainRulesCore v1.11.1\n",
      " \u001b[90m [9e997f8a] \u001b[39mChangesOfVariables v0.1.1\n",
      " \u001b[90m [944b1d66] \u001b[39mCodecZlib v0.7.0\n",
      " \u001b[90m [3da002f7] \u001b[39mColorTypes v0.11.0\n",
      " \u001b[90m [5ae59095] \u001b[39mColors v0.12.8\n",
      " \u001b[90m [861a8166] \u001b[39mCombinatorics v1.0.2\n",
      " \u001b[90m [34da2185] \u001b[39mCompat v3.40.0\n",
      " \u001b[90m [ed09eef8] \u001b[39mComputationalResources v0.3.2\n",
      " \u001b[90m [187b0558] \u001b[39mConstructionBase v1.3.0\n",
      " \u001b[90m [a8cc5b0e] \u001b[39mCrayons v4.0.4\n",
      " \u001b[90m [9a962f9c] \u001b[39mDataAPI v1.9.0\n",
      " \u001b[90m [124859b0] \u001b[39mDataDeps v0.7.7\n",
      " \u001b[90m [a93c6f00] \u001b[39mDataFrames v1.3.0\n",
      " \u001b[90m [864edb3b] \u001b[39mDataStructures v0.18.10\n",
      " \u001b[90m [e2d170a0] \u001b[39mDataValueInterfaces v1.0.0\n",
      " \u001b[90m [e7dc6d0d] \u001b[39mDataValues v0.4.13\n",
      " \u001b[90m [b429d917] \u001b[39mDensityInterface v0.4.0\n",
      " \u001b[90m [b4f34e82] \u001b[39mDistances v0.10.6\n",
      " \u001b[90m [31c24e10] \u001b[39mDistributions v0.25.34\n",
      " \u001b[90m [ffbed154] \u001b[39mDocStringExtensions v0.8.6\n",
      " \u001b[90m [792122b4] \u001b[39mEarlyStopping v0.1.9\n",
      " \u001b[90m [da5c29d0] \u001b[39mEllipsisNotation v1.1.3\n",
      " \u001b[90m [8f5d6c58] \u001b[39mEzXML v1.1.0\n",
      " \u001b[90m [5789e2e9] \u001b[39mFileIO v1.11.2\n",
      " \u001b[90m [8fc22ac5] \u001b[39mFilePaths v0.8.3\n",
      " \u001b[90m [48062228] \u001b[39mFilePathsBase v0.9.17\n",
      " \u001b[90m [1a297f60] \u001b[39mFillArrays v0.12.7\n",
      " \u001b[90m [53c48c17] \u001b[39mFixedPointNumbers v0.8.4\n",
      " \u001b[90m [08572546] \u001b[39mFlameGraphs v0.2.8\n",
      " \u001b[90m [59287772] \u001b[39mFormatting v0.4.2\n",
      " \u001b[90m [cd3eb016] \u001b[39mHTTP v0.9.17\n",
      " \u001b[90m [615f187c] \u001b[39mIfElse v0.1.1\n",
      " \u001b[90m [9b13fd28] \u001b[39mIndirectArrays v1.0.0\n",
      " \u001b[90m [83e8ac13] \u001b[39mIniFile v0.5.0\n",
      " \u001b[90m [842dd82b] \u001b[39mInlineStrings v1.1.0\n",
      " \u001b[90m [3587e190] \u001b[39mInverseFunctions v0.1.2\n",
      " \u001b[90m [41ab1584] \u001b[39mInvertedIndices v1.1.0\n",
      " \u001b[90m [92d709cd] \u001b[39mIrrationalConstants v0.1.1\n",
      " \u001b[90m [b3c1a2ee] \u001b[39mIterationControl v0.4.0\n",
      " \u001b[90m [82899510] \u001b[39mIteratorInterfaceExtensions v1.0.0\n",
      " \u001b[90m [692b3bcd] \u001b[39mJLLWrappers v1.3.0\n",
      " \u001b[90m [9da8a3cd] \u001b[39mJLSO v2.6.0\n",
      " \u001b[90m [682c06a0] \u001b[39mJSON v0.21.2\n",
      " \u001b[90m [7d188eb4] \u001b[39mJSONSchema v0.3.4\n",
      " \u001b[90m [a5e1c1ea] \u001b[39mLatinHypercubeSampling v1.8.0\n",
      " \u001b[90m [7f8f8fb0] \u001b[39mLearnBase v0.4.1\n",
      " \u001b[90m [1d6d02ad] \u001b[39mLeftChildRightSiblingTrees v0.1.3\n",
      " \u001b[90m [1b4a561d] \u001b[39mLegacyStrings v0.5.0\n",
      " \u001b[90m [2ab3a3ac] \u001b[39mLogExpFunctions v0.3.5\n",
      " \u001b[90m [30fc2ffe] \u001b[39mLossFunctions v0.7.2\n",
      " \u001b[90m [add582a8] \u001b[39mMLJ v0.16.11\n",
      " \u001b[90m [a7f614a8] \u001b[39mMLJBase v0.18.26\n",
      " \u001b[90m [50ed68f4] \u001b[39mMLJEnsembles v0.1.2\n",
      " \u001b[90m [614be32b] \u001b[39mMLJIteration v0.3.3\n",
      " \u001b[90m [e80e1ace] \u001b[39mMLJModelInterface v1.3.3\n",
      " \u001b[90m [d491faf4] \u001b[39mMLJModels v0.14.14\n",
      " \u001b[90m [17bed46d] \u001b[39mMLJSerialization v1.1.2\n",
      " \u001b[90m [03970b2e] \u001b[39mMLJTuning v0.6.14\n",
      " \u001b[90m [1914dd2f] \u001b[39mMacroTools v0.5.9\n",
      " \u001b[90m [739be429] \u001b[39mMbedTLS v1.0.3\n",
      " \u001b[90m [f28f55f0] \u001b[39mMemento v1.3.0\n",
      " \u001b[90m [e1d29d7a] \u001b[39mMissings v1.0.2\n",
      " \u001b[90m [2bd173c7] \u001b[39mNodeJS v1.3.0\n",
      " \u001b[90m [8b6db2d4] \u001b[39mOpenML v0.1.1\n",
      " \u001b[90m [bac558e1] \u001b[39mOrderedCollections v1.4.1\n",
      " \u001b[90m [90014a1f] \u001b[39mPDMats v0.11.5\n",
      " \u001b[90m [d96e819e] \u001b[39mParameters v0.12.3\n",
      " \u001b[90m [69de0a69] \u001b[39mParsers v2.1.2\n",
      " \u001b[90m [2dfb63ee] \u001b[39mPooledArrays v1.4.0\n",
      " \u001b[90m [21216c6a] \u001b[39mPreferences v1.2.2\n",
      " \u001b[90m [08abe8d2] \u001b[39mPrettyTables v1.2.3\n",
      " \u001b[90m [4391764f] \u001b[39mProfileVega v1.1.1\n",
      " \u001b[90m [92933f4c] \u001b[39mProgressMeter v1.7.1\n",
      " \u001b[90m [1fd47b50] \u001b[39mQuadGK v2.4.2\n",
      " \u001b[90m [3cdcf5f2] \u001b[39mRecipesBase v1.2.1\n",
      " \u001b[90m [189a3867] \u001b[39mReexport v1.2.2\n",
      " \u001b[90m [ae029012] \u001b[39mRequires v1.1.3\n",
      " \u001b[90m [79098fc4] \u001b[39mRmath v0.7.0\n",
      " \u001b[90m [321657f4] \u001b[39mScientificTypes v2.3.3\n",
      " \u001b[90m [30f210dd] \u001b[39mScientificTypesBase v2.3.0\n",
      " \u001b[90m [52f33b2b] \u001b[39mSelectivelyAdaptiveLasso v0.1.0 `~/Desktop/SelectivelyAdaptiveLasso`\n",
      " \u001b[90m [91c51154] \u001b[39mSentinelArrays v1.3.8\n",
      " \u001b[90m [efcf1570] \u001b[39mSetfield v0.7.1\n",
      " \u001b[90m [a2af1166] \u001b[39mSortingAlgorithms v1.0.1\n",
      " \u001b[90m [276daf66] \u001b[39mSpecialFunctions v1.8.1\n",
      " \u001b[90m [860ef19b] \u001b[39mStableRNGs v1.0.0\n",
      " \u001b[90m [aedffcd0] \u001b[39mStatic v0.4.1\n",
      " \u001b[90m [64bff920] \u001b[39mStatisticalTraits v2.1.0\n",
      " \u001b[90m [82ae8749] \u001b[39mStatsAPI v1.1.0\n",
      " \u001b[90m [2913bbd2] \u001b[39mStatsBase v0.33.13\n",
      " \u001b[90m [4c63d2b9] \u001b[39mStatsFuns v0.9.14\n",
      " \u001b[90m [3783bdb8] \u001b[39mTableTraits v1.0.1\n",
      " \u001b[90m [382cd787] \u001b[39mTableTraitsUtils v1.0.2\n",
      " \u001b[90m [bd369af6] \u001b[39mTables v1.6.0\n",
      " \u001b[90m [3bb67fe8] \u001b[39mTranscodingStreams v0.9.6\n",
      " \u001b[90m [2dacb708] \u001b[39mUCIData v0.1.0 `https://github.com/JackDunnNZ/UCIData.jl#master`\n",
      " \u001b[90m [30578b45] \u001b[39mURIParser v0.4.1\n",
      " \u001b[90m [5c2747f8] \u001b[39mURIs v1.3.0\n",
      " \u001b[90m [3a884ed6] \u001b[39mUnPack v1.0.2\n",
      " \u001b[90m [239c3e63] \u001b[39mVega v2.3.0\n",
      " \u001b[90m [112f6efa] \u001b[39mVegaLite v2.6.0\n",
