[14w] -4 regs hyper params tuned.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2cf475e",
   "metadata": {},
   "source": [
    "# library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c6ae050",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-11 15:53:58.862289: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import tensorflow as tf\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f0b1a1",
   "metadata": {},
   "source": [
    "## mk_cd 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ad1d5d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 함수\n",
    "def mk_cd(x):\n",
    "    if type(x) == str:\n",
    "        if x.isalpha():\n",
    "            return x\n",
    "        else:\n",
    "            if len(x) == 6:\n",
    "                return x\n",
    "            else:\n",
    "                return \"0\"*(6-len(x))+x\n",
    "    elif type(x) == int:\n",
    "        x = str(x)\n",
    "        return \"0\"*(6-len(x))+x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72637196",
   "metadata": {},
   "source": [
    "# 슬라이싱 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bace3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "len_a2012 = 284412\n",
    "len_a2013 = 242826\n",
    "len_a2014 = 242448\n",
    "len_a2015 = 314361\n",
    "len_a2016 = 263593\n",
    "len_a2017 = 216591\n",
    "len_a2018 = 307395\n",
    "len_a2019 = 278610\n",
    "len_a2020 = 371006\n",
    "len_a2021 = 321808"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1294cf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "len1215 = len_a2012+len_a2013+len_a2014+len_a2015 # y:2015\n",
    "len1316 = len1215+len_a2016 # y:2016\n",
    "len1417 = len1316+len_a2017 # y:2017\n",
    "len1518 = len1417+len_a2018 # y:2018\n",
    "len1619 = len1518+len_a2019 # y:2019\n",
    "len1720 = len1619+len_a2020 # y:2020\n",
    "len1821 = len1720+len_a2021 # y:2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dfdbd29",
   "metadata": {},
   "source": [
    "## [0], [1]은 train 슬라이싱 인덱스, [2]는 test인덱스로 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eae1dcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "slicing_dic = {'1215':[0, len1215, len_a2015],\n",
    "               '1316':[len_a2012, len1316, len_a2016],\n",
    "               '1417':[len_a2012+len_a2013, len1417, len_a2017],\n",
    "               '1518':[len_a2012+len_a2013+len_a2014, len1518, len_a2018],\n",
    "               '1619':[len_a2012+len_a2013+len_a2014+len_a2015, len1619, len_a2019],\n",
    "               '1720':[len_a2012+len_a2013+len_a2014+len_a2015+len_a2016,len1720, len_a2020],\n",
    "               '1821':[len_a2012+len_a2013+len_a2014+len_a2015+len_a2016+len_a2017,len1821, len_a2021]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709ddf1b",
   "metadata": {},
   "source": [
    "## 슬라이싱 테스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b0edf41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# d1221[:len1215]\n",
    "# d1221[len_a2012:len1316]\n",
    "# d1221[len_a2012+len_a2013:len1417]\n",
    "# d1221[len_a2012+len_a2013+len_a2014:len1518]\n",
    "# d1221[len_a2012+len_a2013+len_a2014+len_a2015:len1619]\n",
    "# d1221[len_a2012+len_a2013+len_a2014+len_a2015+len_a2016:len1720]\n",
    "# d1221[len_a2012+len_a2013+len_a2014+len_a2015+len_a2016+len_a2017:len1821]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81d8b16",
   "metadata": {},
   "source": [
    "# data load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bcc7c845",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1221 = pd.read_feather('d1221.ftr', columns = None, use_threads = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e68d2d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2843050 entries, 0 to 2843049\n",
      "Columns: 823 entries, date to NextChange\n",
      "dtypes: category(1), float64(821), int64(1)\n",
      "memory usage: 17.4 GB\n"
     ]
    }
   ],
   "source": [
    "d1221.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ed0484",
   "metadata": {},
   "source": [
    "# category to object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ae7ccf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1221['code'] = list(map(mk_cd, d1221['code'])) # mk_cd함수 적용시켜야 ftr만들 때 에러 안남."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08aa8296",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2843050 entries, 0 to 2843049\n",
      "Columns: 823 entries, date to NextChange\n",
      "dtypes: float64(821), int64(1), object(1)\n",
      "memory usage: 17.4+ GB\n"
     ]
    }
   ],
   "source": [
    "d1221.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c42a34",
   "metadata": {},
   "source": [
    "# modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c1dfdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "def my_regressor():\n",
    "    X = d1221.iloc[:,2:-1] # 1221 데이터\n",
    "    y = d1221.iloc[:,-1]\n",
    "    from sklearn.linear_model import LinearRegression # 일반 회귀 모델\n",
    "    from sklearn.linear_model import Ridge, Lasso, ElasticNet # Norm 규제 회귀 모델\n",
    "    from sklearn.linear_model import ARDRegression, BayesianRidge # 베이지안 회귀\n",
    "    from sklearn.tree import DecisionTreeRegressor\n",
    "    from sklearn.ensemble import RandomForestRegressor # decision tree 앙상블 모델, 배깅\n",
    "    from xgboost import XGBRegressor # decision tree 앙상블 모델, 부스팅\n",
    "    import lightgbm as lgb\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "#     from sklearn.model_selection import cross_val_score # model 검증\n",
    "    \n",
    "#     from sklearn.model_selection import StratifiedShuffleSplit\n",
    "    from sklearn.model_selection import ShuffleSplit\n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    \n",
    "    from sklearn.dummy import DummyRegressor\n",
    "    from sklearn.metrics import mean_squared_error as mse # 모델 평가 지표 scoring (mse)\n",
    "    from sklearn.metrics import r2_score as r2\n",
    "    \n",
    "    import ast # convert string to function\n",
    "    \n",
    "    # seed 고정\n",
    "    user_seed = 0\n",
    "    random.seed(user_seed) # seed 고정\n",
    "    \n",
    "    # (같은 줄에 써놓은 애들끼리 하이퍼파리미터 구성 비슷하거나 같음)\n",
    "    linear = LinearRegression(n_jobs=64)\n",
    "    ridge, lasso, elasticnet = Ridge(), Lasso(alpha=0.01, max_iter=10, random_state=user_seed), ElasticNet(alpha=0.01, max_iter=10, random_state=user_seed)\n",
    "    # [lasso 학습시간 단축 하이퍼파라미터 참고](https://tensorflow.blog/%ED%8C%8C%EC%9D%B4%EC%8D%AC-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D/2-3-3-%EC%84%A0%ED%98%95-%EB%AA%A8%EB%8D%B8/comment-page-1/)\n",
    "    # [elastic net 참고](https://soobarkbar.tistory.com/30)\n",
    "    # elastic net은 큰 데이터 셋에서 잘 작동함. + ridge&lasso의 장점 모두 가짐.\n",
    "    # [모델훈련](https://taek98.tistory.com/15)\n",
    "    ardr_linear, bayesian_ridge = ARDRegression(verbose = True), BayesianRidge(verbose = True) # n_iter=10, \n",
    "    DecisionTree = DecisionTreeRegressor(max_depth = 10, random_state=user_seed, max_leaf_nodes=4)\n",
    "    random_forest = RandomForestRegressor(n_estimators=10,\n",
    "                                          n_jobs=64,\n",
    "                                          max_depth=10,\n",
    "                                          random_state=user_seed,\n",
    "                                          verbose=2,\n",
    "                                          max_leaf_nodes=4)\n",
    "    xgboost_linear = XGBRegressor(n_jobs=64, max_depth=10, n_estimators=10, learning_rate=0.05) # 16에서 64로 증가 # , verbosity=2\n",
    "    gbm = lgb.LGBMRegressor(num_leaves=31,\n",
    "                            learning_rate=0.05,\n",
    "                            n_estimators=20)\n",
    "\n",
    "    dummy = DummyRegressor(strategy = 'mean')\n",
    "    \n",
    "    my_model_list = ['DecisionTree', 'random_forest',\n",
    "                     'xgboost_linear', 'gbm', 'dummy',\n",
    "                     \n",
    "                     'linear', 'ridge', 'lasso', 'elasticnet',\n",
    "                     'ardr_linear', 'bayesian_ridge'] # 오래 걸리는걸 앞으로 뺌\n",
    "    \n",
    "    i = 0\n",
    "    \n",
    "    ######################################################################### fitting\n",
    "    for model_nm in tqdm(my_model_list):\n",
    "        cntcnt = 0\n",
    "        # cv\n",
    "        for slicer in slicing_dic:\n",
    "            tmpX = X[slicing_dic[slicer][0]:slicing_dic[slicer][1]].reset_index(drop = True)\n",
    "            tmpy = y[slicing_dic[slicer][0]:slicing_dic[slicer][1]].reset_index(drop = True)\n",
    "#             tmp_all = d1221[slicing_dic[slicer][0]:slicing_dic[slicer][1]].reset_index(drop = True) # 전체데이터\n",
    "\n",
    "            X_train = tmpX[:-slicing_dic[slicer][2]]\n",
    "            X_test = tmpX[-slicing_dic[slicer][2]:]\n",
    "            y_train = tmpy[:-slicing_dic[slicer][2]]\n",
    "            y_test = tmpy[-slicing_dic[slicer][2]:]\n",
    "            \n",
    "            # train끼리 idx 같아야 하므로\n",
    "            len_train = slicing_dic[slicer][1]-(slicing_dic[slicer][2]+slicing_dic[slicer][0]) # 혹은 tmp_train.shape[0]\n",
    "            len(list(range(len_train)))\n",
    "            train_idx = list(range(len_train))\n",
    "            random.shuffle(train_idx) # 자동으로 덮어쓰기\n",
    "\n",
    "            len_train_to_test = slicing_dic[slicer][1]-slicing_dic[slicer][0]\n",
    "            test_idx = list(range(len_train,len_train_to_test)) # +1 안해줘도 되는 거 위 cell에서 확인\n",
    "            len(test_idx)\n",
    "            random.shuffle(test_idx) # 자동으로 덮어쓰기\n",
    "            \n",
    "            X_train = X_train.loc[train_idx]\n",
    "            y_train = y_train.loc[train_idx]\n",
    "            X_test = X_test.loc[test_idx]\n",
    "            y_test = y_test.loc[test_idx]\n",
    "            ###########################################\n",
    "            y_train = np.array(y_train).reshape(-1,1) # 1열짜리로 만드는 것\n",
    "            y_test = np.array(y_test).reshape(-1,1)\n",
    "\n",
    "            ######################################################## cv를 위한 데이터 slicing 작업 끝\n",
    "\n",
    "            # fitting은 cv 안에 넣기\n",
    "            if model_nm == 'gbm':\n",
    "                model_ = eval(model_nm).fit(X_train, y_train,\n",
    "                                            eval_set=[(X_test, y_test)],\n",
    "                                            eval_metric='l1',\n",
    "                                            early_stopping_rounds=5)\n",
    "                y_pred_test = model_.predict(X_test, num_iteration=gbm.best_iteration_) # 예측\n",
    "\n",
    "            elif model_nm == 'xgboost_linear': # xgboost_linear가 느려서\n",
    "                cntcnt += 1\n",
    "                print(f'$$$$$---{cntcnt}')\n",
    "                model_ = eval(model_nm).fit(X_train, y_train,\n",
    "                                            eval_set=[(X_test, y_test)],\n",
    "                                            eval_metric='rmse',\n",
    "                                            early_stopping_rounds=5) # ,verbose = True\n",
    "                y_pred_test = model_.predict(X_test) # 예측\n",
    "                \n",
    "            else:\n",
    "                cntcnt += 1\n",
    "                print(f'$$$$$---{cntcnt}')\n",
    "                model_ = eval(model_nm).fit(X_train, y_train)\n",
    "                y_pred_test = model_.predict(X_test) # 예측\n",
    "\n",
    "            mse_score = mse(y_test, y_pred_test)\n",
    "            r2_score = r2(y_test, y_pred_test)\n",
    "            cv_scores = cross_val_score(eval(model_nm), tmpX, tmpy, cv=7)\n",
    "\n",
    "            results.append([i, f'{model_nm}', mse_score, r2_score, cv_scores]) #[:str(model).index(\"(\")]\n",
    "            i+=1\n",
    "        ################################################################\n",
    "        print(results[-7:])\n",
    "    return results#score_dic#[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8458a5",
   "metadata": {},
   "source": [
    "[검색어: xgbregressor___] <br>\n",
    "\n",
    "[xgb regressor parameter](https://xgboost.readthedocs.io/en/stable/parameter.html) <br>\n",
    "[xgbregressor라고 검색해서 classxgboost.dask.DaskXGBRegressor라고 돼있는 파란 박스 보기](https://xgboost.readthedocs.io/en/stable/python/python_api.html) - 여기서 fit 안에 parameter도 확인 <br>\n",
    "[xgboost](https://injo.tistory.com/44) <br>\n",
    "[xgboost metric 확인](https://deep-deep-deep.tistory.com/160) <br>\n",
    "<br>\n",
    "\n",
    "[Decision Tree regression 병렬처리] <br>\n",
    "[병렬처리를통한 속도 해결 논문](https://patents.google.com/patent/KR20160001375A/ko) <br>\n",
    "[결정 트리의 앙상블](https://tensorflow.blog/%ED%8C%8C%EC%9D%B4%EC%8D%AC-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D/2-3-6-%EA%B2%B0%EC%A0%95-%ED%8A%B8%EB%A6%AC%EC%9D%98-%EC%95%99%EC%83%81%EB%B8%94/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14ec8721",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                    | 0/11 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---1\n",
      "$$$$$---2\n",
      "$$$$$---3\n",
      "$$$$$---4\n",
      "$$$$$---5\n",
      "$$$$$---6\n",
      "$$$$$---7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  9%|███▎                                | 1/11 [4:14:52<42:28:47, 15292.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 'DecisionTree', 20.447291795548274, 0.0035265375603458216, array([0.01405012, 0.01226554, 0.01315876, 0.01519267, 0.01197257,\n",
      "       0.00270937, 0.00612972])], [1, 'DecisionTree', 14.87985054605241, -0.002389782688752895, array([ 0.00946988,  0.01217002,  0.01289997,  0.00196199,  0.00231116,\n",
      "       -0.00037709, -0.00230182])], [2, 'DecisionTree', 13.639345690521145, 0.0011683704216332025, array([ 0.00499926, -0.01249419,  0.00188581,  0.00322636,  0.00106242,\n",
      "       -0.00099545,  0.00223977])], [3, 'DecisionTree', 16.525384090038386, -0.0011049997821852564, array([-0.0182933 , -0.0007949 , -0.00016497,  0.00152865,  0.0029646 ,\n",
      "        0.00178123,  0.00144303])], [4, 'DecisionTree', 14.794120517280113, 0.005122367540025818, array([0.0012748 , 0.00402795, 0.00087567, 0.00449716, 0.00465782,\n",
      "       0.00558872, 0.00466857])], [5, 'DecisionTree', 23.263032413330418, 0.0002753500199895731, array([-0.00449635, -0.00344979, -0.00329516, -0.00480872,  0.00962501,\n",
      "        0.02132431,  0.02335139])], [6, 'DecisionTree', 16.256849056674465, -0.0035039010080668564, array([-0.00227765, -0.00352618, -0.00593414,  0.01915191,  0.03319467,\n",
      "        0.01386662, -0.00356716])]]\n",
      "$$$$$---1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10building tree 5 of 10\n",
      "\n",
      "building tree 6 of 10building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.4min remaining:  5.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.2min remaining:  5.1min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.0min remaining:  4.6min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 7 of 10\n",
      "building tree 5 of 10\n",
      "building tree 3 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.1min remaining:  5.0min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 4 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.1min remaining:  4.8min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 10 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.1min remaining:  7.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 6 of 10\n",
      "building tree 8 of 10\n",
      "building tree 1 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 10 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  7.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 8 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 3 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 5 of 10\n",
      "building tree 7 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.1min remaining:  7.1min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 8 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  1.6min remaining:  3.8min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 10 of 10\n",
      "building tree 9 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.2min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.8min remaining:  9.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.8min remaining:  8.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 6 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.7min remaining:  8.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  1.9min remaining:  4.4min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 3 of 10\n",
      "building tree 7 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.0min remaining:  4.7min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 6 of 10\n",
      "building tree 6 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 2 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.7min remaining:  6.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 3 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 1 of 10\n",
      "building tree 9 of 10\n",
      "building tree 4 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.4min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.6min remaining:  8.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.7min remaining:  8.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.6min remaining:  8.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.7min remaining:  8.6min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.6min remaining:  8.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.7min remaining:  8.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 7 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 7 of 10\n",
      "building tree 1 of 10\n",
      "building tree 3 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 4 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 8 of 10\n",
      "building tree 8 of 10\n",
      "building tree 1 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.4min remaining:  5.6min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.2min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  4.0min remaining:  9.3min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  7.1min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 4 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 3 of 10\n",
      "building tree 9 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 4 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 10 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 4 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 4 of 10\n",
      "building tree 6 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  7.1min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 4 of 10\n",
      "building tree 6 of 10\n",
      "building tree 4 of 10\n",
      "building tree 6 of 10\n",
      "building tree 10 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 5 of 10\n",
      "building tree 3 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  6.9min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  7.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.0min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 8 of 10\n",
      "building tree 6 of 10\n",
      "$$$$$---5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 4 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 4 of 10\n",
      "building tree 1 of 10\n",
      "building tree 3 of 10\n",
      "building tree 9 of 10\n",
      "building tree 1 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 5 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 2 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 6 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.4min remaining:  5.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.2min remaining:  7.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.3min remaining:  7.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.8min remaining:  6.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.8min remaining:  6.6min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.8min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.9min remaining:  6.8min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.9min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 10 of 10\n",
      "building tree 9 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 2 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 4 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 1 of 10\n",
      "building tree 4 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 5 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 10 of 10\n",
      "building tree 9 of 10\n",
      "building tree 8 of 10\n",
      "building tree 1 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  2.4min remaining:  5.7min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.2min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.2min remaining:  7.6min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.3min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.1min remaining:  7.3min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.2min remaining:  7.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.3min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.1min remaining:  7.3min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.2min remaining:  7.5min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.2min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.1min remaining:  7.3min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.2min remaining:  7.5min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 10 of 10\n",
      "building tree 8 of 10\n",
      "building tree 1 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 9 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.2min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$$$$$---7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 4 of 10\n",
      "building tree 3 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 2 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 4 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.0min remaining:  7.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.1min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.5min remaining:  8.3min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.6min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.4min remaining:  8.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.4min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
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      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.5min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.4min remaining:  8.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.5min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
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      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.5min remaining:  8.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 6 of 10\n",
      "building tree 10 of 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  4.7min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.4min remaining:  8.0min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.4min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
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      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=64)]: Using backend LokyBackend with 64 concurrent workers.\n",
      "[Parallel(n_jobs=64)]: Done   3 out of  10 | elapsed:  3.5min remaining:  8.2min\n",
      "[Parallel(n_jobs=64)]: Done  10 out of  10 | elapsed:  3.5min finished\n",
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      " 18%|██████▌                             | 2/11 [7:45:11<34:17:58, 13719.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7, 'random_forest', 20.44565358450195, 0.003606373748426983, array([ 0.00246136,  0.00437214, -0.00323899,  0.01426546,  0.01267653,\n",
      "        0.00297055,  0.00645231])], [8, 'random_forest', 14.845105995140418, -4.919581723150657e-05, array([0.00628882, 0.00923687, 0.01226374, 0.00106842, 0.00491803,\n",
      "       0.00086677, 0.00144416])], [9, 'random_forest', 13.635969174568876, 0.001415638216396009, array([ 0.00544177, -0.00450218,  0.00189107,  0.00287889,  0.0008559 ,\n",
      "       -0.00013156,  0.00241456])], [10, 'random_forest', 16.49362166407226, 0.0008191626618857528, array([-0.00563761,  0.00311593,  0.00045241,  0.00306874,  0.00390843,\n",
      "        0.00097895,  0.00235553])], [11, 'random_forest', 14.823525748098751, 0.003144919378466504, array([0.00147878, 0.0040849 , 0.00258724, 0.00467767, 0.00515655,\n",
      "       0.00575855, 0.00367187])], [12, 'random_forest', 23.258904670913353, 0.00045273901080500334, array([-0.00421108, -0.00324414, -0.00268638, -0.00379628,  0.0114286 ,\n",
      "        0.031977  ,  0.02352195])], [13, 'random_forest', 16.247642584248606, -0.0029356032425116307, array([-0.0022002 , -0.00339645, -0.00558811,  0.0270969 ,  0.02652153,\n",
      "        0.01439309, -0.00292966])]]\n",
      "$$$$$---1\n",
      "[0]\tvalidation_0-rmse:4.53772\n",
      "[1]\tvalidation_0-rmse:4.53511\n",
      "[2]\tvalidation_0-rmse:4.53288\n",
      "[3]\tvalidation_0-rmse:4.53111\n",
      "[4]\tvalidation_0-rmse:4.52893\n",
      "[5]\tvalidation_0-rmse:4.52690\n",
      "[6]\tvalidation_0-rmse:4.52517\n",
      "[7]\tvalidation_0-rmse:4.52434\n",
      "[8]\tvalidation_0-rmse:4.52289\n",
      "[9]\tvalidation_0-rmse:4.52165\n",
      "building tree 9 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 4 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 6 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 7 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 2 of 10\n",
      "building tree 1 of 10\n",
      "building tree 4 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 10 of 10\n",
      "$$$$$---2\n",
      "[0]\tvalidation_0-rmse:3.87281\n",
      "[1]\tvalidation_0-rmse:3.86992\n",
      "[2]\tvalidation_0-rmse:3.86752\n",
      "[3]\tvalidation_0-rmse:3.86547\n",
      "[4]\tvalidation_0-rmse:3.86415\n",
      "[5]\tvalidation_0-rmse:3.86265\n",
      "[6]\tvalidation_0-rmse:3.86122\n",
      "[7]\tvalidation_0-rmse:3.86020\n",
      "[8]\tvalidation_0-rmse:3.85996\n",
      "[9]\tvalidation_0-rmse:3.85935\n",
      "building tree 6 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 2 of 10\n",
      "building tree 6 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 1 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 5 of 10\n",
      "building tree 9 of 10\n",
      "building tree 6 of 10\n",
      "building tree 4 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 4 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 4 of 10\n",
      "building tree 9 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 4 of 10\n",
      "building tree 9 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 2 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 4 of 10\n",
      "building tree 7 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 9 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 3 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 4 of 10\n",
      "building tree 1 of 10\n",
      "building tree 6 of 10\n",
      "building tree 1 of 10\n",
      "building tree 4 of 10\n",
      "building tree 7 of 10\n",
      "building tree 10 of 10\n",
      "building tree 4 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 4 of 10\n",
      "building tree 7 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 5 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 7 of 10\n",
      "building tree 3 of 10\n",
      "building tree 7 of 10\n",
      "building tree 3 of 10\n",
      "building tree 8 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 2 of 10\n",
      "building tree 6 of 10\n",
      "building tree 5 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 9 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "building tree 10 of 10\n",
      "building tree 2 of 10\n",
