books_sites/genetic/genetic-programming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://qiita.com/overlap/items/e7f1077ef8239f454602"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import operator, math, random, time\n",
    "import numpy as np\n",
    "\n",
    "from deap import algorithms, base, creator, tools, gp\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, log_loss\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# サンプルデータの生成\n",
    "X, y = make_classification(n_samples=10000, n_features=10, n_informative=5, n_redundant=0, n_repeated=0,\n",
    "                           n_clusters_per_class=8, random_state=123)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "X_train2, X_valid, y_train2, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's auc: 0.743222\n",
      "Training until validation scores don't improve for 10 rounds.\n",
      "[2]\tvalid_0's auc: 0.774293\n",
      "[3]\tvalid_0's auc: 0.786269\n",
      "[4]\tvalid_0's auc: 0.796968\n",
      "[5]\tvalid_0's auc: 0.798784\n",
      "[6]\tvalid_0's auc: 0.802183\n",
      "[7]\tvalid_0's auc: 0.802674\n",
      "[8]\tvalid_0's auc: 0.805887\n",
      "[9]\tvalid_0's auc: 0.810216\n",
      "[10]\tvalid_0's auc: 0.808829\n",
      "[11]\tvalid_0's auc: 0.812117\n",
      "[12]\tvalid_0's auc: 0.812388\n",
      "[13]\tvalid_0's auc: 0.813328\n",
      "[14]\tvalid_0's auc: 0.815966\n",
      "[15]\tvalid_0's auc: 0.815584\n",
      "[16]\tvalid_0's auc: 0.816796\n",
      "[17]\tvalid_0's auc: 0.819553\n",
      "[18]\tvalid_0's auc: 0.819432\n",
      "[19]\tvalid_0's auc: 0.820362\n",
      "[20]\tvalid_0's auc: 0.822502\n",
      "[21]\tvalid_0's auc: 0.823933\n",
      "[22]\tvalid_0's auc: 0.825941\n",
      "[23]\tvalid_0's auc: 0.826346\n",
      "[24]\tvalid_0's auc: 0.828636\n",
      "[25]\tvalid_0's auc: 0.829256\n",
      "[26]\tvalid_0's auc: 0.829943\n",
      "[27]\tvalid_0's auc: 0.830962\n",
      "[28]\tvalid_0's auc: 0.831575\n",
      "[29]\tvalid_0's auc: 0.83256\n",
      "[30]\tvalid_0's auc: 0.834183\n",
      "[31]\tvalid_0's auc: 0.835503\n",
      "[32]\tvalid_0's auc: 0.836211\n",
      "[33]\tvalid_0's auc: 0.836906\n",
      "[34]\tvalid_0's auc: 0.837768\n",
      "[35]\tvalid_0's auc: 0.838464\n",
      "[36]\tvalid_0's auc: 0.8391\n",
      "[37]\tvalid_0's auc: 0.839569\n",
      "[38]\tvalid_0's auc: 0.840365\n",
      "[39]\tvalid_0's auc: 0.840882\n",
      "[40]\tvalid_0's auc: 0.841443\n",
      "[41]\tvalid_0's auc: 0.841642\n",
      "[42]\tvalid_0's auc: 0.842293\n",
      "[43]\tvalid_0's auc: 0.842808\n",
      "[44]\tvalid_0's auc: 0.844074\n",
      "[45]\tvalid_0's auc: 0.844198\n",
      "[46]\tvalid_0's auc: 0.844253\n",
      "[47]\tvalid_0's auc: 0.844709\n",
      "[48]\tvalid_0's auc: 0.845056\n",
      "[49]\tvalid_0's auc: 0.845164\n",
      "[50]\tvalid_0's auc: 0.846189\n",
      "[51]\tvalid_0's auc: 0.846457\n",
      "[52]\tvalid_0's auc: 0.847409\n",
      "[53]\tvalid_0's auc: 0.847525\n",
      "[54]\tvalid_0's auc: 0.847345\n",
      "[55]\tvalid_0's auc: 0.847442\n",
      "[56]\tvalid_0's auc: 0.848219\n",
      "[57]\tvalid_0's auc: 0.848075\n",
      "[58]\tvalid_0's auc: 0.84803\n",
      "[59]\tvalid_0's auc: 0.848765\n",
      "[60]\tvalid_0's auc: 0.84845\n",
      "[61]\tvalid_0's auc: 0.848434\n",
      "[62]\tvalid_0's auc: 0.8484\n",
      "[63]\tvalid_0's auc: 0.848242\n",
      "[64]\tvalid_0's auc: 0.848037\n",
      "[65]\tvalid_0's auc: 0.848256\n",
      "[66]\tvalid_0's auc: 0.848176\n",
      "[67]\tvalid_0's auc: 0.848564\n",
      "[68]\tvalid_0's auc: 0.848878\n",
      "[69]\tvalid_0's auc: 0.848643\n",
      "[70]\tvalid_0's auc: 0.848679\n",
      "[71]\tvalid_0's auc: 0.848851\n",
      "[72]\tvalid_0's auc: 0.848966\n",
      "[73]\tvalid_0's auc: 0.849241\n",
      "[74]\tvalid_0's auc: 0.849333\n",
      "[75]\tvalid_0's auc: 0.849436\n",
      "[76]\tvalid_0's auc: 0.849368\n",
      "[77]\tvalid_0's auc: 0.849582\n",
      "[78]\tvalid_0's auc: 0.849322\n",
      "[79]\tvalid_0's auc: 0.849762\n",
      "[80]\tvalid_0's auc: 0.849981\n",
      "[81]\tvalid_0's auc: 0.85013\n",
      "[82]\tvalid_0's auc: 0.850722\n",
      "[83]\tvalid_0's auc: 0.850761\n",
      "[84]\tvalid_0's auc: 0.851262\n",
      "[85]\tvalid_0's auc: 0.851254\n",
      "[86]\tvalid_0's auc: 0.851026\n",
      "[87]\tvalid_0's auc: 0.851601\n",
      "[88]\tvalid_0's auc: 0.851721\n",
      "[89]\tvalid_0's auc: 0.851679\n",
      "[90]\tvalid_0's auc: 0.851578\n",
      "[91]\tvalid_0's auc: 0.851559\n",
      "[92]\tvalid_0's auc: 0.851467\n",
      "[93]\tvalid_0's auc: 0.851796\n",
      "[94]\tvalid_0's auc: 0.852392\n",
      "[95]\tvalid_0's auc: 0.852139\n",
      "[96]\tvalid_0's auc: 0.852058\n",
      "[97]\tvalid_0's auc: 0.852125\n",
      "[98]\tvalid_0's auc: 0.851956\n",
      "[99]\tvalid_0's auc: 0.852342\n",
      "[100]\tvalid_0's auc: 0.852528\n",
      "[101]\tvalid_0's auc: 0.852466\n",
      "[102]\tvalid_0's auc: 0.852484\n",
      "[103]\tvalid_0's auc: 0.852339\n",
      "[104]\tvalid_0's auc: 0.852326\n",
      "[105]\tvalid_0's auc: 0.852475\n",
      "[106]\tvalid_0's auc: 0.852994\n",
      "[107]\tvalid_0's auc: 0.853011\n",
      "[108]\tvalid_0's auc: 0.852883\n",
      "[109]\tvalid_0's auc: 0.852735\n",
      "[110]\tvalid_0's auc: 0.852658\n",
      "[111]\tvalid_0's auc: 0.85311\n",
      "[112]\tvalid_0's auc: 0.853057\n",
      "[113]\tvalid_0's auc: 0.853504\n",
      "[114]\tvalid_0's auc: 0.85382\n",
      "[115]\tvalid_0's auc: 0.854024\n",
      "[116]\tvalid_0's auc: 0.854267\n",
      "[117]\tvalid_0's auc: 0.854013\n",
      "[118]\tvalid_0's auc: 0.853917\n",
      "[119]\tvalid_0's auc: 0.853754\n",
      "[120]\tvalid_0's auc: 0.853771\n",
      "[121]\tvalid_0's auc: 0.854457\n",
      "[122]\tvalid_0's auc: 0.854589\n",
      "[123]\tvalid_0's auc: 0.854478\n",
      "[124]\tvalid_0's auc: 0.854692\n",
      "[125]\tvalid_0's auc: 0.854698\n",
      "[126]\tvalid_0's auc: 0.854916\n",
      "[127]\tvalid_0's auc: 0.854981\n",
      "[128]\tvalid_0's auc: 0.855031\n",
      "[129]\tvalid_0's auc: 0.855083\n",
      "[130]\tvalid_0's auc: 0.855041\n",
      "[131]\tvalid_0's auc: 0.855119\n",
      "[132]\tvalid_0's auc: 0.855067\n",
      "[133]\tvalid_0's auc: 0.854903\n",
      "[134]\tvalid_0's auc: 0.854909\n",
      "[135]\tvalid_0's auc: 0.854748\n",
      "[136]\tvalid_0's auc: 0.855022\n",
      "[137]\tvalid_0's auc: 0.855066\n",
      "[138]\tvalid_0's auc: 0.855031\n",
      "[139]\tvalid_0's auc: 0.855328\n",
      "[140]\tvalid_0's auc: 0.855428\n",
      "[141]\tvalid_0's auc: 0.855247\n",
      "[142]\tvalid_0's auc: 0.855222\n",
      "[143]\tvalid_0's auc: 0.855355\n",
      "[144]\tvalid_0's auc: 0.855255\n",
      "[145]\tvalid_0's auc: 0.855694\n",
      "[146]\tvalid_0's auc: 0.855657\n",
      "[147]\tvalid_0's auc: 0.855611\n",
      "[148]\tvalid_0's auc: 0.855525\n",
      "[149]\tvalid_0's auc: 0.855352\n",
      "[150]\tvalid_0's auc: 0.855711\n",
      "[151]\tvalid_0's auc: 0.855816\n",
      "[152]\tvalid_0's auc: 0.855815\n",
      "[153]\tvalid_0's auc: 0.855794\n",
      "[154]\tvalid_0's auc: 0.856196\n",
      "[155]\tvalid_0's auc: 0.856029\n",
      "[156]\tvalid_0's auc: 0.856019\n",
      "[157]\tvalid_0's auc: 0.856037\n",
      "[158]\tvalid_0's auc: 0.85599\n",
      "[159]\tvalid_0's auc: 0.85603\n",
      "[160]\tvalid_0's auc: 0.85606\n",
      "[161]\tvalid_0's auc: 0.855872\n",
      "[162]\tvalid_0's auc: 0.856004\n",
      "[163]\tvalid_0's auc: 0.856154\n",
      "[164]\tvalid_0's auc: 0.856409\n",
      "[165]\tvalid_0's auc: 0.856288\n",
      "[166]\tvalid_0's auc: 0.856066\n",
      "[167]\tvalid_0's auc: 0.855908\n",
      "[168]\tvalid_0's auc: 0.855897\n",
      "[169]\tvalid_0's auc: 0.85576\n",
      "[170]\tvalid_0's auc: 0.855807\n",
      "[171]\tvalid_0's auc: 0.855688\n",
      "[172]\tvalid_0's auc: 0.855702\n",
      "[173]\tvalid_0's auc: 0.855937\n",
      "[174]\tvalid_0's auc: 0.85584\n",
      "Early stopping, best iteration is:\n",
      "[164]\tvalid_0's auc: 0.856409\n"
     ]
    },
    {