      " \u001b[90m [ea10d353] \u001b[39mWeakRefStrings v1.4.1\n",
      " \u001b[90m [009559a3] \u001b[39mXGBoost v1.1.1\n",
      " \u001b[90m [fdbf4ff8] \u001b[39mXLSX v0.7.8\n",
      " \u001b[90m [a5390f91] \u001b[39mZipFile v0.9.4\n",
      " \u001b[90m [94ce4f54] \u001b[39mLibiconv_jll v1.16.1+1\n",
      " \u001b[90m [efe28fd5] \u001b[39mOpenSpecFun_jll v0.5.5+0\n",
      " \u001b[90m [f50d1b31] \u001b[39mRmath_jll v0.3.0+0\n",
      " \u001b[90m [a5c6f535] \u001b[39mXGBoost_jll v1.4.2+0\n",
      " \u001b[90m [02c8fc9c] \u001b[39mXML2_jll v2.9.12+0\n",
      " \u001b[90m [0dad84c5] \u001b[39mArgTools `@stdlib/ArgTools`\n",
      " \u001b[90m [56f22d72] \u001b[39mArtifacts `@stdlib/Artifacts`\n",
      " \u001b[90m [2a0f44e3] \u001b[39mBase64 `@stdlib/Base64`\n",
      " \u001b[90m [ade2ca70] \u001b[39mDates `@stdlib/Dates`\n",
      " \u001b[90m [8bb1440f] \u001b[39mDelimitedFiles `@stdlib/DelimitedFiles`\n",
      " \u001b[90m [8ba89e20] \u001b[39mDistributed `@stdlib/Distributed`\n",
      " \u001b[90m [f43a241f] \u001b[39mDownloads `@stdlib/Downloads`\n",
      " \u001b[90m [9fa8497b] \u001b[39mFuture `@stdlib/Future`\n",
      " \u001b[90m [b77e0a4c] \u001b[39mInteractiveUtils `@stdlib/InteractiveUtils`\n",
      " \u001b[90m [b27032c2] \u001b[39mLibCURL `@stdlib/LibCURL`\n",
      " \u001b[90m [76f85450] \u001b[39mLibGit2 `@stdlib/LibGit2`\n",
      " \u001b[90m [8f399da3] \u001b[39mLibdl `@stdlib/Libdl`\n",
      " \u001b[90m [37e2e46d] \u001b[39mLinearAlgebra `@stdlib/LinearAlgebra`\n",
      " \u001b[90m [56ddb016] \u001b[39mLogging `@stdlib/Logging`\n",
      " \u001b[90m [d6f4376e] \u001b[39mMarkdown `@stdlib/Markdown`\n",
      " \u001b[90m [a63ad114] \u001b[39mMmap `@stdlib/Mmap`\n",
      " \u001b[90m [ca575930] \u001b[39mNetworkOptions `@stdlib/NetworkOptions`\n",
      " \u001b[90m [44cfe95a] \u001b[39mPkg `@stdlib/Pkg`\n",
      " \u001b[90m [de0858da] \u001b[39mPrintf `@stdlib/Printf`\n",
      " \u001b[90m [9abbd945] \u001b[39mProfile `@stdlib/Profile`\n",
      " \u001b[90m [3fa0cd96] \u001b[39mREPL `@stdlib/REPL`\n",
      " \u001b[90m [9a3f8284] \u001b[39mRandom `@stdlib/Random`\n",
      " \u001b[90m [ea8e919c] \u001b[39mSHA `@stdlib/SHA`\n",
      " \u001b[90m [9e88b42a] \u001b[39mSerialization `@stdlib/Serialization`\n",
      " \u001b[90m [1a1011a3] \u001b[39mSharedArrays `@stdlib/SharedArrays`\n",
      " \u001b[90m [6462fe0b] \u001b[39mSockets `@stdlib/Sockets`\n",
      " \u001b[90m [2f01184e] \u001b[39mSparseArrays `@stdlib/SparseArrays`\n",
      " \u001b[90m [10745b16] \u001b[39mStatistics `@stdlib/Statistics`\n",
      " \u001b[90m [4607b0f0] \u001b[39mSuiteSparse `@stdlib/SuiteSparse`\n",
      " \u001b[90m [fa267f1f] \u001b[39mTOML `@stdlib/TOML`\n",
      " \u001b[90m [a4e569a6] \u001b[39mTar `@stdlib/Tar`\n",
      " \u001b[90m [8dfed614] \u001b[39mTest `@stdlib/Test`\n",
      " \u001b[90m [cf7118a7] \u001b[39mUUIDs `@stdlib/UUIDs`\n",
      " \u001b[90m [4ec0a83e] \u001b[39mUnicode `@stdlib/Unicode`\n",
      " \u001b[90m [e66e0078] \u001b[39mCompilerSupportLibraries_jll `@stdlib/CompilerSupportLibraries_jll`\n",
      " \u001b[90m [deac9b47] \u001b[39mLibCURL_jll `@stdlib/LibCURL_jll`\n",
      " \u001b[90m [29816b5a] \u001b[39mLibSSH2_jll `@stdlib/LibSSH2_jll`\n",
      " \u001b[90m [c8ffd9c3] \u001b[39mMbedTLS_jll `@stdlib/MbedTLS_jll`\n",
      " \u001b[90m [14a3606d] \u001b[39mMozillaCACerts_jll `@stdlib/MozillaCACerts_jll`\n",
      " \u001b[90m [05823500] \u001b[39mOpenLibm_jll `@stdlib/OpenLibm_jll`\n",
      " \u001b[90m [83775a58] \u001b[39mZlib_jll `@stdlib/Zlib_jll`\n",
      " \u001b[90m [8e850ede] \u001b[39mnghttp2_jll `@stdlib/nghttp2_jll`\n",
      " \u001b[90m [3f19e933] \u001b[39mp7zip_jll `@stdlib/p7zip_jll`\n",
      "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
      "\u001b[33m  ✓ \u001b[39mSelectivelyAdaptiveLasso\n",
      "  1 dependency successfully precompiled in 2 seconds (122 already precompiled)\n",
      "  \u001b[33m1\u001b[39m dependency precompiled but a different version is currently loaded. Restart julia to access the new version\n",
      "\u001b[32m\u001b[1m     Testing\u001b[22m\u001b[39m Running tests...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[37m\u001b[1mTest Summary: | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "FeatureVector | \u001b[32m 400  \u001b[39m\u001b[36m  400\u001b[39m\n",
      "\u001b[37m\u001b[1mTest Summary:                 | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "1-dim basis selection (right) | \u001b[32m 500  \u001b[39m\u001b[36m  500\u001b[39m\n",
      "\u001b[37m\u001b[1mTest Summary:         | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "changepoint inclusion | \u001b[32m 100  \u001b[39m\u001b[36m  100\u001b[39m\n",
      "\u001b[37m\u001b[1mTest Summary:                    | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "multidim basis selection (right) | \u001b[32m 500  \u001b[39m\u001b[36m  500\u001b[39m\n",
      "\u001b[37m\u001b[1mTest Summary:                   | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "multidim basis selection (left) | \u001b[32m 500  \u001b[39m\u001b[36m  500\u001b[39m\n",
      "\u001b[37m\u001b[1mTest Summary: | \u001b[22m\u001b[39m\u001b[32m\u001b[1mPass  \u001b[22m\u001b[39m\u001b[36m\u001b[1mTotal\u001b[22m\u001b[39m\n",
      "build basis   | \u001b[32m 200  \u001b[39m\u001b[36m  200\u001b[39m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m     Testing\u001b[22m\u001b[39m SelectivelyAdaptiveLasso tests passed \n"
     ]
    }
   ],
   "source": [
    "Pkg.test(\"SelectivelyAdaptiveLasso\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b977ebc-9446-4b7e-a633-170dd9e79be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_data (generic function with 2 methods)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"/Users/aschuler/Desktop/uci-data/csv/\"\n",
    "datasets = [\"boston\", \"concrete\", \"energy\", \"kin8nm\", \"naval\", \"power\", \"protein\", \"wine\", \"yacht\"]\n",
    "\n",
    "function get_data(dataset, path=data_path)\n",
    "    df = CSV.read(\n",
    "        open(data_path * dataset * \".csv\"), \n",
    "        DataFrame, copycols=true\n",
    "    )\n",
    "    Y = float(df[:,end])\n",
    "    X = Matrix(df[:,1:end-1])\n",
    "    \n",
    "    return X, Y\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c0a2490-6221-4eb7-9374-b7f492ddffed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boston(506, 13)(506,)\n",