      "building tree 5 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 6 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 1 of 10\n",
      "building tree 9 of 10\n",
      "building tree 9 of 10\n",
      "building tree 8 of 10\n",
      "building tree 7 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 1 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 4 of 10\n",
      "building tree 10 of 10\n",
      "building tree 4 of 10\n",
      "building tree 4 of 10\n",
      "building tree 1 of 10\n",
      "building tree 7 of 10\n",
      "building tree 8 of 10\n",
      "building tree 2 of 10\n",
      "building tree 7 of 10\n",
      "building tree 5 of 10\n",
      "building tree 10 of 10\n",
      "building tree 6 of 10\n",
      "building tree 4 of 10\n",
      "building tree 2 of 10\n",
      "building tree 9 of 10\n",
      "building tree 5 of 10\n",
      "building tree 5 of 10\n",
      "building tree 8 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 3 of 10\n",
      "building tree 5 of 10\n",
      "building tree 2 of 10\n",
      "building tree 10 of 10\n",
      "$$$$$---3\n",
      "[0]\tvalidation_0-rmse:3.72283\n",
      "[1]\tvalidation_0-rmse:3.72087\n",
      "[2]\tvalidation_0-rmse:3.71950\n",
      "[3]\tvalidation_0-rmse:3.71945\n",
      "[4]\tvalidation_0-rmse:3.71924\n",
      "[5]\tvalidation_0-rmse:3.71935\n",
      "[6]\tvalidation_0-rmse:3.72038\n",
      "[7]\tvalidation_0-rmse:3.71881\n",
      "[8]\tvalidation_0-rmse:3.71979\n",
      "[9]\tvalidation_0-rmse:3.71845\n",
      "$$$$$---4\n",
      "[0]\tvalidation_0-rmse:4.09065\n",
      "[1]\tvalidation_0-rmse:4.08750\n",
      "[2]\tvalidation_0-rmse:4.08490\n",
      "[3]\tvalidation_0-rmse:4.08251\n",
      "[4]\tvalidation_0-rmse:4.08019\n",
      "[5]\tvalidation_0-rmse:4.07836\n",
      "[6]\tvalidation_0-rmse:4.07656\n",
      "[7]\tvalidation_0-rmse:4.07461\n",
      "[8]\tvalidation_0-rmse:4.07315\n",
      "[9]\tvalidation_0-rmse:4.07173\n",
      "$$$$$---5\n",
      "[0]\tvalidation_0-rmse:3.87861\n",
      "[1]\tvalidation_0-rmse:3.87532\n",
      "[2]\tvalidation_0-rmse:3.87228\n",
      "[3]\tvalidation_0-rmse:3.86964\n",
      "[4]\tvalidation_0-rmse:3.86730\n",
      "[5]\tvalidation_0-rmse:3.86501\n",
      "[6]\tvalidation_0-rmse:3.86269\n",
      "[7]\tvalidation_0-rmse:3.86071\n",
      "[8]\tvalidation_0-rmse:3.85936\n",
      "[9]\tvalidation_0-rmse:3.85774\n",
      "$$$$$---6\n",
      "[0]\tvalidation_0-rmse:4.83031\n",
      "[1]\tvalidation_0-rmse:4.82810\n",
      "[2]\tvalidation_0-rmse:4.82683\n",
      "[3]\tvalidation_0-rmse:4.82657\n",
      "[4]\tvalidation_0-rmse:4.82537\n",
      "[5]\tvalidation_0-rmse:4.82467\n",
      "[6]\tvalidation_0-rmse:4.82445\n",
      "[7]\tvalidation_0-rmse:4.82371\n",
      "[8]\tvalidation_0-rmse:4.82223\n",
      "[9]\tvalidation_0-rmse:4.82161\n",
      "$$$$$---7\n",
      "[0]\tvalidation_0-rmse:4.03930\n",
      "[1]\tvalidation_0-rmse:4.03675\n",
      "[2]\tvalidation_0-rmse:4.03454\n",
      "[3]\tvalidation_0-rmse:4.03250\n",
      "[4]\tvalidation_0-rmse:4.03038\n",
      "[5]\tvalidation_0-rmse:4.02872\n",
      "[6]\tvalidation_0-rmse:4.02724\n",
      "[7]\tvalidation_0-rmse:4.02606\n",
      "[8]\tvalidation_0-rmse:4.02493\n",
      "[9]\tvalidation_0-rmse:4.02397\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|██████████                           | 3/11 [8:15:58<18:26:27, 8298.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14, 'xgboost_linear', 20.445327867524377, 0.003622247162259118, array([ 0.00270119, -0.01920002,  0.00051682,  0.01003605, -0.001402  ,\n",
      "        0.00681282, -0.00236984])], [15, 'xgboost_linear', 14.894559049242977, -0.0033806295572236245, array([-3.28202133e-05,  5.87775331e-03,  8.63062709e-03,  5.26461520e-03,\n",
      "        3.63829124e-03, -8.49103957e-04, -1.04103612e-03])], [16, 'xgboost_linear', 13.826892162422073, -0.012565964963726417, array([ 0.00731241,  0.001422  ,  0.00426586,  0.00379795, -0.00232177,\n",
      "       -0.01147504, -0.00355902])], [17, 'xgboost_linear', 16.579007062304484, -0.004353470458950293, array([ 0.00116737, -0.00650393, -0.00175864, -0.00839824, -0.00364066,\n",
      "        0.00363475,  0.00383983])], [18, 'xgboost_linear', 14.882148870249223, -0.0007973787057184989, array([ 4.95597575e-05,  1.59742721e-03, -2.29430973e-03,  9.87612812e-03,\n",
      "        9.01707002e-03,  4.38568683e-03,  2.36826465e-03])], [19, 'xgboost_linear', 23.24790867122966, 0.0009252901270845326, array([-0.00451778,  0.00012292,  0.00073142, -0.00066704,  0.01985202,\n",
      "        0.06359073,  0.0715749 ])], [20, 'xgboost_linear', 16.1923155435495, 0.00047962814576230084, array([ 0.00340726,  0.00091172, -0.00236382,  0.05244229,  0.06705951,\n",
      "        0.02526662,  0.00169304])]]\n",
      "[1]\tvalid_0's l1: 2.71562\tvalid_0's l2: 20.5187\n",
      "[2]\tvalid_0's l1: 2.71461\tvalid_0's l2: 20.5015\n",
      "[3]\tvalid_0's l1: 2.71376\tvalid_0's l2: 20.4871\n",
      "[4]\tvalid_0's l1: 2.71301\tvalid_0's l2: 20.4737\n",
      "[5]\tvalid_0's l1: 2.71242\tvalid_0's l2: 20.4635\n",
      "[6]\tvalid_0's l1: 2.71195\tvalid_0's l2: 20.4534\n",
      "[7]\tvalid_0's l1: 2.71132\tvalid_0's l2: 20.4449\n",
      "[8]\tvalid_0's l1: 2.71075\tvalid_0's l2: 20.4352\n",
      "[9]\tvalid_0's l1: 2.71039\tvalid_0's l2: 20.4285\n",
      "[10]\tvalid_0's l1: 2.70992\tvalid_0's l2: 20.4206\n",
      "[11]\tvalid_0's l1: 2.70949\tvalid_0's l2: 20.4131\n",
      "[12]\tvalid_0's l1: 2.70916\tvalid_0's l2: 20.4072\n",
      "[13]\tvalid_0's l1: 2.70892\tvalid_0's l2: 20.4028\n",
      "[14]\tvalid_0's l1: 2.70865\tvalid_0's l2: 20.3984\n",
      "[15]\tvalid_0's l1: 2.70836\tvalid_0's l2: 20.3941\n",
      "[16]\tvalid_0's l1: 2.70819\tvalid_0's l2: 20.3908\n",
      "[17]\tvalid_0's l1: 2.70802\tvalid_0's l2: 20.3871\n",
      "[18]\tvalid_0's l1: 2.70781\tvalid_0's l2: 20.3837\n",
      "[19]\tvalid_0's l1: 2.70755\tvalid_0's l2: 20.3793\n",
      "[20]\tvalid_0's l1: 2.70744\tvalid_0's l2: 20.3776\n",
      "[1]\tvalid_0's l1: 2.40198\tvalid_0's l2: 14.8384\n",
      "[2]\tvalid_0's l1: 2.40165\tvalid_0's l2: 14.8328\n",
      "[3]\tvalid_0's l1: 2.40123\tvalid_0's l2: 14.8273\n",
      "[4]\tvalid_0's l1: 2.40089\tvalid_0's l2: 14.8221\n",
      "[5]\tvalid_0's l1: 2.40044\tvalid_0's l2: 14.8188\n",
      "[6]\tvalid_0's l1: 2.40016\tvalid_0's l2: 14.8159\n",
      "[7]\tvalid_0's l1: 2.39987\tvalid_0's l2: 14.8109\n",
      "[8]\tvalid_0's l1: 2.39942\tvalid_0's l2: 14.8071\n",
      "[9]\tvalid_0's l1: 2.39923\tvalid_0's l2: 14.8059\n",
      "[10]\tvalid_0's l1: 2.39873\tvalid_0's l2: 14.8014\n",
      "[11]\tvalid_0's l1: 2.39856\tvalid_0's l2: 14.7981\n",
      "[12]\tvalid_0's l1: 2.39841\tvalid_0's l2: 14.7979\n",
      "[13]\tvalid_0's l1: 2.39825\tvalid_0's l2: 14.7971\n",
      "[14]\tvalid_0's l1: 2.39821\tvalid_0's l2: 14.796\n",
      "[15]\tvalid_0's l1: 2.39826\tvalid_0's l2: 14.7957\n",
      "[16]\tvalid_0's l1: 2.39821\tvalid_0's l2: 14.797\n",
      "[17]\tvalid_0's l1: 2.3981\tvalid_0's l2: 14.7962\n",
      "[18]\tvalid_0's l1: 2.39812\tvalid_0's l2: 14.7961\n",
      "[19]\tvalid_0's l1: 2.39807\tvalid_0's l2: 14.7965\n",
      "[20]\tvalid_0's l1: 2.39805\tvalid_0's l2: 14.7961\n",
      "[1]\tvalid_0's l1: 2.2258\tvalid_0's l2: 13.6551\n",
      "[2]\tvalid_0's l1: 2.2255\tvalid_0's l2: 13.6467\n",
      "[3]\tvalid_0's l1: 2.22524\tvalid_0's l2: 13.6413\n",
      "[4]\tvalid_0's l1: 2.22496\tvalid_0's l2: 13.6353\n",
      "[5]\tvalid_0's l1: 2.22459\tvalid_0's l2: 13.6303\n",
      "[6]\tvalid_0's l1: 2.22425\tvalid_0's l2: 13.6242\n",
      "[7]\tvalid_0's l1: 2.22413\tvalid_0's l2: 13.6212\n",
      "[8]\tvalid_0's l1: 2.22377\tvalid_0's l2: 13.616\n",
      "[9]\tvalid_0's l1: 2.22355\tvalid_0's l2: 13.6118\n",
      "[10]\tvalid_0's l1: 2.22327\tvalid_0's l2: 13.6071\n",
      "[11]\tvalid_0's l1: 2.22315\tvalid_0's l2: 13.6058\n",
      "[12]\tvalid_0's l1: 2.223\tvalid_0's l2: 13.6033\n",
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      "[20]\tvalid_0's l1: 2.22153\tvalid_0's l2: 13.5882\n",
      "[1]\tvalid_0's l1: 2.57781\tvalid_0's l2: 16.5116\n",
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      "[20]\tvalid_0's l1: 2.57142\tvalid_0's l2: 16.4351\n",
      "[1]\tvalid_0's l1: 2.37468\tvalid_0's l2: 14.8615\n",
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      "[8]\tvalid_0's l1: 2.37257\tvalid_0's l2: 14.812\n",
      "[9]\tvalid_0's l1: 2.37253\tvalid_0's l2: 14.8085\n",
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      "[11]\tvalid_0's l1: 2.37263\tvalid_0's l2: 14.8039\n",
      "[12]\tvalid_0's l1: 2.37266\tvalid_0's l2: 14.8016\n",
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      "[14]\tvalid_0's l1: 2.37281\tvalid_0's l2: 14.8\n",
      "[1]\tvalid_0's l1: 3.03091\tvalid_0's l2: 23.2755\n",
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      "[11]\tvalid_0's l1: 3.02572\tvalid_0's l2: 23.1465\n",
      "[12]\tvalid_0's l1: 3.02566\tvalid_0's l2: 23.1414\n",
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      "[14]\tvalid_0's l1: 3.025\tvalid_0's l2: 23.134\n",
      "[15]\tvalid_0's l1: 3.02556\tvalid_0's l2: 23.1426\n",
      "[16]\tvalid_0's l1: 3.02559\tvalid_0's l2: 23.1375\n",
      "[17]\tvalid_0's l1: 3.02471\tvalid_0's l2: 23.1278\n",
      "[18]\tvalid_0's l1: 3.02432\tvalid_0's l2: 23.1235\n",
      "[19]\tvalid_0's l1: 3.02412\tvalid_0's l2: 23.117\n",
      "[20]\tvalid_0's l1: 3.02342\tvalid_0's l2: 23.1034\n",
      "[1]\tvalid_0's l1: 2.55475\tvalid_0's l2: 16.1954\n",
      "[2]\tvalid_0's l1: 2.55457\tvalid_0's l2: 16.1903\n",
      "[3]\tvalid_0's l1: 2.55432\tvalid_0's l2: 16.1847\n",
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      "[5]\tvalid_0's l1: 2.55405\tvalid_0's l2: 16.1758\n",
      "[6]\tvalid_0's l1: 2.5539\tvalid_0's l2: 16.1723\n",
      "[7]\tvalid_0's l1: 2.55388\tvalid_0's l2: 16.1691\n",
      "[8]\tvalid_0's l1: 2.55375\tvalid_0's l2: 16.1664\n",
      "[9]\tvalid_0's l1: 2.55358\tvalid_0's l2: 16.1633\n",
      "[10]\tvalid_0's l1: 2.55364\tvalid_0's l2: 16.1613\n",
      "[11]\tvalid_0's l1: 2.55369\tvalid_0's l2: 16.1597\n",
      "[12]\tvalid_0's l1: 2.55386\tvalid_0's l2: 16.1581\n",
      "[13]\tvalid_0's l1: 2.55384\tvalid_0's l2: 16.1562\n",
      "[14]\tvalid_0's l1: 2.55388\tvalid_0's l2: 16.154\n"
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      "\r",
      " 36%|█████████████▍                       | 4/11 [8:25:57<10:13:31, 5258.78s/it]"
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      "       0.0139097 , 0.01578372])], [25, 'gbm', 14.808476405923647, 0.004156960202100102, array([0.0018516 , 0.00570835, 0.00456742, 0.01901596, 0.0192079 ,\n",
      "       0.01137434, 0.00806986])], [26, 'gbm', 23.10335745366295, 0.007137352803111785, array([0.00185658, 0.01153771, 0.00991819, 0.00425886, 0.02870052,\n",
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      "       0.03345655, 0.00451549])]]\n",
      "$$$$$---1\n",
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      "       -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])]]\n",
      "$$$$$---1\n",
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      "$$$$$---1\n",
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      "$$$$$---1\n",
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      "$$$$$---1\n",
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      "[[63, 'ardr_linear', 20.53742794929616, -0.0008661363504212094, array([-1.70009117e-04, -2.34884934e-04, -2.46682078e-04, -9.65110624e-05,\n",
      "       -6.78959358e-06, -5.08244771e-04, -7.06766075e-04])], [64, 'ardr_linear', 14.844799344342729, -2.8538107968767434e-05, array([-4.23316098e-04, -2.68705463e-04, -6.88936727e-05, -2.56941536e-04,\n",
      "       -4.58500448e-04, -1.55067377e-05, -1.97571903e-05])], [65, 'ardr_linear', 13.663214094243353, -0.000579551885362406, array([-8.07156907e-05, -2.86772300e-05, -4.08641203e-04, -2.62154475e-04,\n",
      "       -2.29039216e-05, -1.14380780e-04, -5.96097037e-04])], [66, 'ardr_linear', 16.51848935038189, -0.0006873176088266764, array([-6.17868121e-04, -8.18904985e-04, -1.89771782e-06, -3.93777863e-06,\n",
      "       -2.86095030e-04, -3.43183450e-04, -6.40506254e-04])], [67, 'ardr_linear', 14.870582145802057, -1.953757485351204e-05, array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
      "       -1.88067208e-04, -3.44907231e-07, -2.30689628e-05])], [68, 'ardr_linear', 23.302422240415527, -0.0014174207416099982, array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
      "       -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])], [69, 'ardr_linear', 16.201372683087488, -7.94515889797065e-05, array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
      "       -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])]]\n",
      "$$$$$---1\n",
      "Convergence after  26  iterations\n",
      "Convergence after  13  iterations\n",
      "Convergence after  8  iterations\n",
      "Convergence after  10  iterations\n",
      "Convergence after  15  iterations\n",
      "Convergence after  12  iterations\n",
      "Convergence after  8  iterations\n",
      "Convergence after  11  iterations\n",
      "$$$$$---2\n",
      "Convergence after  14  iterations\n",
      "Convergence after  7  iterations\n",
      "Convergence after  24  iterations\n",
      "Convergence after  19  iterations\n",
      "Convergence after  14  iterations\n",
      "Convergence after  13  iterations\n",
      "Convergence after  12  iterations\n",
      "Convergence after  9  iterations\n",
      "$$$$$---3\n",
      "Convergence after  27  iterations\n",
      "Convergence after  27  iterations\n",
      "Convergence after  20  iterations\n",
      "Convergence after  32  iterations\n",
      "Convergence after  27  iterations\n",
      "Convergence after  21  iterations\n",
      "Convergence after  21  iterations\n",
      "Convergence after  27  iterations\n",
      "$$$$$---4\n",
      "Convergence after  31  iterations\n",
      "Convergence after  19  iterations\n",
      "Convergence after  20  iterations\n",
      "Convergence after  30  iterations\n",
      "Convergence after  67  iterations\n",
      "Convergence after  13  iterations\n",
      "Convergence after  23  iterations\n",
      "Convergence after  59  iterations\n",
      "$$$$$---5\n",
      "Convergence after  35  iterations\n",
      "Convergence after  34  iterations\n",
      "Convergence after  27  iterations\n",
      "Convergence after  32  iterations\n",
      "Convergence after  36  iterations\n",
      "Convergence after  39  iterations\n",
      "Convergence after  260  iterations\n",
      "Convergence after  43  iterations\n",
      "$$$$$---6\n",
      "Convergence after  44  iterations\n",
      "Convergence after  35  iterations\n",
      "Convergence after  11  iterations\n",
      "Convergence after  15  iterations\n",
      "Convergence after  17  iterations\n",
      "Convergence after  39  iterations\n",
      "Convergence after  17  iterations\n",
      "Convergence after  17  iterations\n",
      "$$$$$---7\n",
      "Convergence after  19  iterations\n",
      "Convergence after  22  iterations\n",
      "Convergence after  82  iterations\n",
      "Convergence after  35  iterations\n",
      "Convergence after  29  iterations\n",
      "Convergence after  27  iterations\n",
      "Convergence after  53  iterations\n",
      "Convergence after  30  iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 11/11 [9:29:44<00:00, 3107.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70, 'bayesian_ridge', 21.290845477007363, -0.03758300722055585, array([-6.46374680e+01, -2.25281914e+05, -3.74956710e+01, -2.35734837e+01,\n",
      "       -1.69015181e+00, -3.76910125e+00, -4.51656771e+00])], [71, 'bayesian_ridge', 180.82999854034776, -11.181717980262585, array([-1.12932011e+03, -7.05574260e+03, -7.17926287e-01, -1.14007874e+00,\n",
      "       -1.19648569e+01, -1.49035055e+00, -5.52095245e+00])], [72, 'bayesian_ridge', 42.85486900924617, -2.1383322645470146, array([-4.09705566e+01, -2.32881167e+00, -4.89366481e+00, -2.96265428e+00,\n",
      "       -5.04010826e+00,  2.63795568e-03, -3.12632675e+00])], [73, 'bayesian_ridge', 16.807027371110184, -0.018166902567609444, array([-9.97990736e+01, -1.60375289e+00, -2.19123585e-01, -6.72700761e-01,\n",
      "       -5.44880985e+00, -3.50050028e+00, -1.22563249e-02])], [74, 'bayesian_ridge', 14.92278896467433, -0.003530350961647999, array([ 5.01524843e-04,  4.11234667e-03, -2.27409795e-03,  7.97276160e-03,\n",
      "        8.06883630e-03, -1.89779898e-02, -6.07871152e+00])], [75, 'bayesian_ridge', 23.73534662349114, -0.020022268538240073, array([-1.36836458e+01, -1.62579084e+02, -1.34220388e+03, -1.01345875e+02,\n",
      "       -3.08084185e-01, -1.47939871e+02, -3.05702414e+01])], [76, 'bayesian_ridge', 411.31996449092617, -24.389987167265932, array([-1.33138167e+02, -3.33178391e-01, -1.63988867e+01,  4.89189385e-02,\n",
      "        5.70996334e-02, -2.83811678e+00, -1.05308871e-02])]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       " [12,\n",
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       "  0.00045273901080500334,\n",
       "  array([-0.00421108, -0.00324414, -0.00268638, -0.00379628,  0.0114286 ,\n",
       "          0.031977  ,  0.02352195])],\n",
       " [13,\n",
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       "          0.01439309, -0.00292966])],\n",
       " [14,\n",
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       "  0.003622247162259118,\n",
       "  array([ 0.00270119, -0.01920002,  0.00051682,  0.01003605, -0.001402  ,\n",
       "          0.00681282, -0.00236984])],\n",
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       "  'xgboost_linear',\n",
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       "          3.63829124e-03, -8.49103957e-04, -1.04103612e-03])],\n",
       " [16,\n",
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       "          9.01707002e-03,  4.38568683e-03,  2.36826465e-03])],\n",
       " [19,\n",
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       " [21,\n",
       "  'gbm',\n",
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       "  0.006920775403688517,\n",
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       "  0.004156960202100102,\n",
       "  array([0.0018516 , 0.00570835, 0.00456742, 0.01901596, 0.0192079 ,\n",
       "         0.01137434, 0.00806986])],\n",
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       "  'gbm',\n",
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       "         0.03345655, 0.00451549])],\n",
       " [28,\n",
       "  'dummy',\n",
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       " [29,\n",
       "  'dummy',\n",
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       " [30,\n",
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       " [31,\n",
       "  'dummy',\n",
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       " [32,\n",
       "  'dummy',\n",
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       "  -1.953757485351204e-05,\n",
       "  array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
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       " [33,\n",
       "  'dummy',\n",
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       "  -0.0014174207416097762,\n",
       "  array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
       "         -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])],\n",
       " [34,\n",
       "  'dummy',\n",
       "  16.201372683087488,\n",
       "  -7.945158897992854e-05,\n",
       "  array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
       "         -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])],\n",
       " [35,\n",
       "  'linear',\n",
       "  20.54202370140966,\n",
       "  -0.0010901046424984795,\n",
       "  array([-1.52580320e-03, -1.59972052e+05,  1.79551952e-03,  3.78879766e-03,\n",
       "          3.40729510e-03, -7.08667900e-04,  3.61790592e-03])],\n",
       " [36,\n",
       "  'linear',\n",
       "  14.878200753010933,\n",
       "  -0.0022786434213790674,\n",
       "  array([-5.90552782e-02, -1.07878143e+01,  1.53074598e-03, -8.59846440e-03,\n",
       "          4.86009758e-03,  2.85421155e-03, -1.34814466e-02])],\n",
       " [37,\n",
       "  'linear',\n",
       "  13.7170024447564,\n",
       "  -0.004518560912211811,\n",
       "  array([ 0.00818563, -0.00383357,  0.00561133,  0.0059827 ,  0.00020013,\n",
       "          0.00176022, -0.00278919])],\n",
       " [38,\n",
       "  'linear',\n",
       "  16.8062533650501,\n",
       "  -0.01812001341015379,\n",
       "  array([ 0.00334053,  0.00234252, -0.00289987,  0.00143955, -0.00404967,\n",
       "         -0.01948539,  0.00154861])],\n",
       " [39,\n",
       "  'linear',\n",
       "  14.936050899280056,\n",
       "  -0.0044221918850047,\n",
       "  array([-0.00073986,  0.00415489, -0.00339393,  0.01213842,  0.00899272,\n",
       "          0.00928571,  0.00142224])],\n",
       " [40,\n",
       "  'linear',\n",
       "  23.765181626424372,\n",
       "  -0.021304422443828575,\n",
       "  array([-0.01948649, -0.04238908, -0.01990264, -0.01521807,  0.00951779,\n",
       "          0.06039634,  0.0639197 ])],\n",
       " [41,\n",
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       "          0.01798185, -0.01341024])],\n",
       " [42,\n",
       "  'ridge',\n",
       "  20.49326438832787,\n",
       "  0.0012861201416252133,\n",
       "  array([ 3.63893942e-03, -1.01845248e+04,  6.15737613e-03,  1.09373492e-02,\n",
       "          8.29444866e-03,  4.77399226e-03,  6.43706286e-03])],\n",
       " [43,\n",
       "  'ridge',\n",
       "  14.924352683034593,\n",
       "  -0.005387695018631344,\n",
       "  array([-4.45543497e-02, -3.72614621e+00,  1.30394095e-02,  1.75529260e-03,\n",
       "          7.87957721e-03,  4.11878423e-03,  1.14938266e-04])],\n",
       " [44,\n",
       "  'ridge',\n",
       "  13.687621649028053,\n",
       "  -0.00236695710792878,\n",
       "  array([ 0.01075166,  0.0056566 ,  0.00882092,  0.00822959,  0.00032359,\n",
       "          0.00437414, -0.00134697])],\n",
       " [45,\n",
       "  'ridge',\n",
       "  16.852446943563734,\n",
       "  -0.02091841265799399,\n",
       "  array([ 0.00608359,  0.00458079, -0.00253694,  0.0025402 , -0.00387427,\n",
       "         -0.02235075,  0.0022251 ])],\n",
       " [46,\n",
       "  'ridge',\n",
       "  14.926984204812955,\n",
       "  -0.0038124732122972738,\n",
       "  array([ 2.28361644e-05,  4.25151576e-03, -2.80673562e-03,  1.24744299e-02,\n",
       "          1.16964391e-02,  9.67035512e-03,  2.10778685e-03])],\n",
       " [47,\n",
       "  'ridge',\n",
       "  23.744937227371548,\n",
       "  -0.02043442302167464,\n",
       "  array([-0.01664559, -0.03059095, -0.0189909 , -0.01289432,  0.0128039 ,\n",
       "          0.0599099 ,  0.06459717])],\n",
       " [48,\n",
       "  'ridge',\n",
       "  16.541351631286858,\n",
       "  -0.021065695576932386,\n",
       "  array([-0.04927274, -0.00979938, -0.01485642,  0.05189038,  0.05812156,\n",
       "          0.01877641, -0.0096801 ])],\n",
       " [49,\n",
       "  'lasso',\n",
       "  20.497467796584864,\n",
       "  0.001081272241875153,\n",
       "  array([ 0.00212106, -1.06867761,  0.00122722,  0.00318312,  0.00252694,\n",
       "          0.00267687,  0.00315093])],\n",
       " [50,\n",
       "  'lasso',\n",
       "  14.85593228117927,\n",
       "  -0.0007785148702839084,\n",
       "  array([-1.77160110e-02, -1.06335433e+00,  1.39696222e-03,  2.48675486e-03,\n",
       "          3.08344861e-03,  2.47169462e-03,  7.16289644e-04])],\n",
       " [51,\n",
       "  'lasso',\n",
       "  13.64872690028428,\n",
       "  0.0004813690619094313,\n",
       "  array([0.00085473, 0.00161368, 0.00257512, 0.00394436, 0.00046013,\n",
       "         0.0028046 , 0.00038119])],\n",
       " [52,\n",
       "  'lasso',\n",
       "  16.46084037122641,\n",
       "  0.002805047890846457,\n",
       "  array([ 1.80127845e-03,  2.96667524e-03, -9.20363802e-05,  2.06771003e-03,\n",
       "          1.67301915e-04,  5.38908078e-03,  5.57259603e-03])],\n",
       " [53,\n",
       "  'lasso',\n",
       "  14.87988932737415,\n",
       "  -0.000645428566920625,\n",
       "  array([0.00166798, 0.00278359, 0.0011938 , 0.00366199, 0.00384868,\n",
       "         0.00403075, 0.0015772 ])],\n",
       " [54,\n",
       "  'lasso',\n",
       "  23.330851984712226,\n",
       "  -0.002639183909070253,\n",
       "  array([-0.00329832, -0.00292185, -0.00428743, -0.00258045,  0.00393033,\n",
       "          0.01456471,  0.01599418])],\n",
       " [55,\n",
       "  'lasso',\n",
       "  16.289791305451303,\n",
       "  -0.005537362415027669,\n",
       "  array([-0.00289694, -0.00186393, -0.00425899,  0.01275383,  0.01404195,\n",
       "          0.00616926, -0.00161048])],\n",
       " [56,\n",
       "  'elasticnet',\n",
       "  20.49723284912669,\n",
       "  0.0010927221155058175,\n",
       "  array([ 0.00211263, -1.10871278,  0.0014043 ,  0.00327565,  0.00256519,\n",
       "          0.00282007,  0.00328338])],\n",
       " [57,\n",
       "  'elasticnet',\n",
       "  14.856047623395368,\n",
       "  -0.000786284965728079,\n",
       "  array([-1.74891592e-02, -9.46840157e-01,  1.27334471e-03,  2.65646135e-03,\n",
       "          3.33942066e-03,  2.56541719e-03,  7.98101628e-04])],\n",
       " [58,\n",
       "  'elasticnet',\n",
       "  13.648757353331924,\n",
       "  0.00047913893534057284,\n",
       "  array([0.00083962, 0.00181324, 0.00278631, 0.00406786, 0.00041377,\n",
       "         0.0027894 , 0.00045676])],\n",
       " [59,\n",
       "  'elasticnet',\n",
       "  16.455807675606057,\n",
       "  0.0031099277484318844,\n",
       "  array([ 2.11040005e-03,  3.16080681e-03, -8.42702075e-05,  2.05772570e-03,\n",
       "          1.71542222e-04,  5.81471672e-03,  5.96394543e-03])],\n",
       " [60,\n",
       "  'elasticnet',\n",
       "  14.877740238036827,\n",
       "  -0.0005009062271597031,\n",
       "  array([0.00175604, 0.00283771, 0.00118768, 0.00408041, 0.00424696,\n",
       "         0.00429832, 0.00167063])],\n",
       " [61,\n",
       "  'elasticnet',\n",
       "  23.339208147312345,\n",
       "  -0.002998288499656665,\n",
       "  array([-0.00340366, -0.00301661, -0.00397146, -0.00269889,  0.00413545,\n",
       "          0.01520734,  0.01685657])],\n",
       " [62,\n",
       "  'elasticnet',\n",
       "  16.288091170084243,\n",
       "  -0.005432416341705659,\n",
       "  array([-0.00290652, -0.00178731, -0.00445904,  0.01322118,  0.01457514,\n",
       "          0.00654078, -0.00149189])],\n",
       " [63,\n",
       "  'ardr_linear',\n",
       "  20.53742794929616,\n",
       "  -0.0008661363504212094,\n",
       "  array([-1.70009117e-04, -2.34884934e-04, -2.46682078e-04, -9.65110624e-05,\n",
       "         -6.78959358e-06, -5.08244771e-04, -7.06766075e-04])],\n",
       " [64,\n",
       "  'ardr_linear',\n",
       "  14.844799344342729,\n",
       "  -2.8538107968767434e-05,\n",
       "  array([-4.23316098e-04, -2.68705463e-04, -6.88936727e-05, -2.56941536e-04,\n",
       "         -4.58500448e-04, -1.55067377e-05, -1.97571903e-05])],\n",
       " [65,\n",
       "  'ardr_linear',\n",
       "  13.663214094243353,\n",
       "  -0.000579551885362406,\n",
       "  array([-8.07156907e-05, -2.86772300e-05, -4.08641203e-04, -2.62154475e-04,\n",
       "         -2.29039216e-05, -1.14380780e-04, -5.96097037e-04])],\n",
       " [66,\n",
       "  'ardr_linear',\n",
       "  16.51848935038189,\n",
       "  -0.0006873176088266764,\n",
       "  array([-6.17868121e-04, -8.18904985e-04, -1.89771782e-06, -3.93777863e-06,\n",
       "         -2.86095030e-04, -3.43183450e-04, -6.40506254e-04])],\n",
       " [67,\n",
       "  'ardr_linear',\n",
       "  14.870582145802057,\n",
       "  -1.953757485351204e-05,\n",
       "  array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