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G26BT5tyf91+/47DLYBulP5iK3qAf/UE/+oOp6A366VJ/rFx41LBL2O6Mjo5mZGRkKPeu\nqhWttYM2NW/gK7KqakZVvb+qvlFVV1fV56vqKVPMXTboegAAAICHj7PPPjuLFi3acHzdddfliCOO\nyMjISA499NB84xvfSJKsWLEir3nNa3LkkUfmuc99bj784Q8Pq2Qegq0Rw16cZElr7ZQkqaqDkhyY\n5J+2wr2nVFXHJTl23NAuSS5prX1kSCUBAAAA07Rq1aq8+tWvzs0335z3vOc9G8ZPPPHEXHzxxXni\nE5+YH/3oRznmmGOyfPnyJMknPvGJPOYxj8kDDzyQF7zgBXnd616X2bNnD+sn8B8w0CCrqg5NslNr\n7aL1Y6215UmWD/K+09Fa+3iSj68/rqq/SjI6tIIAAACAadt7772zbNmyLF68OGvWrNkwvuOOO2bP\nPfdMkuy2227Zd999kyTPec5zNsz52c9+ll122SWPfvSjt27RPGSDXpH1ioytyNpIVc1Icn6SA5K0\nJCe31r7bOzeS5OWttVN7xwuSrEkykmRFklcleUySE5OckGT/JDe31t5YVR9NclWStybZNcmZrbXL\nNlVkVc1O8rjW2vcfyo8FAAAAhuu8887Lsccem7lz52bJkiW58MILN5ybP39+vve97+XGG2/Me9/7\n3syYMWOIlfIfMegga68kP55k/NQk17fWFlTV45N8oaoOm8b1Dkvy4iTPTvKNJK9srS2pqguq6pAk\nM5Mc3Fp7YVXtmmRpkk0GWRkLvv5qqpNVdULGQrPMnr1HTp9z/zQuyfZmr53HHpwIk9EfTEVv0I/+\noB/9wVT0Bv10qT9GR0enNe/GG2/M2rVrMzo6mnXr1uWCCy7InDlz8vSnPz2ttbzmNa/Jn/7pn2aX\nXXbJ6173urzuda/Lvffem3POOSerV6/OwQcfPNgf0iGrV6+e9t99WAYdZN2ZZJ8k35kw/pIkhydJ\na+2nVfWtJHOmcb2PtNZaVd2Q5JbW2pLe+A+S7Juxh9ef27vu3VV1V1XNaK3dO9UFe6vD5rbWzpxq\nTmvtwiQXJmNvLezKGx7Yurr09g+2Pv3BVPQG/egP+tEfTEVv0E+X+mPlG0amN2/lyqxZsyYjIyO5\n/PLLc+CBB+b0009PkhxxxBFZs2ZN7rvvvge9je/uu+/OT37yk6G9pW9bNMy3Fk7XoN9a+LVs/ED1\n9dokx23c5xp3bvxT1+4Y9/nnE66xQ5K01u6YMF7p79gk/2cTcwAAAIBt3O67755ly5Zl7dq1SZJ7\n7rknl19+eZ72tKdldHQ0P/vZz5Ik9957bz71qU/lRS960TDL5T9goDFsa220qk6qqnmttcVJUlUH\nJrkiyfFJFvW2Fh6U5JTe125NclBVVZLHJpmX5KxB1Ne7x+uTHDmI6wMAAABbz8EHH5yjjz46hx9+\neGbOnJmqymmnnZb9998/v/zlL/Pf/tt/y+rVq7PjjjvmbW97W575zGcOu2Q209ZYT/j6JOdW1fwk\nDyS5PcmZSd5UVVdlbCXVW1pra6sqrbWVVXVNkmuT3JPkkgHWdmSSq/ptPQQAAAC2XfPmzdvoeP78\n+Zk/f/6D5j33uc/N17/+9a1UFYNSrU3c5Uc/BxxwQLvpppuGXQbboC7sJWZ49AdT0Rv0oz/oR38w\nFb1BP/qDfobZH1W1orV20KbmdeMJbw9BVc3L2PbE8Rav3+oIAAAAQDc87IOsXmC1eMhlAAAAAPAQ\nDfqthQAAAACwRQiyAAAAAOgEQRYAAAAAnSDIAgAAAKATBFkAAAAAdIIgCwAAAIBOEGQBAAAA0AmC\nLAAAAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAAAJ0gyAIAAACgEwRZAAAAAHSCIAsAAACAThBkAQAA\nANAJgiwAAAAAOkGQBQAAAEAnCLIAAAAA6ARBFgAAAACdIMgCAAAAoBMEWQAAAAB0giALAAAAgE4Q\nZAEAAADQCYIsAAAAADpBkAUAAABAJwiyAAAAAOgEQRYAAAAAnSDIAgAAAKATBFkAAAAAdIIgCwAA\nAIBOEGQBAAAA0AmCLAAAAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAAAJ0gyAIAAACgEwRZAAAAAHRC\ntdaGXUOn7PukJ7cdjjl/2GWwDTplzv15//U7DrsMtlH6g6noDfrRH/SjP5iK3qCfrdkfKxcetck5\nZ599dmbNmpUFCxYkSa677rqccsopuf/++7N27dqcc845Oeyww5Ikf//3f58zzzwzSfKyl70s733v\newdX/HZqdHQ0IyMjQ7l3Va1orR20qXkDX5FVVTOq6v1V9Y2qurqqPl9VT5li7rJB1wMAAAAM16pV\nq3LIIYfkAx/4wEbjJ554Yi666KKMjo7m05/+dN7xjnckSe688878xV/8RZYuXZrly5fn3/7t3/KV\nr3xlGKUzZFtja+HFSW5srR3WWjs8yfuSHLgV7rtJVfXsqrqyqpZU1f+tqicMuyYAAAB4uNt7772z\nbNmynHvuuRuN77jjjtlzzz2TJLvttlv23XffJMmll16a448/PjNnzkySvPOd78wnPvGJrVs024SB\nriesqkOT7NRau2j9WGtteZLlg7zvdFTVY5J8NMmrW2uresfW3wIAAMCQnHfeeTn22GMzd+7cLFmy\nJBdeeGGS5J//+Z9z6KGHbpj3uMc9LrfffvuwymSIBh3cvCJjK7I2UlUzkpyf5IAkLcnJrbXv9s6N\nJHl5a+3U3vGCJGuSjCRZkeRVSR6T5MQkJyTZP8nNrbU3VtVHk1yV5K1Jdk1yZmvtsilqW5DkL1tr\nq5KktXbPVD+iqk7o3SuzZ++R0+fcP/2/ANuNvXYe228Ok9EfTEVv0I/+oB/9wVT0Bv1szf4YHR3d\n5Jwbb7wxa9euzejoaNatW5cLLrggc+bMydOf/vS01vKa17wmf/qnf5of//jHWbFiRX71q19t+O49\n99wzrXswfatXr97m/6aDDrL2SvLjScZPTXJ9a21BVT0+yReq6rBpXO+wJC9O8uwk30jyytbakqq6\noKoOSTIzycGttRdW1a5JliaZKsh6VpLlVfU3SX4zyQ1J/ri19m8TJ7bWLkxyYTL2sHcPTmQyHqpJ\nP/qDqegN+tEf9KM/mIreoJ+t+rD3N4xses7KlVmzZk1GRkZy+eWX58ADD8zpp5+eJDniiCOyZs2a\n3HfffZk7d24e85jHbHgQ+W233ZanPOUpQ3sw+cPVMB/2Pl2DfkbWnUn2mWT8JUkWJUlr7adJvpVk\nzjSu95E29prFG5Lc0lpb0hv/QZJ9M/Z7zu1d9+4kd/VWf03mUUnmJTmp9+yuf07yrmnUAAAAAGxh\nu+++e5YtW5a1a9cmGVtxdfnll+dpT3taXvva1+aCCy7YcO7888/PcccdN8xyGZJBx7Bfy9g2vy9N\nGG+THLdxn2vcudlJftL7fMe48Z9PuMYOSdJau2PCeGVy/5zks6219ZtqL0myeIq5AAAAwAAdfPDB\nOfroo3P44Ydn5syZqaqcdtpp2X///ZMkb3nLW/L85z8/O+20U1760pfmyCOPHHLFDMNAg6zW2mhV\nnVRV81pri5Okqg5MckWS45Ms6m0tPCjJKb2v3ZrkoKqqJI/N2KqpswZQ3oVJPlBVv99a+3WSo5Nc\nO4D7AAAAAJOYN2/eRsfz58/P/PnzJ517zDHH5JhjjtkKVbEt2xobY1+f5Nyqmp/kgSS3JzkzyZuq\n6qqMraR6S2ttbVWltbayqq7JWKh0T8ZWSm1xrbUfVNUFSa6oqvsytkLr7Zv63s47PSI3LTxqECXR\ncaOjo9PaA872SX8wFb1BP/qDfvQHU9Eb9KM/6LqBB1m91U6TBUQnTTL3kN5/vjvJuyecXjxu3vq3\nGK4//mDv499MuN5I+ui90XCqh8EDAAAAsA152L/KoqrmZWx74niL1291BAAAAKAbHvZBVi+wWjzk\nMgAAAAB4iHYYdgEAAAAAMB2CLAAAAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAAAJ0gyAIAAACgEwRZ\nAAAAAHSCIAsAAACAThBkAQAAANAJgiwAAAAAOkGQBQAAAEAnCLIAAAAA6ARBFgAAAACdIMgCAAAA\noBMEWQAAAAB0giALAAAAgE4QZAEAAADQCYIsAAAAADpBkAUAAABAJwiyAAAAAOgEQRYAAAAAnSDI\nAgAAAKATBFkAAAAAdIIgCwAAAIBOEGQBAAAA0AmCLAAAAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAA\nAJ0gyAIAAACgEwRZAAAAAHSCIAsAAACAThBkAQAAANAJgiwAAAAAOkGQBQAAAEAnCLIAAAAA6IRq\nrQ27hk7Z90lPbjscc/6wy2AbdMqc+/P+63ccdhlso/QHU9Eb9KM/6Ed/MBW9QT+LX/4bGRkZ6Tvn\n7LPPzqxZs7JgwYKsWrUqb3jDGzY6X1W56qqr8nd/93c5++yz8+hHP3rDuc9+9rPZY489BlE6W8Ho\n6Ogm+2NQqmpFa+2gTc0b+P+6VdWMJO9LcnCSdUnuSnJqa+2fJpm7rLV2yKBrAgAAADa2atWqvPrV\nr87NN9+c97znPUmSvffeO6OjoxvmLFu2LJ/73OeSJKtXr87xxx+fBQsWDKNctlNbI6a/OMmS1top\nSVJVByU5MMmDgqytrar+R5I/7B2uSvKm1trqIZYEAAAAQ7H33ntn2bJlWbx4cdasWTPpnPPPPz//\n+3//761cGfy7gT4jq6oOTbJTa+2i9WOtteWttS8M8r7TUVX/OcmRSQ5rrT0/yXVJ5g+3KgAAANg2\n3XLLLdlxxx3zhCc8YcPY17/+9fz+7/9+5s6dmzPOOCPr1q0bYoVsDwb9sPdXZGxF1kaqakZVLaqq\nq6rqyqp65rhzI1W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AAAw4yKqqQ5Ps1Fq7aP1Ya215kuWDvO807Zbkz1trXx12IQAM1y23\n3JLf/u3fzl/+5V/m85//fJLkXe96V4444oj8+Z//eV772tfmec97Xr75zW/mkksuGXK1AACw/Rr0\niqxXZGxF1kZ62w3PT3JAxp6zdXJr7bu9cyNJXt5aO7V3vCDJmiQjSVYkeVWSxyQ5MckJSfZPcnNr\n7Y1V9dEkVyV5a5Jdk5zZWrtsgL8PgIeB1atX54orrsiRRx6ZK6+8MnfccUde9rKX5ctf/nK++MUv\n5rjjjssRRxyRJz7xiXnzm9+cT3/603nEIx4x7LIBAGC7M+gga68kP55k/NQk17fWFlTV45N8oaoO\nm8b1Dkvy4iTPTvKNJK9srS2pqguq6pAkM5Mc3Fp7YVXtmmRpkn5B1ht6Qdljk3ymtfaRySZV1QkZ\nC80ye/YeOX3O/dMole3NXjuPPdAbJqM/tg2jo6OTjq9evTqPe9zjsscee2yYs99+++XP/uzPstde\ne2XmzJlZunRpZs2aldWrV+eTn/xknvzkJ2+RmlavXj1lXaA/6Ed/MBW9QT/6g3660B+DDrLuTLJP\nku9MGH9JksOTpLX206r6VpI507jeR1prrapuSHJLa21Jb/wHSfZNskOSc3vXvbuq7qqqGa21eye5\n1mVJdm2tXd1bIfapqrqptXbFxImttQuTXJgk+z7pye3912+NR4vRNafMuT96g6noj23DyjeMTHnu\nM5/5TNasWZOXv/zlWb16dW6//fb83u/9XpYuXZq5c+dmhx12yG233ZZVq1blD/7gDzY8N+uhGh0d\nzcjI1HWxfdMf9KM/mIreoB/9QT9d6I9B/1vV1zK2zW/i653aJMdt3Ocad252kp/0Pt8xbvznE66x\nQ5K01u6YMF6ZxPqtjL3P91bVZzMWpj0oyALg4e9DH/pQTjzxxCxcuDBJctZZZ+VFL3pRbr311jz/\n+c/PzJkzM2PGjCxatGiLhVgAAMDmGWiQ1VobraqTqmpea21xklTVgRkLi45Psqi3tfCgJKf0vnZr\nkoOqqjK25W9ekrO2dG1VNSdjq7pW91ZkvSHJe7b0fQDoht122y2f+tSnHjR++umn5/TTTx9CRQAA\nwERbY5/L65OcW1XzkzyQ5PYkZyZ5U1VdlbGVVG9pra2tqrTWVlbVNUmuTXJPkkG9HurRST5TVTsl\neWSSD7fWVgzoXgAAAAA8RAMPslprv07y9klOnTTJ3EN6//nuJO+ecHrxuHnr32K4/viDvY9/M+F6\nI5lCa+3/Zeytiptl550ekZsWHrW5X2M7MDo62vf5O2zf9AcAAMBD97B/8nBVzcvY9sTxFq/f6ggA\nAABANzzsg6xeYLV4yGUAAAAA8BDtMOwCAAAAAGA6BFkAAAAAdIIgCwAAAIBOEGQBAAAA0AmCLAAA\nAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAAAJ0gyAIAAACgEwRZAAAAAHSCIAsAAACAThBkAQAAANAJ\ngiwAAAAAOkGQBQAAAEAnCLIAAAAA6ARBFgAAAACdIMgCAAAAoBMEWQAAAAB0giALAAAAgE4QZAEA\nAADQCYIsAAAAADpBkAUAAABAJwiyAAAAAOgEQRYAAAAAnSDIAgAAAKATBFkAAAAAdIIgCwAAAIBO\nEGQBAAAA0AmCLAAAAAA6QZAFAAAAQCcIsgAAAADoBEEWAAAAAJ0gyAIAAACgEwRZAAAAAHSCIAsA\nAACAThBkAQAAANAJOw67gK759X0PZL9TvzLsMtgGnTLn/szTG0xhc/pj5cKjBlwNAABANwmyADri\ngAMOyG/+5m9uOD7uuONy7LHHJkm+/OUv5yMf+UjuvfferFu3Lh/4wAfynOc8Z1ilAgAADMTAg6yq\nmpHkfUkOTrIuyV1JTm2t/dMkc5e11g4ZdE0AXTRr1qyMjo5Oem7nnXfOF7/4xcyYMSMrV67Mf/2v\n/zVLly7dugUCAAAM2NZYkXVxkiWttVOSpKoOSnJgkgcFWVtTVb0kyduTzEyyW5LzWmufHWZNAP9R\nL37xizd8ftzjHpeqGmI1AAAAgzHQIKuqDk2yU2vtovVjrbXlSZYP8r7T9K9Jjmmt3VtVuyT5h6r6\nu9ZaG3ZhAJO58847c9xxx2XlypWZNWtWzjvvvOy3334bzbn33nvz5je/OaeddtpwigQAABigGmRu\nU1VnJfmH1trfTxifkeT8JAckaUlObq19t6qWJTk1yctba6f25i5IsibJSJIVSV6V5DFJTkxyQpL9\nk9zcWntjVX00yVVJ3ppk1yRnttYum0adT03yF621I6c4f0LvXpk9e4/nnP7BiyabxnZur52T2389\n7CrYVm1Of8zZe9dJx7/+9a/nkEP+//buPtiysr4T/ffHSxDlgrRkENBWESRX7KmMMKRDSXEIMaWi\nQxI18RqNLSAvcyU9CcNcTMpIW0PEMFREiSgotC+jMpUroUwMjBc50EjASMK1xxcIRoziCIIEwogR\nyO/+sXf3PX0459CmOWef1f35VFG917Oes9Zvdz+12Pt7nmet1dlzzz3z1a9+NZdcckn+6I/+aPP+\nu+66Kx/84Afz2te+NqtWrXoyymYJPPTQQ9ljjz0mXQbLlPHBQowP5mNssBDjg4VMcnwcc8wxt3T3\n4U/Ub7GXFu6b5NtztJ+VZGN3n1pV+ye5oqqO2orjHZXk2CQvTrIhyau6+5qq+mBVrc5omeAR3X1M\nVe2V5IYk8wZZVXVlkv2SHJjkLfP16+6Lk1ycJCsPPKjP3+ge+TzeGasejbHBfH6S8XHnb0zN2T41\nNbXF649+9KOb226++eZcccUVufLKK7P33ntvY7Uspenp6S3+bWEm44OFGB/Mx9hgIcYHCxnC+Nhp\nkY9/b5JnzdH+0iQfSJLu/m6Sv0qyNdMHLhwv/ftKkm929zXj9q8lWZnR+zlvfNwHktw3nv01p+4+\nvruPSHJQkt+qqkO36l0BTMCNN964+fV1112XlStXbt5+17velY997GNCLAAAYLu22NNHrs5omd9n\nZrXPXs/YM9o6ycy7FO+T5Dvj1/fMaP/+rGPslCTdfc+s9ie843F3/0NV/UVGSx2/8kT9ASbh8ssv\nz7p16/LII49k//33z0UXXZQk+eEPf5gbb7wxL3/5lquj/+zP/sy0cQAAYLuyqEFWd09X1dqqWtPd\n65NkPOvpcxkt5fvAeGnh4UnOGP/Yt5IcXqNHbq1IsibJf36ya6uqlyf5Qnc/WFV7Jnllktc92ecB\neLJccMEFc7Y/9alPzT33zM7wAQAAtj9LcUOf1yc5r6pOSfJYkruTvDPJCVV1bUYzqU7r7h9XVbr7\nzqq6PsnNSR5M8tFFqquTfKaqdk7ySJJ142WOC9p9151z27nHLVJJDNn09PS89zYC4wMAAGDbLXqQ\n1d0PJ3nrHLvWztF39fjPdUnWzdq9fka/TU8x3LT9nvHLT8063lTm0d1XJblqodoBAAAAWD62+0es\nVdWajJYnzrR+01JHAAAAAIZhuw+yxoHV+gmXAQAAAMA22mnSBQAAAADA1hBkAQAAADAIgiwAAAAA\nBkGQBQAAAMAgCLIAAAAAGARBFgAAAACDIMgCAAAAYBAEWQAAAAAMgiALAAAAgEEQZAEAAAAwCIIs\nAAAAAAZBkAUAAADAIAiyAAAAABgEQRYAAAAAgyDIAgAAAGAQBFkAAAAADIIgCwAAAIBBEGQBAAAA\nMAiCLAAAAAAGQZAFAAAAwCAIsgAAAAAYBEEWAAAAAIMgyAIAAABgEARZAAAAAAyCIAsAAACAQRBk\nAQAAADAIgiwAAAAABkGQBQAAAMAgCLIAAAAAGARBFgAAAACDIMgCAAAAYBAEWQAAAAAMgiALAAAA\ngEEQZAEAAAAwCIIsAAAAAAZBkAUAAADAIOwy6QKG5uFHHstzz/rzSZfBMnTGqkezxthgHutf9rQ5\n2w855JDst99+m7dPPPHEvPGNb0yS3HbbbTn55JPz8MMP59nPfnY+8pGPZI899liSegEAAJajRQ+y\nqmq3JH+Q5Igk/5zkviRndfftc/S9qbtXL3ZNAMvF3nvvnenp6Tn3rV27NpdddlkOPPDAXHnllVm3\nbl3OO++8pS0QAABgGVmKpYUfSfL17j6qu4/OKNQ6dAnOu1Vq5OqqOnXStQBssnHjxqxcuTIHHnhg\nkuT444/PjTfeOOGqAAAAJmtRg6yqOjLJrt19yaa27v5Sd1+xmOf9Ca1N8sNJFwHsmO69996ceOKJ\nOfbYY/Oa17wmd955Z5LkjjvuyCGHHLJF3xUrVuT++++fQJUAAADLw2IvLXxFRjOytjBebnhBkkOS\ndJLf6e5bx/umkrysu88ab5+a5EdJppLckuRXkuyZ5PQkJyc5OMk3uvtNVfX+JNcm+fdJ9kryzu7+\n0/mKq6oXJTkwyZVJnrLtbxfgJ/OOd7wjxx13XFasWJGbbropb37zm3Pttdemu1NVj+s/VxsAAMCO\nYrGDrH2TfHuO9rOSbOzuU6tq/yRXVNVRW3G8o5Icm+TFSTYkeVV3X1NVH6yq1RmFUUd09zFVtVeS\nG5LMGWSNw7R1SX4zyWsXOmlVnZxRaJZ99vnp/P6qR7eiVHY0++4+uuE7zOWhhx6a815Yz372s/Pl\nL3958/bdd9+d6enp/OAHP8j09HRe/OIXb973zW9+M7feeutSlMsSmm9sQGJ8sDDjg/kYGyzE+GAh\nQxgfix1k3ZvkWUn+Zlb7S5McnSTd/d2q+qskq7bieBd2d1fVV5J8s7uvGbd/LcnKjJZKnjc+7gNV\ndV9V7dbd/zTHsf5zkj/s7v/1RDMcuvviJBcnycoDD+rzN3rYI493xqpHY2wwn/Uve1qmpqYe137j\njTfmyCOPTJJcd911edGLXpSpqalMTU3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xNliI8cFCJjk+ntPdP/1EnXZZikq2M7d19+GT\nLoLlp6q+ZGwwH+OD+RgbLMT4YCHGB/MxNliI8cFChjA+LC0EAAAAYBAEWQAAAAAMgiDrJ3fxpAtg\n2TI2WIjxwXyMDRZifLAQ44P5GBssxPhgIct+fLjZOwAAAACDYEYWAAAAAIMgyAIAAABgEHaZdAFD\nUVX7JlmfZEWSB5O8qbu/O9GimIiqOjLJ25LskWS3JOuSfCXJ1Um+P6PrOd39uaWvkEmqqtuS/M8Z\nTR/u7o+N9x2S0Zrz3ZN8O6PryENLXyWTUlU7J7lmVvO+Sd6c5INJ7p/Rfnp3b1yq2pisqnpbkvu7\n+wPj7XmvF1X180nek9EvJG9Nclp3PzqRwll0c4yNVyZ5a0afQXZK8jvdfUtVrY7ryA5n5vioqmdl\ngc+jrh07nlnj44Ak/3VWl+7uY6rqNRl9v/nHGfte293fD9uNub7HdvfVQ/zMIcjaeucnObu7b66q\nw5P8lySvn3BNTMbOSV7f3f9YVXsnuS7Jv0uysbtfN9nSWAbu7+6pefZdkOTN3f13VXV8knckOXPJ\nKmPiuvuxJFObtsdfOv4wyVOS/EV3nzWh0piQ8ReL/zvJ85O8fcauOa8XVbVrkncneUV331dVa5Oc\nkuSPl7h0FtkCY+PhJMd39z9V1XOTfDzJS+I6skOZZ3zsknk+j7p27FjmGh/dfVe2/AyyOsmrx5t7\nJLlkU2DOdmuu77FXZ4CfOSwt3ApV9fQkz+jum5Oku7+UZK9xOzuY7t7Q3Zt+W/EPGX2grAmWxABU\n1aokf9/df5ck3X1lkiMnWxXLwNok75t0EUxOd9/V3aszI9R+guvFy5Jc1d33jbcvSvKaJSyZJTLX\n2Bi3X9Pd/zTe/F4ST27aAc03Phbg2rED2crxsTbJe5eoJJaBub7HDvUzhyBr6zwvyR2z2v5u3M4O\nqqp2ymhm3ocz+hD5/Kr6eFVdW1Xrq2qfyVbIhOxTVR+uqmuq6k/Gvy1PkoOS3Dar7w/Gvw1hB1RV\n/1uSF3X3X46bDq+qy6vquqq6oKqeOsn6mKiFrhdb7OvuHyfZdQlrY5moqt2SfCjJu2Y0u44w3+dR\n1w42q6rnJXm0u789o/mXqurTVXV9VZ09/q7DdmjW99hBfuYwOLdOZe7fdvkN2A6qqv5VRmvMr+/u\ni5Pcm9Hy01O6+5gkV2Y0RZMdz7okZ3b3sRn9D+KycbvrCLOdlNEHiCT5epL3J3lDdx+d5M4kvzeh\nupi8ha4Xc+1zHdnBVNXzk3wyyUXd/dlxs+sIC30ede1gpv+QLb+r3JBRMP7qjJYf7pXkxKUvi8U2\nx/fYQX7mEGRtnTszSiNnev64nR1MVR2YUThx5njqZbr7oe7+VHf/r/H2FRmNEXYw3f2x7v7B+PVN\nGd00MRnN4nzBrO7P6O5/WMr6WB7GN33/d0muSJLu/l53f7q7Hxl3+XiSVZOqj4lb6Hqxxb6q+qkk\nE7/pKkunqn4uo3uWnNjdX9jU7jrCE3wede0gyebb5hw0vl1OkqS77+juz/bIPyf5RFw/tjtzfY/N\nQD9zCLK2wvhL6Q+r6sVJUlX/Osm9voDusM5OckJ3f2dTQ1XtWVWHzth+fZK/nONn2c6Nnway6fXR\nSf4+Sbr71iQvqKrnjPe9LMmGiRTJcvCaJH86vvl7qmr/qlo5Y/9b8vinG7KDeILrxVVJXjVjWfIJ\nSf7b0lfJBL0tyRu7e+bTCV1HeKLPo64dbHJKRk+o26yqDt60DHW87OyEuH5sj87OrO+xQ/3M4amF\nW+8/JPlwVe2R5IGMHpXOjunIJJdXbXF/97VJ1o6fEPJTSb6a5D9OoDYm79er6h0ZrR//bpLTZuxb\nm+TjNRo8d2X0PwN2TCcm+dUZ253kvPFvSXdPcm2ScydRGMvGnNeL7v5RVf1ekquq6tEk/2+S0ydX\nJktpfM+rI5P8xazPIa+M6wjJY0nOmOvzqGsHyeanV748yXmzdu2c5EPja8zuST41Y8YO24+5vsee\nlAF+5qjuZbHEEQAAAAAWZGkhAAAAAIMgyAIAAABgEARZAAAAAAyCIAsAAACAQRBkAQAAADAIu0y6\nAACAIaiqs5P8apIfzGg+t7uvmkxFAAA7HkEWAMDW+0/bGlxV1ZruXv8k1ZOqOjZJdff/82Qdc57z\nPD3JVHf/6WKeBwBgIZYWAgAsrTVP5sG6+5rFDrHGnp7kl5fgPAAA8zIjCwBgG1TVXkn+OMkzk/wo\nyQndfU9V/ZckP5tRAPQ73X19VX06yc9W1XSSS5O8sLvPGh/n1PHPTyX5dpKXJfm/knwvyflJdkvy\n9SSnd/djM86/JslTkqxOckuSX0myZ5LTk5yc5OAk3+juN1XV+iQ3Jfk/kvxUkjO7+4aqelGSP0qy\na5JvJHlrkp9L8qYkL0jyxSQ/n+S549p/KckfzPH+zk7y1SSnJNk7ySXdfVFV7ZvkfUlWJOnx+/rG\nXH9v/8J/BgBgB2FGFgDA1vvDqpqe8d8hGYVMn+juX0zy7iTvHPf9yLjtl5O8K0m6+1eT3NrdU0n+\nfoHzPK+7/22S65N8OMlJ3f0LSf5nktcu8HNHJTk2oyDpmiQf7+6XJPlRVa0e9zmku49O8sok51fV\nT43PccK4rr9M8vZx31cmeXV3/3aS1yW5qrunuvvHc72/sV/OKOhaneQtVbVnkkuSvHvc/9cy+mXq\nfH9vAADzMiMLAGDrPe4eWVX1S0kOqqr/NG764fjPe6rqlCT/OqNZRz+Ji8d//kyS5yT5r1WVjGZl\nLXSPrgu7u6vqK0m+2d3XjNu/lmTl+PWlSdLd91XVLUn+9yQbu/vb4/YPVdWNSf57ks909/fmOdd8\n7+894xljj1XVl5M8L8lTuvuW8fHvT/LFqvqTzP33BgAwL0EWAMC2uWs8k2mzqnpuRrOQfj/JRzOa\nKTVbJ6kZ2/sk+c749T0z+vxFd5+4lbXMXJr3/Vn7Ns3E/8cZbbsneXR8nk21V5J/nuN4mdHnuZn/\n/c3+mcey5fvc5HF/bwAAT8TSQgCAbfPlqnpFklTVblX15ozuHXVjd/9lRkv9njGj/6ZfJH4ryeE1\n8ozMfRP4ryd5QVU9e3z8/apqW2+4fvD4WCszmvH19SQ/U1UHjPefmGR6jp/754zuq5Us/P5mezTJ\nj6vq58bn3buqfj5z/70BACxIkAUAsG3+Y5JfH98E/c8yCqiuTvJvquq6jG7evrGqzhj3v7uqNmQU\nDF2f5OYkl2c0s2kL4yV6JyW5ZHz8C5Pcuo31nlRV/z3JZUlOnHWO65McmeScOX7uu0lWjuu4doH3\nN5cTk5w57v8nSR7O3H9vAAALqu5+4l4AAAze+KmFZ3f3nRMuBQDgX8SMLAAAAAAGwYwsAAAAAAbB\njCwAAAAABkGQBQAAAMAgCLIAAAAAGARBFgAAAACDIMgCAAAAYBAEWQAAAAAMwv8H+yurG9ufUSsA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10812c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# データセットを生成する\n",
    "lgb_train = lgb.Dataset(X_train2, y_train2)\n",
    "lgb_eval = lgb.Dataset(X_valid, y_valid, reference=lgb_train)\n",
    "\n",
    "lgbm_params = {\n",
    "    'learning_rate': 0.1,\n",
    "    'num_leaves': 8,\n",
    "    'boosting_type' : 'gbdt',\n",
    "    'subsample' : 0.8,\n",
    "    'reg_alpha' : 1,\n",
    "    'reg_lambda' : 1,\n",
    "    'objective': 'regression',\n",
    "    'metric': 'auc',\n",
    "    }\n",
    "\n",
    "# 上記のパラメータでモデルを学習する\n",
    "model = lgb.train(lgbm_params, lgb_train,\n",
    "                  # モデルの評価用データを渡す\n",
    "                  valid_sets=lgb_eval,\n",
    "                  # 最大で 1000 ラウンドまで学習する\n",
    "                  num_boost_round=1000,\n",
    "                  # 10 ラウンド経過しても性能が向上しないときは学習を打ち切る\n",
    "                  early_stopping_rounds=10)\n",
    "\n",
    "# 特徴量の重要度をプロットする\n",
    "lgb.plot_importance(model, figsize=(20, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### testでの精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.854653164841\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# テストデータを予測する\n",
    "y_pred = model.predict(X_test, num_iteration=model.best_iteration)\n",
    "\n",
    "# auc を計算する\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Generation by Genetic Programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ベースラインスコアの算出\n",
    "clf = LogisticRegression(penalty=\"l2\", C=1.0)\n",
    "base_train_auc = np.mean(cross_val_score(clf, X_train, y_train, scoring=\"roc_auc\", cv=5))\n",
    "clf.fit(X_train, y_train)\n",
    "base_test_auc = roc_auc_score(y_test, clf.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 除算関数の定義\n",
    "# 左項 / 右項で右項が0の場合1を代入する\n",
    "def protectedDiv(left, right):\n",
    "    eps = 1.0e-7\n",
    "    tmp = np.zeros(len(left))\n",
    "    tmp[np.abs(right) >= eps] = left[np.abs(right) >= eps] / right[np.abs(right) >= eps]\n",
    "    tmp[np.abs(right) < eps] = 1.0\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 乱数シード\n",
    "random.seed(123)\n",
    "\n",
    "# 適合度を最大化するような木構造を個体として定義\n",
    "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
    "creator.create(\"Individual\", gp.PrimitiveTree, fitness=creator.FitnessMax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 初期値の計算\n",
    "# 学習データの5-fold CVのAUCスコアを評価指標の初期値とする\n",
    "n_features = X_train.shape[1]\n",
    "clf = LogisticRegression(penalty=\"l2\", C=1.0)\n",
    "prev_auc = np.mean(cross_val_score(clf, X_train, y_train, scoring=\"roc_auc\", cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# メインループ\n",
    "# resultsに特徴量数、学習データのAUCスコア（5-fold CV）、テストデータのAUCスコアを保持する\n",
    "# exprsに生成された特徴量の表記を保持する\n",
    "results = []\n",
    "exprs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
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      "  0%|          | 0/100 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   \t      \t                     fitness                      \t              size             \n",
      "   \t      \t--------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax    \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.741286\t0.75243\t0.739359\t0.00093841\t4.52667\t14 \t2  \t2.84182\n",
      "1  \t164   \t0.742463\t0.759495\t0.738583\t0.00261917\t4.43667\t14 \t1  \t2.70666\n",
      "2  \t172   \t0.745261\t0.768907\t0.740169\t0.00479189\t4.73   \t14 \t1  \t3.05458\n",
      "3  \t171   \t0.749827\t0.782676\t0.740549\t0.00735729\t5.78333\t17 \t1  \t3.96271\n",
      "4  \t156   \t0.75783 \t0.782676\t0.740453\t0.0105806 \t10.7967\t18 \t1  \t4.05241\n",
      "5  \t168   \t0.764185\t0.792389\t0.740825\t0.0141751 \t13.09  \t22 \t3  \t2.97689\n",