      "concrete(1030, 8)(1030,)\n",
      "energy(768, 8)(768,)\n",
      "kin8nm(8192, 8)(8192,)\n",
      "naval(11934, 17)(11934,)\n",
      "power(9568, 4)(9568,)\n",
      "protein(45730, 9)(45730,)\n",
      "wine(1599, 11)(1599,)\n",
      "yacht(308, 6)(308,)\n"
     ]
    }
   ],
   "source": [
    "for dataset in datasets\n",
    "    X,Y = get_data(dataset)\n",
    "    print(dataset, size(X), size(Y))\n",
    "    print(\"\\n\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b495b0f4-49db-4cdd-afb0-f6f44699ae80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "train_test_split (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using EllipsisNotation\n",
    "import Random: shuffle\n",
    "\n",
    "function train_test_split(arrays...; test_frac=0.1)\n",
    "    n = size(arrays[1])[1]\n",
    "    n_test = Int(round(n*test_frac))\n",
    "\n",
    "    idx = shuffle(1:n)\n",
    "    idx_test = idx[1:n_test]\n",
    "    idx_train = idx[(n_test+1):end]\n",
    "    \n",
    "    return ((A[idx_train,..], A[idx_test,..]) for A in arrays)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1c4b4a-2285-4084-97e9-dab385db167a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Quick test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9811a531-ae40-4587-b9bd-9c96cf0e2610",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = get_data(\"wine\")\n",
    "# Y = Y.-mean(Y)\n",
    "(X_tr_val, X_te), (Y_tr_val, Y_te) = train_test_split(X, Y, test_frac=0.1)\n",
    "(X_tr, X_val), (Y_tr, Y_val) = train_test_split(X_tr_val, Y_tr_val, test_frac=0.2);\n",
    "n,p = size(X_tr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7154c9dc-47ef-4deb-8feb-c8d6eae806ae",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "[726]\ttrain-rmse:0.073929\n",
      "[727]\ttrain-rmse:0.073635\n",
      "[728]\ttrain-rmse:0.073484\n",
      "[729]\ttrain-rmse:0.073324\n",
      "[730]\ttrain-rmse:0.073253\n",
      "[731]\ttrain-rmse:0.073217\n",
      "[732]\ttrain-rmse:0.072963\n",
      "[733]\ttrain-rmse:0.072773\n",
      "[734]\ttrain-rmse:0.072696\n",
      "[735]\ttrain-rmse:0.072658\n",
      "[736]\ttrain-rmse:0.072495\n",
      "[737]\ttrain-rmse:0.072301\n",
      "[738]\ttrain-rmse:0.072161\n",
      "[739]\ttrain-rmse:0.072049\n",
      "[740]\ttrain-rmse:0.072030\n",
      "[741]\ttrain-rmse:0.071997\n",
      "[742]\ttrain-rmse:0.071856\n",
      "[743]\ttrain-rmse:0.071764\n",
      "[744]\ttrain-rmse:0.071406\n",
      "[745]\ttrain-rmse:0.071160\n",
      "[746]\ttrain-rmse:0.071133\n",
      "[747]\ttrain-rmse:0.071104\n",
      "[748]\ttrain-rmse:0.070891\n",
      "[749]\ttrain-rmse:0.070749\n",
      "[750]\ttrain-rmse:0.070726\n",
      "[751]\ttrain-rmse:0.070562\n",
      "[752]\ttrain-rmse:0.070394\n",
      "[753]\ttrain-rmse:0.070335\n",
      "[754]\ttrain-rmse:0.070203\n",
      "[755]\ttrain-rmse:0.069997\n",
      "[756]\ttrain-rmse:0.069864\n",
      "[757]\ttrain-rmse:0.069641\n",
      "[758]\ttrain-rmse:0.069476\n",
      "[759]\ttrain-rmse:0.069367\n",
      "[760]\ttrain-rmse:0.069295\n",
      "[761]\ttrain-rmse:0.069258\n",
      "[762]\ttrain-rmse:0.069132\n",
      "[763]\ttrain-rmse:0.069104\n",
      "[764]\ttrain-rmse:0.068955\n",
      "[765]\ttrain-rmse:0.068738\n",
      "[766]\ttrain-rmse:0.068570\n",
      "[767]\ttrain-rmse:0.068344\n",
      "[768]\ttrain-rmse:0.068245\n",
      "[769]\ttrain-rmse:0.067985\n",
      "[770]\ttrain-rmse:0.067575\n",
      "[771]\ttrain-rmse:0.067325\n",
      "[772]\ttrain-rmse:0.067078\n",
      "[773]\ttrain-rmse:0.066809\n",
      "[774]\ttrain-rmse:0.066664\n",
      "[775]\ttrain-rmse:0.066584\n",
      "[776]\ttrain-rmse:0.066435\n",
      "[777]\ttrain-rmse:0.066306\n",
      "[778]\ttrain-rmse:0.066190\n",
      "[779]\ttrain-rmse:0.066171\n",
      "[780]\ttrain-rmse:0.066087\n",
      "[781]\ttrain-rmse:0.066016\n",
      "[782]\ttrain-rmse:0.065707\n",
      "[783]\ttrain-rmse:0.065588\n",
      "[784]\ttrain-rmse:0.065405\n",
      "[785]\ttrain-rmse:0.065384\n",
      "[786]\ttrain-rmse:0.065190\n",
      "[787]\ttrain-rmse:0.065162\n",
      "[788]\ttrain-rmse:0.064957\n",
      "[789]\ttrain-rmse:0.064919\n",
      "[790]\ttrain-rmse:0.064864\n",
      "[791]\ttrain-rmse:0.064685\n",
      "[792]\ttrain-rmse:0.064399\n",
      "[793]\ttrain-rmse:0.064305\n",
      "[794]\ttrain-rmse:0.064223\n",
      "[795]\ttrain-rmse:0.063958\n",
      "[796]\ttrain-rmse:0.063762\n",
      "[797]\ttrain-rmse:0.063561\n",
      "[798]\ttrain-rmse:0.063342\n",
      "[799]\ttrain-rmse:0.063147\n",
      "[800]\ttrain-rmse:0.062889\n",
      "[801]\ttrain-rmse:0.062739\n",
      "[802]\ttrain-rmse:0.062681\n",
      "[803]\ttrain-rmse:0.062386\n",
      "[804]\ttrain-rmse:0.062215\n",
      "[805]\ttrain-rmse:0.062098\n",
      "[806]\ttrain-rmse:0.062044\n",
      "[807]\ttrain-rmse:0.061854\n",
      "[808]\ttrain-rmse:0.061837\n",
      "[809]\ttrain-rmse:0.061744\n",
      "[810]\ttrain-rmse:0.061725\n",
      "[811]\ttrain-rmse:0.061614\n",
      "[812]\ttrain-rmse:0.061560\n",
      "[813]\ttrain-rmse:0.061491\n",
      "[814]\ttrain-rmse:0.061314\n",
      "[815]\ttrain-rmse:0.061120\n",
      "[816]\ttrain-rmse:0.060864\n",
      "[817]\ttrain-rmse:0.060710\n",
      "[818]\ttrain-rmse:0.060501\n",
      "[819]\ttrain-rmse:0.060292\n",
      "[820]\ttrain-rmse:0.060267\n",
      "[821]\ttrain-rmse:0.060218\n",
      "[822]\ttrain-rmse:0.059997\n",
      "[823]\ttrain-rmse:0.059968\n",
      "[824]\ttrain-rmse:0.059780\n",
      "[825]\ttrain-rmse:0.059634\n",
      "[826]\ttrain-rmse:0.059428\n",
      "[827]\ttrain-rmse:0.059078\n",
      "[828]\ttrain-rmse:0.059033\n",
      "[829]\ttrain-rmse:0.058817\n",
      "[830]\ttrain-rmse:0.058524\n",
      "[831]\ttrain-rmse:0.058243\n",
      "[832]\ttrain-rmse:0.058227\n",
      "[833]\ttrain-rmse:0.058214\n",
      "[834]\ttrain-rmse:0.058179\n",
      "[835]\ttrain-rmse:0.057886\n",
      "[836]\ttrain-rmse:0.057777\n",
      "[837]\ttrain-rmse:0.057684\n",
      "[838]\ttrain-rmse:0.057418\n",
      "[839]\ttrain-rmse:0.057173\n",
      "[840]\ttrain-rmse:0.056927\n",
      "[841]\ttrain-rmse:0.056711\n",
      "[842]\ttrain-rmse:0.056521\n",
      "[843]\ttrain-rmse:0.056336\n",
      "[844]\ttrain-rmse:0.056121\n",
      "[845]\ttrain-rmse:0.056033\n",
      "[846]\ttrain-rmse:0.055901\n",
      "[847]\ttrain-rmse:0.055663\n",
      "[848]\ttrain-rmse:0.055645\n",
      "[849]\ttrain-rmse:0.055507\n",
      "[850]\ttrain-rmse:0.055473\n",