       "         -1.88067208e-04, -3.44907231e-07, -2.30689628e-05])],\n",
       " [68,\n",
       "  'ardr_linear',\n",
       "  23.302422240415527,\n",
       "  -0.0014174207416099982,\n",
       "  array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
       "         -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])],\n",
       " [69,\n",
       "  'ardr_linear',\n",
       "  16.201372683087488,\n",
       "  -7.94515889797065e-05,\n",
       "  array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
       "         -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])],\n",
       " [70,\n",
       "  'bayesian_ridge',\n",
       "  21.290845477007363,\n",
       "  -0.03758300722055585,\n",
       "  array([-6.46374680e+01, -2.25281914e+05, -3.74956710e+01, -2.35734837e+01,\n",
       "         -1.69015181e+00, -3.76910125e+00, -4.51656771e+00])],\n",
       " [71,\n",
       "  'bayesian_ridge',\n",
       "  180.82999854034776,\n",
       "  -11.181717980262585,\n",
       "  array([-1.12932011e+03, -7.05574260e+03, -7.17926287e-01, -1.14007874e+00,\n",
       "         -1.19648569e+01, -1.49035055e+00, -5.52095245e+00])],\n",
       " [72,\n",
       "  'bayesian_ridge',\n",
       "  42.85486900924617,\n",
       "  -2.1383322645470146,\n",
       "  array([-4.09705566e+01, -2.32881167e+00, -4.89366481e+00, -2.96265428e+00,\n",
       "         -5.04010826e+00,  2.63795568e-03, -3.12632675e+00])],\n",
       " [73,\n",
       "  'bayesian_ridge',\n",
       "  16.807027371110184,\n",
       "  -0.018166902567609444,\n",
       "  array([-9.97990736e+01, -1.60375289e+00, -2.19123585e-01, -6.72700761e-01,\n",
       "         -5.44880985e+00, -3.50050028e+00, -1.22563249e-02])],\n",
       " [74,\n",
       "  'bayesian_ridge',\n",
       "  14.92278896467433,\n",
       "  -0.003530350961647999,\n",
       "  array([ 5.01524843e-04,  4.11234667e-03, -2.27409795e-03,  7.97276160e-03,\n",
       "          8.06883630e-03, -1.89779898e-02, -6.07871152e+00])],\n",
       " [75,\n",
       "  'bayesian_ridge',\n",
       "  23.73534662349114,\n",
       "  -0.020022268538240073,\n",
       "  array([-1.36836458e+01, -1.62579084e+02, -1.34220388e+03, -1.01345875e+02,\n",
       "         -3.08084185e-01, -1.47939871e+02, -3.05702414e+01])],\n",
       " [76,\n",
       "  'bayesian_ridge',\n",
       "  411.31996449092617,\n",
       "  -24.389987167265932,\n",
       "  array([-1.33138167e+02, -3.33178391e-01, -1.63988867e+01,  4.89189385e-02,\n",
       "          5.70996334e-02, -2.83811678e+00, -1.05308871e-02])]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "34187.43579483032"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최종\n",
    "import time\n",
    "start = time.time()\n",
    "_ = my_regressor()\n",
    "results\n",
    "time.time()-start\n",
    "\n",
    "# DT에 max_leaf_node 추가 (4시간 15분+ 걸림)\n",
    "# RF 3시간 30분+ 걸림\n",
    "# XGB 30분 걸려야 함. (확인)\n",
    "# gbm 10분 걸려야 함. (확)\n",
    "# dummy 1분 걸려야 함. (확) 00:23분\n",
    "# linear 13분 걸려야 함. (확)\n",
    "# ridge 6분 걸려야 함. (확)\n",
    "# lasso 6분 걸려야 함. (확)\n",
    "# elasicnet 6분 걸려야 함. (확)\n",
    "# ardr_linear 6분 걸려야 함. (7분 걸림)\n",
    "# bayesian_ridge 24분 걸려야 함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71315a11",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                    | 0/11 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:52:27] DEBUG: ../src/gbm/gbtree.cc:155: Using tree method: 2\n",
      "[09:52:32] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 476 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:34] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 466 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:36] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 504 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 446 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 484 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 482 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 542 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 492 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 464 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:52:51] ======== Monitor (0): Learner ========\n",
      "[09:52:51] Configure: 0s, 1 calls @ 0us\n",
      "\n",
      "[09:52:51] EvalOneIter: 3.5e-05s, 10 calls @ 35us\n",
      "\n",
      "[09:52:51] GetGradient: 0.00301s, 10 calls @ 3010us\n",
      "\n",
      "[09:52:51] PredictRaw: 0.662928s, 10 calls @ 662928us\n",
      "\n",
      "[09:52:51] UpdateOneIter: 18.7889s, 10 calls @ 18788872us\n",
      "\n",
      "[09:52:51] ======== Monitor (0): GBTree ========\n",
      "[09:52:51] BoostNewTrees: 22.5074s, 10 calls @ 22507389us\n",
      "\n",
      "[09:52:51] CommitModel: 8e-06s, 10 calls @ 8us\n",
      "\n",
      "[09:52:51] ======== Monitor (0): TreePruner ========\n",
      "[09:52:51] PrunerUpdate: 0.001352s, 10 calls @ 1352us\n",
      "\n",
      "[09:52:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 412 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 498 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 486 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 370 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 438 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 304 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:22] ======== Monitor (0): Learner ========\n",
      "[09:53:22] Configure: 0.000238s, 1 calls @ 238us\n",
      "\n",
      "[09:53:22] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[09:53:22] GetGradient: 0.004251s, 10 calls @ 4251us\n",
      "\n",
      "[09:53:22] PredictRaw: 0.780095s, 10 calls @ 780095us\n",
      "\n",
      "[09:53:22] UpdateOneIter: 27.7942s, 10 calls @ 27794216us\n",
      "\n",
      "[09:53:22] ======== Monitor (0): GBTree ========\n",
      "[09:53:22] BoostNewTrees: 27.0093s, 10 calls @ 27009308us\n",
      "\n",
      "[09:53:22] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:53:22] ======== Monitor (0): TreePruner ========\n",
      "[09:53:22] PrunerUpdate: 0.001291s, 10 calls @ 1291us\n",
      "\n",
      "[09:53:22] ======== Monitor (0): Learner ========\n",
      "[09:53:22] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[09:53:22] ======== Monitor (0): GBTree ========\n",
      "[09:53:22] ======== Monitor (0): TreePruner ========\n",
      "[09:53:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 376 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 346 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 372 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 422 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 320 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 386 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 366 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:53:53] ======== Monitor (0): Learner ========\n",
      "[09:53:53] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[09:53:53] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[09:53:53] GetGradient: 0.003526s, 10 calls @ 3526us\n",
      "\n",
      "[09:53:53] PredictRaw: 0.788539s, 10 calls @ 788539us\n",
      "\n",
      "[09:53:53] UpdateOneIter: 27.58s, 10 calls @ 27580013us\n",
      "\n",
      "[09:53:53] ======== Monitor (0): GBTree ========\n",
      "[09:53:53] BoostNewTrees: 26.7874s, 10 calls @ 26787376us\n",
      "\n",
      "[09:53:53] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:53:53] ======== Monitor (0): TreePruner ========\n",
      "[09:53:53] PrunerUpdate: 0.001265s, 10 calls @ 1265us\n",
      "\n",
      "[09:53:53] ======== Monitor (0): Learner ========\n",
      "[09:53:53] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[09:53:53] ======== Monitor (0): GBTree ========\n",
      "[09:53:53] ======== Monitor (0): TreePruner ========\n",
      "[09:54:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 426 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 422 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 502 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 510 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 482 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 442 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 424 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 402 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:24] ======== Monitor (0): Learner ========\n",
      "[09:54:24] Configure: 0.000247s, 1 calls @ 247us\n",
      "\n",
      "[09:54:24] EvalOneIter: 4.3e-05s, 10 calls @ 43us\n",
      "\n",
      "[09:54:24] GetGradient: 0.00366s, 10 calls @ 3660us\n",
      "\n",
      "[09:54:24] PredictRaw: 0.793539s, 10 calls @ 793539us\n",
      "\n",
      "[09:54:24] UpdateOneIter: 28.2002s, 10 calls @ 28200246us\n",
      "\n",
      "[09:54:24] ======== Monitor (0): GBTree ========\n",
      "[09:54:24] BoostNewTrees: 27.4025s, 10 calls @ 27402482us\n",
      "\n",
      "[09:54:24] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:54:24] ======== Monitor (0): TreePruner ========\n",
      "[09:54:24] PrunerUpdate: 0.001343s, 10 calls @ 1343us\n",
      "\n",
      "[09:54:24] ======== Monitor (0): Learner ========\n",
      "[09:54:24] Configure: 0.000197s, 1 calls @ 197us\n",
      "\n",
      "[09:54:24] ======== Monitor (0): GBTree ========\n",
      "[09:54:24] ======== Monitor (0): TreePruner ========\n",
      "[09:54:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 392 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 386 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 432 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 494 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:54:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 404 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 432 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 434 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:54:56] ======== Monitor (0): Learner ========\n",
      "[09:54:56] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[09:54:56] EvalOneIter: 4e-05s, 10 calls @ 40us\n",
      "\n",
      "[09:54:56] GetGradient: 0.004954s, 10 calls @ 4954us\n",
      "\n",
      "[09:54:56] PredictRaw: 0.786503s, 10 calls @ 786503us\n",
      "\n",
      "[09:54:56] UpdateOneIter: 28.1814s, 10 calls @ 28181401us\n",
      "\n",
      "[09:54:56] ======== Monitor (0): GBTree ========\n",
      "[09:54:56] BoostNewTrees: 27.3893s, 10 calls @ 27389338us\n",
      "\n",
      "[09:54:56] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:54:56] ======== Monitor (0): TreePruner ========\n",
      "[09:54:56] PrunerUpdate: 0.001592s, 10 calls @ 1592us\n",
      "\n",
      "[09:54:56] ======== Monitor (0): Learner ========\n",
      "[09:54:56] Configure: 0.000197s, 1 calls @ 197us\n",
      "\n",
      "[09:54:56] ======== Monitor (0): GBTree ========\n",
      "[09:54:56] ======== Monitor (0): TreePruner ========\n",
      "[09:55:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 484 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 482 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 434 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 538 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 526 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 540 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 470 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 532 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 458 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 552 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:28] ======== Monitor (0): Learner ========\n",
      "[09:55:28] Configure: 0.00024s, 1 calls @ 240us\n",
      "\n",
      "[09:55:28] EvalOneIter: 4.2e-05s, 10 calls @ 42us\n",
      "\n",
      "[09:55:28] GetGradient: 0.00506s, 10 calls @ 5060us\n",
      "\n",
      "[09:55:28] PredictRaw: 0.784702s, 10 calls @ 784702us\n",
      "\n",
      "[09:55:28] UpdateOneIter: 28.8741s, 10 calls @ 28874057us\n",
      "\n",
      "[09:55:28] ======== Monitor (0): GBTree ========\n",
      "[09:55:28] BoostNewTrees: 28.0837s, 10 calls @ 28083730us\n",
      "\n",
      "[09:55:28] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:55:28] ======== Monitor (0): TreePruner ========\n",
      "[09:55:28] PrunerUpdate: 0.001337s, 10 calls @ 1337us\n",
      "\n",
      "[09:55:28] ======== Monitor (0): Learner ========\n",
      "[09:55:28] Configure: 0.000205s, 1 calls @ 205us\n",
      "\n",
      "[09:55:28] ======== Monitor (0): GBTree ========\n",
      "[09:55:28] ======== Monitor (0): TreePruner ========\n",
      "[09:55:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 376 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 466 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 438 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 448 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 346 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 390 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:55:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 372 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 360 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:00] ======== Monitor (0): Learner ========\n",
      "[09:56:00] Configure: 0.000372s, 1 calls @ 372us\n",
      "\n",
      "[09:56:00] EvalOneIter: 4.2e-05s, 10 calls @ 42us\n",
      "\n",
      "[09:56:00] GetGradient: 0.004112s, 10 calls @ 4112us\n",
      "\n",
      "[09:56:00] PredictRaw: 0.804027s, 10 calls @ 804027us\n",
      "\n",
      "[09:56:00] UpdateOneIter: 28.2618s, 10 calls @ 28261849us\n",
      "\n",
      "[09:56:00] ======== Monitor (0): GBTree ========\n",
      "[09:56:00] BoostNewTrees: 27.453s, 10 calls @ 27452993us\n",
      "\n",
      "[09:56:00] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:56:00] ======== Monitor (0): TreePruner ========\n",
      "[09:56:00] PrunerUpdate: 0.001316s, 10 calls @ 1316us\n",
      "\n",
      "[09:56:00] ======== Monitor (0): Learner ========\n",
      "[09:56:00] Configure: 0.0002s, 1 calls @ 200us\n",
      "\n",
      "[09:56:00] ======== Monitor (0): GBTree ========\n",
      "[09:56:00] ======== Monitor (0): TreePruner ========\n",
      "[09:56:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 446 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 440 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 472 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 454 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 400 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 450 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 394 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:31] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:31] ======== Monitor (0): Learner ========\n",
      "[09:56:31] Configure: 0.000245s, 1 calls @ 245us\n",
      "\n",
      "[09:56:31] EvalOneIter: 4.2e-05s, 10 calls @ 42us\n",
      "\n",
      "[09:56:31] GetGradient: 0.005835s, 10 calls @ 5835us\n",
      "\n",
      "[09:56:31] PredictRaw: 0.805525s, 10 calls @ 805525us\n",
      "\n",
      "[09:56:31] UpdateOneIter: 28.1637s, 10 calls @ 28163656us\n",
      "\n",
      "[09:56:31] ======== Monitor (0): GBTree ========\n",
      "[09:56:31] BoostNewTrees: 27.3517s, 10 calls @ 27351716us\n",
      "\n",
      "[09:56:31] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:56:31] ======== Monitor (0): TreePruner ========\n",
      "[09:56:31] PrunerUpdate: 0.001312s, 10 calls @ 1312us\n",
      "\n",
      "[09:56:31] ======== Monitor (0): Learner ========\n",
      "[09:56:31] Configure: 0.000198s, 1 calls @ 198us\n",
      "\n",
      "[09:56:31] ======== Monitor (0): GBTree ========\n",
      "[09:56:31] ======== Monitor (0): TreePruner ========\n",
      "[09:56:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 336 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 360 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 306 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:55] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 390 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:56:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:01] ======== Monitor (0): Learner ========\n",
      "[09:57:01] Configure: 0.000205s, 1 calls @ 205us\n",
      "\n",
      "[09:57:01] EvalOneIter: 4.1e-05s, 10 calls @ 41us\n",
      "\n",
      "[09:57:01] GetGradient: 0.004304s, 10 calls @ 4304us\n",
      "\n",
      "[09:57:01] PredictRaw: 0.687529s, 10 calls @ 687529us\n",
      "\n",
      "[09:57:01] UpdateOneIter: 23.542s, 10 calls @ 23542017us\n",
      "\n",
      "[09:57:01] ======== Monitor (0): GBTree ========\n",
      "[09:57:01] BoostNewTrees: 22.8497s, 10 calls @ 22849661us\n",
      "\n",
      "[09:57:01] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:57:01] ======== Monitor (0): TreePruner ========\n",
      "[09:57:01] PrunerUpdate: 0.001309s, 10 calls @ 1309us\n",
      "\n",
      "[09:57:01] ======== Monitor (0): Learner ========\n",
      "[09:57:01] Configure: 0.000204s, 1 calls @ 204us\n",
      "\n",
      "[09:57:01] ======== Monitor (0): GBTree ========\n",
      "[09:57:01] ======== Monitor (0): TreePruner ========\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:57:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 300 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 298 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 312 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 300 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 334 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 330 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:30] ======== Monitor (0): Learner ========\n",
      "[09:57:30] Configure: 0.000253s, 1 calls @ 253us\n",
      "\n",
      "[09:57:30] EvalOneIter: 4.3e-05s, 10 calls @ 43us\n",
      "\n",
      "[09:57:30] GetGradient: 0.004086s, 10 calls @ 4086us\n",
      "\n",
      "[09:57:30] PredictRaw: 0.78395s, 10 calls @ 783950us\n",
      "\n",
      "[09:57:30] UpdateOneIter: 25.9462s, 10 calls @ 25946163us\n",
      "\n",
      "[09:57:30] ======== Monitor (0): GBTree ========\n",
      "[09:57:30] BoostNewTrees: 25.1575s, 10 calls @ 25157523us\n",
      "\n",
      "[09:57:30] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:57:30] ======== Monitor (0): TreePruner ========\n",
      "[09:57:30] PrunerUpdate: 0.00135s, 10 calls @ 1350us\n",
      "\n",
      "[09:57:31] ======== Monitor (0): Learner ========\n",
      "[09:57:31] Configure: 0.000201s, 1 calls @ 201us\n",
      "\n",
      "[09:57:31] ======== Monitor (0): GBTree ========\n",
      "[09:57:31] ======== Monitor (0): TreePruner ========\n",
      "[09:57:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 332 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 346 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 332 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 312 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 334 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:55] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:57:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 278 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:00] ======== Monitor (0): Learner ========\n",
      "[09:58:00] Configure: 0.000243s, 1 calls @ 243us\n",
      "\n",
      "[09:58:00] EvalOneIter: 4.2e-05s, 10 calls @ 42us\n",
      "\n",
      "[09:58:00] GetGradient: 0.004629s, 10 calls @ 4629us\n",
      "\n",
      "[09:58:00] PredictRaw: 0.775053s, 10 calls @ 775053us\n",
      "\n",
      "[09:58:00] UpdateOneIter: 26.0378s, 10 calls @ 26037761us\n",
      "\n",
      "[09:58:00] ======== Monitor (0): GBTree ========\n",
      "[09:58:00] BoostNewTrees: 25.2575s, 10 calls @ 25257518us\n",
      "\n",
      "[09:58:00] CommitModel: 5e-06s, 10 calls @ 5us\n",
      "\n",
      "[09:58:00] ======== Monitor (0): TreePruner ========\n",
      "[09:58:00] PrunerUpdate: 0.00129s, 10 calls @ 1290us\n",
      "\n",
      "[09:58:00] ======== Monitor (0): Learner ========\n",
      "[09:58:00] Configure: 0.000198s, 1 calls @ 198us\n",
      "\n",
      "[09:58:00] ======== Monitor (0): GBTree ========\n",
      "[09:58:00] ======== Monitor (0): TreePruner ========\n",
      "[09:58:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 354 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 376 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 396 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 378 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 412 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 410 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 432 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 392 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:27] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 370 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:30] ======== Monitor (0): Learner ========\n",
      "[09:58:30] Configure: 0.000247s, 1 calls @ 247us\n",
      "\n",
      "[09:58:30] EvalOneIter: 4.1e-05s, 10 calls @ 41us\n",
      "\n",
      "[09:58:30] GetGradient: 0.004273s, 10 calls @ 4273us\n",
      "\n",
      "[09:58:30] PredictRaw: 0.764648s, 10 calls @ 764648us\n",
      "\n",
      "[09:58:30] UpdateOneIter: 26.8836s, 10 calls @ 26883607us\n",
      "\n",
      "[09:58:30] ======== Monitor (0): GBTree ========\n",
      "[09:58:30] BoostNewTrees: 26.1141s, 10 calls @ 26114114us\n",
      "\n",
      "[09:58:30] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:58:30] ======== Monitor (0): TreePruner ========\n",
      "[09:58:30] PrunerUpdate: 0.001323s, 10 calls @ 1323us\n",
      "\n",
      "[09:58:30] ======== Monitor (0): Learner ========\n",
      "[09:58:30] Configure: 0.000199s, 1 calls @ 199us\n",
      "\n",
      "[09:58:30] ======== Monitor (0): GBTree ========\n",
      "[09:58:30] ======== Monitor (0): TreePruner ========\n",
      "[09:58:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 428 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 394 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 364 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 464 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 488 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 368 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 348 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:55] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 368 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:58:59] ======== Monitor (0): Learner ========\n",
      "[09:58:59] Configure: 0.000249s, 1 calls @ 249us\n",
      "\n",
      "[09:58:59] EvalOneIter: 4.2e-05s, 10 calls @ 42us\n",
      "\n",
      "[09:58:59] GetGradient: 0.003914s, 10 calls @ 3914us\n",
      "\n",
      "[09:58:59] PredictRaw: 0.761406s, 10 calls @ 761406us\n",
      "\n",
      "[09:58:59] UpdateOneIter: 26.1244s, 10 calls @ 26124424us\n",
      "\n",
      "[09:58:59] ======== Monitor (0): GBTree ========\n",
      "[09:58:59] BoostNewTrees: 25.3585s, 10 calls @ 25358497us\n",
      "\n",
      "[09:58:59] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[09:58:59] ======== Monitor (0): TreePruner ========\n",
      "[09:58:59] PrunerUpdate: 0.001266s, 10 calls @ 1266us\n",
      "\n",
      "[09:59:00] ======== Monitor (0): Learner ========\n",
      "[09:59:00] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[09:59:00] ======== Monitor (0): GBTree ========\n",
      "[09:59:00] ======== Monitor (0): TreePruner ========\n",
      "[09:59:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 346 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 332 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:59:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 320 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 310 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 334 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:29] ======== Monitor (0): Learner ========\n",
      "[09:59:29] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[09:59:29] EvalOneIter: 4.1e-05s, 10 calls @ 41us\n",
      "\n",
      "[09:59:29] GetGradient: 0.004015s, 10 calls @ 4015us\n",
      "\n",
      "[09:59:29] PredictRaw: 0.788272s, 10 calls @ 788272us\n",
      "\n",
      "[09:59:29] UpdateOneIter: 26.2719s, 10 calls @ 26271875us\n",
      "\n",
      "[09:59:29] ======== Monitor (0): GBTree ========\n",
      "[09:59:29] BoostNewTrees: 25.479s, 10 calls @ 25479015us\n",
      "\n",
      "[09:59:29] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[09:59:29] ======== Monitor (0): TreePruner ========\n",
      "[09:59:29] PrunerUpdate: 0.001229s, 10 calls @ 1229us\n",
      "\n",
      "[09:59:29] ======== Monitor (0): Learner ========\n",
      "[09:59:29] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[09:59:29] ======== Monitor (0): GBTree ========\n",
      "[09:59:29] ======== Monitor (0): TreePruner ========\n",
      "[09:59:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 274 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 332 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 302 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 296 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 280 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 304 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[09:59:58] ======== Monitor (0): Learner ========\n",
      "[09:59:58] Configure: 0.000243s, 1 calls @ 243us\n",
      "\n",
      "[09:59:58] EvalOneIter: 4.3e-05s, 10 calls @ 43us\n",
      "\n",
      "[09:59:58] GetGradient: 0.003814s, 10 calls @ 3814us\n",
      "\n",
      "[09:59:58] PredictRaw: 0.78077s, 10 calls @ 780770us\n",
      "\n",
      "[09:59:58] UpdateOneIter: 26.1413s, 10 calls @ 26141273us\n",
      "\n",
      "[09:59:58] ======== Monitor (0): GBTree ========\n",
      "[09:59:58] BoostNewTrees: 25.3561s, 10 calls @ 25356126us\n",
      "\n",
      "[09:59:58] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[09:59:58] ======== Monitor (0): TreePruner ========\n",
      "[09:59:58] PrunerUpdate: 0.00124s, 10 calls @ 1240us\n",
      "\n",
      "[09:59:59] ======== Monitor (0): Learner ========\n",
      "[09:59:59] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[09:59:59] ======== Monitor (0): GBTree ========\n",
      "[09:59:59] ======== Monitor (0): TreePruner ========\n",
      "[10:00:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 312 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 298 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 286 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 274 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 354 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:28] ======== Monitor (0): Learner ========\n",
      "[10:00:28] Configure: 0.000246s, 1 calls @ 246us\n",
      "\n",
      "[10:00:28] EvalOneIter: 4.4e-05s, 10 calls @ 44us\n",
      "\n",
      "[10:00:28] GetGradient: 0.004006s, 10 calls @ 4006us\n",
      "\n",
      "[10:00:28] PredictRaw: 0.790593s, 10 calls @ 790593us\n",
      "\n",
      "[10:00:28] UpdateOneIter: 26.6307s, 10 calls @ 26630699us\n",
      "\n",
      "[10:00:28] ======== Monitor (0): GBTree ========\n",
      "[10:00:28] BoostNewTrees: 25.8355s, 10 calls @ 25835544us\n",
      "\n",
      "[10:00:28] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:00:28] ======== Monitor (0): TreePruner ========\n",
      "[10:00:28] PrunerUpdate: 0.001228s, 10 calls @ 1228us\n",
      "\n",
      "[10:00:29] ======== Monitor (0): Learner ========\n",
      "[10:00:29] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:00:29] ======== Monitor (0): GBTree ========\n",
      "[10:00:29] ======== Monitor (0): TreePruner ========\n",
      "[10:00:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 296 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 300 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 278 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 318 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:00:58] ======== Monitor (0): Learner ========\n",
      "[10:00:58] Configure: 0.000199s, 1 calls @ 199us\n",
      "\n",
      "[10:00:58] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[10:00:58] GetGradient: 0.003454s, 10 calls @ 3454us\n",