      "6  \t167   \t0.772111\t0.793657\t0.740836\t0.016202  \t15.1167\t22 \t6  \t2.57612\n",
      "7  \t171   \t0.775656\t0.799636\t0.740179\t0.0174457 \t14.83  \t24 \t4  \t2.81681\n",
      "8  \t157   \t0.782073\t0.801036\t0.73729 \t0.0180503 \t15.55  \t22 \t1  \t2.69459\n",
      "9  \t166   \t0.7822  \t0.801207\t0.739583\t0.0211187 \t16.2   \t24 \t4  \t2.51131\n"
     ]
    },
    {
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     "output_type": "stream",
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      "  1%|          | 1/100 [01:21<2:13:56, 81.18s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t164   \t0.786265\t0.802428\t0.739242\t0.021292  \t16.0267\t21 \t1  \t2.47372\n",
      "11 0.802428302576 0.803795647866 0.797010138382 81.16082692146301\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.802033\t0.814404\t0.761567\t0.00433662\t4.28333\t14 \t2  \t2.69377\n",
      "1  \t164   \t0.803713\t0.814404\t0.764503\t0.00316722\t4.17667\t18 \t1  \t2.61256\n",
      "2  \t164   \t0.806679\t0.81613 \t0.802141\t0.0037591 \t4.33   \t10 \t1  \t1.86577\n",
      "3  \t161   \t0.80984 \t0.81613 \t0.801938\t0.00472126\t4.64333\t11 \t1  \t1.68012\n",
      "4  \t153   \t0.81146 \t0.81613 \t0.802161\t0.00515748\t5.13667\t11 \t3  \t1.38491\n",
      "5  \t152   \t0.81188 \t0.81613 \t0.802048\t0.00516251\t5.62333\t9  \t1  \t1.19225\n",
      "6  \t172   \t0.811213\t0.81613 \t0.801716\t0.00557383\t5.66   \t12 \t1  \t1.32831\n",
      "7  \t157   \t0.811631\t0.81613 \t0.770352\t0.00579245\t5.59333\t11 \t1  \t1.24952\n",
      "8  \t174   \t0.811479\t0.81613 \t0.802029\t0.00534518\t5.46333\t9  \t1  \t1.19805\n",
      "9  \t163   \t0.811848\t0.81613 \t0.802096\t0.00520217\t5.47   \t13 \t1  \t1.23657\n"
     ]
    },
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     "output_type": "stream",
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      "\r",
      "  2%|▏         | 2/100 [02:53<2:18:01, 84.50s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t147   \t0.811569\t0.81613 \t0.80202 \t0.0055067 \t5.51333\t13 \t1  \t1.31776\n",
      "12 0.816129868324 0.817298780873 0.8114929688 92.24004912376404\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.816128\t0.823524\t0.794877\t0.00158977\t4.14667\t13 \t2  \t2.42593\n",
      "1  \t178   \t0.817119\t0.823524\t0.815655\t0.00180078\t3.95   \t13 \t1  \t1.9615 \n",
      "2  \t161   \t0.819406\t0.823525\t0.81579 \t0.00285312\t4.15333\t11 \t1  \t2.09678\n",
      "3  \t161   \t0.820868\t0.823525\t0.815832\t0.00317795\t3.54333\t12 \t1  \t1.74206\n",
      "4  \t165   \t0.821452\t0.823529\t0.815416\t0.00312237\t3.97333\t15 \t1  \t2.32364\n",
      "5  \t149   \t0.821559\t0.829063\t0.815793\t0.00315751\t7.05   \t19 \t1  \t3.62411\n",
      "6  \t167   \t0.821955\t0.829066\t0.815815\t0.00317337\t10.1433\t18 \t1  \t2.69248\n",
      "7  \t161   \t0.823138\t0.829066\t0.815887\t0.00383283\t11.3367\t21 \t2  \t2.93314\n",
      "8  \t166   \t0.825206\t0.829067\t0.815817\t0.005054  \t11.3767\t19 \t1  \t2.6711 \n",
      "9  \t148   \t0.826979\t0.829071\t0.815894\t0.00425297\t12.03  \t20 \t5  \t1.91897\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      "  3%|▎         | 3/100 [04:35<2:25:12, 89.82s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t174   \t0.826045\t0.829499\t0.815601\t0.00521858\t12.1767\t26 \t1  \t3.51835\n",
      "13 0.829499139546 0.830656032717 0.824326109937 102.19790506362915\n",
      "   \t      \t                          fitness                          \t              size             \n",
      "   \t      \t-----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd        \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.829527\t0.835429\t0.828997\t0.000501536\t4.25667\t15 \t2  \t2.75635\n",
      "1  \t175   \t0.829918\t0.835429\t0.829007\t0.000949161\t3.67   \t15 \t1  \t1.94622\n",
      "2  \t169   \t0.830882\t0.839667\t0.828999\t0.00179962 \t3.64333\t9  \t1  \t1.81736\n",
      "3  \t163   \t0.832515\t0.839667\t0.829203\t0.00270011 \t5.36   \t15 \t1  \t2.44071\n",
      "4  \t158   \t0.834084\t0.842093\t0.828785\t0.0036491  \t5.64   \t15 \t1  \t2.51338\n",
      "5  \t172   \t0.836154\t0.842093\t0.82881 \t0.00460951 \t6.01   \t11 \t1  \t1.71753\n",
      "6  \t160   \t0.837306\t0.842498\t0.823182\t0.00462295 \t7.41667\t14 \t1  \t2.43921\n",
      "7  \t162   \t0.838484\t0.842649\t0.829118\t0.00470148 \t9.95667\t15 \t2  \t2.35686\n",
      "8  \t164   \t0.839211\t0.842649\t0.829099\t0.00463014 \t11.06  \t18 \t1  \t2.38532\n",
      "9  \t180   \t0.839412\t0.842883\t0.829007\t0.00469644 \t11.42  \t21 \t1  \t2.59684\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      "  4%|▍         | 4/100 [06:37<2:39:13, 99.51s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t171   \t0.839536\t0.842883\t0.829014\t0.00481196 \t11.94  \t19 \t2  \t2.41586\n",
      "14 0.842882748074 0.843964157486 0.839929087385 122.11120009422302\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.842458\t0.843703\t0.787594\t0.00392329\t4.03667\t15 \t2  \t2.46482\n",
      "1  \t182   \t0.842981\t0.843703\t0.842252\t0.000256409\t4.17333\t14 \t1  \t2.45424\n",
      "2  \t155   \t0.843284\t0.845571\t0.842475\t0.000391736\t4.08   \t13 \t1  \t1.85839\n",
      "3  \t170   \t0.843532\t0.845629\t0.842568\t0.00058395 \t4.82   \t11 \t1  \t1.46319\n",
      "4  \t165   \t0.843697\t0.845777\t0.757425\t0.00507276 \t4.49333\t12 \t1  \t1.41302\n",
      "5  \t158   \t0.844529\t0.846178\t0.842578\t0.00114965 \t5.07333\t12 \t1  \t1.37403\n",
      "6  \t154   \t0.844933\t0.846178\t0.842345\t0.0011011  \t5.38667\t10 \t1  \t1.23982\n",
      "7  \t175   \t0.844656\t0.846526\t0.759171\t0.00510229 \t6.36667\t13 \t1  \t1.87053\n",
      "8  \t154   \t0.845323\t0.84698 \t0.842345\t0.00127064 \t7.99   \t16 \t2  \t1.9958 \n",
      "9  \t164   \t0.845489\t0.847584\t0.842345\t0.00145059 \t9.37   \t15 \t3  \t2.06715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      "  5%|▌         | 5/100 [08:45<2:51:09, 108.10s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t166   \t0.845605\t0.847932\t0.756268\t0.00535856 \t10.24  \t18 \t1  \t2.37678\n",
      "15 0.847932485435 0.849171815009 0.844444711384 128.1265251636505\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg \tmax\tmin\tstd    \n",
      "0  \t300   \t0.847807\t0.851083\t0.820355\t0.00161459\t4.09\t14 \t2  \t2.33136\n",
      "1  \t165   \t0.848223\t0.851105\t0.845306\t0.000740776\t4.52333\t15 \t1  \t2.41028\n",
      "2  \t151   \t0.849131\t0.852687\t0.847522\t0.00122628 \t4.5    \t12 \t1  \t1.84481\n",
      "3  \t164   \t0.850098\t0.852687\t0.847619\t0.00138347 \t4.13667\t11 \t1  \t1.42524\n",
      "4  \t163   \t0.850189\t0.852687\t0.847476\t0.00161932 \t4.82667\t11 \t1  \t1.27408\n",
      "5  \t150   \t0.850659\t0.852687\t0.847619\t0.0018648  \t5.15333\t11 \t1  \t1.4685 \n",
      "6  \t180   \t0.850297\t0.852687\t0.847557\t0.00231168 \t5.03333\t14 \t1  \t1.65898\n",
      "7  \t182   \t0.850447\t0.852687\t0.847591\t0.0023277  \t5.01333\t13 \t1  \t1.68913\n",
      "8  \t171   \t0.850816\t0.852687\t0.847591\t0.00231882 \t4.99667\t11 \t1  \t1.58008\n",
      "9  \t147   \t0.850872\t0.852687\t0.847591\t0.00228322 \t4.94333\t8  \t1  \t1.2437 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      "  6%|▌         | 6/100 [11:03<3:03:13, 116.95s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t164   \t0.850755\t0.852687\t0.847557\t0.00232731 \t4.96667\t9  \t1  \t1.39722\n",
      "16 0.852686520731 0.854126711854 0.845711008072 137.57639479637146\n",
      "   \t      \t                     fitness                      \t             size             \n",
      "   \t      \t--------------------------------------------------\t------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin    \tstd       \tavg \tmax\tmin\tstd   \n",
      "0  \t300   \t0.852425\t0.853307\t0.79346\t0.00341542\t4.22\t14 \t2  \t2.5242\n",
      "1  \t165   \t0.852769\t0.853307\t0.852017\t0.000182739\t4.68\t15 \t1  \t2.99849\n",
      "2  \t163   \t0.852719\t0.853331\t0.801125\t0.00299485 \t4.71333\t14 \t1  \t2.36033\n",
      "3  \t161   \t0.853053\t0.854913\t0.85229 \t0.000314936\t4.28   \t13 \t1  \t2.11067\n",
      "4  \t160   \t0.853153\t0.854983\t0.852372\t0.000433026\t4.29667\t11 \t1  \t2.17608\n",
      "5  \t178   \t0.853307\t0.854983\t0.852255\t0.000714044\t6.14333\t15 \t1  \t2.78977\n",
      "6  \t170   \t0.853939\t0.855048\t0.85235 \t0.00106912 \t7.47667\t16 \t1  \t2.26924\n",
      "7  \t165   \t0.854221\t0.855048\t0.852372\t0.00111744 \t7.11   \t10 \t1  \t1.67866\n",