      "[851]\ttrain-rmse:0.055417\n",
      "[852]\ttrain-rmse:0.055339\n",
      "[853]\ttrain-rmse:0.055155\n",
      "[854]\ttrain-rmse:0.055091\n",
      "[855]\ttrain-rmse:0.054970\n",
      "[856]\ttrain-rmse:0.054876\n",
      "[857]\ttrain-rmse:0.054782\n",
      "[858]\ttrain-rmse:0.054718\n",
      "[859]\ttrain-rmse:0.054538\n",
      "[860]\ttrain-rmse:0.054467\n",
      "[861]\ttrain-rmse:0.054307\n",
      "[862]\ttrain-rmse:0.054279\n",
      "[863]\ttrain-rmse:0.054122\n",
      "[864]\ttrain-rmse:0.053984\n",
      "[865]\ttrain-rmse:0.053734\n",
      "[866]\ttrain-rmse:0.053591\n",
      "[867]\ttrain-rmse:0.053565\n",
      "[868]\ttrain-rmse:0.053294\n",
      "[869]\ttrain-rmse:0.053215\n",
      "[870]\ttrain-rmse:0.053004\n",
      "[871]\ttrain-rmse:0.052888\n",
      "[872]\ttrain-rmse:0.052728\n",
      "[873]\ttrain-rmse:0.052673\n",
      "[874]\ttrain-rmse:0.052469\n",
      "[875]\ttrain-rmse:0.052328\n",
      "[876]\ttrain-rmse:0.052202\n",
      "[877]\ttrain-rmse:0.052077\n",
      "[878]\ttrain-rmse:0.051862\n",
      "[879]\ttrain-rmse:0.051838\n",
      "[880]\ttrain-rmse:0.051764\n",
      "[881]\ttrain-rmse:0.051700\n",
      "[882]\ttrain-rmse:0.051686\n",
      "[883]\ttrain-rmse:0.051575\n",
      "[884]\ttrain-rmse:0.051458\n",
      "[885]\ttrain-rmse:0.051306\n",
      "[886]\ttrain-rmse:0.051210\n",
      "[887]\ttrain-rmse:0.051065\n",
      "[888]\ttrain-rmse:0.050880\n",
      "[889]\ttrain-rmse:0.050740\n",
      "[890]\ttrain-rmse:0.050723\n",
      "[891]\ttrain-rmse:0.050701\n",
      "[892]\ttrain-rmse:0.050587\n",
      "[893]\ttrain-rmse:0.050386\n",
      "[894]\ttrain-rmse:0.050321\n",
      "[895]\ttrain-rmse:0.050218\n",
      "[896]\ttrain-rmse:0.050096\n",
      "[897]\ttrain-rmse:0.050054\n",
      "[898]\ttrain-rmse:0.049984\n",
      "[899]\ttrain-rmse:0.049886\n",
      "[900]\ttrain-rmse:0.049776\n",
      "[901]\ttrain-rmse:0.049660\n",
      "[902]\ttrain-rmse:0.049534\n",
      "[903]\ttrain-rmse:0.049320\n",
      "[904]\ttrain-rmse:0.049137\n",
      "[905]\ttrain-rmse:0.048985\n",
      "[906]\ttrain-rmse:0.048895\n",
      "[907]\ttrain-rmse:0.048853\n",
      "[908]\ttrain-rmse:0.048791\n",
      "[909]\ttrain-rmse:0.048655\n",
      "[910]\ttrain-rmse:0.048590\n",
      "[911]\ttrain-rmse:0.048553\n",
      "[912]\ttrain-rmse:0.048350\n",
      "[913]\ttrain-rmse:0.048283\n",
      "[914]\ttrain-rmse:0.048019\n",
      "[915]\ttrain-rmse:0.047790\n",
      "[916]\ttrain-rmse:0.047526\n",
      "[917]\ttrain-rmse:0.047493\n",
      "[918]\ttrain-rmse:0.047454\n",
      "[919]\ttrain-rmse:0.047376\n",
      "[920]\ttrain-rmse:0.047213\n",
      "[921]\ttrain-rmse:0.047177\n",
      "[922]\ttrain-rmse:0.047092\n",
      "[923]\ttrain-rmse:0.047076\n",
      "[924]\ttrain-rmse:0.047029\n",
      "[925]\ttrain-rmse:0.046817\n",
      "[926]\ttrain-rmse:0.046701\n",
      "[927]\ttrain-rmse:0.046678\n",
      "[928]\ttrain-rmse:0.046648\n",
      "[929]\ttrain-rmse:0.046610\n",
      "[930]\ttrain-rmse:0.046498\n",
      "[931]\ttrain-rmse:0.046434\n",
      "[932]\ttrain-rmse:0.046409\n",
      "[933]\ttrain-rmse:0.046176\n",
      "[934]\ttrain-rmse:0.045990\n",
      "[935]\ttrain-rmse:0.045947\n",
      "[936]\ttrain-rmse:0.045857\n",
      "[937]\ttrain-rmse:0.045841\n",
      "[938]\ttrain-rmse:0.045824\n",
      "[939]\ttrain-rmse:0.045693\n",
      "[940]\ttrain-rmse:0.045631\n",
      "[941]\ttrain-rmse:0.045609\n",
      "[942]\ttrain-rmse:0.045575\n",
      "[943]\ttrain-rmse:0.045541\n",
      "[944]\ttrain-rmse:0.045467\n",
      "[945]\ttrain-rmse:0.045324\n",
      "[946]\ttrain-rmse:0.045118\n",
      "[947]\ttrain-rmse:0.044957\n",
      "[948]\ttrain-rmse:0.044884\n",
      "[949]\ttrain-rmse:0.044819\n",
      "[950]\ttrain-rmse:0.044705\n",
      "[951]\ttrain-rmse:0.044581\n",
      "[952]\ttrain-rmse:0.044519\n",
      "[953]\ttrain-rmse:0.044465\n",
      "[954]\ttrain-rmse:0.044429\n",
      "[955]\ttrain-rmse:0.044404\n",
      "[956]\ttrain-rmse:0.044352\n",
      "[957]\ttrain-rmse:0.044210\n",
      "[958]\ttrain-rmse:0.044029\n",
      "[959]\ttrain-rmse:0.043918\n",
      "[960]\ttrain-rmse:0.043846\n",
      "[961]\ttrain-rmse:0.043829\n",
      "[962]\ttrain-rmse:0.043620\n",
      "[963]\ttrain-rmse:0.043541\n",
      "[964]\ttrain-rmse:0.043515\n",
      "[965]\ttrain-rmse:0.043507\n",
      "[966]\ttrain-rmse:0.043477\n",
      "[967]\ttrain-rmse:0.043298\n",
      "[968]\ttrain-rmse:0.043152\n",
      "[969]\ttrain-rmse:0.043071\n",
      "[970]\ttrain-rmse:0.043018\n",
      "[971]\ttrain-rmse:0.043001\n",
      "[972]\ttrain-rmse:0.042981\n",
      "[973]\ttrain-rmse:0.042835\n",
      "[974]\ttrain-rmse:0.042679\n",
      "[975]\ttrain-rmse:0.042518\n",
      "[976]\ttrain-rmse:0.042503\n",
      "[977]\ttrain-rmse:0.042485\n",
      "[978]\ttrain-rmse:0.042378\n",
      "[979]\ttrain-rmse:0.042308\n",
      "[980]\ttrain-rmse:0.042287\n",
      "[981]\ttrain-rmse:0.042182\n",
      "[982]\ttrain-rmse:0.042177\n",
      "[983]\ttrain-rmse:0.042084\n",
      "[984]\ttrain-rmse:0.041979\n",
      "[985]\ttrain-rmse:0.041787\n",
      "[986]\ttrain-rmse:0.041620\n",
      "[987]\ttrain-rmse:0.041472\n",
      "[988]\ttrain-rmse:0.041242\n",
      "[989]\ttrain-rmse:0.041068\n",
      "[990]\ttrain-rmse:0.040881\n",
      "[991]\ttrain-rmse:0.040734\n",
      "[992]\ttrain-rmse:0.040718\n",
      "[993]\ttrain-rmse:0.040632\n",
      "[994]\ttrain-rmse:0.040545\n",
      "[995]\ttrain-rmse:0.040407\n",
      "[996]\ttrain-rmse:0.040264\n",
      "[997]\ttrain-rmse:0.040171\n",
      "[998]\ttrain-rmse:0.040157\n",
      "[999]\ttrain-rmse:0.040052\n",
      "[1000]\ttrain-rmse:0.039985\n"
     ]
    }
   ],
   "source": [
    "import XGBoost as XGB\n",
    "bst = XGB.xgboost(X_tr, 1000, label = Y_tr, eta = 0.1, max_depth = 4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2dfedf0b-a29c-440b-ab50-3622799135ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000-element Vector{Float64}:\n",
       " 4.666500916494118\n",
       " 4.207695330112024\n",
       " 3.7965402923658083\n",
       " 3.4247710740374555\n",
       " 3.093070009398494\n",
       " 2.797838336353295\n",
       " 2.5332177435914383\n",
       " 2.2928553246497603\n",
       " 2.079164572594345\n",
       " 1.8886666794280071\n",
       " 1.718432302862012\n",
       " 1.5683323044854849\n",
       " 1.4344486151255067\n",
       " ⋮\n",
       " 0.6142125214355677\n",
       " 0.6142067400457613\n",