      "\n",
      "[10:00:58] PredictRaw: 0.706047s, 10 calls @ 706047us\n",
      "\n",
      "[10:00:58] UpdateOneIter: 23.2856s, 10 calls @ 23285555us\n",
      "\n",
      "[10:00:58] ======== Monitor (0): GBTree ========\n",
      "[10:00:58] BoostNewTrees: 22.5755s, 10 calls @ 22575526us\n",
      "\n",
      "[10:00:58] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:00:58] ======== Monitor (0): TreePruner ========\n",
      "[10:00:58] PrunerUpdate: 0.00125s, 10 calls @ 1250us\n",
      "\n",
      "[10:00:58] ======== Monitor (0): Learner ========\n",
      "[10:00:58] Configure: 0.0002s, 1 calls @ 200us\n",
      "\n",
      "[10:00:58] ======== Monitor (0): GBTree ========\n",
      "[10:00:58] ======== Monitor (0): TreePruner ========\n",
      "[10:01:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 368 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 378 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 298 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 256 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 252 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:01:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 252 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:27] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 258 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:27] ======== Monitor (0): Learner ========\n",
      "[10:01:27] Configure: 0.000246s, 1 calls @ 246us\n",
      "\n",
      "[10:01:27] EvalOneIter: 4.4e-05s, 10 calls @ 44us\n",
      "\n",
      "[10:01:27] GetGradient: 0.00534s, 10 calls @ 5340us\n",
      "\n",
      "[10:01:27] PredictRaw: 0.765335s, 10 calls @ 765335us\n",
      "\n",
      "[10:01:27] UpdateOneIter: 25.7569s, 10 calls @ 25756929us\n",
      "\n",
      "[10:01:27] ======== Monitor (0): GBTree ========\n",
      "[10:01:27] BoostNewTrees: 24.9857s, 10 calls @ 24985702us\n",
      "\n",
      "[10:01:27] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:01:27] ======== Monitor (0): TreePruner ========\n",
      "[10:01:27] PrunerUpdate: 0.001225s, 10 calls @ 1225us\n",
      "\n",
      "[10:01:27] ======== Monitor (0): Learner ========\n",
      "[10:01:27] Configure: 0.000198s, 1 calls @ 198us\n",
      "\n",
      "[10:01:27] ======== Monitor (0): GBTree ========\n",
      "[10:01:27] ======== Monitor (0): TreePruner ========\n",
      "[10:01:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 398 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 418 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 372 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 330 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 306 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 312 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 298 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:55] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:01:55] ======== Monitor (0): Learner ========\n",
      "[10:01:55] Configure: 0.000243s, 1 calls @ 243us\n",
      "\n",
      "[10:01:55] EvalOneIter: 4.3e-05s, 10 calls @ 43us\n",
      "\n",
      "[10:01:55] GetGradient: 0.004478s, 10 calls @ 4478us\n",
      "\n",
      "[10:01:55] PredictRaw: 0.751613s, 10 calls @ 751613us\n",
      "\n",
      "[10:01:55] UpdateOneIter: 24.9462s, 10 calls @ 24946169us\n",
      "\n",
      "[10:01:55] ======== Monitor (0): GBTree ========\n",
      "[10:01:55] BoostNewTrees: 24.1895s, 10 calls @ 24189525us\n",
      "\n",
      "[10:01:55] CommitModel: 5e-06s, 10 calls @ 5us\n",
      "\n",
      "[10:01:55] ======== Monitor (0): TreePruner ========\n",
      "[10:01:55] PrunerUpdate: 0.001217s, 10 calls @ 1217us\n",
      "\n",
      "[10:01:55] ======== Monitor (0): Learner ========\n",
      "[10:01:55] Configure: 0.000218s, 1 calls @ 218us\n",
      "\n",
      "[10:01:55] ======== Monitor (0): GBTree ========\n",
      "[10:01:55] ======== Monitor (0): TreePruner ========\n",
      "[10:02:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 208 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:05] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 206 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 212 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 260 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 224 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 310 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 238 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 246 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 236 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:24] ======== Monitor (0): Learner ========\n",
      "[10:02:24] Configure: 0.000238s, 1 calls @ 238us\n",
      "\n",
      "[10:02:24] EvalOneIter: 4.6e-05s, 10 calls @ 46us\n",
      "\n",
      "[10:02:24] GetGradient: 0.00439s, 10 calls @ 4390us\n",
      "\n",
      "[10:02:24] PredictRaw: 0.764249s, 10 calls @ 764249us\n",
      "\n",
      "[10:02:24] UpdateOneIter: 25.4968s, 10 calls @ 25496850us\n",
      "\n",
      "[10:02:24] ======== Monitor (0): GBTree ========\n",
      "[10:02:24] BoostNewTrees: 24.7276s, 10 calls @ 24727624us\n",
      "\n",
      "[10:02:24] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[10:02:24] ======== Monitor (0): TreePruner ========\n",
      "[10:02:24] PrunerUpdate: 0.001375s, 10 calls @ 1375us\n",
      "\n",
      "[10:02:24] ======== Monitor (0): Learner ========\n",
      "[10:02:24] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:02:24] ======== Monitor (0): GBTree ========\n",
      "[10:02:24] ======== Monitor (0): TreePruner ========\n",
      "[10:02:32] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 266 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:34] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:36] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 322 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 332 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 278 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 258 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 254 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:02:52] ======== Monitor (0): Learner ========\n",
      "[10:02:52] Configure: 0.000245s, 1 calls @ 245us\n",
      "\n",
      "[10:02:52] EvalOneIter: 4.3e-05s, 10 calls @ 43us\n",
      "\n",
      "[10:02:52] GetGradient: 0.004665s, 10 calls @ 4665us\n",
      "\n",
      "[10:02:52] PredictRaw: 0.746176s, 10 calls @ 746176us\n",
      "\n",
      "[10:02:52] UpdateOneIter: 24.5807s, 10 calls @ 24580697us\n",
      "\n",
      "[10:02:52] ======== Monitor (0): GBTree ========\n",
      "[10:02:52] BoostNewTrees: 23.8293s, 10 calls @ 23829299us\n",
      "\n",
      "[10:02:52] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[10:02:52] ======== Monitor (0): TreePruner ========\n",
      "[10:02:52] PrunerUpdate: 0.001298s, 10 calls @ 1298us\n",
      "\n",
      "[10:02:52] ======== Monitor (0): Learner ========\n",
      "[10:02:52] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[10:02:52] ======== Monitor (0): GBTree ========\n",
      "[10:02:52] ======== Monitor (0): TreePruner ========\n",
      "[10:03:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 292 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 336 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 344 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 296 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 296 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 288 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 242 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 266 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:20] ======== Monitor (0): Learner ========\n",
      "[10:03:20] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[10:03:20] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[10:03:20] GetGradient: 0.004835s, 10 calls @ 4835us\n",
      "\n",
      "[10:03:20] PredictRaw: 0.770018s, 10 calls @ 770018us\n",
      "\n",
      "[10:03:20] UpdateOneIter: 25.437s, 10 calls @ 25437005us\n",
      "\n",
      "[10:03:20] ======== Monitor (0): GBTree ========\n",
      "[10:03:20] BoostNewTrees: 24.6616s, 10 calls @ 24661595us\n",
      "\n",
      "[10:03:20] CommitModel: 8e-06s, 10 calls @ 8us\n",
      "\n",
      "[10:03:20] ======== Monitor (0): TreePruner ========\n",
      "[10:03:20] PrunerUpdate: 0.001498s, 10 calls @ 1498us\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:03:21] ======== Monitor (0): Learner ========\n",
      "[10:03:21] Configure: 0.000194s, 1 calls @ 194us\n",
      "\n",
      "[10:03:21] ======== Monitor (0): GBTree ========\n",
      "[10:03:21] ======== Monitor (0): TreePruner ========\n",
      "[10:03:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:31] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 302 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 304 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 306 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 294 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:49] ======== Monitor (0): Learner ========\n",
      "[10:03:49] Configure: 0.000241s, 1 calls @ 241us\n",
      "\n",
      "[10:03:49] EvalOneIter: 4e-05s, 10 calls @ 40us\n",
      "\n",
      "[10:03:49] GetGradient: 0.004557s, 10 calls @ 4557us\n",
      "\n",
      "[10:03:49] PredictRaw: 0.733502s, 10 calls @ 733502us\n",
      "\n",
      "[10:03:49] UpdateOneIter: 25.1772s, 10 calls @ 25177157us\n",
      "\n",
      "[10:03:49] ======== Monitor (0): GBTree ========\n",
      "[10:03:49] BoostNewTrees: 24.4385s, 10 calls @ 24438546us\n",
      "\n",
      "[10:03:49] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:03:49] ======== Monitor (0): TreePruner ========\n",
      "[10:03:49] PrunerUpdate: 0.001471s, 10 calls @ 1471us\n",
      "\n",
      "[10:03:49] ======== Monitor (0): Learner ========\n",
      "[10:03:49] Configure: 0.000194s, 1 calls @ 194us\n",
      "\n",
      "[10:03:49] ======== Monitor (0): GBTree ========\n",
      "[10:03:49] ======== Monitor (0): TreePruner ========\n",
      "[10:03:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 274 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:03:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 254 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 250 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 270 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 272 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 256 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 228 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 244 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 218 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 230 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:17] ======== Monitor (0): Learner ========\n",
      "[10:04:17] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[10:04:17] EvalOneIter: 4.6e-05s, 10 calls @ 46us\n",
      "\n",
      "[10:04:17] GetGradient: 0.003961s, 10 calls @ 3961us\n",
      "\n",
      "[10:04:17] PredictRaw: 0.758733s, 10 calls @ 758733us\n",
      "\n",
      "[10:04:17] UpdateOneIter: 25.3702s, 10 calls @ 25370233us\n",
      "\n",
      "[10:04:17] ======== Monitor (0): GBTree ========\n",
      "[10:04:17] BoostNewTrees: 24.607s, 10 calls @ 24606965us\n",
      "\n",
      "[10:04:17] CommitModel: 8e-06s, 10 calls @ 8us\n",
      "\n",
      "[10:04:17] ======== Monitor (0): TreePruner ========\n",
      "[10:04:17] PrunerUpdate: 0.001537s, 10 calls @ 1537us\n",
      "\n",
      "[10:04:17] ======== Monitor (0): Learner ========\n",
      "[10:04:17] Configure: 0.000198s, 1 calls @ 198us\n",
      "\n",
      "[10:04:17] ======== Monitor (0): GBTree ========\n",
      "[10:04:17] ======== Monitor (0): TreePruner ========\n",
      "[10:04:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 336 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 314 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 288 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 280 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:47] ======== Monitor (0): Learner ========\n",
      "[10:04:47] Configure: 0.000203s, 1 calls @ 203us\n",
      "\n",
      "[10:04:47] EvalOneIter: 4.7e-05s, 10 calls @ 47us\n",
      "\n",
      "[10:04:47] GetGradient: 0.003091s, 10 calls @ 3091us\n",
      "\n",
      "[10:04:47] PredictRaw: 0.69234s, 10 calls @ 692340us\n",
      "\n",
      "[10:04:47] UpdateOneIter: 23.3304s, 10 calls @ 23330390us\n",
      "\n",
      "[10:04:47] ======== Monitor (0): GBTree ========\n",
      "[10:04:47] BoostNewTrees: 22.6344s, 10 calls @ 22634412us\n",
      "\n",
      "[10:04:47] CommitModel: 1e-05s, 10 calls @ 10us\n",
      "\n",
      "[10:04:47] ======== Monitor (0): TreePruner ========\n",
      "[10:04:47] PrunerUpdate: 0.001406s, 10 calls @ 1406us\n",
      "\n",
      "[10:04:47] ======== Monitor (0): Learner ========\n",
      "[10:04:47] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:04:47] ======== Monitor (0): GBTree ========\n",
      "[10:04:47] ======== Monitor (0): TreePruner ========\n",
      "[10:04:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 438 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:04:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 442 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 418 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 404 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 348 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 450 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 446 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 460 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:19] ======== Monitor (0): Learner ========\n",
      "[10:05:19] Configure: 0.000245s, 1 calls @ 245us\n",
      "\n",
      "[10:05:19] EvalOneIter: 4.4e-05s, 10 calls @ 44us\n",
      "\n",
      "[10:05:19] GetGradient: 0.004071s, 10 calls @ 4071us\n",
      "\n",
      "[10:05:19] PredictRaw: 0.80746s, 10 calls @ 807460us\n",
      "\n",
      "[10:05:19] UpdateOneIter: 28.5531s, 10 calls @ 28553130us\n",
      "\n",
      "[10:05:19] ======== Monitor (0): GBTree ========\n",
      "[10:05:19] BoostNewTrees: 27.741s, 10 calls @ 27741008us\n",
      "\n",
      "[10:05:19] CommitModel: 8e-06s, 10 calls @ 8us\n",
      "\n",
      "[10:05:19] ======== Monitor (0): TreePruner ========\n",
      "[10:05:19] PrunerUpdate: 0.001783s, 10 calls @ 1783us\n",
      "\n",
      "[10:05:19] ======== Monitor (0): Learner ========\n",
      "[10:05:19] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:05:19] ======== Monitor (0): GBTree ========\n",
      "[10:05:19] ======== Monitor (0): TreePruner ========\n",
      "[10:05:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 314 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 320 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:05:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 310 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 396 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 396 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 298 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 418 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 348 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 356 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:05:51] ======== Monitor (0): Learner ========\n",
      "[10:05:51] Configure: 0.000242s, 1 calls @ 242us\n",
      "\n",
      "[10:05:51] EvalOneIter: 4.4e-05s, 10 calls @ 44us\n",
      "\n",
      "[10:05:51] GetGradient: 0.004133s, 10 calls @ 4133us\n",
      "\n",
      "[10:05:51] PredictRaw: 0.798833s, 10 calls @ 798833us\n",
      "\n",
      "[10:05:51] UpdateOneIter: 28.3928s, 10 calls @ 28392751us\n",
      "\n",
      "[10:05:51] ======== Monitor (0): GBTree ========\n",
      "[10:05:51] BoostNewTrees: 27.5892s, 10 calls @ 27589178us\n",
      "\n",
      "[10:05:51] CommitModel: 8e-06s, 10 calls @ 8us\n",
      "\n",
      "[10:05:51] ======== Monitor (0): TreePruner ========\n",
      "[10:05:51] PrunerUpdate: 0.001382s, 10 calls @ 1382us\n",
      "\n",
      "[10:05:51] ======== Monitor (0): Learner ========\n",
      "[10:05:51] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[10:05:51] ======== Monitor (0): GBTree ========\n",
      "[10:05:51] ======== Monitor (0): TreePruner ========\n",
      "[10:05:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 390 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 394 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:05] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 394 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 364 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:10] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 392 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 412 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 398 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 414 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:23] ======== Monitor (0): Learner ========\n",
      "[10:06:23] Configure: 0.000247s, 1 calls @ 247us\n",
      "\n",
      "[10:06:23] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[10:06:23] GetGradient: 0.003795s, 10 calls @ 3795us\n",
      "\n",
      "[10:06:23] PredictRaw: 0.798954s, 10 calls @ 798954us\n",
      "\n",
      "[10:06:23] UpdateOneIter: 28.4213s, 10 calls @ 28421276us\n",
      "\n",
      "[10:06:23] ======== Monitor (0): GBTree ========\n",
      "[10:06:23] BoostNewTrees: 27.6179s, 10 calls @ 27617941us\n",
      "\n",
      "[10:06:23] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[10:06:23] ======== Monitor (0): TreePruner ========\n",
      "[10:06:23] PrunerUpdate: 0.001527s, 10 calls @ 1527us\n",
      "\n",
      "[10:06:23] ======== Monitor (0): Learner ========\n",
      "[10:06:23] Configure: 0.000199s, 1 calls @ 199us\n",
      "\n",
      "[10:06:23] ======== Monitor (0): GBTree ========\n",
      "[10:06:23] ======== Monitor (0): TreePruner ========\n",
      "[10:06:31] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 302 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:34] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:36] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 314 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 334 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 434 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 420 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 438 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 406 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:06:54] ======== Monitor (0): Learner ========\n",
      "[10:06:54] Configure: 0.000244s, 1 calls @ 244us\n",
      "\n",
      "[10:06:54] EvalOneIter: 3.8e-05s, 10 calls @ 38us\n",
      "\n",
      "[10:06:54] GetGradient: 0.004023s, 10 calls @ 4023us\n",
      "\n",
      "[10:06:54] PredictRaw: 0.807496s, 10 calls @ 807496us\n",
      "\n",
      "[10:06:54] UpdateOneIter: 28.489s, 10 calls @ 28488983us\n",
      "\n",
      "[10:06:54] ======== Monitor (0): GBTree ========\n",
      "[10:06:54] BoostNewTrees: 27.6769s, 10 calls @ 27676901us\n",
      "\n",
      "[10:06:54] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:06:54] ======== Monitor (0): TreePruner ========\n",
      "[10:06:54] PrunerUpdate: 0.001291s, 10 calls @ 1291us\n",
      "\n",
      "[10:06:55] ======== Monitor (0): Learner ========\n",
      "[10:06:55] Configure: 0.000189s, 1 calls @ 189us\n",
      "\n",
      "[10:06:55] ======== Monitor (0): GBTree ========\n",
      "[10:06:55] ======== Monitor (0): TreePruner ========\n",
      "[10:07:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 396 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 440 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 470 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 440 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 444 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 414 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 404 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 400 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 410 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:26] ======== Monitor (0): Learner ========\n",
      "[10:07:26] Configure: 0.000248s, 1 calls @ 248us\n",
      "\n",
      "[10:07:26] EvalOneIter: 4e-05s, 10 calls @ 40us\n",
      "\n",
      "[10:07:26] GetGradient: 0.003709s, 10 calls @ 3709us\n",
      "\n",
      "[10:07:26] PredictRaw: 0.804684s, 10 calls @ 804684us\n",
      "\n",
      "[10:07:26] UpdateOneIter: 28.6477s, 10 calls @ 28647691us\n",
      "\n",
      "[10:07:26] ======== Monitor (0): GBTree ========\n",
      "[10:07:26] BoostNewTrees: 27.8387s, 10 calls @ 27838733us\n",
      "\n",
      "[10:07:26] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[10:07:26] ======== Monitor (0): TreePruner ========\n",
      "[10:07:26] PrunerUpdate: 0.001239s, 10 calls @ 1239us\n",
      "\n",
      "[10:07:27] ======== Monitor (0): Learner ========\n",
      "[10:07:27] Configure: 0.000201s, 1 calls @ 201us\n",
      "\n",
      "[10:07:27] ======== Monitor (0): GBTree ========\n",
      "[10:07:27] ======== Monitor (0): TreePruner ========\n",
      "[10:07:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 294 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 364 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:07:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 208 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:55] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 362 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 372 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:07:57] ======== Monitor (0): Learner ========\n",
      "[10:07:57] Configure: 0.000256s, 1 calls @ 256us\n",
      "\n",
      "[10:07:57] EvalOneIter: 4.1e-05s, 10 calls @ 41us\n",
      "\n",
      "[10:07:57] GetGradient: 0.004358s, 10 calls @ 4358us\n",
      "\n",
      "[10:07:57] PredictRaw: 0.807431s, 10 calls @ 807431us\n",
      "\n",
      "[10:07:57] UpdateOneIter: 27.7993s, 10 calls @ 27799310us\n",
      "\n",
      "[10:07:57] ======== Monitor (0): GBTree ========\n",
      "[10:07:57] BoostNewTrees: 26.987s, 10 calls @ 26986952us\n",
      "\n",
      "[10:07:57] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:07:57] ======== Monitor (0): TreePruner ========\n",
      "[10:07:57] PrunerUpdate: 0.001448s, 10 calls @ 1448us\n",
      "\n",
      "[10:07:58] ======== Monitor (0): Learner ========\n",
      "[10:07:58] Configure: 0.0002s, 1 calls @ 200us\n",
      "\n",
      "[10:07:58] ======== Monitor (0): GBTree ========\n",
      "[10:07:58] ======== Monitor (0): TreePruner ========\n",
      "[10:08:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 402 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 354 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 348 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 368 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:27] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 352 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 312 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:29] ======== Monitor (0): Learner ========\n",
      "[10:08:29] Configure: 0.000249s, 1 calls @ 249us\n",
      "\n",
      "[10:08:29] EvalOneIter: 4e-05s, 10 calls @ 40us\n",
      "\n",
      "[10:08:29] GetGradient: 0.004017s, 10 calls @ 4017us\n",
      "\n",
      "[10:08:29] PredictRaw: 0.812578s, 10 calls @ 812578us\n",
      "\n",
      "[10:08:29] UpdateOneIter: 28.3512s, 10 calls @ 28351204us\n",
      "\n",
      "[10:08:29] ======== Monitor (0): GBTree ========\n",
      "[10:08:29] BoostNewTrees: 27.534s, 10 calls @ 27534010us\n",
      "\n",
      "[10:08:29] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:08:29] ======== Monitor (0): TreePruner ========\n",
      "[10:08:29] PrunerUpdate: 0.001323s, 10 calls @ 1323us\n",
      "\n",
      "[10:08:30] ======== Monitor (0): Learner ========\n",
      "[10:08:30] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:08:30] ======== Monitor (0): GBTree ========\n",
      "[10:08:30] ======== Monitor (0): TreePruner ========\n",
      "[10:08:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 492 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:42] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 480 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 484 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 488 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 476 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 456 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 510 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 470 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:08:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 466 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:00] ======== Monitor (0): Learner ========\n",
      "[10:09:00] Configure: 0.000199s, 1 calls @ 199us\n",
      "\n",
      "[10:09:00] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[10:09:00] GetGradient: 0.003202s, 10 calls @ 3202us\n",
      "\n",
      "[10:09:00] PredictRaw: 0.657787s, 10 calls @ 657787us\n",
      "\n",
      "[10:09:00] UpdateOneIter: 24.0596s, 10 calls @ 24059645us\n",
      "\n",
      "[10:09:00] ======== Monitor (0): GBTree ========\n",
      "[10:09:00] BoostNewTrees: 23.3981s, 10 calls @ 23398142us\n",
      "\n",
      "[10:09:00] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:09:00] ======== Monitor (0): TreePruner ========\n",
      "[10:09:00] PrunerUpdate: 0.001321s, 10 calls @ 1321us\n",
      "\n",
      "[10:09:00] ======== Monitor (0): Learner ========\n",
      "[10:09:00] Configure: 0.000196s, 1 calls @ 196us\n",
      "\n",
      "[10:09:00] ======== Monitor (0): GBTree ========\n",
      "[10:09:00] ======== Monitor (0): TreePruner ========\n",
      "[10:09:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 580 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 590 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:14] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 592 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 566 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 594 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:21] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 552 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 530 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:27] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 602 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 582 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:32] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 594 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:32] ======== Monitor (0): Learner ========\n",
      "[10:09:32] Configure: 0.000241s, 1 calls @ 241us\n",
      "\n",
      "[10:09:32] EvalOneIter: 3.9e-05s, 10 calls @ 39us\n",
      "\n",
      "[10:09:32] GetGradient: 0.004259s, 10 calls @ 4259us\n",
      "\n",
      "[10:09:32] PredictRaw: 0.770286s, 10 calls @ 770286us\n",
      "\n",
      "[10:09:32] UpdateOneIter: 28.5591s, 10 calls @ 28559134us\n",
      "\n",
      "[10:09:32] ======== Monitor (0): GBTree ========\n",
      "[10:09:32] BoostNewTrees: 27.784s, 10 calls @ 27784031us\n",
      "\n",
      "[10:09:32] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:09:32] ======== Monitor (0): TreePruner ========\n",
      "[10:09:32] PrunerUpdate: 0.001315s, 10 calls @ 1315us\n",
      "\n",
      "[10:09:32] ======== Monitor (0): Learner ========\n",
      "[10:09:32] Configure: 0.000209s, 1 calls @ 209us\n",
      "\n",
      "[10:09:32] ======== Monitor (0): GBTree ========\n",
      "[10:09:32] ======== Monitor (0): TreePruner ========\n",