      "8  \t165   \t0.85435 \t0.855048\t0.852372\t0.00104431 \t7.37667\t12 \t1  \t1.63955\n",
      "9  \t160   \t0.854314\t0.855049\t0.852347\t0.0011091  \t7.36333\t11 \t1  \t1.66673\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      "  7%|▋         | 7/100 [13:48<3:23:31, 131.30s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t188   \t0.854266\t0.855049\t0.852362\t0.00115677 \t7.36333\t11 \t2  \t1.62214\n",
      "17 0.855048986868 0.85657656536 0.847930280607 164.75059580802917\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.854564\t0.855822\t0.731169\t0.00714094\t4.14667\t14 \t2  \t2.58299\n",
      "1  \t153   \t0.855071\t0.857286\t0.839383\t0.000937062\t3.96667\t11 \t2  \t2.12577\n",
      "2  \t162   \t0.855322\t0.857286\t0.850464\t0.000504503\t4.88   \t16 \t1  \t2.57402\n",
      "3  \t175   \t0.855619\t0.857529\t0.854722\t0.000635606\t4.86667\t13 \t1  \t2.12968\n",
      "4  \t174   \t0.85604 \t0.857605\t0.854726\t0.000952738\t6.25333\t14 \t1  \t2.76571\n",
      "5  \t154   \t0.856475\t0.857607\t0.85455 \t0.00108882 \t8.23   \t17 \t1  \t2.44754\n",
      "6  \t172   \t0.856503\t0.857607\t0.854708\t0.00118773 \t9.73333\t17 \t1  \t2.47162\n",
      "7  \t169   \t0.856518\t0.857608\t0.854714\t0.001217   \t10.0067\t19 \t1  \t2.38885\n",
      "8  \t166   \t0.856694\t0.857648\t0.854707\t0.00116707 \t10.1067\t18 \t1  \t2.28516\n",
      "9  \t172   \t0.856552\t0.857648\t0.854713\t0.00122236 \t9.83   \t17 \t1  \t2.17127\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      "  8%|▊         | 8/100 [17:18<3:57:41, 155.02s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t167   \t0.856716\t0.8578  \t0.854715\t0.00120108 \t10.1267\t17 \t1  \t2.31314\n",
      "18 0.857799666473 0.859549128451 0.851489925684 210.31481289863586\n",
      "   \t      \t                          fitness                          \t              size             \n",
      "   \t      \t-----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd        \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.857763\t0.861459\t0.852446\t0.000419837\t4.06333\t15 \t2  \t2.54545\n",
      "1  \t173   \t0.858011\t0.861459\t0.857254\t0.000633707\t3.84667\t18 \t1  \t2.43512\n",
      "2  \t167   \t0.858658\t0.861459\t0.857182\t0.00111542 \t5.71333\t18 \t1  \t4.18543\n",
      "3  \t166   \t0.859848\t0.861459\t0.85724 \t0.00149267 \t4.72333\t19 \t1  \t3.65241\n",
      "4  \t167   \t0.860374\t0.86146 \t0.857317\t0.00154562 \t3.11333\t9  \t1  \t0.774912\n",
      "5  \t167   \t0.860409\t0.86146 \t0.85703 \t0.00154042 \t3.07333\t8  \t1  \t0.669295\n",
      "6  \t168   \t0.860277\t0.86146 \t0.85753 \t0.00155383 \t3.10333\t7  \t1  \t0.522164\n",
      "7  \t172   \t0.860453\t0.86146 \t0.857504\t0.00150848 \t3.07667\t8  \t1  \t0.619776\n",
      "8  \t175   \t0.860235\t0.86146 \t0.855745\t0.00162243 \t3.17333\t9  \t1  \t0.862142\n",
      "9  \t169   \t0.860418\t0.86146 \t0.857544\t0.00153877 \t3.05333\t9  \t1  \t0.6662  \n"
     ]
    },
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     "output_type": "stream",
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      "\r",
      "  9%|▉         | 9/100 [21:27<4:37:44, 183.13s/it]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t160   \t0.860416\t0.86146 \t0.824005\t0.00256048 \t3.09   \t9  \t1  \t0.649538\n",
      "19 0.861459640296 0.86317846929 0.85342390608 248.68981790542603\n",
      "   \t      \t                 fitness                  \t              size             \n",
      "   \t      \t------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg    \tmax     \tmin    \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.86124\t0.862244\t0.82738\t0.00241753\t4.11333\t14 \t2  \t2.62941\n",
      "1  \t159   \t0.86158\t0.862736\t0.860695\t0.00027733\t4.92667\t14 \t1  \t2.94301\n",
      "2  \t177   \t0.861735\t0.862792\t0.844961\t0.00104733\t5.06667\t17 \t1  \t3.40131\n",
      "3  \t157   \t0.861934\t0.863297\t0.836051\t0.00157545\t7.62333\t16 \t1  \t3.45275\n",
      "4  \t164   \t0.862149\t0.864243\t0.860527\t0.000645915\t5.88667\t16 \t1  \t3.06602\n",
      "5  \t151   \t0.862364\t0.864243\t0.860976\t0.000771197\t5.72   \t13 \t2  \t2.05627\n",
      "6  \t174   \t0.862577\t0.864245\t0.85769 \t0.00103323 \t6.21   \t12 \t1  \t2.0492 \n",
      "7  \t168   \t0.862926\t0.864417\t0.858827\t0.00121661 \t7.01667\t15 \t1  \t2.51722\n",
      "8  \t179   \t0.863138\t0.864477\t0.858398\t0.0013414  \t6.99333\t12 \t1  \t1.3976 \n",
      "9  \t165   \t0.863387\t0.864486\t0.860368\t0.00128294 \t6.94333\t12 \t1  \t1.521  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|█         | 10/100 [26:22<5:25:13, 216.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t152   \t0.863318\t0.864654\t0.836341\t0.00206684 \t7.39   \t15 \t1  \t1.63031\n",
      "20 0.864653998154 0.866487796609 0.854253755846 295.38154196739197\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.864622\t0.865441\t0.864099\t0.00015414\t3.87333\t14 \t2  \t2.40499\n",
      "1  \t156   \t0.86441 \t0.865984\t0.762702\t0.00588973\t4.15   \t13 \t1  \t2.36802\n",
      "2  \t168   \t0.864737\t0.866004\t0.808657\t0.00327122\t3.87   \t15 \t2  \t2.00826\n",
      "3  \t166   \t0.865119\t0.866004\t0.860856\t0.000470518\t3.09   \t10 \t1  \t1.41959\n",
      "4  \t169   \t0.865267\t0.866004\t0.860269\t0.000656486\t2.91667\t9  \t1  \t1.15314\n",
      "5  \t178   \t0.865133\t0.866013\t0.83921 \t0.00202541 \t3.67333\t11 \t1  \t1.05513\n",
      "6  \t170   \t0.865327\t0.866013\t0.860072\t0.00126961 \t4.10667\t10 \t1  \t0.89179\n",
      "7  \t129   \t0.86549 \t0.866013\t0.859575\t0.00117117 \t4.03667\t8  \t1  \t0.722488\n",
      "8  \t154   \t0.865464\t0.866013\t0.858381\t0.0012777  \t4.16667\t10 \t1  \t0.958587\n",
      "9  \t156   \t0.865271\t0.866013\t0.859377\t0.00139422 \t4.07   \t10 \t1  \t0.835723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 11%|█         | 11/100 [32:15<6:22:14, 257.69s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t188   \t0.865227\t0.866013\t0.858681\t0.00153133 \t4.04667\t10 \t1  \t0.915326\n",
      "21 0.866012717609 0.867726098928 0.852664128067 352.9923450946808\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.865574\t0.866715\t0.753601\t0.00647949\t3.95333\t14 \t2  \t2.52147\n",
      "1  \t151   \t0.866092\t0.866775\t0.865552\t0.000214298\t3.74333\t15 \t1  \t2.22653\n",
      "2  \t156   \t0.866314\t0.866775\t0.864542\t0.000349959\t3.25333\t11 \t1  \t1.85898\n",
      "3  \t176   \t0.866591\t0.866775\t0.86574 \t0.000282753\t2.26   \t8  \t1  \t0.886416\n",
      "4  \t158   \t0.866532\t0.866776\t0.865676\t0.000360715\t2.71   \t8  \t1  \t0.807816\n",
      "5  \t154   \t0.866572\t0.866777\t0.86571 \t0.000352942\t3.04667\t8  \t1  \t0.768866\n",
      "6  \t171   \t0.866477\t0.866778\t0.86571 \t0.000391157\t3.10333\t8  \t1  \t0.97262 \n",
      "7  \t153   \t0.866549\t0.866778\t0.865748\t0.000362031\t3.10333\t9  \t1  \t0.816081\n",
      "8  \t158   \t0.866533\t0.866778\t0.865778\t0.00037166 \t2.97   \t8  \t1  \t0.665157\n",
      "9  \t159   \t0.866535\t0.866778\t0.865751\t0.000370605\t3.10333\t8  \t1  \t0.791195\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      " 12%|█▏        | 12/100 [37:34<6:44:41, 275.92s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t166   \t0.86653 \t0.866778\t0.865714\t0.000371997\t3.11333\t9  \t1  \t0.934785\n",
      "22 0.866778364859 0.868600198358 0.854380886027 318.4031250476837\n",
      "   \t      \t                      fitness                      \t             size             \n",
      "   \t      \t---------------------------------------------------\t------------------------------\n",
      "gen\tnevals\tavg     \tmax    \tmin     \tstd        \tavg    \tmax\tmin\tstd   \n",
      "0  \t300   \t0.866723\t0.86719\t0.866316\t0.000119344\t4.00667\t13 \t2  \t2.3338\n",
      "1  \t169   \t0.866815\t0.867517\t0.866346\t0.000152342\t4.63667\t14 \t1  \t2.59576\n",
      "2  \t179   \t0.866876\t0.867594\t0.863979\t0.000317811\t4.42667\t14 \t1  \t2.536  \n",
      "3  \t166   \t0.866945\t0.867594\t0.854919\t0.000749558\t3.90667\t11 \t1  \t1.68264\n",
      "4  \t169   \t0.867117\t0.86807 \t0.866167\t0.000387287\t4.55   \t12 \t1  \t2.29074\n",
      "5  \t171   \t0.867328\t0.86807 \t0.862662\t0.00045752 \t6.47333\t14 \t1  \t2.75487\n",
      "6  \t184   \t0.867435\t0.868127\t0.866538\t0.000437682\t6.85   \t16 \t1  \t2.6244 \n",
      "7  \t173   \t0.86761 \t0.869358\t0.866626\t0.000579139\t4.96667\t16 \t1  \t2.89578\n",
      "8  \t168   \t0.867628\t0.869358\t0.866307\t0.000669097\t3.66   \t10 \t1  \t1.34823\n",