       " 0.6142401342145614\n",
       " 0.6142366233740776\n",
       " 0.6142352280471185\n",
       " 0.6142530379353464\n",
       " 0.6142674630200889\n",
       " 0.6143227432069405\n",
       " 0.6144064733937143\n",
       " 0.6144096731698874\n",
       " 0.6143662676128816\n",
       " 0.614365212209941"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = [ \n",
    "    begin \n",
    "        Ŷ_val = XGB.predict(bst, X_val, ntree_limit = b)\n",
    "        sqrt(mean((Y_val-Ŷ_val).^2))\n",
    "    end \n",
    "    for b in 1:1000\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "32a9ef7d-b043-43a1-bd3c-597e82870d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6067444209569907, 689)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findmin(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3a782b40-7fe2-49b5-8e80-163b6af14cb2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.5796436727090666, 0.6794874691775642, 55, 55)\n",
      "(0.5351282332524843, 0.6651994856404053, 90, 90)\n",
      "(0.5171870395029655, 0.6663624251158546, 106, 106)\n",
      "(0.5049264318392231, 0.6667255927576533, 120, 120)\n",
      "(0.49366513899029413, 0.6665778030957333, 120, 120)\n",
      "(0.48914019243154117, 0.6612353577538014, 131, 131)\n",
      "(0.47575680912290963, 0.661380644485238, 143, 143)\n",
      "(0.47185250215000074, 0.6595235845332801, 146, 146)\n",
      "(0.4607988807953613, 0.6582229944482233, 154, 154)\n",
      "(0.45545471503305235, 0.6522854352529966, 161, 161)\n",
      "(0.45250869563933516, 0.6502066777801666, 173, 173)\n",
      "(0.449483300511894, 0.6494921017110655, 168, 168)\n",
      "(0.44835278173367527, 0.6505535800025327, 176, 176)\n",
      "(0.4428975638681363, 0.6544561158949491, 177, 177)\n",
      "(0.4405733946658019, 0.6566819681077991, 180, 180)\n",
      "(0.4383343954615867, 0.6547090554317313, 180, 180)\n",
      "(0.4355767066495148, 0.6539388107678858, 184, 184)\n",
      "(0.4323850235275253, 0.6558602063593563, 181, 181)\n",
      "(0.43128325184282645, 0.6558885557417905, 185, 185)\n",
      "(0.4286727955882764, 0.6548916951108912, 184, 184)\n",
      "(0.42712874233710174, 0.6553569300901669, 188, 188)\n",
      "(0.42504169629318006, 0.6534951622797777, 189, 189)\n",
      "(0.42427422926681024, 0.6540197000908486, 193, 193)\n",
      "(0.42175074530186135, 0.6571129607319168, 195, 195)\n",
      "(0.42028776310535443, 0.657070056282454, 201, 201)\n",
      "(0.41989925773262643, 0.657396725742442, 203, 203)\n",
      "(0.4175435180242655, 0.6563782606131201, 202, 202)\n",
      "(0.4156653942355938, 0.6598441256144605, 203, 203)\n",
      "(0.4144340199186897, 0.6593119206840775, 202, 202)\n",
      "(0.41253885034462534, 0.6547986697082547, 202, 202)\n",
      "(0.41223207627309877, 0.6547233573645252, 205, 205)\n",
      "(0.4118273067030807, 0.6540384741280225, 206, 206)\n",
      "(0.4095784046302741, 0.6498316846446377, 209, 209)\n",
      "(0.40791026572596906, 0.6459350902339588, 215, 215)\n",
      "(0.4073043145281288, 0.6458707745003467, 216, 216)\n",
      "(0.4050913114520328, 0.6412345239844587, 215, 215)\n",
      "(0.40453559276462, 0.6403638059486373, 214, 214)\n",
      "(0.40331420249675604, 0.6393531545047648, 211, 211)\n",
      "(0.4029415745508876, 0.6399203959356012, 216, 216)\n",
      "(0.4024318412670942, 0.6409483068980533, 217, 217)\n",
      "(0.4020578055788547, 0.6412674856607176, 222, 222)\n",
      "(0.4011899043738875, 0.6406524699389564, 222, 222)\n",
      "(0.4009192638126991, 0.6409856629311663, 222, 222)\n",
      "(0.3989322421826609, 0.6381230402250182, 221, 221)\n",
      "(0.39802476573449497, 0.6367390067728209, 226, 226)\n",
      "(0.39703628482188247, 0.6378224110329448, 224, 224)\n",
      "(0.3961328160917453, 0.6381956598465265, 223, 223)\n",
      "(0.3954471345621091, 0.6389160703822655, 218, 218)\n",
      "(0.3953923526563949, 0.6397957812289417, 220, 220)\n",
      "(0.3945909083487133, 0.6372110637471418, 219, 219)\n",
      "(0.39451060784265324, 0.6349606202711074, 218, 218)\n",
      "(0.3941973211622671, 0.6345648241571821, 220, 220)\n",
      "(0.3932441576014532, 0.6352192607804424, 223, 223)\n",
      "(0.393159471499828, 0.6352508214233116, 225, 225)\n",
      "(0.3928549635619173, 0.6346195398500044, 228, 228)\n",
      "(0.3914754817578394, 0.6327374296788484, 231, 231)\n",
      "(0.3897630466101962, 0.6318359868353364, 232, 232)\n",
      "(0.38810562625827416, 0.63450160469957, 238, 238)\n",
      "(0.3872215391970449, 0.6358548609310957, 236, 236)\n",
      "(0.3863453247139519, 0.6368977441478054, 240, 240)\n",
      "(0.3858548166885275, 0.6373026488748915, 243, 243)\n",
      "(0.38516484581628413, 0.6375733213090566, 247, 247)\n",
      "(0.38427657153793754, 0.6367411455681171, 249, 249)\n",
      "(0.38406524129166364, 0.6366560947512254, 251, 251)\n",
      "(0.3838629797148582, 0.6363416885496893, 247, 247)\n",
      "(0.38284819258881153, 0.6353417961675136, 249, 249)\n",
      "(0.38255410341581647, 0.6349594145316555, 254, 254)\n",
      "(0.38185215307620124, 0.6338891622651165, 257, 257)\n",
      "(0.3815984802796795, 0.6339979683915439, 254, 254)\n",
      "(0.38095051939157837, 0.6329026272314671, 255, 255)\n",
      "(0.38014500966253695, 0.6338504585946987, 256, 256)\n",
      "(0.37968929053906353, 0.6341759237644239, 262, 262)\n",
      "(0.3796325416642485, 0.6342060350270319, 264, 264)\n",
      "(0.37770211407586596, 0.6354724216319644, 261, 261)\n",
      "(0.3776976766143348, 0.6354851613400904, 262, 262)\n",
      "(0.3770053189012146, 0.6353150161320666, 261, 261)\n",
      "(0.37542990818992045, 0.6328209182788468, 257, 257)\n",
      "(0.3746986455574541, 0.6320197577722901, 260, 260)\n",
      "(0.37447905732167924, 0.6311400005153724, 256, 256)\n",
      "(0.3742254255481576, 0.6305821191315634, 261, 261)\n",
      "(0.3732742209257818, 0.6295903799469802, 260, 260)\n",
      "(0.37221525617978857, 0.6285025780800416, 263, 263)\n",
      "(0.37170808453301785, 0.6264121043131767, 260, 260)\n",
      "(0.3709217283302666, 0.6262897105307333, 258, 258)\n",
      "(0.37091862315356566, 0.6262817131233016, 259, 259)\n",
      "(0.3707997535960429, 0.6261733316365683, 261, 261)\n",
      "(0.3698644714815177, 0.6244984572094828, 260, 260)\n",
      "(0.3697286470085632, 0.6246619607041038, 261, 261)\n",
      "(0.3695080593068969, 0.6245553720349446, 262, 262)\n",