      "[10:09:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 642 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:43] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 600 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 618 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 648 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 634 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 638 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 596 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:09:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 616 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 600 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 732 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:04] ======== Monitor (0): Learner ========\n",
      "[10:10:04] Configure: 0.000244s, 1 calls @ 244us\n",
      "\n",
      "[10:10:04] EvalOneIter: 3.7e-05s, 10 calls @ 37us\n",
      "\n",
      "[10:10:04] GetGradient: 0.006543s, 10 calls @ 6543us\n",
      "\n",
      "[10:10:04] PredictRaw: 0.753279s, 10 calls @ 753279us\n",
      "\n",
      "[10:10:04] UpdateOneIter: 28.6934s, 10 calls @ 28693387us\n",
      "\n",
      "[10:10:04] ======== Monitor (0): GBTree ========\n",
      "[10:10:04] BoostNewTrees: 27.933s, 10 calls @ 27932997us\n",
      "\n",
      "[10:10:04] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:10:04] ======== Monitor (0): TreePruner ========\n",
      "[10:10:04] PrunerUpdate: 0.001335s, 10 calls @ 1335us\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:10:04] ======== Monitor (0): Learner ========\n",
      "[10:10:04] Configure: 0.0002s, 1 calls @ 200us\n",
      "\n",
      "[10:10:04] ======== Monitor (0): GBTree ========\n",
      "[10:10:04] ======== Monitor (0): TreePruner ========\n",
      "[10:10:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 534 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 566 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 550 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 540 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 546 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 576 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 684 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 572 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 682 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:36] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 622 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:36] ======== Monitor (0): Learner ========\n",
      "[10:10:36] Configure: 0.000247s, 1 calls @ 247us\n",
      "\n",
      "[10:10:36] EvalOneIter: 4.1e-05s, 10 calls @ 41us\n",
      "\n",
      "[10:10:36] GetGradient: 0.003981s, 10 calls @ 3981us\n",
      "\n",
      "[10:10:36] PredictRaw: 0.777854s, 10 calls @ 777854us\n",
      "\n",
      "[10:10:36] UpdateOneIter: 28.697s, 10 calls @ 28697033us\n",
      "\n",
      "[10:10:36] ======== Monitor (0): GBTree ========\n",
      "[10:10:36] BoostNewTrees: 27.9146s, 10 calls @ 27914624us\n",
      "\n",
      "[10:10:36] CommitModel: 6e-06s, 10 calls @ 6us\n",
      "\n",
      "[10:10:36] ======== Monitor (0): TreePruner ========\n",
      "[10:10:36] PrunerUpdate: 0.001264s, 10 calls @ 1264us\n",
      "\n",
      "[10:10:36] ======== Monitor (0): Learner ========\n",
      "[10:10:36] Configure: 0.000195s, 1 calls @ 195us\n",
      "\n",
      "[10:10:36] ======== Monitor (0): GBTree ========\n",
      "[10:10:36] ======== Monitor (0): TreePruner ========\n",
      "[10:10:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 538 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 534 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 524 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 532 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:10:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 488 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:11:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 522 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:11:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 560 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m_ctypes/callbacks.c\u001b[0m in \u001b[0;36m'calling callback function'\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_log_callback\u001b[0;34m(msg)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_log_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m     \u001b[0;34m\"\"\"Redirect logs from native library into Python console\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:11:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 562 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/11 [18:48<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:11:10] ======== Monitor (0): Learner ========\n",
      "[10:11:10] Configure: 0.000248s, 1 calls @ 248us\n",
      "\n",
      "[10:11:10] EvalOneIter: 4.8e-05s, 10 calls @ 48us\n",
      "\n",
      "[10:11:10] GetGradient: 0.003825s, 10 calls @ 3825us\n",
      "\n",
      "[10:11:10] PredictRaw: 0.771881s, 10 calls @ 771881us\n",
      "\n",
      "[10:11:10] UpdateOneIter: 28.506s, 10 calls @ 28505984us\n",
      "\n",
      "[10:11:10] ======== Monitor (0): GBTree ========\n",
      "[10:11:10] BoostNewTrees: 27.7297s, 10 calls @ 27729707us\n",
      "\n",
      "[10:11:10] CommitModel: 7e-06s, 10 calls @ 7us\n",
      "\n",
      "[10:11:10] ======== Monitor (0): TreePruner ========\n",
      "[10:11:10] PrunerUpdate: 0.060397s, 10 calls @ 60397us\n",
      "\n",
      "[10:11:11] ======== Monitor (0): Learner ========\n",
      "[10:11:11] Configure: 0.00014s, 1 calls @ 140us\n",
      "\n",
      "[10:11:11] ======== Monitor (0): GBTree ========\n",
      "[10:11:11] ======== Monitor (0): TreePruner ========\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_293367/1390773533.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_regressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_293367/505122673.py\u001b[0m in \u001b[0;36mmy_regressor\u001b[0;34m()\u001b[0m\n\u001b[1;32m    103\u001b[0m             \u001b[0mmse_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0mr2_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m             \u001b[0mcv_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_nm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{model_nm}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmse_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv_scores\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#[:str(model).index(\"(\")]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    507\u001b[0m     \u001b[0mscorer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_scoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m     cv_results = cross_validate(\n\u001b[0m\u001b[1;32m    510\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# independent, and that it is pickle-able.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    268\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    269\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    859\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 861\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    862\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    678\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[1;32m    759\u001b[0m         \"\"\"\n\u001b[1;32m    760\u001b[0m         \u001b[0mevals_result\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingCallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvalsLog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 761\u001b[0;31m         train_dmatrix, evals = _wrap_evaluation_matrices(\n\u001b[0m\u001b[1;32m    762\u001b[0m             \u001b[0mmissing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36m_wrap_evaluation_matrices\u001b[0;34m(missing, X, y, group, qid, sample_weight, base_margin, feature_weights, eval_set, sample_weight_eval_set, base_margin_eval_set, eval_group, eval_qid, create_dmatrix, enable_categorical, label_transform)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m     \"\"\"\n\u001b[0;32m--> 286\u001b[0;31m     train_dmatrix = create_dmatrix(\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    773\u001b[0m             \u001b[0meval_group\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m             \u001b[0meval_qid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m             \u001b[0mcreate_dmatrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnthread\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    776\u001b[0m             \u001b[0menable_categorical\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_categorical\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m         )\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, label, weight, base_margin, missing, silent, feature_names, feature_types, nthread, group, qid, label_lower_bound, label_upper_bound, feature_weights, enable_categorical)\u001b[0m\n\u001b[1;32m    614\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m         handle, feature_names, feature_types = dispatch_data_backend(\n\u001b[0m\u001b[1;32m    617\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0mmissing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/data.py\u001b[0m in \u001b[0;36mdispatch_data_backend\u001b[0;34m(data, missing, threads, feature_names, feature_types, enable_categorical)\u001b[0m\n\u001b[1;32m    770\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_from_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    771\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_pandas_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 772\u001b[0;31m         return _from_pandas_df(data, enable_categorical, missing, threads,\n\u001b[0m\u001b[1;32m    773\u001b[0m                                feature_names, feature_types)\n\u001b[1;32m    774\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_pandas_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/data.py\u001b[0m in \u001b[0;36m_from_pandas_df\u001b[0;34m(data, enable_categorical, missing, nthread, feature_names, feature_types)\u001b[0m\n\u001b[1;32m    312\u001b[0m     data, feature_names, feature_types = _transform_pandas_df(\n\u001b[1;32m    313\u001b[0m         data, enable_categorical, feature_names, feature_types)\n\u001b[0;32m--> 314\u001b[0;31m     return _from_numpy_array(data, missing, nthread, feature_names,\n\u001b[0m\u001b[1;32m    315\u001b[0m                              feature_types)\n\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/xgboost/data.py\u001b[0m in \u001b[0;36m_from_numpy_array\u001b[0;34m(data, missing, nthread, feature_names, feature_types)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     _check_call(\n\u001b[0;32m--> 179\u001b[0;31m         _LIB.XGDMatrixCreateFromDense(\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0m_array_interface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "## try 1\n",
    "# verbosity = 3일 때 결과\n",
    "import time\n",
    "start = time.time()\n",
    "_ = my_regressor()\n",
    "results\n",
    "time.time()-start\n",
    "# lasso, elasocnet 하이퍼 파라미터 튜닝해주니 빨라짐. (max_iter=10으로 튜닝)\n",
    "# (ardr_linear, bayesian_ridge의 n_iter는 안하고 걍 vervose만 켬, 그냥 자체로 빠름)\n",
    "# xgbregressor nthread=16으로 튜닝해줌.\n",
    "# linear_regression 잘돼다 갑자기 안돼서 n_jobs =16으로 튜닝함\n",
    "# 순서대로 학습에\n",
    "# linear: 2분, (?? 갑자기 13분... cv_score 때문일 것 같음;;;),\n",
    "# ridge: 1분, (예상대로 linear 소요시간의 절반, 6분),\n",
    "# lasso: 1분 (튜닝후, 예상대로 6분),\n",
    "# elasicnet: 1분 (튜닝후, 49분~55분, 예상대로 6분),\n",
    "# ardr_linear: 1분 (55분~01분, 예상대로 6분),\n",
    "# bayesian_ridge: 3분 (24분 걸림, 예상보다 6분 단축),\n",
    "# xgbregressor: 원래 개별로만 돌릴 때는 2분 걸렸었음.(28분~) # nthread에서 njobs로 바꿈.\n",
    "\n",
    "# xgb verbosity 종류 0~3까지\n",
    "# verbosity: Verbosity of printing messages. Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug).\n",
    "# Sometimes XGBoost tries to change configurations based on heuristics, which is displayed as warning message.\n",
    "# If there’s unexpected behaviour, please try to increase value of verbosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08c3fef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                    | 0/11 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[10:38:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 476 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:4.53772\n",
      "[10:38:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 466 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:4.53511\n",
      "[10:38:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 504 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:4.53288\n",
      "[10:38:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 446 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:4.53112\n",
      "[10:38:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 484 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:4.52893\n",
      "[10:38:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 482 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:4.52690\n",
      "[10:39:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 542 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:4.52517\n",
      "[10:39:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 492 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:4.52434\n",
      "[10:39:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:4.52289\n",
      "[10:39:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 464 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[9]\tvalidation_0-rmse:4.52165\n",
      "[10:39:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 412 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 498 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 384 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 486 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 370 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:30] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 438 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 304 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:46] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 376 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:49] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 346 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 372 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 422 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:39:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 350 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 320 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 386 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 366 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 426 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 422 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 502 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 510 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:27] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 482 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 442 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:32] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 424 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:37] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 402 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 436 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 392 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 386 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:53] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 408 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:56] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 432 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:40:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 494 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:41:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 430 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:41:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:41:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 404 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:41:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 432 extra nodes, 0 pruned nodes, max_depth=10\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:42:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 360 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "2\n",
      "[10:42:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 336 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:3.87281\n",
      "[10:43:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 358 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:3.86992\n",
      "[10:43:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:3.86752\n",
      "[10:43:05] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 360 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:3.86548\n",
      "[10:43:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 306 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:3.86415\n",
      "[10:43:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:3.86265\n",
      "[10:43:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 290 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:3.86122\n",
      "[10:43:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 390 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:3.86020\n",
      "[10:43:16] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 328 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:3.85996\n",
      "[10:43:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[9]\tvalidation_0-rmse:3.85935\n",
      "[10:43:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 300 extra nodes, 0 pruned nodes, max_depth=10\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:45:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[10:46:39] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 274 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[10:46:44] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 354 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[10:46:47] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "3\n",
      "[10:46:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:3.72283\n",
      "[10:47:00] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 282 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:3.72087\n",
      "[10:47:02] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 296 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:3.71950\n",
      "[10:47:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 300 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:3.71945\n",
      "[10:47:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 324 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:3.71924\n",
      "[10:47:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:3.71935\n",
      "[10:47:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 308 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:3.72038\n",
      "[10:47:13] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 278 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:3.71881\n",
      "[10:47:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 318 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:3.71979\n",
      "[10:47:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 284 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[9]\tvalidation_0-rmse:3.71845\n",
      "[10:47:25] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 382 extra nodes, 0 pruned nodes, max_depth=10\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "4\n",
      "[10:50:48] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:4.09065\n",
      "[10:50:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 340 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:4.08750\n",
      "[10:50:52] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 336 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:4.08490\n",
      "[10:50:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 338 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:4.08251\n",
      "[10:50:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 326 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:4.08019\n",
      "[10:50:59] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 314 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:4.07836\n",
      "[10:51:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 342 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:4.07655\n",
      "[10:51:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 316 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:4.07461\n",
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      "[8]\tvalidation_0-rmse:4.07315\n",
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      "[9]\tvalidation_0-rmse:4.07173\n",
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     ]
    },
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     "output_type": "stream",
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      "5\n",
      "[10:55:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 492 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:3.87861\n",
      "[10:55:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 480 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:3.87532\n",
      "[10:55:08] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 484 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:3.87228\n",
      "[10:55:11] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 488 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:3.86964\n",
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      "[4]\tvalidation_0-rmse:3.86730\n",
      "[10:55:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 456 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:3.86501\n",
      "[10:55:17] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 510 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:3.86269\n",
      "[10:55:19] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 470 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:3.86071\n",
      "[10:55:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 478 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:3.85936\n",
      "[10:55:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 466 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[9]\tvalidation_0-rmse:3.85774\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[10:56:18] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 638 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "6\n",
      "[10:59:20] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 536 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:4.83031\n",
      "[10:59:22] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 530 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:4.82810\n",
      "[10:59:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 532 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:4.82683\n",
      "[10:59:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 538 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:4.82657\n",
      "[10:59:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 566 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:4.82537\n",
      "[10:59:31] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 554 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:4.82467\n",
      "[10:59:33] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 496 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[6]\tvalidation_0-rmse:4.82445\n",
      "[10:59:36] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 564 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[7]\tvalidation_0-rmse:4.82371\n",
      "[10:59:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 568 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:4.82223\n",
      "[10:59:40] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 496 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[9]\tvalidation_0-rmse:4.82161\n",
      "[10:59:50] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 924 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[10:59:58] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 976 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:00:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 886 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "7\n",
      "[11:04:01] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 868 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[0]\tvalidation_0-rmse:4.03930\n",
      "[11:04:04] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 886 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[1]\tvalidation_0-rmse:4.03675\n",
      "[11:04:07] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 848 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[2]\tvalidation_0-rmse:4.03454\n",
      "[11:04:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 946 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[3]\tvalidation_0-rmse:4.03250\n",
      "[11:04:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 954 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[4]\tvalidation_0-rmse:4.03038\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:04:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1062 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[5]\tvalidation_0-rmse:4.02872\n",
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      "[6]\tvalidation_0-rmse:4.02724\n",
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      "[7]\tvalidation_0-rmse:4.02606\n",
      "[11:04:23] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1044 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[8]\tvalidation_0-rmse:4.02493\n",
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      "[9]\tvalidation_0-rmse:4.02397\n",
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      "[11:07:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 916 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[11:07:24] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1022 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:07:28] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 996 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[11:07:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1048 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:07:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1020 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:07:57] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1076 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[11:08:03] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 978 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:06] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 962 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:09] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1084 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:12] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 974 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:15] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1016 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:26] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 950 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:29] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 974 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:32] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1070 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:35] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1024 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:38] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1082 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:08:41] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1086 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:45] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1060 extra nodes, 0 pruned nodes, max_depth=10\n",
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      "[11:08:51] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 990 extra nodes, 0 pruned nodes, max_depth=10\n",