      "9  \t175   \t0.867772\t0.86936 \t0.866267\t0.000772876\t4.49667\t12 \t1  \t1.52643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 13%|█▎        | 13/100 [43:51<7:24:19, 306.44s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t180   \t0.868132\t0.869417\t0.8665  \t0.00102104 \t5.46333\t11 \t1  \t1.55841\n",
      "23 0.8694168759 0.871426692778 0.858424026203 377.5869927406311\n",
      "   \t      \t                          fitness                          \t             size             \n",
      "   \t      \t-----------------------------------------------------------\t------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd        \tavg \tmax\tmin\tstd   \n",
      "0  \t300   \t0.869333\t0.869937\t0.864228\t0.000370727\t4.05\t14 \t2  \t2.5886\n",
      "1  \t153   \t0.869067\t0.869937\t0.748753\t0.00696002 \t3.82333\t14 \t2  \t2.21783\n",
      "2  \t164   \t0.869566\t0.869937\t0.854024\t0.00093537 \t2.93667\t10 \t1  \t1.58514\n",
      "3  \t168   \t0.869874\t0.869937\t0.86922 \t0.000171076\t2.14667\t8  \t1  \t0.803216\n",
      "4  \t178   \t0.869875\t0.869937\t0.86916 \t0.000171566\t2.16333\t8  \t1  \t0.814446\n",
      "5  \t169   \t0.869879\t0.869937\t0.869155\t0.00017331 \t2.08333\t7  \t1  \t0.624277\n",
      "6  \t161   \t0.869885\t0.869937\t0.868477\t0.000173896\t2.11333\t7  \t1  \t0.668697\n",
      "7  \t169   \t0.869881\t0.869937\t0.869245\t0.000161981\t2.10333\t8  \t1  \t0.662814\n",
      "8  \t167   \t0.869027\t0.869937\t0.614384\t0.0147274  \t2.11333\t7  \t1  \t0.554216\n",
      "9  \t150   \t0.869894\t0.869937\t0.869115\t0.000149762\t2.09333\t8  \t1  \t0.609335\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 14%|█▍        | 14/100 [49:46<7:39:50, 320.82s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t156   \t0.869882\t0.869937\t0.869157\t0.000163253\t2.12333\t7  \t1  \t0.664421\n",
      "24 0.869937428082 0.872046656471 0.858455057979 354.31483483314514\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg \tmax\tmin\tstd    \n",
      "0  \t300   \t0.869664\t0.870283\t0.819549\t0.00293885\t4.06\t15 \t2  \t2.57224\n",
      "1  \t168   \t0.86997 \t0.870283\t0.869491\t0.000136344\t3.4 \t10 \t1  \t1.70294\n",
      "2  \t181   \t0.870036\t0.870663\t0.869531\t0.000190518\t3.67333\t9  \t1  \t1.66932\n",
      "3  \t188   \t0.869007\t0.870663\t0.54096 \t0.0189728  \t4.48   \t11 \t1  \t1.93811\n",
      "4  \t156   \t0.868964\t0.870684\t0.490688\t0.02188    \t5.42667\t12 \t1  \t1.90909\n",
      "5  \t172   \t0.870318\t0.870691\t0.865306\t0.000537014\t6.16   \t12 \t1  \t1.53441\n",
      "6  \t164   \t0.870332\t0.870781\t0.86341 \t0.000765505\t5.53333\t11 \t1  \t1.37679\n",
      "7  \t171   \t0.870418\t0.87081 \t0.869713\t0.000370474\t4.9    \t11 \t1  \t1.51327\n",
      "8  \t169   \t0.870437\t0.870822\t0.864642\t0.000502711\t5.34333\t13 \t1  \t2.26983\n",
      "9  \t168   \t0.870208\t0.870852\t0.784974\t0.0049466  \t8.25667\t18 \t1  \t2.74058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 15%|█▌        | 15/100 [56:47<8:16:57, 350.80s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t178   \t0.870293\t0.871088\t0.795834\t0.0043251  \t10.04  \t19 \t1  \t2.5778 \n",
      "25 0.8710876351 0.873208643065 0.860753411493 420.68731713294983\n",
      "   \t      \t                     fitness                      \t              size             \n",
      "   \t      \t--------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg    \tmax     \tmin     \tstd       \tavg \tmax\tmin\tstd    \n",
      "0  \t300   \t0.87072\t0.871573\t0.775945\t0.00548234\t4.24\t14 \t2  \t2.67377\n",
      "1  \t168   \t0.871118\t0.871621\t0.868214\t0.000231546\t4.69\t14 \t1  \t2.92357\n",
      "2  \t161   \t0.869894\t0.871621\t0.728686\t0.0122212  \t5.98667\t13 \t1  \t3.13472\n",
      "3  \t159   \t0.866156\t0.871815\t0.496168\t0.0371511  \t7.09333\t14 \t1  \t2.46129\n",
      "4  \t166   \t0.863228\t0.871817\t0.496168\t0.0458803  \t6.04   \t14 \t2  \t2.38853\n",
      "5  \t169   \t0.871479\t0.872009\t0.870773\t0.000322956\t4.14333\t10 \t1  \t1.07523\n",
      "6  \t150   \t0.871587\t0.872016\t0.870773\t0.000347815\t3.95667\t8  \t1  \t1.0808 \n",
      "7  \t191   \t0.871255\t0.872016\t0.787578\t0.00485468 \t4.11667\t9  \t1  \t1.13859\n",
      "8  \t157   \t0.871621\t0.872016\t0.870852\t0.000358107\t4.65667\t8  \t1  \t1.12196\n",
      "9  \t170   \t0.871576\t0.87212 \t0.870623\t0.000419658\t5.12   \t12 \t1  \t1.35607\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 16%|█▌        | 16/100 [1:04:13<8:51:08, 379.39s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t160   \t0.871736\t0.87212 \t0.870754\t0.000401721\t5.33333\t10 \t3  \t1.37679\n",
      "26 0.872120329445 0.874251249615 0.860497149081 446.03529500961304\n",
      "   \t      \t                         fitness                          \t              size             \n",
      "   \t      \t----------------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg     \tmax     \tmin     \tstd       \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.871952\t0.872388\t0.844608\t0.00159558\t3.99667\t15 \t2  \t2.70986\n",
      "1  \t170   \t0.872079\t0.87261 \t0.858097\t0.00082847\t4.20333\t19 \t1  \t2.47427\n",
      "2  \t166   \t0.872195\t0.87261 \t0.871479\t0.000177403\t3.45   \t14 \t1  \t2.02011\n",
      "3  \t159   \t0.872257\t0.87261 \t0.871769\t0.00020992 \t3.30667\t9  \t1  \t1.58513\n",
      "4  \t161   \t0.872328\t0.872924\t0.869782\t0.000307733\t4.89333\t10 \t1  \t1.74412\n",
      "5  \t160   \t0.872409\t0.872924\t0.871766\t0.000317778\t5.90667\t12 \t1  \t1.5159 \n",
      "6  \t164   \t0.872513\t0.872924\t0.871537\t0.000376167\t5.93667\t13 \t1  \t1.57881\n",
      "7  \t163   \t0.872625\t0.872924\t0.87137 \t0.000399094\t6.07667\t12 \t2  \t1.61373\n",
      "8  \t147   \t0.872364\t0.872924\t0.785823\t0.00502018 \t6.01667\t12 \t1  \t1.49099\n",
      "9  \t171   \t0.872555\t0.872924\t0.871646\t0.000422229\t6.02   \t12 \t1  \t1.61233\n"
     ]
    },
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     "output_type": "stream",
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      "\r",
      " 17%|█▋        | 17/100 [1:11:42<9:13:47, 400.34s/it]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t171   \t0.87266 \t0.872924\t0.870762\t0.000400823\t5.99667\t15 \t1  \t1.42478\n",
      "27 0.872923635008 0.875051970946 0.86098064418 449.14445900917053\n",
      "   \t      \t                      fitness                      \t              size             \n",
      "   \t      \t---------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg    \tmax     \tmin     \tstd        \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.87282\t0.873334\t0.870583\t0.000200191\t4.05333\t14 \t2  \t2.51869\n",
      "1  \t164   \t0.872921\t0.87348 \t0.872455\t0.000122134\t4.54   \t14 \t1  \t2.75954\n",
      "2  \t174   \t0.871751\t0.873488\t0.508188\t0.0210266  \t6.30333\t17 \t1  \t3.25955\n",
      "3  \t161   \t0.87306 \t0.873488\t0.870661\t0.000285023\t6.63   \t17 \t1  \t2.99551\n",
      "4  \t181   \t0.873098\t0.873488\t0.872232\t0.000322762\t5.37333\t18 \t2  \t2.67469\n",
      "5  \t162   \t0.873175\t0.873491\t0.862047\t0.000725667\t4.21333\t14 \t1  \t1.12598\n",
      "6  \t163   \t0.873205\t0.873491\t0.866599\t0.000506145\t4.03667\t9  \t1  \t0.869093\n",
      "7  \t166   \t0.873198\t0.873492\t0.872542\t0.000348138\t4.05   \t11 \t1  \t0.849019\n",
      "8  \t168   \t0.873233\t0.873492\t0.872543\t0.000332944\t4.10667\t10 \t1  \t0.83384 \n",
      "9  \t184   \t0.873212\t0.873492\t0.872176\t0.000336661\t4.15   \t10 \t1  \t0.956121\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      " 18%|█▊        | 18/100 [1:19:33<9:36:03, 421.50s/it]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t158   \t0.873236\t0.873496\t0.872527\t0.000330624\t4.11   \t10 \t3  \t0.760197\n",
      "28 0.873495655487 0.875755438167 0.861473148504 470.81135058403015\n",
      "   \t      \t                      fitness                      \t              size             \n",
      "   \t      \t---------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg   \tmax     \tmin     \tstd        \tavg    \tmax\tmin\tstd    \n",
      "0  \t300   \t0.8734\t0.874088\t0.857579\t0.000922923\t3.86333\t13 \t2  \t2.19043\n",
      "1  \t174   \t0.873193\t0.874088\t0.777559\t0.00553349 \t3.85333\t10 \t1  \t1.81984\n",
      "2  \t162   \t0.873617\t0.874101\t0.872522\t0.000236797\t3.61333\t11 \t1  \t1.60535\n",
      "3  \t153   \t0.873548\t0.874117\t0.813262\t0.00349991 \t4.09667\t9  \t2  \t1.49911\n",