      "(0.3694989095504642, 0.6245533699094995, 263, 263)\n",
      "(0.3691344639578985, 0.6239709688903954, 264, 264)\n",
      "(0.36892294828406724, 0.6235833946008039, 267, 267)\n",
      "(0.3682031022918829, 0.6214193613082272, 264, 264)\n",
      "(0.3678297731658349, 0.6206874926048054, 263, 263)\n",
      "(0.3672438385850395, 0.6194973417344705, 266, 266)\n",
      "(0.3666635863601929, 0.6179983918064444, 264, 264)\n",
      "(0.3665439750185403, 0.6179818529839728, 261, 261)\n",
      "(0.3656508501044474, 0.6174879369449496, 261, 261)\n",
      "(0.3653776398132907, 0.616752044909964, 263, 263)\n",
      "(0.36414648612282724, 0.6161420355397879, 263, 263)\n",
      " 49.742586 seconds (208.10 M allocations: 31.053 GiB, 8.50% gc time, 0.87% compilation time)\n"
     ]
    }
   ],
   "source": [
    "import SelectivelyAdaptiveLasso as SAL\n",
    "# for testing\n",
    "sal_spec = SAL.SALSpec(\n",
    "    max_iter=100,\n",
    "    λ = 0.001, \n",
    "    bases_per_iter=200,\n",
    "    subsample_pct = 0.1,\n",
    "    feat_pct = 0.3,\n",
    "    tol = 1e-6,\n",
    ")\n",
    "@time sal_fit, (mse_tr, mse_val) = SAL.fit(\n",
    "    sal_spec, \n",
    "    X_tr, Y_tr, \n",
    "    X_val=X_val, Y_val=Y_val,\n",
    "    verbose=true, print_iter=1,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17afbc85-5e08-48d4-8474-e60e1a9bb248",
   "metadata": {
    "tags": []
   },
   "source": [
    "# SAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9b60cd9c-09a2-49ef-9700-17e8122df803",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 43.584397 seconds (257.58 M allocations: 48.502 GiB, 22.35% gc time)\n",
      " 24.656601 seconds (145.63 M allocations: 27.550 GiB, 22.14% gc time)\n",
      " 79.830830 seconds (466.42 M allocations: 89.161 GiB, 23.04% gc time)\n",
      " 39.386551 seconds (227.48 M allocations: 42.610 GiB, 20.53% gc time)\n",
      " 34.530613 seconds (179.21 M allocations: 34.133 GiB, 21.20% gc time)\n",
      "  6.321461 seconds (30.90 M allocations: 5.195 GiB, 11.51% gc time)\n",
      "115.303534 seconds (616.39 M allocations: 116.031 GiB, 19.42% gc time)\n",
      " 62.473194 seconds (261.65 M allocations: 46.533 GiB, 12.96% gc time)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"data-frame\"><p>8 rows × 2 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>dataset</th><th>rmse</th></tr><tr><th></th><th title=\"String\">String</th><th title=\"Any\">Any</th></tr></thead><tbody><tr><th>1</th><td>concrete</td><td>3.95255</td></tr><tr><th>2</th><td>concrete</td><td>3.61847</td></tr><tr><th>3</th><td>concrete</td><td>4.97134</td></tr><tr><th>4</th><td>concrete</td><td>4.36299</td></tr><tr><th>5</th><td>concrete</td><td>3.01248</td></tr><tr><th>6</th><td>concrete</td><td>5.22059</td></tr><tr><th>7</th><td>concrete</td><td>4.97357</td></tr><tr><th>8</th><td>concrete</td><td>3.3533</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& dataset & rmse\\\\\n",
       "\t\\hline\n",
       "\t& String & Any\\\\\n",
       "\t\\hline\n",
       "\t1 & concrete & 3.95255 \\\\\n",
       "\t2 & concrete & 3.61847 \\\\\n",
       "\t3 & concrete & 4.97134 \\\\\n",
       "\t4 & concrete & 4.36299 \\\\\n",
       "\t5 & concrete & 3.01248 \\\\\n",
       "\t6 & concrete & 5.22059 \\\\\n",
       "\t7 & concrete & 4.97357 \\\\\n",
       "\t8 & concrete & 3.3533 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m8×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m dataset  \u001b[0m\u001b[1m rmse    \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m String   \u001b[0m\u001b[90m Any     \u001b[0m\n",
       "─────┼───────────────────\n",
       "   1 │ concrete  3.95255\n",
       "   2 │ concrete  3.61847\n",
       "   3 │ concrete  4.97134\n",
       "   4 │ concrete  4.36299\n",
       "   5 │ concrete  3.01248\n",
       "   6 │ concrete  5.22059\n",
       "   7 │ concrete  4.97357\n",
       "   8 │ concrete  3.3533"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import SelectivelyAdaptiveLasso as SAL\n",
    "result_path = \"/Users/aschuler/Desktop/SAL-results/\"\n",
    "\n",
    "λs = ℯ .^range(-14, -5, length=10)\n",
    "\n",
    "# loop over datasets\n",
    "# change λ to denser range?\n",
    "\n",
    "dataset_name = \"concrete\"\n",
    "X,Y = get_data(dataset_name)\n",
    "test_rmses = []\n",
    "\n",
    "Threads.@threads for run in 1:8\n",
    "    (X_tr_val, X_te), (Y_tr_val, Y_te) = train_test_split(X, Y, test_frac=0.1)\n",
    "    (X_tr, X_val), (Y_tr, Y_val) = train_test_split(X_tr_val, Y_tr_val, test_frac=0.2);\n",
    "    n,p = size(X_tr)\n",
    "    metrics = []\n",
    "    \n",
    "    for λ in λs\n",
    "        sal_spec = SAL.SALSpec(\n",
    "            max_iter=10000,\n",
    "            λ = λ, \n",
    "            bases_per_iter=1,\n",
    "            subsample_pct = 0.5,\n",
    "            feat_pct = sqrt(p)/p,\n",
    "            tol = 1e-2,\n",
    "        )\n",
    "        stats = @timed sal_fit, (mse_tr, mse_val) = SAL.fit(\n",
    "            sal_spec, \n",
    "            X_tr, Y_tr, \n",
    "            X_val=X_val, Y_val=Y_val,\n",
    "            # verbose=true, print_iter=100,\n",
    "        )\n",
    "        mse, iter = findmin(mse_val)\n",
    "        run_metrics = Dict(\n",
    "            :mse => mse,\n",
    "            :best_iter => iter,\n",
    "            :time => stats.time,\n",
    "            :spec => sal_spec,\n",
    "        )\n",
    "        # Core.println(run_metrics)\n",
    "        push!(metrics, run_metrics)\n",
    "    end\n",
    "\n",
    "    min_mse, run_id = findmin([run[:mse] for run in metrics])\n",
    "    params = metrics[run_id]\n",
    "    new_spec = deepcopy(params[:spec])\n",
    "    new_spec.max_iter = params[:best_iter]\n",
    "\n",
    "    @time sal_fit, (mse_tr_val, mse_te) = SAL.fit(\n",
    "        new_spec, \n",
    "        X_tr_val, Y_tr_val, \n",
    "        X_val=X_te, Y_val=Y_te,\n",
    "        # verbose=true, print_iter=100,\n",
    "    )\n",
    "    test_rmse = sqrt(mse_te[end])\n",
    "    push!(test_rmses, test_rmse)\n",
    "end\n",
    "result_df = DataFrame(:dataset=>dataset_name, :rmse=>test_rmses)\n",
    "CSV.write(result_path*dataset_name*\".csv\", result_df)\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98dbee1-c5c7-4d16-8eb8-9491ca7606af",
   "metadata": {},
   "source": [
    "## for Year MSD Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d3efca5-4212-42b6-8db6-e58fe74a8aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = \"yearmsd\"\n",