      "[11:08:54] INFO: ../src/tree/updater_prune.cc:101: tree pruning end, 1102 extra nodes, 0 pruned nodes, max_depth=10\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "  9%|███▋                                    | 1/11 [30:18<5:03:00, 1818.09s/it]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[[0, 'xgboost_linear', 20.445327867524394, 0.0036222471622583408, array([ 0.00270119, -0.01920002,  0.00051682,  0.01003605, -0.001402  ,\n",
      "        0.00681282, -0.00236984])], [1, 'xgboost_linear', 14.89455904924298, -0.0033806295572238465, array([-3.28202133e-05,  5.87775331e-03,  8.63062709e-03,  5.26461520e-03,\n",
      "        3.63829124e-03, -8.49103957e-04, -1.04103612e-03])], [2, 'xgboost_linear', 13.826892162422073, -0.012565964963726417, array([ 0.00731241,  0.001422  ,  0.00426586,  0.00379795, -0.00232177,\n",
      "       -0.01147504, -0.00355902])], [3, 'xgboost_linear', 16.579007062304484, -0.004353470458950293, array([ 0.00116737, -0.00650393, -0.00175864, -0.00839824, -0.00364066,\n",
      "        0.00363475,  0.00383983])], [4, 'xgboost_linear', 14.88214887024922, -0.0007973787057182768, array([ 4.95597575e-05,  1.59742721e-03, -2.29430973e-03,  9.87612812e-03,\n",
      "        9.01707002e-03,  4.38568683e-03,  2.36826465e-03])], [5, 'xgboost_linear', 23.247908671229652, 0.0009252901270845326, array([-0.00451778,  0.00012292,  0.00073142, -0.00066704,  0.01985202,\n",
      "        0.06359073,  0.0715749 ])], [6, 'xgboost_linear', 16.19231554354951, 0.0004796281457616347, array([ 0.00340726,  0.00091172, -0.00236382,  0.05244229,  0.06705951,\n",
      "        0.02526662,  0.00169304])]]\n",
      "[1]\tvalid_0's l1: 2.71558\tvalid_0's l2: 20.5187\n",
      "[2]\tvalid_0's l1: 2.71456\tvalid_0's l2: 20.5031\n",
      "[3]\tvalid_0's l1: 2.71381\tvalid_0's l2: 20.4888\n",
      "[4]\tvalid_0's l1: 2.71304\tvalid_0's l2: 20.4758\n",
      "[5]\tvalid_0's l1: 2.71241\tvalid_0's l2: 20.4643\n",
      "[6]\tvalid_0's l1: 2.71185\tvalid_0's l2: 20.4541\n",
      "[7]\tvalid_0's l1: 2.71133\tvalid_0's l2: 20.4452\n",
      "[8]\tvalid_0's l1: 2.71082\tvalid_0's l2: 20.4356\n",
      "[9]\tvalid_0's l1: 2.71041\tvalid_0's l2: 20.4274\n",
      "[10]\tvalid_0's l1: 2.71002\tvalid_0's l2: 20.4193\n",
      "[11]\tvalid_0's l1: 2.70953\tvalid_0's l2: 20.4119\n",
      "[12]\tvalid_0's l1: 2.70934\tvalid_0's l2: 20.4065\n",
      "[13]\tvalid_0's l1: 2.70916\tvalid_0's l2: 20.4026\n",
      "[14]\tvalid_0's l1: 2.70895\tvalid_0's l2: 20.398\n",
      "[15]\tvalid_0's l1: 2.70864\tvalid_0's l2: 20.393\n",
      "[16]\tvalid_0's l1: 2.70846\tvalid_0's l2: 20.3899\n",
      "[17]\tvalid_0's l1: 2.70824\tvalid_0's l2: 20.3869\n",
      "[18]\tvalid_0's l1: 2.70809\tvalid_0's l2: 20.3851\n",
      "[19]\tvalid_0's l1: 2.708\tvalid_0's l2: 20.383\n",
      "[20]\tvalid_0's l1: 2.70779\tvalid_0's l2: 20.3803\n",
      "[1]\tvalid_0's l1: 2.40196\tvalid_0's l2: 14.8373\n",
      "[2]\tvalid_0's l1: 2.40147\tvalid_0's l2: 14.8293\n",
      "[3]\tvalid_0's l1: 2.40118\tvalid_0's l2: 14.8244\n",
      "[4]\tvalid_0's l1: 2.40084\tvalid_0's l2: 14.8196\n",
      "[5]\tvalid_0's l1: 2.40043\tvalid_0's l2: 14.8159\n",
      "[6]\tvalid_0's l1: 2.40009\tvalid_0's l2: 14.8118\n",
      "[7]\tvalid_0's l1: 2.3997\tvalid_0's l2: 14.8088\n",
      "[8]\tvalid_0's l1: 2.39948\tvalid_0's l2: 14.8061\n",
      "[9]\tvalid_0's l1: 2.39919\tvalid_0's l2: 14.8035\n",
      "[10]\tvalid_0's l1: 2.39874\tvalid_0's l2: 14.8004\n",
      "[11]\tvalid_0's l1: 2.39849\tvalid_0's l2: 14.7989\n",
      "[12]\tvalid_0's l1: 2.39844\tvalid_0's l2: 14.7978\n",
      "[13]\tvalid_0's l1: 2.39837\tvalid_0's l2: 14.7983\n",
      "[14]\tvalid_0's l1: 2.39831\tvalid_0's l2: 14.7975\n",
      "[15]\tvalid_0's l1: 2.3984\tvalid_0's l2: 14.797\n",
      "[16]\tvalid_0's l1: 2.39836\tvalid_0's l2: 14.7961\n",
      "[17]\tvalid_0's l1: 2.39848\tvalid_0's l2: 14.797\n",
      "[18]\tvalid_0's l1: 2.39838\tvalid_0's l2: 14.7948\n",
      "[19]\tvalid_0's l1: 2.39826\tvalid_0's l2: 14.7924\n",
      "[20]\tvalid_0's l1: 2.39821\tvalid_0's l2: 14.7927\n",
      "[1]\tvalid_0's l1: 2.22571\tvalid_0's l2: 13.653\n",
      "[2]\tvalid_0's l1: 2.22535\tvalid_0's l2: 13.646\n",
      "[3]\tvalid_0's l1: 2.2251\tvalid_0's l2: 13.6391\n",
      "[4]\tvalid_0's l1: 2.22471\tvalid_0's l2: 13.633\n",
      "[5]\tvalid_0's l1: 2.2244\tvalid_0's l2: 13.627\n",
      "[6]\tvalid_0's l1: 2.22405\tvalid_0's l2: 13.6219\n",
      "[7]\tvalid_0's l1: 2.22389\tvalid_0's l2: 13.617\n",
      "[8]\tvalid_0's l1: 2.2238\tvalid_0's l2: 13.6141\n",
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      "[11]\tvalid_0's l1: 2.22296\tvalid_0's l2: 13.6022\n",
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      "[13]\tvalid_0's l1: 2.22258\tvalid_0's l2: 13.5965\n",
      "[14]\tvalid_0's l1: 2.22243\tvalid_0's l2: 13.5951\n",
      "[15]\tvalid_0's l1: 2.2222\tvalid_0's l2: 13.5938\n",
      "[16]\tvalid_0's l1: 2.222\tvalid_0's l2: 13.5911\n",
      "[17]\tvalid_0's l1: 2.22182\tvalid_0's l2: 13.589\n",
      "[18]\tvalid_0's l1: 2.22174\tvalid_0's l2: 13.5885\n",
      "[19]\tvalid_0's l1: 2.22162\tvalid_0's l2: 13.588\n",
      "[20]\tvalid_0's l1: 2.22149\tvalid_0's l2: 13.5855\n",
      "[1]\tvalid_0's l1: 2.57778\tvalid_0's l2: 16.5112\n",
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      "[3]\tvalid_0's l1: 2.57666\tvalid_0's l2: 16.498\n",
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      "[1]\tvalid_0's l1: 2.3746\tvalid_0's l2: 14.8612\n",
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      "[10]\tvalid_0's l1: 2.37269\tvalid_0's l2: 14.81\n",
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      "[1]\tvalid_0's l1: 3.03087\tvalid_0's l2: 23.2742\n",
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      "[4]\tvalid_0's l1: 3.02741\tvalid_0's l2: 23.2025\n",
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      "[11]\tvalid_0's l1: 3.02654\tvalid_0's l2: 23.1501\n",
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      "[1]\tvalid_0's l1: 2.55475\tvalid_0's l2: 16.1955\n",
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      "[11]\tvalid_0's l1: 2.5542\tvalid_0's l2: 16.1615\n",
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      "[13]\tvalid_0's l1: 2.5544\tvalid_0's l2: 16.1588\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|███████▎                                | 2/11 [40:03<2:43:58, 1093.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7, 'gbm', 20.380274109745688, 0.006792561544555542, array([0.01750447, 0.01610233, 0.01174407, 0.01943906, 0.01377155,\n",
      "       0.00827517, 0.00767508])], [8, 'gbm', 14.79270719905892, 0.0034806795437288818, array([0.00602095, 0.01526369, 0.01495503, 0.00869939, 0.01065227,\n",
      "       0.0058684 , 0.00563005])], [9, 'gbm', 13.585469717920976, 0.005113796157469319, array([0.01414884, 0.00929487, 0.01125351, 0.01031913, 0.00491586,\n",
      "       0.00616736, 0.00611833])], [10, 'gbm', 16.4425766339739, 0.003911462033995838, array([0.00708529, 0.00906092, 0.00280058, 0.00544855, 0.0038169 ,\n",
      "       0.0139097 , 0.01578372])], [11, 'gbm', 14.806235712719168, 0.004307642734917239, array([0.0018516 , 0.00570835, 0.00456742, 0.01901596, 0.0192079 ,\n",
      "       0.01137434, 0.00806986])], [12, 'gbm', 23.162132529391602, 0.0046115044542650585, array([0.00185658, 0.01153771, 0.00991819, 0.00425886, 0.02870052,\n",
      "       0.08113481, 0.08733085])], [13, 'gbm', 16.16731642809157, 0.00202277527078798, array([0.01035773, 0.01029788, 0.00229656, 0.07079085, 0.08378315,\n",
      "       0.03345655, 0.00451549])]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|███████████▏                             | 3/11 [41:33<1:24:38, 634.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14, 'dummy', 20.537427949296163, -0.0008661363504212094, array([-1.70009117e-04, -2.34884934e-04, -2.46682078e-04, -9.65110624e-05,\n",
      "       -6.78959358e-06, -5.08244771e-04, -7.06766075e-04])], [15, 'dummy', 14.844799344342725, -2.8538107968323345e-05, array([-4.23316098e-04, -2.68705463e-04, -6.88936727e-05, -2.56941536e-04,\n",
      "       -4.58500448e-04, -1.55067377e-05, -1.97571903e-05])], [16, 'dummy', 13.663214094243353, -0.000579551885362406, array([-8.07156907e-05, -2.86772300e-05, -4.08641203e-04, -2.62154475e-04,\n",
      "       -2.29039216e-05, -1.14380780e-04, -5.96097037e-04])], [17, 'dummy', 16.518489350381895, -0.0006873176088264543, array([-6.17868121e-04, -8.18904985e-04, -1.89771782e-06, -3.93777863e-06,\n",
      "       -2.86095030e-04, -3.43183450e-04, -6.40506254e-04])], [18, 'dummy', 14.870582145802056, -1.953757485351204e-05, array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
      "       -1.88067208e-04, -3.44907231e-07, -2.30689628e-05])], [19, 'dummy', 23.30242224041552, -0.0014174207416097762, array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
      "       -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])], [20, 'dummy', 16.201372683087484, -7.945158897926241e-05, array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
      "       -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██████████                           | 3/11 [4:57:44<13:13:59, 5954.90s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_295989/3092523739.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_regressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_295989/3137870862.py\u001b[0m in \u001b[0;36mmy_regressor\u001b[0;34m()\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0mmse_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0mr2_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0mcv_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_nm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'{model_nm}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmse_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv_scores\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#[:str(model).index(\"(\")]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    507\u001b[0m     \u001b[0mscorer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_scoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m     cv_results = cross_validate(\n\u001b[0m\u001b[1;32m    510\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# independent, and that it is pickle-able.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    268\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    269\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    859\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 861\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    862\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    678\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1313\u001b[0m         \"\"\"\n\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1315\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m   1316\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/package/anaconda3.8/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    418\u001b[0m             )\n\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "## try 2\n",
    "# verbosity = 2일 때 결과\n",
    "import time\n",
    "start = time.time()\n",
    "_ = my_regressor()\n",
    "results\n",
    "time.time()-start\n",
    "# xgboost 한 타임에 4분..., 총 30분 걸릴 것으로 예상;; 31분 걸림. (11:09에 끝남.)\n",
    "# gbm 10분 정도 걸림\n",
    "# dummy 짱빨리 끝남\n",
    "# DT안돌아감"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abd5d3bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f692adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0,\n",
       "  'DecisionTree',\n",
       "  20.447291795548274,\n",
       "  0.0035265375603458216,\n",
       "  array([0.01405012, 0.01226554, 0.01315876, 0.01519267, 0.01197257,\n",
       "         0.00270937, 0.00612972])],\n",
       " [1,\n",
       "  'DecisionTree',\n",
       "  14.87985054605241,\n",
       "  -0.002389782688752895,\n",
       "  array([ 0.00946988,  0.01217002,  0.01289997,  0.00196199,  0.00231116,\n",
       "         -0.00037709, -0.00230182])],\n",
       " [2,\n",
       "  'DecisionTree',\n",
       "  13.639345690521145,\n",
       "  0.0011683704216332025,\n",
       "  array([ 0.00499926, -0.01249419,  0.00188581,  0.00322636,  0.00106242,\n",
       "         -0.00099545,  0.00223977])],\n",
       " [3,\n",
       "  'DecisionTree',\n",
       "  16.525384090038386,\n",
       "  -0.0011049997821852564,\n",
       "  array([-0.0182933 , -0.0007949 , -0.00016497,  0.00152865,  0.0029646 ,\n",
       "          0.00178123,  0.00144303])],\n",
       " [4,\n",
       "  'DecisionTree',\n",
       "  14.794120517280113,\n",
       "  0.005122367540025818,\n",
       "  array([0.0012748 , 0.00402795, 0.00087567, 0.00449716, 0.00465782,\n",
       "         0.00558872, 0.00466857])],\n",
       " [5,\n",
       "  'DecisionTree',\n",
       "  23.263032413330418,\n",
       "  0.0002753500199895731,\n",
       "  array([-0.00449635, -0.00344979, -0.00329516, -0.00480872,  0.00962501,\n",
       "          0.02132431,  0.02335139])],\n",
       " [6,\n",
       "  'DecisionTree',\n",
       "  16.256849056674465,\n",
       "  -0.0035039010080668564,\n",
       "  array([-0.00227765, -0.00352618, -0.00593414,  0.01915191,  0.03319467,\n",
       "          0.01386662, -0.00356716])],\n",
       " [7,\n",
       "  'random_forest',\n",
       "  20.44565358450195,\n",
       "  0.003606373748426983,\n",
       "  array([ 0.00246136,  0.00437214, -0.00323899,  0.01426546,  0.01267653,\n",
       "          0.00297055,  0.00645231])],\n",
       " [8,\n",
       "  'random_forest',\n",
       "  14.845105995140418,\n",
       "  -4.919581723150657e-05,\n",
       "  array([0.00628882, 0.00923687, 0.01226374, 0.00106842, 0.00491803,\n",
       "         0.00086677, 0.00144416])],\n",
       " [9,\n",
       "  'random_forest',\n",
       "  13.635969174568876,\n",
       "  0.001415638216396009,\n",
       "  array([ 0.00544177, -0.00450218,  0.00189107,  0.00287889,  0.0008559 ,\n",
       "         -0.00013156,  0.00241456])],\n",
       " [10,\n",
       "  'random_forest',\n",
       "  16.49362166407226,\n",
       "  0.0008191626618857528,\n",
       "  array([-0.00563761,  0.00311593,  0.00045241,  0.00306874,  0.00390843,\n",
       "          0.00097895,  0.00235553])],\n",
       " [11,\n",
       "  'random_forest',\n",
       "  14.823525748098751,\n",
       "  0.003144919378466504,\n",
       "  array([0.00147878, 0.0040849 , 0.00258724, 0.00467767, 0.00515655,\n",
       "         0.00575855, 0.00367187])],\n",
       " [12,\n",
       "  'random_forest',\n",
       "  23.258904670913353,\n",
       "  0.00045273901080500334,\n",
       "  array([-0.00421108, -0.00324414, -0.00268638, -0.00379628,  0.0114286 ,\n",
       "          0.031977  ,  0.02352195])],\n",
       " [13,\n",
       "  'random_forest',\n",
       "  16.247642584248606,\n",
       "  -0.0029356032425116307,\n",
       "  array([-0.0022002 , -0.00339645, -0.00558811,  0.0270969 ,  0.02652153,\n",
       "          0.01439309, -0.00292966])],\n",
       " [14,\n",
       "  'xgboost_linear',\n",
       "  20.445327867524377,\n",
       "  0.003622247162259118,\n",
       "  array([ 0.00270119, -0.01920002,  0.00051682,  0.01003605, -0.001402  ,\n",
       "          0.00681282, -0.00236984])],\n",
       " [15,\n",
       "  'xgboost_linear',\n",
       "  14.894559049242977,\n",
       "  -0.0033806295572236245,\n",
       "  array([-3.28202133e-05,  5.87775331e-03,  8.63062709e-03,  5.26461520e-03,\n",
       "          3.63829124e-03, -8.49103957e-04, -1.04103612e-03])],\n",
       " [16,\n",
       "  'xgboost_linear',\n",
       "  13.826892162422073,\n",
       "  -0.012565964963726417,\n",
       "  array([ 0.00731241,  0.001422  ,  0.00426586,  0.00379795, -0.00232177,\n",
       "         -0.01147504, -0.00355902])],\n",
       " [17,\n",
       "  'xgboost_linear',\n",
       "  16.579007062304484,\n",
       "  -0.004353470458950293,\n",
       "  array([ 0.00116737, -0.00650393, -0.00175864, -0.00839824, -0.00364066,\n",
       "          0.00363475,  0.00383983])],\n",
       " [18,\n",
       "  'xgboost_linear',\n",
       "  14.882148870249223,\n",
       "  -0.0007973787057184989,\n",
       "  array([ 4.95597575e-05,  1.59742721e-03, -2.29430973e-03,  9.87612812e-03,\n",
       "          9.01707002e-03,  4.38568683e-03,  2.36826465e-03])],\n",
       " [19,\n",
       "  'xgboost_linear',\n",
       "  23.24790867122966,\n",
       "  0.0009252901270845326,\n",
       "  array([-0.00451778,  0.00012292,  0.00073142, -0.00066704,  0.01985202,\n",
       "          0.06359073,  0.0715749 ])],\n",
       " [20,\n",
       "  'xgboost_linear',\n",
       "  16.1923155435495,\n",
       "  0.00047962814576230084,\n",
       "  array([ 0.00340726,  0.00091172, -0.00236382,  0.05244229,  0.06705951,\n",
       "          0.02526662,  0.00169304])],\n",
       " [21,\n",
       "  'gbm',\n",
       "  20.377643205573385,\n",
       "  0.006920775403688517,\n",
       "  array([0.01750447, 0.01610233, 0.01174407, 0.01943906, 0.01377155,\n",
       "         0.00827517, 0.00767508])],\n",
       " [22,\n",
       "  'gbm',\n",
       "  14.796051220430611,\n",
       "  0.0032554075999213783,\n",
       "  array([0.00602095, 0.01526369, 0.01495503, 0.00869939, 0.01065227,\n",
       "         0.0058684 , 0.00563005])],\n",
       " [23,\n",
       "  'gbm',\n",
       "  13.58815543170958,\n",
       "  0.004917116936858479,\n",
       "  array([0.01414884, 0.00929487, 0.01125351, 0.01031913, 0.00491586,\n",
       "         0.00616736, 0.00611833])],\n",
       " [24,\n",
       "  'gbm',\n",
       "  16.435068917226683,\n",
       "  0.004366278256822564,\n",
       "  array([0.00708529, 0.00906092, 0.00280058, 0.00544855, 0.0038169 ,\n",
       "         0.0139097 , 0.01578372])],\n",
       " [25,\n",
       "  'gbm',\n",
       "  14.808476405923647,\n",
       "  0.004156960202100102,\n",
       "  array([0.0018516 , 0.00570835, 0.00456742, 0.01901596, 0.0192079 ,\n",
       "         0.01137434, 0.00806986])],\n",
       " [26,\n",
       "  'gbm',\n",
       "  23.10335745366295,\n",
       "  0.007137352803111785,\n",
       "  array([0.00185658, 0.01153771, 0.00991819, 0.00425886, 0.02870052,\n",
       "         0.08113481, 0.08733085])],\n",
       " [27,\n",
       "  'gbm',\n",
       "  16.163302259078527,\n",
       "  0.0022705621727793224,\n",
       "  array([0.01035773, 0.01029788, 0.00229656, 0.07079085, 0.08378315,\n",
       "         0.03345655, 0.00451549])],\n",
       " [28,\n",
       "  'dummy',\n",
       "  20.537427949296163,\n",
       "  -0.0008661363504212094,\n",
       "  array([-1.70009117e-04, -2.34884934e-04, -2.46682078e-04, -9.65110624e-05,\n",
       "         -6.78959358e-06, -5.08244771e-04, -7.06766075e-04])],\n",
       " [29,\n",
       "  'dummy',\n",
       "  14.844799344342732,\n",
       "  -2.853810796898948e-05,\n",
       "  array([-4.23316098e-04, -2.68705463e-04, -6.88936727e-05, -2.56941536e-04,\n",
       "         -4.58500448e-04, -1.55067377e-05, -1.97571903e-05])],\n",
       " [30,\n",
       "  'dummy',\n",
       "  13.663214094243353,\n",
       "  -0.000579551885362628,\n",
       "  array([-8.07156907e-05, -2.86772300e-05, -4.08641203e-04, -2.62154475e-04,\n",
       "         -2.29039216e-05, -1.14380780e-04, -5.96097037e-04])],\n",
       " [31,\n",
       "  'dummy',\n",
       "  16.518489350381895,\n",
       "  -0.0006873176088264543,\n",
       "  array([-6.17868121e-04, -8.18904985e-04, -1.89771782e-06, -3.93777863e-06,\n",
       "         -2.86095030e-04, -3.43183450e-04, -6.40506254e-04])],\n",
       " [32,\n",
       "  'dummy',\n",
       "  14.870582145802054,\n",
       "  -1.953757485351204e-05,\n",
       "  array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
       "         -1.88067208e-04, -3.44907231e-07, -2.30689628e-05])],\n",
       " [33,\n",
       "  'dummy',\n",
       "  23.30242224041552,\n",
       "  -0.0014174207416097762,\n",
       "  array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
       "         -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])],\n",
       " [34,\n",
       "  'dummy',\n",
       "  16.201372683087488,\n",
       "  -7.945158897992854e-05,\n",
       "  array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
       "         -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])],\n",
       " [35,\n",
       "  'linear',\n",
       "  20.54202370140966,\n",
       "  -0.0010901046424984795,\n",
       "  array([-1.52580320e-03, -1.59972052e+05,  1.79551952e-03,  3.78879766e-03,\n",
       "          3.40729510e-03, -7.08667900e-04,  3.61790592e-03])],\n",
       " [36,\n",
       "  'linear',\n",
       "  14.878200753010933,\n",
       "  -0.0022786434213790674,\n",
       "  array([-5.90552782e-02, -1.07878143e+01,  1.53074598e-03, -8.59846440e-03,\n",
       "          4.86009758e-03,  2.85421155e-03, -1.34814466e-02])],\n",
       " [37,\n",
       "  'linear',\n",
       "  13.7170024447564,\n",
       "  -0.004518560912211811,\n",
       "  array([ 0.00818563, -0.00383357,  0.00561133,  0.0059827 ,  0.00020013,\n",
       "          0.00176022, -0.00278919])],\n",
       " [38,\n",
       "  'linear',\n",
       "  16.8062533650501,\n",
       "  -0.01812001341015379,\n",
       "  array([ 0.00334053,  0.00234252, -0.00289987,  0.00143955, -0.00404967,\n",
       "         -0.01948539,  0.00154861])],\n",
       " [39,\n",
       "  'linear',\n",
       "  14.936050899280056,\n",
       "  -0.0044221918850047,\n",
       "  array([-0.00073986,  0.00415489, -0.00339393,  0.01213842,  0.00899272,\n",
       "          0.00928571,  0.00142224])],\n",
       " [40,\n",
       "  'linear',\n",
       "  23.765181626424372,\n",
       "  -0.021304422443828575,\n",
       "  array([-0.01948649, -0.04238908, -0.01990264, -0.01521807,  0.00951779,\n",
       "          0.06039634,  0.0639197 ])],\n",
       " [41,\n",
       "  'linear',\n",
       "  16.63500495831269,\n",
       "  -0.026846734613781953,\n",
       "  array([-0.06277622, -0.01036436, -0.01635746,  0.0525075 ,  0.05791837,\n",
       "          0.01798185, -0.01341024])],\n",
       " [42,\n",
       "  'ridge',\n",
       "  20.49326438832787,\n",
       "  0.0012861201416252133,\n",
       "  array([ 3.63893942e-03, -1.01845248e+04,  6.15737613e-03,  1.09373492e-02,\n",
       "          8.29444866e-03,  4.77399226e-03,  6.43706286e-03])],\n",
       " [43,\n",
       "  'ridge',\n",
       "  14.924352683034593,\n",
       "  -0.005387695018631344,\n",
       "  array([-4.45543497e-02, -3.72614621e+00,  1.30394095e-02,  1.75529260e-03,\n",
       "          7.87957721e-03,  4.11878423e-03,  1.14938266e-04])],\n",
       " [44,\n",
       "  'ridge',\n",
       "  13.687621649028053,\n",
       "  -0.00236695710792878,\n",
       "  array([ 0.01075166,  0.0056566 ,  0.00882092,  0.00822959,  0.00032359,\n",
       "          0.00437414, -0.00134697])],\n",
       " [45,\n",
       "  'ridge',\n",
       "  16.852446943563734,\n",
       "  -0.02091841265799399,\n",
       "  array([ 0.00608359,  0.00458079, -0.00253694,  0.0025402 , -0.00387427,\n",
       "         -0.02235075,  0.0022251 ])],\n",
       " [46,\n",
       "  'ridge',\n",
       "  14.926984204812955,\n",
       "  -0.0038124732122972738,\n",
       "  array([ 2.28361644e-05,  4.25151576e-03, -2.80673562e-03,  1.24744299e-02,\n",
       "          1.16964391e-02,  9.67035512e-03,  2.10778685e-03])],\n",
       " [47,\n",
       "  'ridge',\n",
       "  23.744937227371548,\n",
       "  -0.02043442302167464,\n",
       "  array([-0.01664559, -0.03059095, -0.0189909 , -0.01289432,  0.0128039 ,\n",
       "          0.0599099 ,  0.06459717])],\n",
       " [48,\n",
       "  'ridge',\n",
       "  16.541351631286858,\n",
       "  -0.021065695576932386,\n",
       "  array([-0.04927274, -0.00979938, -0.01485642,  0.05189038,  0.05812156,\n",
       "          0.01877641, -0.0096801 ])],\n",
       " [49,\n",
       "  'lasso',\n",
       "  20.497467796584864,\n",
       "  0.001081272241875153,\n",
       "  array([ 0.00212106, -1.06867761,  0.00122722,  0.00318312,  0.00252694,\n",
       "          0.00267687,  0.00315093])],\n",
       " [50,\n",
       "  'lasso',\n",
       "  14.85593228117927,\n",
       "  -0.0007785148702839084,\n",
       "  array([-1.77160110e-02, -1.06335433e+00,  1.39696222e-03,  2.48675486e-03,\n",
       "          3.08344861e-03,  2.47169462e-03,  7.16289644e-04])],\n",
       " [51,\n",
       "  'lasso',\n",
       "  13.64872690028428,\n",
       "  0.0004813690619094313,\n",
       "  array([0.00085473, 0.00161368, 0.00257512, 0.00394436, 0.00046013,\n",
       "         0.0028046 , 0.00038119])],\n",
       " [52,\n",
       "  'lasso',\n",
       "  16.46084037122641,\n",
       "  0.002805047890846457,\n",
       "  array([ 1.80127845e-03,  2.96667524e-03, -9.20363802e-05,  2.06771003e-03,\n",
       "          1.67301915e-04,  5.38908078e-03,  5.57259603e-03])],\n",
       " [53,\n",
       "  'lasso',\n",
       "  14.87988932737415,\n",
       "  -0.000645428566920625,\n",
       "  array([0.00166798, 0.00278359, 0.0011938 , 0.00366199, 0.00384868,\n",
       "         0.00403075, 0.0015772 ])],\n",
       " [54,\n",
       "  'lasso',\n",
       "  23.330851984712226,\n",
       "  -0.002639183909070253,\n",
       "  array([-0.00329832, -0.00292185, -0.00428743, -0.00258045,  0.00393033,\n",
       "          0.01456471,  0.01599418])],\n",
       " [55,\n",
       "  'lasso',\n",
       "  16.289791305451303,\n",
       "  -0.005537362415027669,\n",
       "  array([-0.00289694, -0.00186393, -0.00425899,  0.01275383,  0.01404195,\n",
       "          0.00616926, -0.00161048])],\n",
       " [56,\n",
       "  'elasticnet',\n",
       "  20.49723284912669,\n",
       "  0.0010927221155058175,\n",
       "  array([ 0.00211263, -1.10871278,  0.0014043 ,  0.00327565,  0.00256519,\n",
       "          0.00282007,  0.00328338])],\n",
       " [57,\n",
       "  'elasticnet',\n",
       "  14.856047623395368,\n",
       "  -0.000786284965728079,\n",
       "  array([-1.74891592e-02, -9.46840157e-01,  1.27334471e-03,  2.65646135e-03,\n",
       "          3.33942066e-03,  2.56541719e-03,  7.98101628e-04])],\n",
       " [58,\n",
       "  'elasticnet',\n",
       "  13.648757353331924,\n",
       "  0.00047913893534057284,\n",
       "  array([0.00083962, 0.00181324, 0.00278631, 0.00406786, 0.00041377,\n",
       "         0.0027894 , 0.00045676])],\n",
       " [59,\n",
       "  'elasticnet',\n",
       "  16.455807675606057,\n",
       "  0.0031099277484318844,\n",
       "  array([ 2.11040005e-03,  3.16080681e-03, -8.42702075e-05,  2.05772570e-03,\n",
       "          1.71542222e-04,  5.81471672e-03,  5.96394543e-03])],\n",
       " [60,\n",
       "  'elasticnet',\n",
       "  14.877740238036827,\n",
       "  -0.0005009062271597031,\n",
       "  array([0.00175604, 0.00283771, 0.00118768, 0.00408041, 0.00424696,\n",
       "         0.00429832, 0.00167063])],\n",
       " [61,\n",
       "  'elasticnet',\n",
       "  23.339208147312345,\n",
       "  -0.002998288499656665,\n",
       "  array([-0.00340366, -0.00301661, -0.00397146, -0.00269889,  0.00413545,\n",
       "          0.01520734,  0.01685657])],\n",
       " [62,\n",
       "  'elasticnet',\n",
       "  16.288091170084243,\n",
       "  -0.005432416341705659,\n",
       "  array([-0.00290652, -0.00178731, -0.00445904,  0.01322118,  0.01457514,\n",
       "          0.00654078, -0.00149189])],\n",
       " [63,\n",
       "  'ardr_linear',\n",
       "  20.53742794929616,\n",
       "  -0.0008661363504212094,\n",
       "  array([-1.70009117e-04, -2.34884934e-04, -2.46682078e-04, -9.65110624e-05,\n",
       "         -6.78959358e-06, -5.08244771e-04, -7.06766075e-04])],\n",
       " [64,\n",
       "  'ardr_linear',\n",
       "  14.844799344342729,\n",
       "  -2.8538107968767434e-05,\n",
       "  array([-4.23316098e-04, -2.68705463e-04, -6.88936727e-05, -2.56941536e-04,\n",
       "         -4.58500448e-04, -1.55067377e-05, -1.97571903e-05])],\n",
       " [65,\n",
       "  'ardr_linear',\n",
       "  13.663214094243353,\n",
       "  -0.000579551885362406,\n",