      "4  \t173   \t0.873835\t0.874123\t0.873085\t0.000352327\t4.81   \t12 \t1  \t1.85128\n",
      "5  \t165   \t0.873788\t0.874123\t0.853776\t0.00122965 \t5.51333\t11 \t1  \t2.17175\n",
      "6  \t163   \t0.873771\t0.874126\t0.843375\t0.00178737 \t6.94667\t15 \t1  \t1.45046\n",
      "7  \t169   \t0.873914\t0.874126\t0.873034\t0.000319155\t7.15667\t14 \t1  \t1.29053\n",
      "8  \t176   \t0.873929\t0.874169\t0.872909\t0.000288122\t7.38   \t14 \t1  \t1.61522\n",
      "9  \t169   \t0.873722\t0.874169\t0.822442\t0.0029834  \t8.31667\t15 \t5  \t1.6542 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 19%|█▉        | 19/100 [1:28:30<10:15:43, 456.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \t158   \t0.873935\t0.87417 \t0.872638\t0.000312879\t9.11333\t15 \t1  \t1.84404\n",
      "29 0.874169694807 0.87644046711 0.861510186431 536.7007830142975\n",
      "   \t      \t                     fitness                      \t              size             \n",
      "   \t      \t--------------------------------------------------\t-------------------------------\n",
      "gen\tnevals\tavg    \tmax     \tmin     \tstd       \tavg \tmax\tmin\tstd    \n",
      "0  \t300   \t0.87347\t0.874464\t0.763249\t0.00720991\t3.74\t14 \t2  \t2.25516\n",
      "1  \t173   \t0.873701\t0.874724\t0.767308\t0.00642646\t3.83333\t12 \t1  \t2.15071\n",
      "2  \t170   \t0.874204\t0.874732\t0.866589\t0.000514518\t3.89   \t10 \t2  \t1.63439\n",
      "3  \t159   \t0.874194\t0.874732\t0.836526\t0.00218975 \t3.65   \t9  \t1  \t1.09278\n",
      "4  \t179   \t0.874368\t0.874734\t0.872428\t0.000302147\t3.80667\t9  \t1  \t0.970887\n",
      "5  \t170   \t0.874507\t0.874734\t0.873641\t0.000306773\t4.06   \t9  \t1  \t0.745922\n",
      "6  \t169   \t0.874476\t0.874734\t0.873922\t0.000313726\t4.07667\t10 \t1  \t0.957839\n",
      "7  \t163   \t0.874347\t0.874734\t0.825061\t0.00286728 \t4.08667\t9  \t1  \t0.791089\n",
      "8  \t175   \t0.874481\t0.874734\t0.873929\t0.00031285 \t4.12   \t9  \t1  \t0.819919\n",
      "9  \t160   \t0.874507\t0.874734\t0.873674\t0.000309855\t4.10333\t9  \t1  \t0.848129\n",
      "10 \t184   \t0.874463\t0.874734\t0.872328\t0.000339189\t4.12333\t10 \t1  \t1.01067 \n",
      "30 0.874734038742 0.877274002327 0.861353025498 625.84836769104\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(100)):\n",
    "    # 構文木として利用可能な演算の定義\n",
    "    pset = gp.PrimitiveSet(\"MAIN\", n_features)\n",
    "    pset.addPrimitive(operator.add, 2)\n",
    "    pset.addPrimitive(operator.sub, 2)\n",
    "    pset.addPrimitive(operator.mul, 2)\n",
    "    pset.addPrimitive(protectedDiv, 2)\n",
    "    pset.addPrimitive(operator.neg, 1)\n",
    "    pset.addPrimitive(np.cos, 1)\n",
    "    pset.addPrimitive(np.sin, 1)\n",
    "    pset.addPrimitive(np.tan, 1)\n",
    "\n",
    "    # 関数のデフォルト値の設定\n",
    "    toolbox = base.Toolbox()\n",
    "    toolbox.register(\"expr\", gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
    "    toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.expr)\n",
    "    toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
    "    toolbox.register(\"compile\", gp.compile, pset=pset)\n",
    "\n",
    "    # 評価関数の設定\n",
    "    # 新しく生成した変数を元の変数に加えて5-fold CVを求める\n",
    "    def eval_genfeat(individual):\n",
    "        func = toolbox.compile(expr=individual)\n",
    "        features_train = [X_train[:,i] for i in range(n_features)]\n",
    "        new_feat_train = func(*features_train)\n",
    "        X_train_tmp = np.c_[X_train, new_feat_train]\n",
    "        return np.mean(cross_val_score(clf, X_train_tmp, y_train, scoring=\"roc_auc\", cv=5)),\n",
    "\n",
    "    # 評価、選択、交叉、突然変異の設定\n",
    "    # 選択はサイズ10のトーナメント方式、交叉は1点交叉、突然変異は深さ2のランダム構文木生成と定義\n",
    "    toolbox.register(\"evaluate\", eval_genfeat)\n",
    "    toolbox.register(\"select\", tools.selTournament, tournsize=10)\n",
    "    toolbox.register(\"mate\", gp.cxOnePoint)\n",
    "    toolbox.register(\"expr_mut\", gp.genFull, min_=0, max_=2)\n",
    "    toolbox.register(\"mutate\", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n",
    "\n",
    "    # 構文木の制約の設定\n",
    "    # 交叉や突然変異で深さ5以上の木ができないようにする\n",
    "    toolbox.decorate(\"mate\", gp.staticLimit(key=operator.attrgetter(\"height\"), max_value=5))\n",
    "    toolbox.decorate(\"mutate\", gp.staticLimit(key=operator.attrgetter(\"height\"), max_value=5)) \n",
    "\n",
    "    # 世代ごとの個体とベスト解を保持するクラスの生成\n",
    "    pop = toolbox.population(n=300)\n",
    "    hof = tools.HallOfFame(1)\n",
    "\n",
    "    # 統計量の表示設定\n",
    "    stats_fit = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "    stats_size = tools.Statistics(len)\n",
    "    mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)\n",
    "    mstats.register(\"avg\", np.mean)\n",
    "    mstats.register(\"std\", np.std)\n",
    "    mstats.register(\"min\", np.min)\n",
    "    mstats.register(\"max\", np.max)\n",
    "\n",
    "    # 進化の実行\n",
    "    # 交叉確率50%、突然変異確率10%、10世代まで進化\n",
    "    start_time = time.time()\n",
    "    pop, log = algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 10, stats=mstats, halloffame=hof, verbose=True)\n",
    "    end_time = time.time()\n",
    "\n",
    "    # ベスト解とAUCの保持\n",
    "    best_expr = hof[0]\n",
    "    best_auc = mstats.compile(pop)[\"fitness\"][\"max\"]\n",
    "\n",
    "    # 5-fold CVのAUCスコアが前ステップのAUCを超えていた場合\n",
    "    # 生成変数を学習、テストデータに追加し、ベストAUCを更新する\n",
    "    if prev_auc < best_auc:\n",
    "        # 生成変数の追加\n",
    "        func = toolbox.compile(expr=best_expr)\n",
    "        features_train = [X_train[:,i] for i in range(n_features)]\n",
    "        features_test = [X_test[:,i] for i in range(n_features)]\n",
    "        new_feat_train = func(*features_train)\n",
    "        new_feat_test = func(*features_test)\n",
    "        X_train = np.c_[X_train, new_feat_train]\n",
    "        X_test = np.c_[X_test, new_feat_test]\n",
    "\n",
    "        ### テストAUCの計算（プロット用）\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_auc = roc_auc_score(y_train, clf.predict_proba(X_train)[:,1])\n",
    "        test_auc = roc_auc_score(y_test, clf.predict_proba(X_test)[:,1])\n",
    "\n",
    "        # ベストAUCの更新と特徴量数の加算\n",
    "        prev_auc = best_auc\n",
    "        n_features += 1\n",
    "\n",
    "        # 表示と出力用データの保持\n",
    "        print(n_features, best_auc, train_auc, test_auc, end_time - start_time)\n",
    "        results.append([n_features, best_auc, train_auc, test_auc])\n",
    "        exprs.append(best_expr)\n",
    "\n",
    "        # 変数追加後の特徴量数が30を超えた場合break\n",
    "        if n_features >= 30:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "### Results\n",
      "Baseline AUC train : 0.741195900486\n",
      "Baseline AUC test : 0.731194743417\n",
      "Best AUC train : 0.874734038742\n",
      "Best AUC test : 0.861353025498\n"
     ]
    }
   ],
   "source": [
    "# 結果の出力\n",
    "print()\n",
    "print(\"### Results\")\n",
    "print(\"Baseline AUC train :\", base_train_auc)\n",
    "print(\"Baseline AUC test :\", base_test_auc)\n",
    "print(\"Best AUC train :\", results[-1][1])\n",
    "print(\"Best AUC test :\", results[-1][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 結果のプロット\n",
    "res = np.array(results)\n",
    "plt.plot(res[:,0], res[:,1],\"o-\", color=\"b\", label=\"train(5-fold CV)\")\n",
    "plt.plot(res[:,0], res[:,3],\"o-\", color=\"r\", label=\"test\")\n",
    "plt.plot(10, base_train_auc, \"d\", color=\"b\", label = \"train baseline(5-fold CV)\")\n",
    "plt.plot(10, base_test_auc, \"d\", color=\"r\", label = \"test baseline\")\n",
    "plt.xlim(9,31)\n",
    "plt.grid(which=\"both\")\n",
    "plt.xlabel('n_features')\n",
    "plt.ylabel('AUC')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"gp_featgen.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "### Generated feature expression\n",
      "mul(sub(add(mul(ARG9, ARG2), cos(neg(ARG6))), neg(ARG6)), sub(ARG7, cos(cos(neg(ARG6)))))\n",
      "mul(neg(ARG10), mul(ARG9, ARG8))\n",
      "sub(add(ARG1, ARG8), add(mul(add(ARG2, ARG6), ARG8), cos(protectedDiv(ARG10, ARG3))))\n",
      "sub(sub(sub(neg(mul(ARG9, ARG12)), cos(ARG9)), cos(ARG9)), mul(ARG2, ARG12))\n",
      "mul(add(add(ARG6, neg(ARG7)), ARG8), add(sin(ARG6), neg(ARG7)))\n",
      "sin(cos(add(ARG8, ARG2)))\n",
      "cos(cos(add(cos(ARG9), sin(sin(ARG2)))))\n",
      "mul(sub(ARG10, mul(mul(ARG7, ARG2), cos(ARG6))), ARG16)\n",
      "mul(ARG14, ARG8)\n",
      "mul(sub(sub(ARG14, ARG9), sin(ARG6)), add(ARG9, ARG2))\n",
      "mul(ARG11, cos(ARG16))\n",