    "X,Y = get_data(dataset_name)\n",
    "Y = Y .- mean(Y)\n",
    "idx_tr = 1:463715\n",
    "X_tr_val, X_te = X[idx_tr, :], X[Not(idx_tr), :]\n",
    "Y_tr_val, Y_te = Y[idx_tr], Y[Not(idx_tr)]\n",
    "(X_tr, X_val), (Y_tr, Y_val) = train_test_split(X_tr_val, Y_tr_val, test_frac=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1816f7ee-5c5e-4d55-885f-a6a8ceb1a077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10.041811256140306, 10.043134471693634, 11, 11)\n",
      "(9.907521520329254, 9.935300890648499, 21, 21)\n",
      "(9.81912463415585, 9.866805149724138, 31, 31)\n",
      "(9.764408853024333, 9.81358860582111, 38, 38)\n",
      "(9.69750878796522, 9.752903757375783, 47, 47)\n",
      "(9.655291777285665, 9.717036435319107, 55, 55)\n",
      "(9.63229581731181, 9.6984763050341, 63, 63)\n",
      "(9.604891261206282, 9.67702783169259, 67, 67)\n",
      "(9.599109428014135, 9.671118612974839, 68, 68)\n",
      "(9.593858048412685, 9.665815774040764, 71, 71)\n",
      "(9.59047861150177, 9.662151582316937, 73, 73)\n",
      "(9.581505857973516, 9.65407498424761, 76, 76)\n",
      "(9.574452875269758, 9.647951307104139, 79, 79)\n",
      "(9.569399248656401, 9.643360066467082, 81, 81)\n",
      "(9.555991611574209, 9.631164487691006, 84, 84)\n",
      "(9.551058202413904, 9.625761705644386, 88, 88)\n",
      "(9.544609476279277, 9.620234026605383, 90, 90)\n",
      "(9.538991640932382, 9.61542845597215, 93, 93)\n",
      "(9.534851249185936, 9.611603468600459, 94, 94)\n",
      "(9.531798211699334, 9.608493445174153, 96, 96)\n",
      "(9.529771020812868, 9.606014576719659, 98, 98)\n",
      "(9.527139904903354, 9.603136379311483, 98, 98)\n",
      "(9.524625716077406, 9.600455486330272, 100, 100)\n",
      "(9.521402448930118, 9.597071100750426, 102, 102)\n",
      "(9.510610487206671, 9.587141027638808, 106, 106)\n",
      "(9.505652557175601, 9.582270905085105, 106, 106)\n",
      "(9.502527207598765, 9.579644667582672, 109, 109)\n",
      "(9.500299861701112, 9.577538257270573, 110, 110)\n",
      "(9.496806672979892, 9.574977202085387, 112, 112)\n",
      "(9.495269994997637, 9.57277768845135, 111, 111)\n",
      "(9.494006093177672, 9.5711882971834, 113, 113)\n",
      "(9.49218557550187, 9.569453645982712, 112, 112)\n",
      "(9.48847934240765, 9.566256433773194, 114, 114)\n",
      "(9.482927813229903, 9.560703791379524, 115, 115)\n",
      "(9.480286786976713, 9.557867788680117, 115, 115)\n",
      "(9.477983689813232, 9.556469175627045, 117, 117)\n",
      "(9.474463404683922, 9.554691456349643, 119, 119)\n",
      "(9.470510355640792, 9.553295707918421, 121, 121)\n",
      "(9.468360858612376, 9.552536971835616, 122, 122)\n",
      "(9.467316686928601, 9.552077163406864, 122, 122)\n",
      "(9.466614476660835, 9.551769638859437, 123, 123)\n",
      "(9.465815920500773, 9.551158113110993, 124, 124)\n",
      "(9.462924974612092, 9.548097436163204, 127, 127)\n",
      "(9.460284804249065, 9.54526840044077, 129, 129)\n",
      "(9.457684166714197, 9.542452743457593, 129, 129)\n",
      "(9.45683385186753, 9.541264852164346, 129, 129)\n",
      "(9.456276308384398, 9.540455727312706, 132, 132)\n",
      "(9.454862897171964, 9.53893275540166, 133, 133)\n",
      "(9.452493055843409, 9.536518270271177, 134, 134)\n",
      "(9.450677914337557, 9.532846077405482, 135, 135)\n",
      "(9.450581578201914, 9.531459824755355, 135, 135)\n",
      "(9.449279308557138, 9.529633161483636, 136, 136)\n",
      "(9.445083824326783, 9.524548012182622, 140, 140)\n",
      "(9.438259507712841, 9.5180778237755, 143, 143)\n",
      "(9.435220093391571, 9.515618003288107, 142, 142)\n",
      "(9.432765791265458, 9.513056327009703, 141, 141)\n",
      "(9.431943925337736, 9.511897640793366, 140, 140)\n",
      "(9.430327557304306, 9.510604285411656, 141, 141)\n",
      "(9.429291117389518, 9.509705895998144, 141, 141)\n",
      "(9.428603706936677, 9.509071136654589, 141, 141)\n",
      "(9.428304226904787, 9.508755419952056, 141, 141)\n",
      "(9.427182677397463, 9.508089244948675, 143, 143)\n",
      "(9.425587485960412, 9.506418648554533, 144, 144)\n",
      "(9.425535595678806, 9.506029076386422, 146, 146)\n",
      "(9.424964714879941, 9.50541953016475, 147, 147)\n",
      "(9.4244503524826, 9.504940534442236, 147, 147)\n",
      "(9.42415868337859, 9.50467023385717, 145, 145)\n",
      "(9.42398293632618, 9.504509149075481, 145, 145)\n",
      "(9.42387334682033, 9.504408881537156, 145, 145)\n",
      "(9.423802506608254, 9.504343813046757, 145, 145)\n",
      "(9.423560441448702, 9.504163800569877, 147, 147)\n",
      "(9.423367131023511, 9.50402392199275, 147, 147)\n",
      "(9.422784339922359, 9.503939840555747, 148, 148)\n",
      "(9.42118014912033, 9.502402258494861, 148, 148)\n",
      "(9.417758066742214, 9.49959866638744, 151, 151)\n",
      "(9.416370604812023, 9.499234073571317, 149, 149)\n",
      "(9.416347256994024, 9.499803395511858, 149, 149)\n",
      "(9.416531564793658, 9.500363861591282, 149, 149)\n",
      "(9.416523956045506, 9.50058759027621, 149, 149)\n",
      "(9.41615294455563, 9.50039285291942, 149, 149)\n",
      "(9.415689743118538, 9.500068169611732, 149, 149)\n",
      "(9.415344283682021, 9.499789087829747, 149, 149)\n",
      "(9.415258696327003, 9.499719803161033, 150, 150)\n",
      "(9.415042776737256, 9.499620235043473, 152, 152)\n"
     ]
    },
    {
     "ename": "LoadError",
     "evalue": "InterruptException:",
     "output_type": "error",
     "traceback": [
      "InterruptException:",
      "",
      "Stacktrace:",
      "  [1] Array",
      "    @ ./boot.jl:448 [inlined]",
      "  [2] similar",
      "    @ ./array.jl:357 [inlined]",
      "  [3] getindex",
      "    @ ./array.jl:813 [inlined]",
      "  [4] best_split_sorted(I::Vector{Int64}, X::Vector{Float64}, Y::Vector{Float64}, Y2::Vector{Float64}, off_limits::Set{Float64})",
      "    @ SelectivelyAdaptiveLasso ~/Desktop/SelectivelyAdaptiveLasso/src/bases.jl:113",
      "  [5] interact_basis(I::Vector{Int64}, X::Vector{SelectivelyAdaptiveLasso.FeatureVector}, Y::Vector{Float64}, Y2::Vector{Float64}, features::Vector{Int64}, off_limits::Set{Tuple{Int64, Float64}})",
      "    @ SelectivelyAdaptiveLasso ~/Desktop/SelectivelyAdaptiveLasso/src/bases.jl:131",