       "  array([-8.07156907e-05, -2.86772300e-05, -4.08641203e-04, -2.62154475e-04,\n",
       "         -2.29039216e-05, -1.14380780e-04, -5.96097037e-04])],\n",
       " [66,\n",
       "  'ardr_linear',\n",
       "  16.51848935038189,\n",
       "  -0.0006873176088266764,\n",
       "  array([-6.17868121e-04, -8.18904985e-04, -1.89771782e-06, -3.93777863e-06,\n",
       "         -2.86095030e-04, -3.43183450e-04, -6.40506254e-04])],\n",
       " [67,\n",
       "  'ardr_linear',\n",
       "  14.870582145802057,\n",
       "  -1.953757485351204e-05,\n",
       "  array([-1.66068984e-04, -1.10042060e-04, -2.56316696e-05, -6.46524839e-05,\n",
       "         -1.88067208e-04, -3.44907231e-07, -2.30689628e-05])],\n",
       " [68,\n",
       "  'ardr_linear',\n",
       "  23.302422240415527,\n",
       "  -0.0014174207416099982,\n",
       "  array([-2.29570719e-04, -4.60741031e-04, -6.60444023e-04, -1.52595709e-04,\n",
       "         -3.49722012e-07, -9.11531881e-04, -9.22495219e-04])],\n",
       " [69,\n",
       "  'ardr_linear',\n",
       "  16.201372683087488,\n",
       "  -7.94515889797065e-05,\n",
       "  array([-7.44804620e-04, -8.05680419e-04, -2.80853567e-04, -3.61663631e-04,\n",
       "         -6.34012034e-04, -7.77266992e-05, -1.12185534e-04])],\n",
       " [70,\n",
       "  'bayesian_ridge',\n",
       "  21.290845477007363,\n",
       "  -0.03758300722055585,\n",
       "  array([-6.46374680e+01, -2.25281914e+05, -3.74956710e+01, -2.35734837e+01,\n",
       "         -1.69015181e+00, -3.76910125e+00, -4.51656771e+00])],\n",
       " [71,\n",
       "  'bayesian_ridge',\n",
       "  180.82999854034776,\n",
       "  -11.181717980262585,\n",
       "  array([-1.12932011e+03, -7.05574260e+03, -7.17926287e-01, -1.14007874e+00,\n",
       "         -1.19648569e+01, -1.49035055e+00, -5.52095245e+00])],\n",
       " [72,\n",
       "  'bayesian_ridge',\n",
       "  42.85486900924617,\n",
       "  -2.1383322645470146,\n",
       "  array([-4.09705566e+01, -2.32881167e+00, -4.89366481e+00, -2.96265428e+00,\n",
       "         -5.04010826e+00,  2.63795568e-03, -3.12632675e+00])],\n",
       " [73,\n",
       "  'bayesian_ridge',\n",
       "  16.807027371110184,\n",
       "  -0.018166902567609444,\n",
       "  array([-9.97990736e+01, -1.60375289e+00, -2.19123585e-01, -6.72700761e-01,\n",
       "         -5.44880985e+00, -3.50050028e+00, -1.22563249e-02])],\n",
       " [74,\n",
       "  'bayesian_ridge',\n",
       "  14.92278896467433,\n",
       "  -0.003530350961647999,\n",
       "  array([ 5.01524843e-04,  4.11234667e-03, -2.27409795e-03,  7.97276160e-03,\n",
       "          8.06883630e-03, -1.89779898e-02, -6.07871152e+00])],\n",
       " [75,\n",
       "  'bayesian_ridge',\n",
       "  23.73534662349114,\n",
       "  -0.020022268538240073,\n",
       "  array([-1.36836458e+01, -1.62579084e+02, -1.34220388e+03, -1.01345875e+02,\n",
       "         -3.08084185e-01, -1.47939871e+02, -3.05702414e+01])],\n",
       " [76,\n",
       "  'bayesian_ridge',\n",
       "  411.31996449092617,\n",
       "  -24.389987167265932,\n",
       "  array([-1.33138167e+02, -3.33178391e-01, -1.63988867e+01,  4.89189385e-02,\n",
       "          5.70996334e-02, -2.83811678e+00, -1.05308871e-02])]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "931d5d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bayesian_ridge': -5.398477134480513,\n",
       " 'linear': -0.011225810189836911,\n",
       " 'ridge': -0.010385648064833315,\n",
       " 'xgboost_linear': -0.0022957540357875545,\n",
       " 'lasso': -0.0007475429380959163,\n",
       " 'elasticnet': -0.0007194438907102617,\n",
       " 'dummy': -0.0005254219797174997,\n",
       " 'ardr_linear': -0.0005254219797174679,\n",
       " 'DecisionTree': 0.0004419917232842011,\n",
       " 'random_forest': 0.0009220048508910164,\n",
       " 'gbm': 0.004717779053611736}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test cell\n",
    "dic = {i:[] for i in ['linear', 'ridge', 'lasso', 'elasticnet',\n",
    "                       'ardr_linear', 'bayesian_ridge',\n",
    "                       'DecisionTree', 'random_forest',\n",
    "                       'xgboost_linear', 'gbm', 'dummy']}\n",
    "for i in results:\n",
    "    dic[i[1]].append(i[3])\n",
    "\n",
    "for i in dic.keys():\n",
    "    dic[i] = sum(dic[i])/len(dic[i])\n",
    "\n",
    "score_lst = sorted(dic.items(), key = lambda t : t[1])\n",
    "\n",
    "# score_lst\n",
    "\n",
    "# score_dic = {}\n",
    "# for i in score_lst:\n",
    "#     score_dic[i[0]] = i[1]\n",
    "# score_dic\n",
    "\n",
    "score_dic = {i[0]:i[1] for i in score_lst}\n",
    "score_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53067bcc",
   "metadata": {},
   "source": [
    "# plot을 위한 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b591239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('gbm', 17.038864984800767),\n",
       " ('random_forest', 17.107203345934888),\n",
       " ('DecisionTree', 17.11512487277789),\n",
       " ('dummy', 17.134043972509883),\n",
       " ('ardr_linear', 17.134043972509886),\n",
       " ('elasticnet', 17.137555008127638),\n",
       " ('lasso', 17.137642852401786),\n",
       " ('xgboost_linear', 17.15259417521747),\n",
       " ('ridge', 17.310136961060802),\n",
       " ('linear', 17.325673964034888),\n",
       " ('bayesian_ridge', 101.68012006811473)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('bayesian_ridge', -5.398477134480513),\n",
       " ('linear', -0.011225810189836911),\n",
       " ('ridge', -0.010385648064833315),\n",
       " ('xgboost_linear', -0.0022957540357875545),\n",
       " ('lasso', -0.0007475429380959163),\n",
       " ('elasticnet', -0.0007194438907102617),\n",
       " ('dummy', -0.0005254219797174997),\n",
       " ('ardr_linear', -0.0005254219797174679),\n",
       " ('DecisionTree', 0.0004419917232842011),\n",
       " ('random_forest', 0.0009220048508910164),\n",
       " ('gbm', 0.004717779053611736)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('bayesian_ridge', -4811.252901313237),\n",
       " ('linear', -3264.9552931690337),\n",
       " ('ridge', -207.91967448978886),\n",
       " ('lasso', -0.04146327361786053),\n",
       " ('elasticnet', -0.0397491315758709),\n",
       " ('ardr_linear', -0.00029877558233826657),\n",
       " ('dummy', -0.00029877558233826657),\n",
       " ('DecisionTree', 0.004382666428617596),\n",
       " ('random_forest', 0.004641618186416895),\n",
       " ('xgboost_linear', 0.006744282088719398),\n",
       " ('gbm', 0.01615828967311104)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_list = ['linear', 'ridge', 'lasso', 'elasticnet',\n",
    "              'ardr_linear', 'bayesian_ridge',\n",
    "              'DecisionTree', 'random_forest',\n",
    "              'xgboost_linear', 'gbm', 'dummy']\n",
    "mse_dic = {i:[] for i in model_list}\n",
    "r2_dic = {i:[] for i in model_list}\n",
    "cv_dic = {i:[] for i in model_list}\n",
    "for i in results:\n",
    "    mse_dic[i[1]].append(i[2])\n",
    "    r2_dic[i[1]].append(i[3])\n",
    "    cv_dic[i[1]].append(i[4].mean())\n",
    "\n",
    "\n",
    "# import math\n",
    "# 리스트에 mean 함수가 내장이 안돼있다니...\n",
    "mean_mse_score = {i:[] for i in model_list}\n",
    "mean_r2_score = {i:[] for i in model_list}\n",
    "mean_cv_score = {i:[] for i in model_list}\n",
    "for i in model_list:\n",
    "    mean_mse_score[i] = sum(mse_dic[i])/len(mse_dic[i])\n",
    "    mean_r2_score[i] = sum(r2_dic[i])/len(r2_dic[i])\n",
    "    mean_cv_score[i] = sum(cv_dic[i])/len(cv_dic[i])\n",
    "\n",
    "\n",
    "# 정렬\n",
    "mse_score_lst = sorted(mean_mse_score.items(), key = lambda t : t[1]) #mse 작은 순으로 줄 세워짐.\n",
    "r2_score_lst = sorted(mean_r2_score.items(), key = lambda t : t[1]) #r2 작은 순으로 줄 세워짐.\n",
    "cv_score_lst = sorted(mean_cv_score.items(), key = lambda t : t[1]) #cv 작은 순으로 줄 세워짐.\n",
    "mse_score_lst\n",
    "r2_score_lst\n",
    "cv_score_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aee9251a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다시 dic\n",
    "mse_score_dic = {}\n",
    "r2_score_dic = {}\n",
    "cv_score_dic = {}\n",
    "\n",
    "for i in mse_score_lst:\n",
    "    mse_score_dic[i[0]] = i[1]\n",
    "r2_score_dic = {i[0]:i[1] for i in r2_score_lst}\n",
    "cv_score_dic = {i[0]:i[1] for i in cv_score_lst}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f949f15a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>gbm</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>17.115125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dummy</td>\n",
       "      <td>17.134044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ardr_linear</td>\n",
       "      <td>17.134044</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>elasticnet</td>\n",
       "      <td>17.137555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lasso</td>\n",
       "      <td>17.137643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgboost_linear</td>\n",
       "      <td>17.152594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ridge</td>\n",
       "      <td>17.310137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>linear</td>\n",
       "      <td>17.325674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bayesian_ridge</td>\n",
       "      <td>101.680120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key       value\n",
       "0              gbm   17.038865\n",
       "1    random_forest   17.107203\n",
       "2     DecisionTree   17.115125\n",
       "3            dummy   17.134044\n",
       "4      ardr_linear   17.134044\n",
       "5       elasticnet   17.137555\n",
       "6            lasso   17.137643\n",
       "7   xgboost_linear   17.152594\n",
       "8            ridge   17.310137\n",
       "9           linear   17.325674\n",
       "10  bayesian_ridge  101.680120"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xgboost_linear</td>\n",
       "      <td>-0.002296</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lasso</td>\n",
       "      <td>-0.000748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>elasticnet</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ardr_linear</td>\n",
       "      <td>-0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.000442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.000922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gbm</td>\n",
       "      <td>0.004718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key     value\n",
       "0   bayesian_ridge -5.398477\n",
       "1           linear -0.011226\n",
       "2            ridge -0.010386\n",
       "3   xgboost_linear -0.002296\n",
       "4            lasso -0.000748\n",
       "5       elasticnet -0.000719\n",
       "6            dummy -0.000525\n",
       "7      ardr_linear -0.000525\n",
       "8     DecisionTree  0.000442\n",
       "9    random_forest  0.000922\n",
       "10             gbm  0.004718"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <td>linear</td>\n",
       "      <td>-3264.955293</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>lasso</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>elasticnet</td>\n",
       "      <td>-0.039749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ardr_linear</td>\n",
       "      <td>-0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dummy</td>\n",
       "      <td>-0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.004383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.004642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>xgboost_linear</td>\n",
       "      <td>0.006744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gbm</td>\n",
       "      <td>0.016158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key        value\n",
       "0   bayesian_ridge -4811.252901\n",
       "1           linear -3264.955293\n",
       "2            ridge  -207.919674\n",
       "3            lasso    -0.041463\n",
       "4       elasticnet    -0.039749\n",
       "5      ardr_linear    -0.000299\n",
       "6            dummy    -0.000299\n",
       "7     DecisionTree     0.004383\n",
       "8    random_forest     0.004642\n",
       "9   xgboost_linear     0.006744\n",
       "10             gbm     0.016158"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_df = pd.DataFrame(mse_score_dic.items(), columns=['key', 'value'])\n",
    "mse_df\n",
    "\n",
    "r2_df = pd.DataFrame(r2_score_dic.items(), columns=['key', 'value'])\n",
    "r2_df\n",
    "\n",
    "cv_df = pd.DataFrame(cv_score_dic.items(), columns=['key', 'value'])\n",
    "cv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f3f7112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummy_mean test cell\n",
    "df[df.key=='dummy']['value'].values\n",
    "f'dummy\\n: {dummy_mean[0]:.2f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e36424",
   "metadata": {},
   "source": [
    "# MSE plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "808512c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "d0fbbe83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 750x450 with 0 Axes>"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M/fbbr9jASUkHH3wwZsbo0aOLzb/99ttp27YtdevWpV+/fhU+uJKIiIgsU2aAHrtZbAjMcPc3y5oqprgiha9u3bocffTRlNZl54gRI5gzZ87S6dhjj6VPnz40a9Zspba300478c477zBr1iwmTJjA+uuvX+LooS+++GKJgffrr7/OWWedxRNPPMGUKVNo2bIlhxxyyArssYiIiGRTeSkuqW4WTwbW+H7QRbKhe/fudO/enZEjR5a77oIFC3jooYe49dZbV3p77dq1W/q/u1OlShV++OGHYuvMmTOHU045heeee45OnToVW3bvvfdy6KGH0qNHDwCuuOIK1lprLX7++WfWW2+9cvdBREREsqvMGnR3LwLUzaJIwpVXXsmee+6ZlW0NHz6cKlWqsO+++67Sdt5++20aNWpEnTp1uPbaaznvvPOKLT/nnHMYOHAgHTt2XO65X3zxBd26dVv6uGXLlrRq1Yovv/xylcokIiIiK0fdLIqsoLPPPjtr27rrrrs47LDDqFmz5iptp2fPnsycOZNp06Zx11130aVLl6XL3n//fV577TU+++yzEp87Z86c5XLbGzVqxOzZs1epTCIia4pvb/k930UoV6cTW+a7CJJF6mZRJE9++eUX3njjDf773/9mbZvNmjVj0KBBbLDBBkycOJEaNWpw7LHHcuutt5Z6EVCvXj1mzSp+7T1r1izq16+ftXKJiIhI5tTNokie3H333XTv3p2uXbtmdbuLFi1i1qxZTJkyBTPjm2++4cADDyy2Tp8+fTj//PM55ZRT2GSTTYr16jJlyhQmT57MxhtvnNVyiUjl8vhTJfdMVUgG9C+5cb5IvmUSoO+Y81KIrEGKiopYuHAhCxcuBGD+/PkA1KpVa+k6S5YsYdiwYVx44YWrvL17772XXXbZhTZt2vD7779z0kknseGGG9K+fXuKioqYOHFise21bt2axx57jK233hqAo446iv79+3PYYYfRtWtXzj33XHbYYQc1EBUREcmTcgN0daEoUtzll1/OqFGjGDFiRInL33rrLXbccdl1be3atYHQw0rKyy+/zMyZMznooIOWe/5DDz3E5Zdfztdff53R9kaPHs3555/PzJkzadiwITvssAMjRozAzKhatSqtWrVa7jWaNm1K3bp1gdBN4xVXXMF+++3HjBkz2GGHHXjooYdW6D0RERGR7LFk0FDZDRkyxEvra1pEREQytyaluKxJjUR/v/7DHJdk1bU8pXu+i1DhzOw6dx+SelxiN4tm9qmZ7ZZ4XNvMrjKz9UtY9yAzm5ub4opIIbj/llt4O4N+3UVERGTVldYPejegSeJxHeA0oF0J61YFVq2POBEpWJ9/9hnPDh3KfQccwJAjjmDuXF2Pi4iI5FKZAxWlsZyVQkQK0uLFiznt4IO5eepU7pw2jV0efpg9N96Yd95U0xQREZFcWZEAfZWZ2WAz+9jMFpjZ02nL5qRNi8zsi1K2U9PM7jSzX8xstpl9a2ZHp60zzMwWpm1zmxzunsgax91pv+GGXNW4MXOB3RcvZvjPP3Pv/vurNl1ERCRHKjRAByYBl1K8b3UA3L1ecgLGAI+Wsp1qwGSgD9AAOBK4xsx2TVvvlrTtvpetHRGpDKpXr85dzz7Lzvfcw55t2/KuGY2Au6ZNo49q00VERHKiQgN0dx/u7k8DZTbtNrPuQBdgWCnb+cvdL3D3nzx4H3gD6JnlIosIsMe++/LU6NHc3a8fpzVuzDxgj0Rt+mlHHsm8ecsNMCwiIiIroawAfX8zu9jMLgbOJowWemRqXmJZ/xyUaxAwwt0nZbKymdUCugPpKTGHm9l0M/vazE4zs9J6relrZteOHTt2lQotsiZr3Lgxdz/3HDvddRf90mrTd37oIfp17cq7b72V72KKiIis9soaqGjfOCUdWsq6WetM3czqAAcBh2e4vgF3AT8AwxOLbgDOAKYDWwGPA0XAdenbcPeXgZeHDBly6ioVXqQS6Lfffmy7446cfvjhDH/nHS6ZMYM9Fi9mm59/5vT+/Rm+555ccsstSwdUEhERkRVTWg36uis4ZXNM8AHAXOCF8laMwfmtQEdgX3cvSi1z90/dfaq7L4kpMFcCB2axnCKVVqo2vfedd7Jn27a8Z0Zj4O5p09jxwQfZc+ONeW/UqHwXU0REZLVUYoDu7r+u6JTFMh0D3Ofui8taKQbnNxNSW3Z191nlbLeonOUisoL27N+fJ0eP5s499uCMmJveb/FinvzpJ+7q358zjj5auekiIiIrqKK7WawW88WrAVXMrJaZ1Ugs7whsC9yTweZuArYDdnH3GSW81gAza2DBloQ8+qeysiMislTjxo255/nn6XXHHezZti3vQ6hNnzqV3g88wJ4bb8z777yT72KKiIisNiq6m8XzgXnAecBe8f9XEssHAaPc/fv0J5rZbWZ2W/y/HXAiIbXl10Q/57clnjIYGAfMBh4CbgGuyf4uiQjAXvvvz5OjR3P7HntwZuPGzGdZbfodf/sbZw4axPz58/NdTBERkYJX0d0sDnV3S5t6J5af6e47lPLc4939+Pj/r/G5tdL6OT8+sX4vd28U53d096uSOeoikn2NGzfm3hdeYPs77mCPddZZWpt+z9SpbH///fRTbbqIiEi5KroGXUQqgVRt+m27785ZsTZ9r8WLefLHH7n9b3/jrGOOUW26iIhIKRSgi0hONGnShGEvvsh2t99Ov3XW4QNCbfq9U6fS87776Lfxxnzw7rv5LqaIiEjBUYAuIjm19wEH8MTo0dyy227FatOf+PFHbt13X8469ljVpouIiCQoQBeRnGvSpAn3jRjBtrfdRr911uFDoAkwbOpUtiujNt09a2OgiYiIrDaWC9DNrMjMlqzolI/Ci8jqZZ8BA5bWpp/dqBHzgb0XLeKJH3/klrTa9AULFtCjUyc+Hz06r2UWERGpaCXVoF9cwvQ54IQuEf8L3AC8GueNjuuIiJSrSZMmDBsxgq3TatPvmzqVbWNt+ofvvcfFp5zC4b/8wr+OOUY16SIiUqlUS5/h7kOTj83sUKAtsJm7f5m2bFPgdeDHHJZRRNZA+x54INv36cOphxxCmw8+YOjMmeyzaBE9f/yRIfvsQ1XgskWLmP7jjzz7xBPsM2BAvossIiJSITLJQT8LuCk9OAdw98+Bm4Fzsl0wEVnzNW3alPtfeoket93GHmuvzUdAU0Jt+j1TpwJw+qxZ3HDOOSxYsCCvZRUREakomQToHYA/ylg+DVg/O8URkcpo3wMP5PHRo7mxb1+GNm5cbFkd4JiJE7npssvyUzgREZEKtlyKSwnGAwea2a3uvji5wMyqAwfFdUREVlrTpk3559Ch3LT33sstO2jBAvrdcw+HDR5MixYt8lA6kYpxwFNf5bsI5Xqif9d8F0FkjZdJDfr1wLbAKDM7yMw2M7NuZnYwMAroEdcREVlp8+fP5+zDD+famNqSZMCFEydy4YknVnzBREREKli5NejufouZ1QEuAh5KLDJgPnCOu9+So/Kt+Q47DMaMyXcpRPJu8oIFNJ40iSNr1YKiIuq5076oiHWLimjvTntg5tNP80XnzmxSt26+iyuSE1fOmJfvIpTvitoZrdZnxuLyV8q3KzJJJIB2UxfluCBZcE/1jFZr8vtfOS5IFjyYp3N8587wwAP5ee00GR2Z7v4fM7sL6Au0JwTnPwOvuvuM3BVPRFZ3v0//IaP16gA3t6gKVAVgTpEzfnER4xcV8fGiJTy9sIi/Fhfx0u+/0LJRzayWsWWTDTJa7/uZv2b1dXNhw0btMlrvhxnL36koNBs0bp7Rej/MmJnbgmTBBo0b5bsIIrIayezSEXD3mcBjuStKJVUgV2oiufLww31XeRu1gQ3iBPDwKm+xuFMHvpzReic/c3yWXzn7RuxzW0brnfLU7Tkuyap7of/fM1rvtCefyXFJVt2z+++T0Xpnr0E56P97alqOS7LqBvRvltF6v97ye45Lsuo6ndgyo/WmX/9hjkuy6lqe0j3fRci7jAN0M9sQ2BloAdzv7r+YWU2gDTDR3dUHmoiIiIjIKiq3kagFNwHfEPo8vwBYNy6uThhJ9B+5KqCIiIiISGWSSQ36qcCJwDXAS8CrqQXuPsfMngb2Aa7NRQFFKqPn79k930Uo155Hj8h3EURERNZImQToxwKPufsZZta0hOVfAn2yWyyRFffTjZnleObT+v8s/FxZERERya9M+kFfF3ijjOUzgCbZKY6IiIiISOWWSYD+J9C4jOUbAIXfX5eIiIiIyGogkwB9JHCEmS2XDmNmLYBBJPLSRURERERk5WUSoA8F1gHeAQbGeduZ2ZmEHlyqAZflonAiIiIiIpVNuQG6u38D7EIY6O+/cfZFwJXATGBXd/8pVwUUEREREalMMhqoyN3fBzY2s42BToTA/gfgM3f3HJZPRERERKRSKTNAN7N6wK/Ale5+tbt/SehWUUREREREcqDMFBd3nwM4oScXERERERHJsUwaib4C9M11QUREREREJLMA/Qygg5ndaWabmFldM6uSPuW6oCIiIiIilUEmjUTHE9JcugJHl7KOZ7gtKTBTb30w30UoV/MTDs13EUREREQqTCZB9f2EAFxERERERHKs3ADd3Y+sgHKIiIiIiAiZ5aCLiIiIiEgFWaEA3czqmdnaZtY2fcrw+YPN7GMzW2BmT6ctm5M2LTKzL8rYVnUzu8nMpsfpRjOrlulyEREREZFClFGAbmZHmtkYYBZh4KJfSpgyMQm4FLgzfYG710tOwBjg0TK2dT7QE9goTtsD567AchERERGRglNugG5mhwD3ABMJQa8B1wNXAdOATym9d5di3H24uz8dn1fWa3YHugDDyljtaOBSd5/s7pOBy4BBK7BcRERERKTgZFKDfiowyt37AHfEeS+4+zmErhfXAupkuVyDgBHuPqmkhWbWGFgbGJ2YPRpoa2YNy1tewvb6mtm1Y8eOzUbZRURERERWWiYBehdgePy/KP6tBuDuUwlB++BsFcjM6gAHAXeVsVq9+HdmYl7q//oZLC/G3V929yHt27dfobKKiIiIiGRbJgH6QmBB/H8OoU/05onlE4F1s1imAcBc4IUy1pkT/yZrw1P/z85guYiIiIhIQcokQP8Z2BDA3RcB3wO7JpbvCPyWxTIdA9zn7otLW8HdZwATgG6J2d2A8e4+q7zlWSyriIiIiEhWZRKgvwr0NzOLj28HDjOz183sDUI6yn2ZvJiZVTOzWoQUmSpmVsvMaiSWdwS2JTRKLc+9wHlm1srMWhF6aLlrBZaLiIiIiBScTPoFvxJ4PK67yN2vN7PqwMHAEuCCuE4mzgcuTDyeB7wJ9I6PBxEapH6f/kQzuw3A3Y+Psy4BmhK6YwR4CLg88ZTylouIiIiIFJxyA/SYLvJJ2ryrgatX9MXcfSgwtIzlZ5ax7Pi0x4uAf8SppPXLXC4iIiIiUohWaCRRERERERHJrXJr0M3sggy24+5+SRbKIyIiIiJSqWWSgz60jGVOGFnUCTnfIiIiIiKyCspNcXH3KukTIbDvCNwGfAQ0y3E5RUREREQqhZXKQXf3Inf/wd3/AUwCrs1usUREREREKqdsNBJ9Bdg7C9sREREREan0shGgrw3UKHctEREREREpVya9uLQtZVFjYCfgVMJooyIiIiIisooy6cVlLKGXlpIYYSTQE7NVIBERERGRyiyTAP1olg/QHZgB/ODu32a9VCIiIiIilVS5Abq7D6uAcoiIiIiICNlpJCoiIiIiIlmSSSPRC1Ziu+7uGllURERERGQFZZKDPpRlOeiWtqys+QrQRURERERWUCYpLq2A0cBwYBugUZy2Bf4vLmvp7lUSU9VcFFZEREREZE2XSYB+HfCbux/g7h+4+59xet/d9wd+j+uIiIiIiMgqyiRA3x14oYzlz8d1RERERERkFWUSoFcD2pexfF2gelZKIyIiIiJSyWUSoL8GDDazvdIXmNnewOC4joiIiIiIrKJMenE5GXgbeNrMfgS+JfTS0hnoAEwETslVAUVEREREKpNya9DdfRywCXA1sATYFegb/78a6Obuv+aykCIiIiIilUUmNei4+0zg7DiJiIiIiEiOZJKDLiIiIiIiFaTEAN3MWppZLzOrnza/qpmdb2Y/mNlcM/vYzHarmKKKiIiIiKz5SqtBP4vQv/mStPnXAhcBzYBvCA1FnzWzHjkroYiIiIhIJVJagL498Ky7z03NMLPmwAnAT8D67r4lsDEwAxiS64KKiIiIiFQGpQXo7YAv0ubtQmhUeo27Twdw95+B+4Ftc1ZCEREREZFKpLQAvT7wR9q8rQn9n6cPSvQd0CLL5RIRERERqZRKC9AnAOulzesJzHT3H9PmVwPmZLtgIiIiIiKVUWkB+vvAkWbWGsDMtgG6AS+XsG5XwmiiIiIiIiKyikoL0C8FGgPfmdmnhLSWRcB/kiuZWRVgH+CdXBZSRERERKSyKDFAd/fvgJ2AUUBtYCSws7t/mrbqjsBMYHgmL2Zmg2Pf6QvM7OkSlu9tZqPN7C8zm2Rmx5exrTlp0yIz+yKxfJiZLUxbZ5tMyikiIiIiki/VSlvg7u8D/cp6sru/RuhqMVOTCLXzfYC1kwvigEe3AIcSLgwaAC3LeO16ac//Ang0bbVb3P2UFSifiIiIiEhelRqg54K7Dwcws26kBejAJcDF7j4yPp4Rp3KZWXegCzAsG+UUEREREcmX0nLQK5SZ1QW2ABqY2bdm9puZPWZmrTLcxCBghLtPSpt/uJlNN7Ovzey0mDNf0uv3NbNrx44du/I7ISIiIiKSBQURoBMapBpwGNAX6EBolPpAeU80szrAQcBdaYtuADoCzQkB/MlxWo67v+zuQ9q3b7+SxRcRERERyY5CCdBT/ajf4O6/uvsc4EJg51i7XpYBwFzgheRMd//U3ae6+5KYT38lcGC2Cy4iIiIikk0FEaC7+0xgHGGk0nRWztOPAe5z98XlrFe0EkUTEREREalQFRqgm1k1M6tFaJxaxcxqmVmNuPgO4CQza2NmtYELgNdibXpp2+sIbAvcU8KyAWbWwIItgbOBp7K9TyIiIiIi2VShvbgA5xNSV1LmAW8CvQkpKE2Az+OyNwg56QCY2W0A7p7sG30QMMrdvy/htQYTgv5qhJFObwGuycZOiIiIiIjkSkYBupkdBfwdWJ8QRKdzdy93W+4+FBhayrIlwGlxKmn5coMWufuZZbxWr/LKIyIiIiJSaMoNqs3sEuBc4AvgITLsm1xERERERFZcJjXoxwDPuvvfcl0YEREREZHKLpNGog2AEbkuiIiIiIiIZBagvw90ynVBREREREQkswD9ZOBAM9s3x2UREREREan0MslBv4XQHeJTZvYbMBZYkraOu/sOWS6biIiIiEilk0mA3pYwwue4+Hit3BVHRERERKRyy6Tv8vYVUA4RERERESGzHHQREREREakgCtBFRERERApIJjnomFknQm8uWwGNWD6wd3dfP7tFExERERGpfMqtQTez7sAnwH7AZGA94GfgN6AdMBt4K4dlFBERERGpNDJJcbmYEIx3Bo6K8y53922BnYF1gftzUzwRERERkcolkwC9B3C3u08HipLPc/eRwDDgklwUTkRERESksskkQK8B/B7/nxf/Nkws/wLYNJuFEhERERGprDIJ0CcScs1x93mEdJfNEss3AP7KftFERERERCqfTHpxGQXsBlwQHz8JDDGzBYQAf3CcJyIiIiIiqyiTAP06oK+Z1Y416OcB6wND4/LXgdNyUzwRERERkcql3ADd3b8Cvko8ng30M7OGwBJ3n5PD8omIiIiIVCoZDVRUEneflc2CiIiIiIhIZo1EMbMmZnaZmX1gZr+Y2bZxfjMzu9jMuuS2mCIiIiIilUO5Nehm1gZ4B2gD/AC0BWoBuPs0MzuQ0O3iyTksp4iIiIhIpZBJisvlQANgC2ASMCVt+TPAHlkul4iIiIhIpZRJistuwE3u/gXgJSz/BVg7q6USEREREamkMgnQGwLjylhePU4iIiIiIrKKMgnQxwJdy1jeE/g+K6UREREREankMgnQHwOOMbPuiXkOYGZHAP2Bh3NQNhERERGRSieTRqJXAn2AUcAnhOD8EjNrAnQE3gauz1UBRUREREQqk3Jr0N19HtAbOB+oCcwn9OiyGDgP2MXdF+WwjCIiIiIilUZGI4nGAPzqOImIiIiISI5kNJKoiIiIiIhUjBJr0M1spxXdkLu/Xt46ZjYYOBLYGBjh7vumLd8buBjYAJgFXOzut5WyrWHAQGBhYvYu7v5eXF4duC6uA/AQcKq7L854p0REREREKlhpKS7/Y9mgREbJAxSRtrxqBq83CbiU0Oi02OBGZrYbcAtwKKFBagOgZTnbu8XdTyll2fmELiA3io9HAOcSLgBERERERApSWTno84FnCcF6Vmqd3X04gJl1Y/nRRy8h1JiPjI9nxGllHU2oMZ8cX/My4D8oQBcRERGRAlZagD4EOAw4ENgBeAS4390/z0UhzKwuoWeYx8zsW6AR8CZwsrv/VsZTDzezw4HJwD3Ade5eZGaNCRcAoxPrjgbamllDd5+V9vp9gb5/+9vfsrRHIiIiIiIrp8RGou5+vbtvQRhB9EFgAPCpmX1hZqeb2VpZLkdjQqrMYUBfoAOwCHigjOfcQOiHvTkwCDg5TgD14t+ZifVT/9dP35C7v+zuQ9q3b79ShRcRERERyZYye3Fx92/c/UygLbA78AUwFPjVzF42s22yVI458e8N7v6ru88BLgR2jrXrJZXtU3ef6u5L3P19woBKB6Ztr2HiKan/Z2epzCIiIiIiWZdRN4sevOLuhxJqt98kNPTcJRuFcPeZwDhKboxqGW6mKLG9GcAEoFtieTdgfHp6i4iIiIhIIckoQLdgVzN7APiBkJf+EqFnlIyZWTUzq0XIfa9iZrXMrEZcfAdwkpm1MbPawAXAa7E2vaRtDTCzBrFsWwJnA08lVrkXOM/MWplZK0IPLnetSHlFRERERCpamSOJmtkmhLzwgUBrQkPLfwEPu/uUlXi98wmpKynzCLXxvQkpKk2AVEPUN+Jrp8pyG4C7Hx9nDSYE9dWAiYQuGq9JbPsSoCkwJj5+CLh8JcosIiIiIlJhShuo6HRCcNyV0EPKQ8B97v71qryYuw8l5LCXtGwJcFqcSlp+fNrjXuW81iLgH3ESEREREVktlFaDfhWhdvsJQj/oRUAPM+tR2obc/Z7sF09EREREpHIpK8WlNqF7xQMov6GmE/ohFxERERGRVVBagL5jhZZCRERERESAUgJ0d3+zogsiIiIiIiIZdrMoIiIiIiIVQwG6iIiIiEgBUYAuIiIiIlJAFKCLiIiIiBQQBegiIiIiIgVEAbqIiIiISAFRgC4iIiIiUkAUoIuIiIiIFBAF6CIiIiIiBUQBuoiIiIhIAVGALiIiIiJSQBSgi4iIiIgUEAXoIiIiIiIFRAG6iIiIiEgBUYAuIiIiIlJAFKCLiIiIiBQQBegiIiIiIgVEAbqIiIiISAFRgC4iIiIiUkAUoIuIiIiIFBAF6CIiIiIiBUQBuoiIiIhIAVGALiIiIiJSQBSgi4iIiIgUEAXoIiIiIiIFRAG6iIiIiEgBUYAuIiIiIlJAFKCLiIiIiBSQCg3QzWywmX1sZgvM7OkSlu9tZqPN7C8zm2Rmx5eynZpmdqeZ/WJms83sWzM7Om2dYWa20MzmJKZtcrRrIiIiIiJZUa2CX28ScCnQB1g7ucDMdgNuAQ4FRgENgJalbKcaMDlu52egBzDCzCa4+yuJ9W5x91OyuQMiIiIiIrlUoQG6uw8HMLNupAXowCXAxe4+Mj6eEaeStvMXcEFi1vtm9gbQE3ilpOeIiIiIiKwOCiIH3czqAlsADWK6ym9m9piZtcrw+bWA7sAXaYsON7PpZva1mZ1mZiXur5n1NbNrx44duyq7ISIiIiKyygoiQAcaAwYcBvQFOgCLgAfKe6KZGXAX8AMwPLHoBqAj0BwYBJwcp+W4+8vuPqR9+/YrvwciIiIiIllQKAH6nPj3Bnf/1d3nABcCO8fa9RLF4PxWQiC+r7sXpZa5+6fuPtXdl7j7+8CVwIG52wURERERkVVXEAG6u88ExgFewmIr6TkxOL+ZkNqyq7vPKudlispZLiIiIiKSdxXdzWK1mC9eDahiZrXMrEZcfAdwkpm1MbPahEagr8Xa9JLcBGwH7OLuyzUmNbMBZtbAgi2Bs4Gnsr5TIiIiIiJZVNHdLJ5PSF1JmQe8CfQmpKA0AT6Py94g5KQDYGa3Abj78WbWDjgRWAD8GirTAXjQ3VN9pw8mBP3VgImELhyvyfoeiYiIiIhkUUV3szgUGFrKsiXAaXEqafnxif9/pZTUl8Q6vVa2nCIiIiIi+VIQOegiIiIiIhIoQBcRERERKSAK0EVERERECogCdBERERGRAqIAXURERESkgChAFxEREREpIArQRUREREQKiAJ0EREREZECogBdRERERKSAKEAXERERESkgCtBFRERERAqIAnQRERERkQKiAF1EREREpIAoQBcRERERKSAK0EVERERECogCdBERERGRAqIAXURERESkgChAFxEREREpIArQRUREREQKSLV8F0AK1zmP3c+Ln3/MlD9nMfmm+wAY9tZr3Pf260vX+X7yRO465p/svukWyz3/ouGP8MLnH1PFqnDe3gew1+bdiy1/+uP3Oe6em3l2yPls3aFjTvfloie/5JXPJzNt9gK+u34vAB5+eyyPvDN26To//jabG47akl02aV3suRP+mMsZD37KV+NnsefmbbhiYLelyx5/71fufv0nfp4yh0dO2o4t12+a0/0AuP2hb3jvs9+ZMWshz9zZF4ARI8cxYuT4peuMnzSHs07oxtabtSz23N+nzeW6u77kx1//pFeP1px0ZNely6658wt++GUm1atXpU3LOgw+siv16lTP6b4Mv28MX340hdmzFvKfB3YB4N3/jefd1yYsK/PEORxx0qZ03bJFsedOnzqPh2/9kglj/2SzbVpz4LEbLV32wmM/8M1nU6lSxahRsyr7H92Z1uvUz+m+fHPH2/z+wVgWzphL3+HHATDupW8Y/9I3S9eZM2EG3c7YhZY92hd77tzfZ/Plf1/nz5+m0Xr7DnQdvMPSZd8/8AFTPh6HVTGq1qrORsdvT/12TXK6L1/f/Si/fzCaBTP/ZPfHbwHg15ffYtyrbyX2ZTKbDzmWlt27Fd+XKdP4/MZh/PnzOFr33IpNTjhsue1PeudjRl93F1tfcjpNOnfI6b58efcd/PbhByyYOYM9HxsOwNhXXuLXV15aus7sCRPY8rQzaLVVj7R9+Z3Pbvovs37+iTbbbc+mJwxebvsT33mbT6//D9tefDlNO3fJ6b58dvflTPrgNebP/IP+j48G4KeXH+fnVx9fus6fE35imyHXsFb3nYo9968pE/noxnOZ8fMY1um5O1uecNHSZR/ddB5Tv/qI6vUaALDhnofTrvfeOd2Xe+4+m48+eJGZM6fwyOO/AfDqy8N49dVhS9eZMOF7Th1yN1t1373Yc7//7iPuuvMMAJYsWcKufY+i725HA/D6aw/y3LO3MHnSjwy9+Fk6dd46p/sBcOnj5/K/z0cw7c8pfHXjRAAeHXUfj426f+k6P/32PdcNupOdN92t2HM/+/ljLn70LAAWFy3m4F5HMrDXUQBc98zljPzqVapWqUrtGnW48OB/s+FanbNa9nOf+g8jvhzJlNnTmXjNu0vnX/zsjbz45UiqmHHOHiewV7edAXj4/We59Y2H+HHqrzw9+DZ6rNet1G3/99VhPP7RC1StUpXN223E9Qf/C4DHP3qRm19/gKpVqtCsXhNuOmQoLRrk/rdzdaMAXUq1zxY9OHX3fdj03JOWzjuy184c2St8USdO/4Pel53Ljl02Xu65b3zzJZ+M/Yl3L7iKqbNnsftVF7Fjl42pV6s2ADP+msNdb77KFu3Xr5B96bfZWvxj1w3Y7oJXl84b2LM9A3u2B2DSjHnseeVIenVusdxz69Wqxhl7deHbSX/y5biZxZZt0rYRtx/bnXMf/TyXxS+mZ/fWDNhzfY48feTSebv3bsvuvdsCMHX6PP554Tts3rXZcs+tU6saR+y/IWMnzOaHsX8WW9areytOHbQxVaoY9z31HY8++yPHHJTdH4N03bZuxS77rsdFg5cFftv2WYdt+6wDwIw/5vOfs9+l4ybLn7xr1q5Gv4M2ZPK42Yz/pfi+7LRXe/oduAEAX30yheH3fcs/zt8qh3sCrXuuz/oDNmfk0Q8undd2ty603S0EbfOmzuGdU56g2WZrL/fcanWqs+HhPZg9djp//ji12LJ19+vGhoeFwHHKh2MZc+c7dL90rxzuCbTedks69N+D1487e+m8dn170a5vLwDmTZvOqNMupVm3jZZ7brXatel0yN/489eJzPr51+WWL5z9F7+OeINGG6ybux1IWGvbnmzYfwCv/v3opfPa77ob7XcNgdK8aVN58/RTaL7pZss9t1rtOnQ+5HD+/HUss376cbnlC2fP5pcRL9Bogw1ztwMJ62y7G537H8cLx/VZOm/9vgNYv+8AAOZO+41XT9uPlt22W+651WvXo+shpzLr1++Z8fPXyy3faOA/adcrt8dV0rbb7st+/YdwwnGbLJ23S98j2aXvkQBMmzaRM0/bgU277bjcc9u134gr/v0/qlatxvx5czjt1O3ZbPM+tGjRlvXX34wzz36AO24dUlG7wu6b78Pxu51K7/O6LZ130PZHcND2RwAwecZE9r1sJ3p26b3cczut3YUnznqZalWr8df8v9jr0l7ssNHOtGnalmN2Hcyp+5wLwOtfvMxlj5/HfacMz2rZ99msD6fuehTdhi777Ed++z6f/voVb5/9GFNnT6fffwexY6etqVerLt3adua+Y67mtMeuKHO7wz95mc/Gfc0bZz5MjWrVmfLnHwDMX7SAC56+jnfOeYKm9Rpx+Qu3cOvIh7hw75PK3F5lpBQXKdXWHTrSokHDUpc/9dG79Ou2JbWq11hu2QujP+agrbenWtWqtG7UhB4dNuSNMV8tXX7hUw9zZr/9qFk9tzW0KVuu35RmDWqVuvzZjyew66atqVm96nLLGtWtwebrNaFm9eW/Lp3aNKR9i3pZLWt5NtqgMY0b1ix1+cj3J7Pt5i2pUcK+1K9Xg84dGlO92vL7stWmLahSxQDo0K4hU/6Yn71Cl2K9jo2pX8a+fPrOZDbeqiXVayy/L3XrVWfdDRtRrYTPpXai5n/B/CXZKWw5GndpTc1GdUpdPvmtH2i59bpUrbF8vUiN+rVo3KkVVUr4zKrXXfb+LJ63KDuFLUeTzh2o2ahBqcsnjfqQVj26UbXG8t/fGvXr0rjT+iXuJ8CY+59kgwF7UaV6xdQPNe3chZqNGpW6fMKot2jVfWuq1lj+PFajfn2adOxE1VLOU9/cfy8dDzyo1OXZ1qzz5tRqtPyFd8q4Uc/TpkcfqtZY/jtVo35DmnXqVuJ+5kOnzlvTqNHyFSIpb496ku49+lGjxvLn7Zo161C1ajh+Fi6cT1HREnAHQvDeunXFVPykbNGhB80aNC91+fMfDadPtz2oWX35faldow7V4r4sWDSPIi/C477Ur73sO/jX/DlZLnXQY71uNK9fvALkxS9GMqB7P6pVrUbrRi3ovu6mjPzuAwC6rLUB6zVvW+527xr1GGfsdiw1qoXvRqqGvCju29yF83B3Zs//i5YNSj+mKzMF6LLSnvzwHQ7o0bPEZZNnTmetxstuw7dp3JTJM6YDMHLMlxS506vT8rVv+fLMxxPYd6vlazZXRyPfm8SO26y10s93d15+azxblFADX9E+eWcSW/ZsXf6KJXh5+E9cespbPP/I9+x/VG7vBGRi0ps/sFbvlatp/fGxT3jzuIf57v4P6PL3kr9zFWniWx/SpleP8ldMM/Xzb6DIabZJpxyUauVMHPUma+/Qe4WfN/Xz0bg7zTfeNPuFWknj3nqBtitZCz7m8dt4dch+fHjDOcyfMS3LJVtxo956ku17DSh1+dhfvuT0Ib048YTN2HPvE2nRsl0Flm7FPPvhk+zTff9Sl48Z/xV7X9abnf61JUftfAJrN1u2Lze/eA19LtiKa565lAsOurIiisukWVNo02hZemSbxq2YPHPKCm3jpynjeH3Me+x23VHs9d9jefuHjwGoU6MWVx1wNjtdfQjdhu7JlxO+46iepb83lZkCdFkpX44fy+z589l2g9J/aM1s6f++9Kp5AZc98wRD9zs452XM1NfjZzFn/mJ6dFj9c+B++vVP5s5fTNeOK5+j/MDwH6hZoyq79srvBcuEsX8yf94S1u/ceKWe33e/9Tn/+l70P6ozzz78fZZLt2L+/Hkai+cuoknXlbvY6HDgFuxwx0A2+ntPvhv2fpZLt2Jm/TyOxfPm0WSjFbvYWLJgId899DSdDu+fo5KtuFm//MyiuXNp2qVr+SsnLF6wgDEPP0CXw47MTcFWwoyfx7Bo3hyab7TlCj+368CT6Xvj8/T5z5PUX6s9H918fg5KmLlffv6CefNm02WjbUtdp/26G/Ofa9/ipps/5oP3n2PSxOVTkArBN+O/5K/5c9hqg9L3pfM6XXn2vJG8etGHvPLZ8/zy+7J9+ccep/G/iz/iggOv5KrhF5W6jWwzlv/9XhGLi5Ywa94cXjr1Xv59wJmc+MAFzJn/F7Pnz+H+d4fz5lmP8PlFL9Brw+5c+PT1WSz5mkMBuqyUJz58l/5bbVMsCE9aq3ETJk7/Y+njyTNn0LpxE8ZOncLE6dPo++8L2eL8U/nkl5845q4bee3risvhTvfMx+PZe4s2pe7L6mTk+5PovXXrld6XZ14dyzc/zOD04zbN+/vxyduT2Xzbld+XlK5btGDC2D+ZM3thlkq24iaN/IHWO3RY5X1p0b09f/48jYV/zstSyVbcpFEfstb23Vd4X/76bSrzp83g3bOv4I0TzmXmD7/w2TV3MOXTr8p/co5MeGska2+/wwrvy9zfJjNv2jRGnXM6/zvxWGb88D2fXHs1v3/2SY5KWr5xo56n7fb9VuoYq92kBWaGVanCBv0OY