      "sin(cos(ARG7))\n",
      "mul(add(cos(ARG9), tan(ARG21)), ARG8)\n",
      "cos(ARG22)\n",
      "mul(sub(ARG23, ARG21), sub(sin(ARG9), add(sub(sin(ARG9), add(ARG13, ARG3)), ARG3)))\n",
      "mul(add(add(ARG21, neg(neg(ARG16))), ARG2), ARG23)\n",
      "mul(ARG2, neg(mul(ARG9, ARG9)))\n",
      "mul(cos(ARG14), ARG26)\n",
      "sub(mul(mul(cos(ARG13), ARG0), ARG26), mul(ARG17, ARG26))\n",
      "mul(cos(ARG6), ARG2)\n"
     ]
    }
   ],
   "source": [
    "# 生成した構文木の出力\n",
    "print()\n",
    "print(\"### Generated feature expression\")\n",
    "for expr in exprs:\n",
    "    print(expr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lightGBM(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's auc: 0.752588\n",
      "Training until validation scores don't improve for 10 rounds.\n",
      "[2]\tvalid_0's auc: 0.770969\n",
      "[3]\tvalid_0's auc: 0.791902\n",
      "[4]\tvalid_0's auc: 0.796553\n",
      "[5]\tvalid_0's auc: 0.79827\n",
      "[6]\tvalid_0's auc: 0.80239\n",
      "[7]\tvalid_0's auc: 0.807133\n",
      "[8]\tvalid_0's auc: 0.812738\n",
      "[9]\tvalid_0's auc: 0.813093\n",
      "[10]\tvalid_0's auc: 0.816273\n",
      "[11]\tvalid_0's auc: 0.822135\n",
      "[12]\tvalid_0's auc: 0.821824\n",
      "[13]\tvalid_0's auc: 0.824187\n",
      "[14]\tvalid_0's auc: 0.825901\n",
      "[15]\tvalid_0's auc: 0.827086\n",
      "[16]\tvalid_0's auc: 0.830691\n",
      "[17]\tvalid_0's auc: 0.833257\n",
      "[18]\tvalid_0's auc: 0.835706\n",
      "[19]\tvalid_0's auc: 0.837942\n",
      "[20]\tvalid_0's auc: 0.839303\n",
      "[21]\tvalid_0's auc: 0.840747\n",
      "[22]\tvalid_0's auc: 0.842322\n",
      "[23]\tvalid_0's auc: 0.843171\n",
      "[24]\tvalid_0's auc: 0.844819\n",
      "[25]\tvalid_0's auc: 0.845503\n",
      "[26]\tvalid_0's auc: 0.846534\n",
      "[27]\tvalid_0's auc: 0.847227\n",
      "[28]\tvalid_0's auc: 0.848505\n",
      "[29]\tvalid_0's auc: 0.848983\n",
      "[30]\tvalid_0's auc: 0.849424\n",
      "[31]\tvalid_0's auc: 0.850356\n",
      "[32]\tvalid_0's auc: 0.85121\n",
      "[33]\tvalid_0's auc: 0.85239\n",
      "[34]\tvalid_0's auc: 0.853012\n",
      "[35]\tvalid_0's auc: 0.853595\n",
      "[36]\tvalid_0's auc: 0.853648\n",
      "[37]\tvalid_0's auc: 0.854018\n",
      "[38]\tvalid_0's auc: 0.854271\n",
      "[39]\tvalid_0's auc: 0.854744\n",
      "[40]\tvalid_0's auc: 0.855324\n",
      "[41]\tvalid_0's auc: 0.855804\n",
      "[42]\tvalid_0's auc: 0.855819\n",
      "[43]\tvalid_0's auc: 0.856335\n",
      "[44]\tvalid_0's auc: 0.856781\n",
      "[45]\tvalid_0's auc: 0.856906\n",
      "[46]\tvalid_0's auc: 0.85694\n",
      "[47]\tvalid_0's auc: 0.85692\n",
      "[48]\tvalid_0's auc: 0.857414\n",
      "[49]\tvalid_0's auc: 0.857977\n",
      "[50]\tvalid_0's auc: 0.858709\n",
      "[51]\tvalid_0's auc: 0.859017\n",
      "[52]\tvalid_0's auc: 0.859218\n",
      "[53]\tvalid_0's auc: 0.859462\n",
      "[54]\tvalid_0's auc: 0.859701\n",
      "[55]\tvalid_0's auc: 0.859748\n",
      "[56]\tvalid_0's auc: 0.860258\n",
      "[57]\tvalid_0's auc: 0.860489\n",
      "[58]\tvalid_0's auc: 0.860553\n",
      "[59]\tvalid_0's auc: 0.860841\n",
      "[60]\tvalid_0's auc: 0.860857\n",
      "[61]\tvalid_0's auc: 0.860671\n",
      "[62]\tvalid_0's auc: 0.860827\n",
      "[63]\tvalid_0's auc: 0.860801\n",
      "[64]\tvalid_0's auc: 0.860918\n",
      "[65]\tvalid_0's auc: 0.861313\n",
      "[66]\tvalid_0's auc: 0.86132\n",
      "[67]\tvalid_0's auc: 0.86154\n",
      "[68]\tvalid_0's auc: 0.861734\n",
      "[69]\tvalid_0's auc: 0.862042\n",
      "[70]\tvalid_0's auc: 0.861942\n",
      "[71]\tvalid_0's auc: 0.862031\n",
      "[72]\tvalid_0's auc: 0.862022\n",
      "[73]\tvalid_0's auc: 0.862054\n",
      "[74]\tvalid_0's auc: 0.862475\n",
      "[75]\tvalid_0's auc: 0.862814\n",
      "[76]\tvalid_0's auc: 0.86306\n",
      "[77]\tvalid_0's auc: 0.863075\n",
      "[78]\tvalid_0's auc: 0.86359\n",
      "[79]\tvalid_0's auc: 0.863638\n",
      "[80]\tvalid_0's auc: 0.863316\n",
      "[81]\tvalid_0's auc: 0.863784\n",
      "[82]\tvalid_0's auc: 0.863845\n",
      "[83]\tvalid_0's auc: 0.863704\n",
      "[84]\tvalid_0's auc: 0.863962\n",
      "[85]\tvalid_0's auc: 0.864307\n",
      "[86]\tvalid_0's auc: 0.864583\n",
      "[87]\tvalid_0's auc: 0.864482\n",
      "[88]\tvalid_0's auc: 0.864564\n",
      "[89]\tvalid_0's auc: 0.864442\n",
      "[90]\tvalid_0's auc: 0.864475\n",
      "[91]\tvalid_0's auc: 0.864698\n",
      "[92]\tvalid_0's auc: 0.864937\n",
      "[93]\tvalid_0's auc: 0.865117\n",
      "[94]\tvalid_0's auc: 0.865189\n",
      "[95]\tvalid_0's auc: 0.865119\n",
      "[96]\tvalid_0's auc: 0.865\n",
      "[97]\tvalid_0's auc: 0.86492\n",
      "[98]\tvalid_0's auc: 0.865122\n",
      "[99]\tvalid_0's auc: 0.865066\n",
      "[100]\tvalid_0's auc: 0.865047\n",
      "[101]\tvalid_0's auc: 0.865105\n",
      "[102]\tvalid_0's auc: 0.865441\n",
      "[103]\tvalid_0's auc: 0.865536\n",
      "[104]\tvalid_0's auc: 0.865811\n",
      "[105]\tvalid_0's auc: 0.865863\n",
      "[106]\tvalid_0's auc: 0.86588\n",
      "[107]\tvalid_0's auc: 0.865937\n",
      "[108]\tvalid_0's auc: 0.86608\n",
      "[109]\tvalid_0's auc: 0.866273\n",
      "[110]\tvalid_0's auc: 0.866474\n",
      "[111]\tvalid_0's auc: 0.866301\n",
      "[112]\tvalid_0's auc: 0.866396\n",
      "[113]\tvalid_0's auc: 0.866099\n",
      "[114]\tvalid_0's auc: 0.86632\n",
      "[115]\tvalid_0's auc: 0.866438\n",
      "[116]\tvalid_0's auc: 0.866553\n",
      "[117]\tvalid_0's auc: 0.866928\n",
      "[118]\tvalid_0's auc: 0.867195\n",
      "[119]\tvalid_0's auc: 0.867054\n",
      "[120]\tvalid_0's auc: 0.867018\n",
      "[121]\tvalid_0's auc: 0.867162\n",
      "[122]\tvalid_0's auc: 0.867084\n",
      "[123]\tvalid_0's auc: 0.867173\n",
      "[124]\tvalid_0's auc: 0.86722\n",
      "[125]\tvalid_0's auc: 0.867295\n",
      "[126]\tvalid_0's auc: 0.867222\n",
      "[127]\tvalid_0's auc: 0.867164\n",
      "[128]\tvalid_0's auc: 0.867306\n",
      "[129]\tvalid_0's auc: 0.867409\n",
      "[130]\tvalid_0's auc: 0.867342\n",
      "[131]\tvalid_0's auc: 0.867251\n",
      "[132]\tvalid_0's auc: 0.867286\n",
      "[133]\tvalid_0's auc: 0.867217\n",
      "[134]\tvalid_0's auc: 0.867086\n",
      "[135]\tvalid_0's auc: 0.8673\n",
      "[136]\tvalid_0's auc: 0.867037\n",
      "[137]\tvalid_0's auc: 0.866912\n",
      "[138]\tvalid_0's auc: 0.866721\n",
      "[139]\tvalid_0's auc: 0.866903\n",
      "Early stopping, best iteration is:\n",
      "[129]\tvalid_0's auc: 0.867409\n"
     ]
    },
    {
     "data": {
      "image/png": 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TRlniKCGbN7vZQqKj3ZrhkZFAdrb/W21tMSZjTBkWMHGI43eKRRGpLiKWdAop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\nQIngAKDkAE+XEV7ZNgkAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10af2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CkrZepVLZ4h/2JG0956BUPuehuuPnP/85X/rSl95T4JKk/q7MIte9VLcO3tkhnp2cZ81x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\nkiRJmvT+f9e5Q2yACEJLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105134198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train2, X_valid, y_train2, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=123)\n",
    "\n",
    "# データセットを生成する\n",
    "lgb_train = lgb.Dataset(X_train2, y_train2)\n",
    "lgb_eval = lgb.Dataset(X_valid, y_valid, reference=lgb_train)\n",
    "\n",
    "lgbm_params = {\n",
    "    'learning_rate': 0.1,\n",
    "    'num_leaves': 8,\n",
    "    'boosting_type' : 'gbdt',\n",
    "    'subsample' : 0.8,\n",
    "    'reg_alpha' : 1,\n",
    "    'reg_lambda' : 1,\n",
    "    'objective': 'regression',\n",
    "    'metric': 'auc',\n",
    "    }\n",
    "\n",
    "# 上記のパラメータでモデルを学習する\n",
    "model2 = lgb.train(lgbm_params, lgb_train,\n",
    "                  # モデルの評価用データを渡す\n",
    "                  valid_sets=lgb_eval,\n",
    "                  # 最大で 1000 ラウンドまで学習する\n",
    "                  num_boost_round=1000,\n",
    "                  # 10 ラウンド経過しても性能が向上しないときは学習を打ち切る\n",
    "                  early_stopping_rounds=10)\n",
    "\n",
    "# 特徴量の重要度をプロットする\n",
    "lgb.plot_importance(model2, figsize=(20, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### testでの検証"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.877752818887\n"
     ]
    }
   ],
   "source": [
    "# テストデータを予測する\n",
    "y_pred = model2.predict(X_test, num_iteration=model2.best_iteration)\n",
    "\n",
    "# auc を計算する\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}