      "  [6] basis_search(X::Vector{SelectivelyAdaptiveLasso.FeatureVector}, Y::Vector{Float64}; subsample_n::Int64, feat_n::Int64, off_limits::Base.KeySet{Set{Tuple{Int64, Float64}}, Dict{Set{Tuple{Int64, Float64}}, SelectivelyAdaptiveLasso.BasisVector}})",
      "    @ SelectivelyAdaptiveLasso ~/Desktop/SelectivelyAdaptiveLasso/src/bases.jl:174",
      "  [7] fit(sal::SelectivelyAdaptiveLasso.SALSpec, X::Matrix{Float64}, Y::Vector{Float64}; X_val::Matrix{Float64}, Y_val::Vector{Float64}, verbose::Bool, print_iter::Int64)",
      "    @ SelectivelyAdaptiveLasso ~/Desktop/SelectivelyAdaptiveLasso/src/SelectivelyAdaptiveLasso.jl:100",
      "  [8] top-level scope",
      "    @ ./timing.jl:368 [inlined]",
      "  [9] top-level scope",
      "    @ ./In[8]:0",
      " [10] eval",
      "    @ ./boot.jl:360 [inlined]",
      " [11] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
      "    @ Base ./loading.jl:1116"
     ]
    }
   ],
   "source": [
    "import SelectivelyAdaptiveLasso as SAL\n",
    "\n",
    "_,p = size(X)\n",
    "\n",
    "sal_spec = SAL.SALSpec(\n",
    "    max_iter=10000,\n",
    "    λ = 0.01, # got down to 9.420 with 0.0001 @~1340 bases\n",
    "    bases_per_iter=1,\n",
    "    subsample_pct = 0.1,\n",
    "    feat_pct = 0.5,\n",
    "    tol = 1e-2,\n",
    ")\n",
    "stats = @timed sal_fit, (mse_tr, mse_val) = SAL.fit(\n",
    "    sal_spec, \n",
    "    X_tr_val, Y_tr_val, \n",
    "    X_val=X_te, Y_val=Y_te,\n",
    "    verbose=true, print_iter=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37dd2db4-c0f9-4ac4-bb9c-7b62ea7a88bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import SelectivelyAdaptiveLasso as SAL\n",
    "result_path = \"/Users/aschuler/Desktop/SAL-results/\"\n",
    "\n",
    "λs = ℯ .^range(-14, -5, length=10)\n",
    "\n",
    "test_rmses = []\n",
    "\n",
    "n,p = size(X_tr)\n",
    "metrics = []\n",
    "\n",
    "Threads.@threads for λ in λs\n",
    "    sal_spec = SAL.SALSpec(\n",
    "        max_iter=200,\n",
    "        λ = λ, \n",
    "        bases_per_iter=100,\n",
    "        subsample_pct = 0.5,\n",
    "        feat_pct = sqrt(p)/p,\n",
    "        tol = 1e-2,\n",
    "    )\n",
    "    stats = @timed sal_fit, (mse_tr, mse_val) = SAL.fit(\n",
    "        sal_spec, \n",
    "        X_tr, Y_tr, \n",
    "        X_val=X_val, Y_val=Y_val,\n",
    "        # verbose=true, print_iter=100,\n",
    "    )\n",
    "    mse, iter = findmin(mse_val)\n",
    "    run_metrics = Dict(\n",
    "        :mse => mse,\n",
    "        :best_iter => iter,\n",
    "        :time => stats.time,\n",
    "        :spec => sal_spec,\n",
    "    )\n",
    "    # Core.println(run_metrics)\n",
    "    push!(metrics, run_metrics)\n",
    "end\n",
    "\n",
    "min_mse, run_id = findmin([run[:mse] for run in metrics])\n",
    "params = metrics[run_id]\n",
    "new_spec = deepcopy(params[:spec])\n",
    "new_spec.max_iter = params[:best_iter]\n",
    "\n",
    "@time sal_fit, (mse_tr_val, mse_te) = SAL.fit(\n",
    "    new_spec, \n",
    "    X_tr_val, Y_tr_val, \n",
    "    X_val=X_te, Y_val=Y_te,\n",
    "    # verbose=true, print_iter=100,\n",
    ")\n",
    "test_rmse = sqrt(mse_te[end])\n",
    "push!(test_rmses, test_rmse)\n",
    "\n",
    "result_df = DataFrame(:dataset=>dataset_name, :rmse=>test_rmses)\n",
    "CSV.write(result_path*dataset_name*\".csv\", result_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8c32c7-e280-4d39-a3d5-43ab23b22c2a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a596366-3492-4994-9afa-e4d27e347c98",
   "metadata": {},
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stroke-width=\"1.5\" opacity=\"1\"/></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">SAL</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"/></g><g transform=\"translate(0,13)\"><path class=\"background\" aria-hidden=\"true\" d=\"M0,0h58v11h-58Z\" pointer-events=\"none\" opacity=\"1\"/><g><g class=\"mark-symbol role-legend-symbol\" pointer-events=\"none\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" stroke=\"#f58518\" stroke-width=\"1.5\" opacity=\"1\"/></g><g class=\"mark-text role-legend-label\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(16,9)\" font-family=\"sans-serif\" font-size=\"10px\" fill=\"#000\" opacity=\"1\">XGBoost</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"/></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"/></g></g><g class=\"mark-text role-legend-title\" pointer-events=\"none\"><text text-anchor=\"start\" transform=\"translate(0,9)\" font-family=\"sans-serif\" font-size=\"11px\" font-weight=\"bold\" fill=\"#000\" opacity=\"1\">learner</text></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" pointer-events=\"none\" display=\"none\"/></g></g></g><path class=\"foreground\" aria-hidden=\"true\" d=\"\" display=\"none\"/></g></g></g></svg>\n"
      ],
      "text/plain": [
       "@vlplot(\n",
       "    width=400,\n",
       "    height=200,\n",
       "    mark=\"line\",\n",
       "    encoding={\n",
       "        x={\n",
       "            field=\"n_iter\"\n",
       "        },\n",
       "        y={\n",
       "            field=\"loss\"\n",
       "        },\n",
       "        color={\n",
       "            field=\"learner\"\n",
       "        }\n",
       "    },\n",
       "    data={\n",
       "        values=...\n",
       "    }\n",
       ")"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = vcat(df_xgb, df_sal);\n",
    "df |>\n",
    "@vlplot(\n",
    "    :line,\n",
    "    x=:n_iter,\n",
    "    y=:loss,\n",
    "    color=:learner,\n",
    "    width=400,\n",
    "    height=200\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca93938e-0ecf-44fe-983f-95d7060797c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Distributions as Dist\n",
    "# import LinearAlgebra: diagm\n",
    "\n",
    "# n, p = 3000, 10\n",
    "\n",
    "# X_dist = Dist.reshape(\n",
    "#     Dist.MvNormal(zeros(p), 0.1ones(p,p) + 0.9diagm(ones(p))), \n",
    "#     (1,p)\n",
    "# )\n",
    "# ϵ_dist = Dist.Normal(0,1)\n",
    "\n",
    "# X = vcat(rand(X_dist, n)...)\n",
    "# ϵ = rand(ϵ_dist, n)\n",
    "\n",
    "# Y = 5X[:,1] + X[:,2].^2 - 3X[:,3].*X[:,4] + ϵ\n",
    "# Y = Y .- mean(Y)\n",
    "\n",
    "# n_val = Int(round(n/2))\n",
    "# X_val, Y_val = X[1:n_val,:], Y[1:n_val]\n",
    "# X, Y = X[(n_val+1):end,:], Y[(n_val+1):end];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4621926a-deac-4195-ab22-d06a9925030d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.6.3"
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