/oPX+SghJkbNepJem7fP6N9adS4JZ06b80nH79cASVbcc99+CR7brVfRvvSvGELtujQgze+fGW5ZTtt0pdvxn/JjDl/lPDM7FqrUUsmzPxt6ePJs6bQuox0pJK30YL+W4RODLqstQFrNW7J2D8mMvLbD2hRvylrxRr6/lvsxjs/5u97U8gUoMsKW1JUxP99/F6p6S0A/bptyWPvj2JJURG/zZzBBz99z46du9KlzTp8c9UtfHLpdXxy6XVsse763HXMP9l5o/zcJl5S5Dz/yUT23WqdvLx+Ni0pct76YDI7btNmpZ7/2jsTef3dSfzrpM2pWULOd0UqKnI+e++3lU5v+X3SX0v///nbGZgZdetVTJ5wOl9SxORRP9JmJdNb5kyYsfT/6V9PBoPq9UtvT5FLvqSISW9/tFLpLQ3ataHPPVez462Xs+Otl9Nog3XZ7LTjaLH5itVeZ4svWcLEd0axdq/eK/zcBu3a0/fu++hzy530ueVOGm+wIVsMOYOWmy3fm1VF8CVLGP/2iJVu5Dlv+rL0hfHvjKBhu4pp9FqSoiVLePft4fQqI73lt99+YfHi0B5j3rw5fPH5SNZpWzhpUylLipbwwidPs3cZ6S3jpo5l0ZKwL3/Nn8M7Y95kg9ZhX37+7Yel63384/tUsSo0qpvbHpwA+m3Sm8c/epElRUv4bdZUPvz5c3p3XLHv/J6b7rQ0b33ijN+ZNON31mnSmrWbtObTcV8zO+bUv/Hte2zYsmIajK9u1IuLlOq0h+/hf1+NZklREZueexJ9unbjmoFH89a3X9OiQUM6ti4eCF753FO0atiII3vtTO/OG/PmmK/Y5qIzMIyL+h+8tAeXfDj/0c954+vfWVLkbPevV9hxo5ZcetCmvPvdVJo1qMUGrYt3wXf9C9/SomEtBvZsz7yFi+lzyevMX7SEhYuLeGvMFK4+dDO27dicpz4Yx7XPf8v0OQs58e6PaN6gJi+cvXyvA9l00/1f8fEXUykqco48/Q223KQ5gw/vyuff/EGjhjVpu1bxRqsPPv0DTRvVZPfebZm/YAnHn/cWCxYUsWhxEZ9+NZVTj96ETbs05cb7vqJp41qcc9WHALRfuz5DjtmkpCJkzeN3f8OYz8K+XDT4TTpv1pwBg7rww1d/UL9RDVqtXXxfRjz5Iw0b1WTbPuuwcMESrjjtbRYuXMKSxUV8+/k0Dj6+Kxt2bcrzj3zPtN/nUrVqFWrVrspRp3bL+R2Br255i6mfjMOLnDcGPUjzLdrS9cRe/PHFRGo2rkO9dYqn6vzw8EfUbFKXtrt1YcmCRbx14mMULVhM0aIlTP1sPJuc1Jumm67Nd/d9wLzf/sSqVqFanepsdnbfnO/Ll7c/xNRPvsSLinj9uLNpvsXGbPz3Q5j25bfUbNSA+usUb+Pw/aPPUrNxI9r17cWSBQt5858XsGThQooWLWbaZ1+zyeAj85Z3/vnttzDl00/woiJe/fsgWmy+BZv+/USmfvUFNRs1pv46xS/Ov330YWo1aUL7XXdj8YIFvHHSiSxZsICixYuYMvozug0+KW9555/cfhG/ffIWXrSEF47bmVZb9GKLv1/I719+QK1GzWiwTvHGkV8/ehO1Grdg/b4DWLxgHi//c08WL5xP0aJF/P7Z22w5+DJabrI1H914LvNn/YFZFWo1bs6WJ16S83254/bT+OyTVykqWsIJx23CZlvswnF/v4Yvv3yLho1asPY6xbveffzRK2ncuBW79D2Sb75+hxeeu40qVauBO712GEC3zULvYiPfeITHHrmCP//8g2uuPopGjVpw9bVv5nRfLnz4DN78+n8sKVpC7/M2Y4eN+nDRwKt5/7tRNGvQgg6ti+/LDc//mxYNW3HQ9kfw4Q/vMuy126hWtRruzj49DmD7jUIXmf95+hLGTR1L9arVqVurPjced0/Wv/tnPH4F//vmHZYULWGzoXvSp8t2XD3gHN787kO2u2IABly4z8nUq1UXgEc/fJ5/v3gbf8yZydH3nk2L+k1548yHANj56kN56LjraNWwOf/Y8VBOevhiHv3weapVqcpVB5xFw9r12axtFwb22Is9rhtE9WrVaFavCdcedF5W92lNYSuTW7SmGjJkiF977bX5LkaFmnrrg+WvlGfNTzg0o/V+unGfHJdk1a3/z2cyWu/5e3Yvf6U82/PoERmtd93DfXNcklV36sDMbo/v/szxOS7Jqhuxz20ZrdfvqdtzXJJV90L/v2e03t5PZva9yqdn98/s/HTAU/lL+cnUE/0zu+vx+FP5b2xangH9M2sM/+0tv+e4JKuu04kty18J+P36D3NcklXX8pTu5a+0hjGz69x9af+gSnERERERESkgCtBFRERERAqIAnQRERERkQJSoQG6mQ02s4/NbIGZPV3C8r3NbLSZ/WVmk8ys1IRPM6tuZjeZ2fQ43Whm1TJdLiIiIiJSiCq6Bn0ScClwZ/oCM9sNuAU4BWgAbASMLGNb5wM943obAdsD567AchERERGRglOhAbq7D3f3p4GSmnZfAlzs7iPdfYm7z3D3b8vY3NHApe4+2d0nA5cBg1ZguYiIiIhIwclLN4tmNhTo5u77xsd1gdnAmcAxQCPgTeBkd/+thOc3BqYDG7j7j3HeBsD38blVylru7rPSttcX6At0Bwq//6HC1g74Nd+FyBLtS2HSvhQm7Uth0r4UJu2LpGvn7v1TDwolJ7sxYMBhhED5D+A24AFglxLWT41eMjMxL/V//bitspYXC9Dd/WWgMMcJXs2Y2bXJfjxXZ9qXwqR9KUzal8KkfSlM2hcpT6H04jIn/r3B3X919znAhcDOsXa9tPUbJual/p+dwXLJnTXpQkf7Upi0L4VJ+1KYtC+FSfsiZSqIAN3dZwLjgJLybZYb19bdZwATgG6J2d2A8e4+q7zl2SizlCzejVgjaF8Kk/alMGlfCpP2pTBpX6Q8Fd3NYjUzq0VIraliZrXMrEZcfAdwkpm1MbPawAXAa7E2vST3AueZWSsza0XooeWuFVguIiIiIlJwKjoH/XxC6krKPEJj0N7AlUAT4PO47A1CTjoAZnYbgLun+ka/BGgKjImPHwIuT2y7vOUiIiIiIgUnL724iIiIiIhIyQoiB11ERERERAIF6CJSqZhZoXQvWyEsWK3P9WZWPd9lkOwws6ppj1frY1MkV/TFkAphZsv1xiNlS3/P9EOWHe6+GMDMmuW7LBXBgyIza7e6fQ/NbHcz29XdF5lZAzPbswDKVCX+Xa3ey0JgZubuS+L/N5pZc3cvyub2k3/XFKmLmjVtv3JlTXmf9IMvOWdm1VyNHVZY6j0zs8PMrEEMsjqa2cb5Llsm0mvK4ryCOHGa2Qjg7Pj/GnkeTL7/ZnYi8AvQc3XZ3zhi9GHAg2Z2EGEAu175vAMSz2VFsOz7GeevFu9pvrm7m1ltM7sKGACsna1tJ39n1pTPJlV2d19iZlsC/4q90kkp1qR4o1Ld6pX8SNRYngL8CXzr7u/G2pQ14ouUK2a2NnAfcJmZLQHOAf4OfJnXgpXDzKomaso2ASa5+7R8fN7J4yzx/8fAjslyrmnij3pjoDuwFnCAu4/Kc7Ey5u4zzOw/wN7APcAx7n5fnsu0OHYVfAVQFZhCGGDvz3yWa3VhZnsDxxJ+B7Z291+ytF2Ln00VQm9xtQljqzy8Oo99kroYNLOuwIvArcDcvBaqgJlZlUS8cTLhOzoReN3dp65uMYcCdMmJ+EVJnVwaE04urQgnl+Zmdri7v7S6fWEqSup9cfcJ8URzNeFHbVd3fzPPxStXDA7XIXRv2gyobmZfAGe4+88VXBZPBeKJY+1XwiBozYDfK7I8FcXMahLGfzgNGA1cn8/yZCL1OSXOH+2ByUBLYGFcp7q7L8pT+XoAzwJfATMJFw/7mdkgd/8sH2UqVOnn9hg8dwE2Baa5+y/ZOv/H73h34P+A7wjf6W2B481s+9X1AsrM6gCPEbqLvtHdLymUu5CFKN5l7kD4ji4BvgY6AwPN7Ex3/y6vBVxBq+2tHylc8Uc2mVfYHPgAWB/4G/Ao8KiZtVJwvrwSbtFtDywAZgOf5qdUK8bMOgGvAN8TgphewC7AVWbWpoLLsjNwm5ltl5j9MrAVIWBYrW+DQyh/+g+3uy8AngReBWq7+7TUunkoYrnS7mbUjd+D4YQ7ALcBN5jZOjEfPV+VS8cCb7r7zsD+wEZAXeAsM2udpzIVnJLSDOJvwkPAc0BnM+ucunjO0sseBzzp7ju5+8HA08DGhHPPaqGE7/BcoBZwOrBcapUUF88LZwDvufvG7n4QMBzYC9gxr4VbCQV5opbVU+rkEmvAmpnZI2Z2HeG23Cx3L3L374F/EwK3B/JY3IJjUbxVW83MTjWzLdx9AOEipypwYxZ/0HKpCzDO3Y9x9x+BQwk/NG8Ck3L1oiUFqoRa8lbAi2Y20Mxau/t4wl2dfrDsVvLqKHVBHIOd9cxsXTNrEhd/ShiluV28EwPhzkHBSd11MbNXgKeAT8xsd2A+cDOhZvT+uG7qNnZOvgtp+fupRqGNge2Ad+Oi6u7+FzCIUPGwWXL9yqiEc9hpZnamme1qZjXj9+5h4D3C70Dqc8/4mCxp3ZgKuB/hOMHMniUEaoe7+/NlPbcQpM5bpQTfRxHOmRvaspHXK71SPss6hAv6YXGd24FTgZPc/baKK112VNoTiWRfIs+3AzCScFu6PeFHrUEigJ9IuO2+bSpoqMw/aikxpcVjje84wlX/embWxN0XEmqIDgMOzmc5k8oIkHoDf1hoEDYaOALYyd1vjM+rnYOyWCJQ3dBC7jvu/pi77wXcBPwTuN9CDy4zCcF7zgK9ipDI9f8v8BLhzsUTZtYrpoK8CVwHXGhmG8aAqOD218y2Bz4i5HVfRqj5vwY4091/JeR9dzSzoXH9/sDZ2T6WrHj7iaOBY2LKzQxgOjEQB1KpOG8Tgvb+sHpf7K2qxDlsB0Ia2T6ExqBDCccg7v4O8CDQ3szOTT0vk+3HzybZeL5mXLSAEPQfaWbjgBrAdu7+oJnVM7MjVuR1KlI8hlLnrZ3M7GozG2JmnQHcfQKhBv1QYNe8FrZApB0HPc0s9Z1sCTQgVEh8Q/iu9nb3m+IF4262OjWydXdNmlZ5YtmotNcTGulcHh/XJVzBzgO6J9ZP3YoqAjrnu/yFMgE7EX7YTovvUdW05dcBvxHyOC2ud0Ceylol8X9voBvQND4+MJZzIfBfoEnqOAHOBA7JYbn+Q8jXH0e4vbleYtlWwChgBPACIeBqnO/PfRX3txnhbsDnQE+gB3A3MD61b4Q8zJeBtxPPs3yXPW0/LgfuSzy+hNBm5ej4uD5wSjxnvBP/9s9RWdYipOV9B5wErB/nHwuMBXaLj6sQ7gy9A5yb7/ewECZgG8KF1jmJeXfGz+vI+Lhl/Ly/AnbMYJuW+L9BPP8VAbfHefUIF6YLgX+nPXf7+P3YJN/vTTn7eHM8Hz0bz1HT43e5alw+jNATU5t8lzWP75GxLNaoQrhgeR94BKgV5z8Tj41LCXe5Us/tRWhsvnG+9yPj/c13ATStvhOJAC0x74P45bgybf4IQs8ZjRPzmhFqxZrne18K6P07Evgg8bgz0AfYIzHvU+DHOP1KHi9wgC0J6UofAtOAJwi5fi2Bt4B309bfOq57Rkn7vxKvn34Bs3n8oe4F7EG4SLgrFWCl3nfg4hhEFAH98n0srOJ70IcQkKcujnYHJsR9ezGx3q6EC5dr8lze9M+sGuFC/jXCBWobwsXG10CvuI4B1RL7d1pqf3NQvjbA24R0moZpy9oTUvMmEgK/DoQ7WpNW9+NoJd+rks5hHVLn//i5PhG/hy8SuspcNy7binCn9e4VeL2BhAvP+wgBbBFwYFy2B+Gi/IZYhpaE2vtxwLXpx12hTPH4P50QaG6SmDePcFHdKc5rQLhgfDT1XaisE6E90zeEwHx8PK5Ojsu2jsfGv4F2hAv7PoSGtvcQ2uPkfR8y2s98F0DT6jMRbhu2jP8nr0yTNan145flYYoH4+sQbl1fRQk1dyXNW5On5I9F2nt5MqHHjZsINQHPEVIxxgO3xXUaELpa/EdFv38Ur8nqRAii/kMIejsSan4mxMdHEwKt9wm1GdcSakT/nYOyHEDIQT0BOC0xf8/43p0K1E3MrxZP3lNT65OFC4Ycv/cllo9Qg5sKZC8lBEOnExrnFhG6JwRoFI+bHQphH4AN0paNBL6N5487gEZxfn3gGKBHLsqT/t0BNiRc/G4UH6caGh4BNCS0Z3gylvN7woXpEfk+PvLwWSbPYQ3SljWJ56mRhMC8DeFifgbwbGK95Wq1CUH9K+nHKSFdYTIhTa0B4W7Ro/H8mPpdGhLPQQsIdzWmAScktpH335n073E8Fx2c+A73J1zwPU/oGOBWlt2B3C1+pzfL935UwPtUnXD39fDk8UaohBkHnAe0jcfF24QUpx3iOqk7uJOBN4BZwMX53qcVfg/yXQBNhT8Raq+aEALHS4F6cf7GhEB8WDyptI3zD4onkQEkrvQJtR9FwPb53qdCmIAWhBqAhwm1vBsBNYGzCHcbLiUEne0J3eWNSfwQJYPTnNSmsHxNZ5X4N5U2sT/wUWL5PYTuzU6Ij2sQbkUPJ9SiPU3IB1zl4zHxfy1C3vUMQupBEXBZ2vrXE26l756YVz3+vZLQK0fej4dy9jkZ2O5KuBDZLfWdi/M3jcfNPvHxRsBf8T3pWtp7mIfyb0G4cPshHhN7xvl7x8/xP2nP3SX+APfNcpmSAeb2wOGEAHxjwl2eJwiNVZ8jpBZMAD5NPKczIbVrtU6RWsX3sAXhYuUFQneAByWW7QN8xrL0oC7Az/F4PKu0zyI+fhf4hOKVPIMIdy7qJOatF4+lVxLzGhDuxPQlni/Tj8E8vVfJ70BdwsVh6q5Qi/j3oPi9ODU+PoVwATIQqBnntcv3515B71c7wm/HJ8Daifn9CL8zyXPftsDr8VhsFudtQrjbdhDQsbRjrZCnvBdA0+ozEa5mvyI0XtwlfkkeJ9R2fBv/poL3R+PJeOO0bRxU0eUuxImQBvJHPAEdG/9+Cewdl1dPW/8G4J4StpP1QIviAXBjQjpK6ofkeEKKzZaE2qobCLWLvxBSWjrH9eoCG8b/c/LDSLhFPgS4hdDLTapx8usUb+9ghABvBGk1doS8z+Xe10Kc4n6k0gVeJQSRn7Cspvk0YHJi/Z6EVIBnKZD0C8JFw5OE1LYDCLVbowh5y9UJ3SkuBAbH5RcCc4ChOSzTnYQg6BLihUx87WeB2+P/GxFq6hYSGjvn/b3M90QIiqbGz/MAQoPeiSy7QD+MUAO8Vnx8PKE2eEegRinbTFUCtCbccb2eZTWnfeKxv158bHE6hhD0D0rNT9tmQQVkhPP9+/E7/BSJFE9CZdddLAvGL4v79geJgLSyTMAOhEbujyTmHUm4c7te2rrnxXPFv0o7ttKPjUKf8l4ATYU/UTwF4y1Cre8NwOmJ+fsQcnofSMz7lVA73KKEbRZ0OkGW3z9L/PCkGrjcCVyVWOe/JBrExXnrEWoBHog/hHtUQFmTtYqHEgZ7uJQQcHcA/seyW45Hxx+PP4GLEvtYNZ5Ez0z90OSgnB0Jt7CnJk/IhIuFD0k0TI3zewI/kQjQCd1xFQFD8n2MZLjPJxOC2VSAsmMs/7/j420IXRJeTKhx+5rQe0Ze8lUpXmNYkxCUv0vI7U4FIFsSAuHnWXZxfy2h5vVDQpuWPjkomxFGm3wgnrc2Yfla3JppjwcQLvRaZrs8q+NEuPt0Q+LxFYQLmCGJz/YdQjrCq4RgvV9i/RLv0MX/awGHAIuId04IFwTvES6kaiTWPRhYHI/9dUvadiFM8Zi7IZ6zjiP8Zr5GqJjZMC4fE89dNQjn3HsIKR275rv8Ffg+VUn8ltQnXKx/w7JUvXXiZ30yxSuT+hBSmt4B9kq95/nen1V6L/JdAE2FPaV9AaoSgrSJJBrnxGW140nnh9QPKuHqtygXP7Cry5T2/u1CSAVqHk8iWwNrxyDkB2DntOdeSAhoXidxiy+HZU3+QN5HyNtL1Up9SMjneyBtvTcIqU8tEvP2JtxpOZHsNAQtMcAk1MiNB+5Im38mIdXj+LT5ddMetwO2yvcxkul7QAgOB8fHxxEaQt3Nslu6dQjpUd8S7mhcWtJxWFHlTf+fUBs4CXgibd1D43fgysS8WoRa1JyVm9Dzx9vAofFxW2ADQmpEKi2jBaF3ogsIF6IX5PtYyMOxt1ywSwggXyWkD7Qq4xy2ESGg/m+m57D4/f2REJwWxXNJ67js6njuPDE+NsJF6BmEBpWP5fv9KmO/GhMC8t7x8VqEO2CfJY63I+M+v0m4Q/0msXeSNX1KP88TUpreJtxdXkho79EtLvsX4YJv19R5PZ77bie0ebiOtLvQq+OU9wJoKsyJRK1vfHwSoaarEaEBxuJ4YkzWuG5IaGW+Z2Le5vnel0KY4omkiHA7tgUhsHyQUJtyP8t64GhAGJhi7fjDt11iGzmpFUr7nHeMZUu/ANuD0PDqkbT1OxFqLd4npF88SKjdyHqtNOHW8L4sa7xXl1Aj9SHF88uNMGLhhywfMBT0bc7SPmNCMPkYoZHnk4Rb/YcmlvcmNtQjNMhrWd42c7gPqbtEteMP5qmEC4zGhFv4Y4CtE+vXItT6f0Ki+81sfU5lvKcbEALLuwmNsv8P+IJwIfoLofauHyEN4XMqUS1mSe8doVeU5Gf7PiE/fxrhgj55oXhi8jNObi9+PzeJ73fLtOWHE9KN+hHukg0kVAg9Gpc3j9/5mfH1PyW0DdiUcBHwJuE3Km/fcdJ+OxPz+xBT0AiVC3/G70fDOC91ITuQ0Obo7/n+/CvgvWpJaEOU3nC2P6ECYlD8bE+N541kr1RPEy4KPyAE5bMItes3AV/me9+y8v7kuwCaCnsiBGznEHJ7fyP2bUtIXXmfRB+28Qd4EonALrGs0qS0pO33WoTA9VyKB1RnEYLgf6Stvxfhdn/3tPk5D7IIudwLCL2y3EeoiU3eSr6akAuZyjNP/VhvTMh/vpWQ073K3T5S/CKgOSF4G0u4ePiYZa31tyAECU8Ta9ni/M0IFws5v/OQo8/iWEJO5YDEvCfjMfMqib6QCTW/95C4ME59PvkKVOJ5YwqhXcphxDxbQmPMV+NnUy+xfmdCG4HLs1VmwsVYMsDck3C7fB+W5TUfGd/XNwkNb3ck1JhPiOWuRbjTtcafv0i7W5r4f+N4rv+ScIHeI84/Lh6PQ9O2sysh6NoleQymfafbkrjDk/i8bgOeS5u/GyHVLnU3rzrhgvQ8Qvpd/Tj/ARL96OfpPUw/bzVLPO5KqP3/Ih5fBySWbU/lvDuzbeo4SHvvriExZkOcdwwh1eXCxHGwHyEl7maWpTfdGo/Tgq2Iyfj9yXcBNBXuBPyDkAN4ASHd4gNCgLQroVeX7+MP2wGE2o5UI6Gu+S57Ht+z9AZKXQgXNkUkauAI6RUfxh++/QgB5ZmEWoOL8lDu4+NrbxofbxNPhsmBY+rEH5dnyVFueXyd9NzUrYHr4/+94g/xDyzLYz6CcCv0inx//lnY92rx/Z1AyLedndj3toS+kW+I/1eJf0cQ2oa0r+Cy1ojH7+5p8+sQgvBrS3neP+Lz0nvyWOXyE+48/ZPYcDZRzqfjues9QvrEy8nypm2jE+GCcMt8Hw8V/Hk2KmFeQ0Ia2zWEi/D/ES6Ua8Tl/yO0dTiFkNJ4DqF2+/Kyjpu0xxuzrPb4DuC9xLKq8Th/hHBXo3Pac1O5yucT7nzsn6f3Lv2O89B4vI0mXHRWJfxmvkjoPGGnxLpV4jrPUMnaN6S9Z6kG2tXj+zGCRJ/lhArA5+P72rOU7R1JuCt9dK7KXKHvT74LoKlwp/gje03icV1CTfoIQppGX0I6w1TCbeD3yEFfxavDRCk1bDE42I9woXNEnJeqUWoV3+NxhFrhH4iNW5LrVVS5KV6DViOe7GZSvOu0rYi9bJSwrVUqb9rrNyP0ZnBuPCmfkVi2NbE2L/U8QoO1H4G/ZfK55Hsi3I04MW3evoTawusJ6RWNCA0Ti4DD4jpHxR/9mYRAfjKhi7u85KkSap3TG1N2J9xu3irx+SQHGqpLSEd4n9hrURbLsx+hMXCqtrUhobb+FZZ1UZpKNzsv8V00QsrLfoQA6glWowFNVvE9qx7fn5sT86rF79/bwE2J+V0IqQbPxMe1CHdvfoqf59dlncPSvuNbx219z7K7cocSKgb6J9dn2Uik7yaPdULlUOocuksFv28dgVZp82rE7+iHhHSdswm1/xfH5bsTGiW/TkgV3ZtwkfMTaXdN1+SJ4r879Qi9cRWxLE3q9Pie9Ew7Zq4hpNeOYVnueTVCTfwbhLt2ORulusLfp3wXQFNhToQAfDTLRudK3RLejfDje218fFP8Im2VeG5BBkU5eI9SAUfyZDMAuJEQMHZOrHN7PHk0jI9T/XDXIwTqXRPzSsxhzFX5S5ifqpVqEfdlMsW7Ajsznkxz0sCSUIM5jtBjyXuEWuNTEsurE9IPZrAscO1MaCdRYvdthTYRahyTXUHWjoFGESG9KPV9s/ijNJ2YrkPo3efoOO2d2EZO06AoHsymB157JY7fjvGz6ZleLmJtNaFniteAgTko562ESoRtCAHkv4h9RxNyo/+Ix1ayh5BehKD8ZxJD1FeWibS+tQkXiGcTLgRvTR5jhKByEXBc6jMl1G62TZzvljuHUfw8OYhwl2hI/Dxuj9/rdoTa8lHEOyrxNW+O3/lT0rbZiHDOreh2FpvF8m+W2jdCrzU/ENqK7JtYd3Dcx/3i410Jd3S+J9ypeZBKNDJo2nFwdjwGdiRcDL6QWPYloRJi0/g41Q3rNaTVkBN+Rw8n0XPXmjDlvQCaCneKX5gn4//JH9kP4g/ZQfFL0yOxrOC6t8rB+7JePBEngyMjBFYzCCkY3xBuxZ0Vl7WM8/4vbVt57bOXEMAcT8jpbFPCZ70pIUgekSwjIe97hxyU5ypCCsT58XFbQo7hTIo3fGxOaKlfROxnObGsIC8QCTVmW6bN24RlQevGhB4rHkvuB+HW+KfA82Vsu8KOG4oPFGOEnjqKiL3mEAL0dwkXd6lBrarF4+Ywlg1n3jxH5WsQz1G3x/+bxHJeQmjEfnBc7y1CrW+qh5A+wDr5Pk4q6DOsQahxPDnts7yTZT1lrEtoazSJ4r3y1CP0yDOXEtp4lHUsEtrk/J3QMH7/OG9Pit8l2oFQoz+HEOx+Gaemaduq8BxjQgpVqiel5unlIJzji4Dd0p73HCEY3ygxr076uauyTPF8cAghJXb7+F70JzQ4To3svFn83L8B7iVctH0GdMjnMVCh71O+C6CpcCfCVe0iijdUa0aonXo7nnTax/lr9Bclsf/t4on2obT5u8cf/y6JedfHk8oh8fEuhJEdU4FMPn5gkrUXWxEuKMYQuvR6LhWgsKw2tArhtv8kiqeZrHLZWb6GrSrhwqcodZKO89sQbgu/krb+5oRUiaYkanfzfYyUsb9TCbXGreLjpnFf/5Mqe/zRWvoDn9ivHuS5z3bCxfi/iSO1EmoGT4n/X0SoJewSH59PuLC7LO5Xg7hv40g0jsthWXvGH/PUiIz1CRcNqb6U6xDS9YqIPYRUtolwgZv8zBoSUjO+TKyzTTw/3J/23PaERuT/XcHX3J9wN+hnit+Vu4tE+yVCAHdO/H7/O20b+eyhZT9CLnwqhWorQpuQVLk7ElJaLqL4hWwTQu7+XSQaeFfGiRB4jyFUZPVPzG9EaPQ7nWU9dXUj3G18hHDxmExvKthzfdbeq3wXQFPhToTg7EpCisFFhK727iJczfYh5CJvke9yVvB70oGQ4rNBfLxP/HsCIUBvwbKgai3CLbqHCbVONQg9pCwidomXx/3oTxjoIRXAHETIhXw+sU4qzaIh4bbiDHIwrDlhoJEO8f+u8f0t1psHoZZlDokav7RtFOzJOvE+donfmbOI+c3xuJlL7A2JkOpyNyGtqF7ado4iUQOXh/2oGYOR9wg11ItYNvJtVUIA/FZ8XIvQsHxSnD6Nn+uhFVjeSwi1xLsQul8rYlnaTYf4g787pTQ4W9On+Jm9R8iHTh2PGxMu1lMpjLUIjbBnsuzOQ+r8tlI9JBGC7j9I67aSEPg+QaI3prTlBZEGwrIUqk0Jtf1zCWkajeLyswkVMTunvV+HELpWXOVerlbnifBb+DlpHSfEZRsS+rN/L21+sjexgjgOKuS9yncBNBX+RLiqfZdQA/ABofakSTzZ7Jnv8lXg+1CFkFrxDOH2+E/A+3HZ4BiINIyPU0HZUfEHL5Wu0Ji0IYrzsB9HxR/Inyl+y/XguE9npfY3sWwdcnA7llDjVETIE071430a4aIwmaOdqlErIg7qkVhWsMF5ooyp4+Gs1I934of7KUKNUqqBVDvCbd1HStlWhabwEPuujv/vHz+DKSzLv03lHXeJ+3Zh4rntCTnCx5KWolAB5a5BuGPxMCEt7SHCRd4jhIvN2/N9XOR7Snxm5yfmpUYITgWYrQl3A6eS1igy+fmvwGvWJ9wRuyO5PcIdsSJCw/D0QWsK5jtOuBv0YSy/EXo5+5ZYWRPXGUX8rUx7bqXqpaWM93BLQpe+55awrE88v9xUwrKCTF/M2fuU7wJoWj2mGCC1Szw+l3AVXGJtx5o8xR/8IhL9tBJqPn8nrXsxQq3Jt+kn5oo60VBCPiihEeZd8Yc52Xd4Y8KAMb+yrAeOVR6NLQZoJeWqpoK+cwm3t/dMzHuF0LNN48T6zYBj8/35r+R7kLwbkPrxTvXb25xwcXd7Yp3dSBssKh9lpvhFWk1CY9BrCN2rXptYlroAOZFwcVUQtdLxWP+UkHLTmHAhOIw1qKeHLLxH/4jBUuruQl1C+sEEYkoBYbyBL4gpQll4zZ7xcxmSNv+CfB7zK1j+z4ET4uORhBTBbvFxc8Ido1uI/bRrWu49PJ/QjewmafPrEXpxWa6nsMo25b0AmlafiZAjtichbWM6ifyxyjIRGvU9R2gQOg7YJs43QgO4IkJvIlsRcqffJjSIqujGn8nAqkUM+Lom5vUi5NI/kva8LoQeBn7MUjkGxB/6M+Pj2oRa5PQLljdi0JfKPWxLqEW5ihJqz0qaV+hTIohtTrgt/h+W3TXYh5C7mupergahUWm+uk9MXlBsThhgJVnLejGhgeWRJRxvzxEuVhvm+z2P5TmBkI9+UHpZNS19j54lNMhrEh+vR6hYeDg+rkpi0J0sveYlhJ6L+uV7/1eh/G8TLl46xvPcpSzrzvN4wh3JCr1ztLpMhDvSb8UpPZ2v0qSxlPke5bsAmlafiVCDORh4ND3AqmwTIQ3hKcKt2mQPB2cQctEnxhP2iHwFWbE8fyfczv8+BrwjiKkq8QdkDMuPZrofWRwsidCbxhuEXOBOxBFUKZ5XuA4hz/U6lqV6HBnX3TTfn3cW34tUOshhhP58/5YI3G+OQXq7tOfkrWekeDzPJfSksxfLLijWIdxJ+h+wcWL9+oQL07PyUd5S9qEKYVj5PfJdlnzse4brrUVaWkE8NouA7VZmmxm8Zg1iJQEFcjG3EuVPpVDVIVwI/kjI2U+lNFaai8GV2VfC2APTSWsInFi+2lXEZPU9zXcBNK1eUz6DzUKbCA3MvgBuTJvfnnALdPvEvJwFWYTarjuIDVcT8/ckpE7sF8vUPV40PE6oVW9CaLD1eTIIzsaPCuGOQmq0wXaEmrJHCD2BnBWD8e2SrwecGucfnHjudqtalgo8HlboMyY0iPueZb1o1CUM+pL1hrgruT8dCKk4A9Lmp3r46RMDlOcJPTO8SxiAqeCCEipZjRwrUdtNaDi+kGUN32sQ2xnksJzrAhvm+/1ahfJ3iufPs+PjNwijha5R/XGX8x4k77bVWtHzF6GXll8pYSTbyj5VQWQFuPv8fJch38zM4r9vEnrc+JuZ7R2XVXX3se7+truPSsxbksMiNQGmuvsPafO3INTovACMc/cPCSPv7UrofWM64W7INMIAEAC4e9GqFsiDhWa2MyHgLiJ023mGu/+bUHN/mZmtk3i9RYQGWJcQLjpw93eg2HtekMzMUp+xmV1kZtuUsW7V+O+BhKD8MjNr5u5/ufvR7j6jovfXzEr6LVibkKr1hpmtb2bHmNmNwHNm1tvd/0c4/usTgvRphEBllY+fbHP3xfkuQ0Uxs2MIg99gZjuZ2QNm1ry857n7U4SGtE+YWXN3X+jun8Xt5CRWcPdf3P37XG0/19z9W0KvLgeb2Z6EVLVD4rl1jWdmVTwVpZsdQbigH2FmQ8ysSSbbcPfrCQ3/Z+asoKup1fJLIZJtFmT0fUidkNx9LiFfewRwp5k1LSkQz2VwHgPDj939vPh4s0Rwtzah5nCBuxeZWXV3f48QlJ8Qy/YeodHc9Tko2wnAS4TGhR8Rau9PjEH73wh9LP/dzDrEp7Qh1OJdFX/4lkq954UiFfCkjhl3dzNrYGY3A/sS0ldK5O5LzKxaDGSPJdRULz1G4mdaIftrZlXi6xWZWVUza5BYPIpwh+gjQu14X8LFYC3gBjNrRTiW+hGGWd87xxeikpl5QH0z+5Bw5+prd5+a4XNPIozTUGz9XF90FeJF3Qq4g9Ajlrv7n+4+I98Fqiipz83MDiCMMP0IoevOC4GTUkF6aRUOZlYtbmdxfKyYNMEK7HdPpMIlAyIzawrMdfd58XGV8n48zKwPIV/6HHcfn+vyJl63WNlicNgL+Lu7vxtrdO4jjND3YgzQF5nZGYTeZXZM1dbmIiA0s5eBMe5+Snxcj1CbPze+/k6EfrVnEmrYawNbp4KDigxUV4SZHU0YVnqQu/8U5+1L6BN5bcJx8GMG2yn32MqltOO+H6FXhSWELh5vd/dP4g/swYTb+JPd/Scz60y4e9TT3b/PU/ElIXmXzsxaEy6qWhKOxf9k8l0q4XyS1+NzdRIvuCvNXRpYGnSvT7jjCfCSu98Xl51NOG9c7+73lvDcVMVGUdzOHu7+QsWUfPWhqxWp9BJByjmE/m3fMLPTzax2PIGU+D1J1Aq87u6HVnBwXjVRe7F2nH0dobb6IDNrRrjd+AJwVUyhWBTX2wr4OFXTk+0gON6NaEZoVDwuUd45wBXAdoQeQZ4k1JjfBTzo7usna+4KMTiPFhFy6f+RmLcjMIhwq/ZHyCgtp9hxlevaIzNraGb7pF4rcdxfSuhW72VCg951gYvNbCt3n+7uN8eUrZ9iGU8mNO6rFLfxC11acN6KcIfjGsLdvR3NrHO8w1O1nG2kzid14+xC/f4VnMoWnMPS8/MkQirlgcD8xLIrCQNPHWxm2yWflzrW4m/rToT88wPNrFbFlX71oABdKqX0YCieKPYnDIbzLSFX+xIo/fZr/NGrlvhhy/n3KRX0xTSJtc3sWeBSM2sTA8PLCOkjf4vB7nWE/ni/NrP/M7O3CQ1Yb8lVGT2YRqgp3y5t2UvEQT3M7FR3f8/dr40n9KW3PAtR4r1PBbNbmdnxcfE5hFSnJma2QznbSdUeLTazpjEVqCJu8x8B/J+ZdU3VXJnZpoRUo/7uPjTuWwNga+DUVMqLmXU3s/MINbO9CSPQTstxeSUD8VzQ3MyeJ4zyPM/dryN071oPODlekC0pKUhPC/BPB540s4YFfIEsFSx53CQrHjykeR5JaH/SzcwaJp52NqGS5jAza5faTuJYuw4YTkgROsrVvm05CtCl0om3e1NBdfdYA70N8IC7P07ofnA4sKeZDYjrlVjbGYOsZma2da4CLDM7wMxaxtdL1XoeSAh0ZxJqP4nL7yMMmnG8mW3voZHXjoRA/QdCv73ruPunuShrmguAvc1sQOKknBrA40+gagwSLS6z1agm6gtCX98Hmdkm8YfqOsIP1QALjew8vRY97YLuYEKXnD3NrGYFlPlBQmPOB2HpsfQ9Ief/DTPb3czGE2rFbiAE6cfE5y4mdIn2rLt38rQ2ApI/ZrYH4XOcBdxIGFQOd3+OMODXZsTPMQbpbcysWtrFfjMze42Qg36Tu8/Kw65IAUmcl6skzt8nANea2dVm1joG3O8SGovvS/itAZY2oL2L0JvYOnHeEjPbyMxGE1Icd3f3S11tV0rmBdCVjCZNFTWxrEs/IwThcwiB6y/ATon11iN0efcJIaCFZf1VJ/s9P5iQt/tfctBnK3AQId+3SWJeK0IQPjht3VT5GhAGHbmPPHZhRqgAuIJw6/NSQj/adxMCxA75PhZWcp96Efq4fzK+x0WEH6HUiIunxWPmpMRzjMSonPH/hwlDp+d8ZFSKDyK0CaG/62uTywhdYb5HyFlOlXE88BXLut3TiIgFNhH6336E5UcwTnVT2jx+5z4jtJu4N/7fIrHuQOAP4LHkfE2VcyJ2+5o2rzHhYm88YbyGMYQGyPsk1nmLkFKZPjJo58T/uxIqZ24G6uZ7Xwt9ynsBNGmq6IlQE7g18AxhBLh/ErojfDFtvVQ/z48n5i0dgCL+ME4lNBasiHI3in9bEWo5BxL6Kh5EyDl9Czg6rrNb/NG9kDz3XQ9cRKi5/5kw+mTb9PezEKcYpFZNPG4Rg5urCRdBDYCbCLXpZyTWuz/+mO1ZwjZ3ju/DG1TAxRMl9P8NHEW4sOiTmLcH4Y5Ap/h4kxiwvwX8M9+fhaaS+9knpLD8Qaj5rksYmOwawoXVqXHeRizraeRNYN34XCMMtjYVOCbf+6cp/1P83k8jNNZPzasdA+rniCN+ElIX5xEqubaM8zYljJx6HWkXeizrkKQHsG2+93N1mfJeAE2aKnIitDovItSY/z3Oq0oYkv4PQhdjqXWrAEMIt4+7JebvBIwFXidtcKAsljO91vVMQpd27eOP8p3Ab/HH9Zn4Q3tz3LeO8TnXAAfn+z1PvMcbJB/nu0wrUPYNgG6ExpPTSAxNDjQl3Kn4EOgV53UhpK5cRhzUJ3HczACGUgED5yR+FGsS2lMMJbSt6BB/RCcDteM6BxDyy08jdHf5AHAVodFr3j+Dyj5R/EJxK8LFYv34+ApCreRM4Nl4PP43Hmt7pY4FYL3k9ggDmN2XOl9o0kSodFgnbV4dQv/um8fHF8Zjazjh7vM1xMGJgH8RUi/bp22jUo8IutKfR74LoElTrqaSgsB4sjmHkJayZ2J+E+BKQpCe/CFrBrROPD4kBsHn5irITPsxrpF43d8INWVV434cHIO+9Qh5p7UJFw775Pu9X9HPpZAmiqeEHBQ/7yOA7QkXdrsk1yN0rziPkLbSIs7bqoTttiNHF3Rl7Mv2hJrxN1jWR/ELhIaeo4HH4nr1CekPv8b1PwCa5vuz0FTss+xCSKsaE7/nzxBT3wh5vr2BtVg20ut3hMZ36dupFv/WUOCkqaQJ6AoMTzxOBeBnEbpc3Ts+figeiyemr6tp1Sf1gy5rpLTW4hsDM9x9QnzcjFDjXNPdt048pxOh5qm1u2+Str0qHnq+aAms5XGEvRyUe2nfw2Z2KmEo6X94aIx6PSHt5gx3H5G+r2Y2lFDTsa+7/5qL8lUWZtaIMABPB+BXdx8W539C6DryUHf/K85bH/gf4Y7LHe5+WWI7uR5FtlRmVodQE/61u18Q591FSI06nNA24GlCWtT9FsYAaEKoQXs9H2WWkpnZhoSeg14kNATdjpDOUh3o4e4L43rV4rliCGEQrMPc/eM8FVtWA7ExaJXkecrMdie0s7nW3f8V51UhpO694+4XxnkPEgYw+4hw9znZra4afq4i9eIia6QYsDY1s5GEmqaPzOwZM+vloXu404AuZnZB4jmpYZsXm9l6ad1JFcW/v+cqOE+9Tuwy7T3CD/AUQm0GhHSbv4BjzawLgJl1BQaZ2VuEH+RTFZyvuPReegh3Jh4ATiHUWqYcS2jsOsTMNojz+hAa7Z5NyE9fKs8/Uk0I+cfDLPSB/gKwOyFoe5IQ8F0O3GFma7v7H+7+g4LzgrQZodvSC9z9W3e/m3B3pzWhtyTMrD2ha8xXCcfiGQrOpSwxkPb4e9nBzAbEC/vXgfMIXXT2jKuvS2gsusjM6loYAdoI40GcmArOIe/nvTWGAnRZ7ZnZoWa2V/y/SvzbhlDb9Dshv/YoQm7t+TFI/5iQL3eBmW2Z2NwIQnrCz56H20vxouBMQk7pNu7+L3cfnahZPwXoDhxgZrXj0zYg1GC0dfeRFV3m1Vns5TF51yL1eDhwLSGVKHVMVfHQPeWJhBroN81sFCH3/wl3f8TdF5YQ7OdLM0La066EvNDFhNrWpyyM6roHoZZsDKEWTPLMSh8HoBbQ0t3/iOtVdfefCRdYx8U7PvMIAdNXQDt3fz7ZjalIusRd5rMJvyF9CT1sLQD+j/Abenc89/0EvEOopHiXkCI3w90fd/exBXTeW2MoxUVWa2a2LqGm809Cl3UT4/wtCTXnu7v7F3Fed0JDuQmEGvQl8bk7As28goe5jv1+e9q8hoQT363uflUifaUqUOTubmbnEi44LnL3B82svrvPzmVZ10TJ99/C4EL/Ityh+IPQF34tQuPPzwkpLYsSz90U2BxoC9ybuLVb7pDqFSnWpu5MuP18R2J+f0JPP1cCU3T85FcMos2XDX2+M/BH6m6dme1CuGD8r7vflXjeYYRzWi93n2hmtTwO+GKVcPh5WTHxWLuS0J7paELbkzmJ8+I2wDDgfXc/wsyqEy74uxJGo34ttZ1COu+tKXTFI6s1d/+FkJPZgJACkrIBsJAQbKVOIB8ScoV7EboenEvoAvBVQiO55HZzHZxXK+WEVpswhPq8WI4lib+pwZEuJ1yQdI4XEgquVkLiR+hCwsXcZ4Rbu1sT+oSuSagt6g8cmvbcz939Xne/yN3HxQsoCvBH6jLCsdTKzDqaWWMzO5LQ1mKqu/+k4yf/YppBkZn1JvQ1fRMwysz+Z2GU4zeBbwgj8G6TeGpzQoXDrLidVHBeRcG5pCvhbkptoDNwjbv/j9DZQDMz297CYGvvAf8mDNp3iLsvcvcX3P3f7v5a4o5joZ331giqQZfVlpnVSDSOupQwhP2d7v5QTHEZT+jF4D5b1sizA6HbxB7u/lGeyp2suf0XoVeWxcDV7r4g5pPPJOT1TUitb2Hkybnu/oyZNXL3mfko/5rEzHoQci1v9djw1sxeJNQuX07oUuwCQlpLL3cfU8I2Crr2yMwOJXTFV4vQs0dn4DSPDV+lMJjZJoSUo2GEvvSbEkZ0XUTo09yB/xDy0Z8kfJ6HEPqpvzMPRZbVRPIOTdr8WoTfw5cIF3odCXcFexAuCs8jtMG5mzDadsfUb67kngJ0Wa3FH7XBhL7BdwW+Bk5w92/M7GpCKkhPj0OTm9nhhEYte6TyOeP8nKW0lJLKsiGhz+J5wChC7t8vhPzzKnHejYT+zscTbineQzhpnk6odMtpLX9lYGbrARvHi54dCIHRZ8CnwPnA/oQ7LB8SGun1TKa6rC7MrCOhQWFDYJS7T89zkSq1Us4JexIaqW+XSJnqTujD/mfCeasV4ZzWmXDX79J8VTTI6iGtQqgXIZVlJvChuz8cz3tDCcfW/YQ7NdOB2wkVFzdaaBA/w0MHC1JBFKDLaiueWFIDc7wHbEvo2WA4ocazCiGlZR3C8Os/EPoRv85j11E5LFuJtaqJnPKbCH0YD4zzjwNuI9Sa32ZmpxBqx7oSRnPsSch1HpzLcldWZtaWcNw8R6g5h3Bh9C0hH92BNq4eTmQVmVn1ki7yzOyfhCC8u7v/mbhzdjahH/5uHhrvpT+vCjFLJueFl9WWhW54TyOMh1BEaCT+NGFMD9x9TmLdGoSGoJe6+9OJ+eo+sQIpB11WZz2B79z9JA89aPyT0KPGloRRQhcRusC7g3CbeGNgYAUE51UTNRZbmllvM+tvoQ/1avHk15MwmiNm9hAhBeGf7n4bgLtfT+jO7wRCa/pdFJzn1NaEHlsejcfNxoRbv72A3u7+nbu/XkIOp0iZzKyNmT0YG+bi7ossdAF7qpntY7HLVOB5YEPC9z7pXUIXd+uUsO0q7l6k4FySUm1iEo83IBxX+7r7ce5+PKGThK2AVqng3Mw6mdnmwNvAAkKj0aUUnFes0rp0EikY6b0R2LLunBoBM8ysZqJm6RZCUH6MmX3i7h+Y2b9jTVQqD73EfLwslNNgaR/sDQkpKW0IDblaEhpyXU/ojaEqsL+Z3UNo8NnL3b+OwfvOwKfu/hshH1VybwlhRNa2ZjaPUJP5FHCgu09OraRASFZCM0JwfYiFcRk6EFLVxhBG/pxrZkPc/f/M7BrgRjP7knCBOB/YJa4/IX3DSnOTkviy7hNTv509gb9iw879COmTXxMqsn6J665N+G3qDLzm7sfkp/SSohp0KWjxNu/i2Fp8n9jIs1b8YfqDUNPZNi43d59CGDSmIzDUzBqmgqoYnFcJHSZk/4ctbjfVmHMioU/ik4G9CTVjHwNnEHrWuI+QSz7S3bdz96/jZjYnBIdtsl0+KZ27P0XoweVhQv75psCTqeDc1MevrCR3/5xQcdCC0OjuEMIgQpsRKhOeBu6PNelnEdpAPAe8bGZPEdIS7vbYQ4tIJszscWL6CjAH6GBmNxIafP4X2M3dfzGzjc1sZw8jbV9DqJQ4Jm5Dlbh5pBx0KUjJXDcL3Yo9GRfNJww1fHhc9gvwPjDYlw3icSPhlvCXwGXJ3LoKKPcFhAY3R7j7A2nLmhHSWrYndOPXidDQa39CekVXQs3GO4Rc9LkVVW5ZegekO9DY3V+K83LeH76smdLzdc3sSsIItS0IwVGq4XotwgBp7u47xaDoUMIosLWBqzwxSqNIJszsv4QeWfYn/B4+RKgo2tLDoEOp9a4GqgNn+7JuOnNyl1lWjK6OpCAlgvPNCd07/ZdwgtkX+JeZXebuqdqoV4B6ZvYe4XbyjsAx7v5+Hopeh1AD9jssC/Di32lmdifQjXBr+w7gKkJDxAmE2+D/dfdL81DuSi/eaVmac6kGUbKy4t281Dmsq7t/ReiRqQPh+18jLqvm7vPN7HLgYTPb3MNotcPStqdjUUpUWocEwG+EHHMDJhFS9oYAu5vZS4SufYcSUiqPTd6hidtT7W2eqQZdClJMKRhMyNn+FBjg7j+bWU1CrxrXAdu6+/tmtgchjWRLQuv0f7r7B6ntVGQtgJnVIXRVVSeW46f0MpjZk0B7d98y5qp3BRoDX7n72Ioqq4hkV9qdv4bAIMJ5rIe7TzWzvoQUtzHAIF82jsOehAv27VO1m4leXHQXR8pkZlsRAu1nPI7VYGbtCeMe9Hb398ysFTCQMLbDDEK3sbOBA3SHpjApQJeCUNKPkJltSTiZbOLu7RLzWxD6C97C3dsn5jdNpLnk7UfNQt/sNwGfe+hZZmkOc6xNPxs4DtjcNdiQyBrHzE4nVBi0IvQEdHPiXHAOYWj1Zwk1mEboc/9vhNSXSfkos6w+0mvNzex44O+EEbUPBz4n9MIyAnjF3a9KrNuRMEpyY3d/M87THZoCpIZPkldpgWsdM2sZa8khpIrcCTSKP2qpE9MUwuAdVc1seGJz0+M6VfNZ4+TuXxAaG25lZoOWzfYiM2tO6H/2BQXnIqu/ZANiM6sSe2I5h9Cv/s2EIGkfM0v1inEXocH4WfHvfcApwJkKzqUsFrtPjHdWaqfme+ied3fgDcKdmH8TeqaaQUirSjb4/N7dv1BwXvgUoEvexGC7KP5/CKHv1ZeBx8xsp3jSeJswvPXZZtYtUWvwFaH2aV6iB5dUby2FcLK5m3Abe6CZbZEo98GELhYfyVvJRCRrEuewqoTc8p2Boe7+uLs/QeiV6RXgJDPr6O5TCUHUx4Q0g/uBFu7+UmycJ7KceFc4lT41GHjKzO41sxMB3P232PvKlYRxHZ4njBjaz8waeOyqOD1fvUB+L6UECtClwqUH1LHG6QZCbfnFhAaTj5pZCw9Dkj9G6Af4rtQ24snmfnc/JNW9YYXvSBk8DHZzYXx4nIWBSYYRLiqucfd381U2EckuC+MZHE3oDcNIdMAQ25U8BTQkfP+JDdgfBM5395fcfYElBjgTSRfvwNY3sxcJXfR+SOhq+AYzOyxxN/oBYDtCfnkvQq9BG+Wp2LIKFKBLhUoF5vEWXU8zO4nQ1eAe7n4roca8G6E3lkcA3P17wgAKreMPIXH+orjNgjyOY8ObmwmDREwlnCS7uPvwMp8oIgXL0kZpjLoBRe4+mxAYdY3pbAC4+4g4fw8zOyPOvsXdX4/bXNrri0hK6o5KrNOqSbgI/Avo7u5DCYPhzSfcZd4irls19shyDHAEIRe9a1xWkL+VUjJ9WFJh4i26VK350YRGUouBlz2M+Lk/MBoYS+i7taeZnRqf/gnh1t3n6dst8B4Onibc3v6nu2/lYXRQEVlNJdIMGiRmNyXk/EIY7OUAYG8LIwNjZm0JPWo8FOevl6wtV825AMQa8mrx/2qJtE33MFr2t4SueKfEhsjvEzpM+I4wAm2TxIXenHinZhjhwlD9mq9m1A+6VJh4i24tYC/CLbhB7v5/ALG2aQjh5PPv+HgScJWZve3uH5nZrak8utVF3OfTdGIUWTPExnmvANXN7BlCY/YxwF8xCPo/M9uFcD77m5k9Tega9mPgVWAfwh3Cn/NRfilMsXeV2wkB9TAPI2j3Bg4EJhM6Fng5rnsYcBRwqLs/a2H06ocI7RwuSkv7rAFM0UXg6kc16FLRtgWuIKR9jErM3w7oTDhBQegu6jNCzfkusDTvfOltv9WFgnORNYe7zyP0Zf4osB9wG7ArsBmwXlznROAiwm/sccCX7n48YXTjuoQ8dZGkH4B5wJ5m1tXMDiV0mtCEEIw/ZGapdk17A+8ReggCaBOffwHQPrXB2OXvMYTKLlnNqB90qXAWhiA+FDjY3V+J8zoRBiS6FHiJcKKZSUgNmZ2nooqIlMrM6hJyf0cSgqC5hPPYM8C77v6rmdXyZUOoP0AYRfiA2ABeZCkLgwk9T7hDsy7wmLs/bcsGGfoPob3DicAmhEC9FWGk7aHAT8muOs1sO6CZuz9TgbshWaIAXSqcmdUHXifUkF/o7pMtjLp3HHAmodHLN8Df3H1ufI5G0xORghIbjFYBXiPUqP8EHEKoWb/S3S81s2bAP4F9CekGf3P3b/NTYil0sS3WFYTBhLb0MO4HZtYIuAXoRBih9n+EsT/WAR5y90FxvaVji1R44SWrFKBLXphZT0LXig+5+zWJ+R2AOh4G+9EgCiJS0MysMSEw38fdR8V5zWN/56l1TgIaufvFeSqmrEbM7CZCDywD3H1EqvczM9uP0BXxHoSLvS7AH+7+TnxesRFGZfWmRqKSF+7+tpm9AOxmZt+5+/Nx/o+pdRSci0ghi7WVS4AphJGNUwHSH3F5NXdf7O435LOcsto5mzDY0P5m9qW7T4jzFxNSWiz+Vv4IS9tlqZeWNYwaiUo+XUJoAHNITHEpRsG5iBSyGBA1BtoCsxPd4hXFv6tVr1NSGNx9DnAKoVOFy81sg9hV52HEHoPS1ncF52sepbhIXpnZukD1OBiRiMhqJeaht3L3ifkui6xZzOxfhBGpxwC/AC0JDYzH5bVgUiEUoEtBUCNQEVmdpbp/VQ6wZIuZVQc+IAxQdL27fxjnK/2zElCALiIiIlKAzGx9AHf/KT5WcF5JKEAXERERKWDqoaXyUYAuIiIiIlJA1IuLiIiIiEgBUYAuIiIiIlJAFKCLiIiIiBQQBegiIgKAmbmZjcx3OQqZmfWO79PQVdzOWDMbm51SiciaRgG6iIiIiEgBqZbvAoiISMHoDMzNdyFERCo7BegiIgKAu3+b7zKIiIhSXESkkkjmDpvZNmb2spnNNDNPrGNmdrSZvWNmf5rZXDP72MyOLmWbzczsDjObEtf9yMz+ZmZHxtc6MrFu+zhvmJl1MrPhZjYtzmufWG8fM3vNzGaY2Xwz+8rMTjezqmmvXcXMjjGzD81senz9sWb2tJn1Slu3v5m9Gcs538zGm9lLZrZv2nol5qCbWVMzu87MfjGzBXE7j5lZlxLWHZbaJzM70czGxNf81cwuNLOMfnfSPq9tzewNM5ttZlPN7BYzqx3X2y1+Xn+Z2e9m9u/09yquV83MTjWzz81snpnNitvsV8rr1zazK+N7lfocji2nzOua2V1mNi6+T5Pj+9Euw32uZWanxTLOMrM5ZvaTmT1iZhtnsg0RWTOoBl1EKpttgXOBN4A7gLYQgnPgQWAg8D3wMLAQ2AW428y6uPvpqY2YWT3gTaAL8Hac2gCPAK+U8fodgPeBr4H7gCbxdTCzy4FzgAnAU8CfQC/gaqAHcEBiO1cAZwI/xbLOjq+/PbAT8Fbc5gnALcBk4P+AP4DWQHdgX+Dpst4sM2say9sBGAk8CrQH9gf6mdku7v5eCU+9GugNPB/fj32BoUAN4LyyXjNND+As4GXgdmBH4ASggZk9Q3gPnwU+APoR3pM/gcsS+2DAY8B+hM/2ZqAuMAB43sxOdvcbEutXidvsA3xJeH+bAtcRjpvlmFmPWMa6wHPAj4T36RBgdzPbxt1/Lmdf74tl+gK4F1hAOD53jNv+spzni8iawt01adKkaY2fCMGix+noEpYfG5fdBVRLzK9BCNYc2CIx/5I476YyXufIxPz2ifkXl/D6u8RlLwJ1EvMNuDUu65+Y/wchkK+Tth0DmiQef0II9JqX8JpN0x47MDJt3t1x/uVp8/vG+d8DVRLzh8X5PwOtE/ObATMIwXONFfy89knMrw58DhQBU4GtEsvqA78D09I+w8NS+5Z8bWDtuP5CYN3E/CPj+iOAqon5G8f30oGhaWX6Je7bpmn70RNYDDyXNn8sMDbxuGHcp4+SrxmXVQUa5fs7pEmTpoqblOIiIpXNZ+5+TwnzBwN/AYPdfXFqprsvZFmN78GJ9Q8lBGsXJTfi7iMJtZ2l+Q24tJTXB/i7uy9tqOnuDpxNCAoPTnvOQkLwl3x9d/fpaestihNp6/5RRjkxsxrxNf9IL7O7v0zYzw0IdyXSXeLukxPrTwOeIQTRHct63TQj3f2ZxHYWAU8SLkSec/ePEstmE2rsmxKC75Qj498z4+eZWn8CoVa8OqGmO+Xw+Pc8d1+SWP9L4IESyrgn4QLsKnf/PLnA3d8m7PceZtagjP30uE8Lkq8Zt7HE3WeW8VwRWcMoxUVEKpsP02eYWR1C7egk4OyQEVFM9fi3U1y/ASEg+9rdp5bwGu8SaphL8nkySEzYmnCBMKiE1weYl3r96HHgeOArM3uMkG7znrv/lfa8x4Er43qPEmqR384w4OsE1CYEySX17jKSsJ/dCCk+SZ+WsP6E+LdRBq+d8lkJ81KB/+gylrUh1FIDbAbMc/flPnvCPkDYh5RNgbnuXtI+jAIGpc3bOv7tZCX3j96K0OZrQ+DjEpbj7n+a2UvAbmb2KeEiZBTwQSnHi4iswRSgi0hl83sJ8xoTai/bABeW8dy68W+qJrSk4Ly01yhvWRPCOTmT1wc4iZBGciRwfpzmm9njwGmxxhrgKkIN+PHAEOA0YLGZvQic4u6/lPF6qf0srcy/xb8NS1g2q4R5qdr+5RpxluHPMrZT1rLqiXkNgPGlbL+kfWhYxvolvRdN4t9DSliWVLec5fsT2kcczLIc+tlmdg9wbikXSSKyBlKKi4hUNl7CvFSg94m7WxnTjmnrNy/lNVqu4OuntvlHOa+/7tKNuC9y96vdfSPChcVAQo3r4cBDifXc3e9y9y1jef8GDAf2Bl4oqceTtDKVtT8t09YrVH+yYvswC2hRzvrp2wfYq5zP782yCunuf7n7ee6+HrAeoab+W+BkQiqOiFQSCtBFpNKLuctjgM5m1iiD9f8kpE90MLOSgvSScrLL8wHQ1Mw2WNEnuvskd38E2A34AeiT6oYwbb0/3P1pdz8QeJ0wMFGHMjb9LTAf2CqmAaXbIf4dvaJlrmCfAbXNrHsJy0rah8+BOma2eQnrb1/CvA/i321WuoRp3P2X2FZiB2AO4YJKRCoJBegiIsENQB3gTjNbLhUh9nHdPjHrIaAmaSkpZtab0vPPy3t9gHti14bpr9/KzDrH/2ua2U62fLJ6XUIjzEXAkrhuXzOrlrat6ixLy5hXWoFi7vMjhB5YzknbRh9gd0J3gu9ktIf5c1/8e0XcdwDMrA0h7WcxibsOLGsIelnyDkPsi/ywErb/DDAOGGJpfdDH51U3s55lFdDMmpdyAdGYcJyV+jmJyJpHOegiIsHthMZ+RwDbmdn/CI1GWxIaS/YgpJGMjev/G+gP/MPMNiE0klyb0I/1c8BehG7zMuLuL5nZJcC/gB9jg8FfCT2SdCDU3J5PqOmvDbwG/GxmHxCCw3qE3kRaAf9ONCx8DJhrZm/H7VUndOnYBXjM3ceVU7SzCLW455vZtoTa4vaEfOm5wFHunvF+5skDhD7Q9wG+MLPnWdYPelNCzn6yj/L7CJ/1bsBnZjaCcEFzMKFP9z2TG3f3BWa2P6FbxjfN7DXgq7i4LeGz+4PijXzTtQE+MLOvCQ1sJ8ay7UP4zK5auV0XkdWRAnQREZZ2Z3hkbDx5LCEIqwdMIaSNnA78L7H+7FhbegUhiNqKMPjQwYT84b34//buXqWBIIri+LnPIAjWdjZ2diKYRmxsBR/AKmgjYqFoZacvYWOnFj6AnQipBD9AsBfBZhEbr8UZUEKy4CJkI/9fGTJhwhZ7dubunV/WZmfmXkRcyS+AduRuJ69yj+19fa/yVnJw7sjhb1LuMX4vaTszT3/87I4cNOfKnCp51Xtd0qB2k/1zeimH8OyW/zkv12ifSzrIzNu68W2QmVkC9Ib8ANaVW1T2JB1l5kXf9z8jYkXeHVkr457k1fZH9QX0MuYmImYlbUlalvuff8hB+0zeiajzLF/jRfmApAm5n3tP0nFm1h1+BeCfCd+TAAB/JSJO5GA3k5l3o54PAGC8UIMOAA1FxNSAzxYkrUp6IJwDAJqgxAUAmruMiHe5A0gl13UvyS9odkc4LwDAGKPEBQAaiohNuZRlWu6e8iZ3NDnMzOvhIwEAGI6ADgAAALQINegAAABAixDQAQAAgBYhoAMAAAAt8gXMj2ZbqOlUbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9), dpi = 50)\n",
    "g = sns.barplot(x = 'key', y = 'value', data=mse_df, capsize=.2)\n",
    "_ = g.set_xticks(range(len(mse_df)))\n",
    "_ = g.set_xticklabels(g.get_xticklabels(), rotation=35, fontsize = 20)\n",
    "_ = plt.setp(g.get_yticklabels(), fontsize =18)\n",
    "_ = g.set_xlabel(\"regression models\", fontsize = 28)\n",
    "_ = g.set_ylabel(\"Mean Squared Errors\", fontsize = 25)\n",
    "_ = g.set_ylim([16, 18]) # g.set_ylim([0, 18]) # ax.set(ylim=(0.4, 0.6))\n",
    "\n",
    "mse_dummy_mean = mse_df[mse_df.key=='dummy']['value'].values\n",
    "_ = g.plot([-1,len(mse_df)], [mse_dummy_mean, mse_dummy_mean], color = 'red', linewidth = 2.3)\n",
    "_ = g.annotate(text = f'dummy\\n: {mse_dummy_mean[0]:.4f}', xy = (3, mse_dummy_mean), xytext = (1.1, 17.3),\n",
    "               arrowprops = dict(facecolor = 'red', shrink = 0), fontsize = 18)\n",
    "\n",
    "\n",
    "val = mse_df['value']\n",
    "cnt = 0\n",
    "for i in range(len(mse_df)):\n",
    "    tmp_fval = f'{val[i]:.2f}'\n",
    "    if tmp_fval.index('.') == 3:\n",
    "        _ = g.text(cnt-0.355, 16.05, tmp_fval, fontsize=15)\n",
    "    elif tmp_fval.index('.') == 2:\n",
    "        _ = g.text(cnt-0.285, 16.05, tmp_fval, fontsize=15)\n",
    "    cnt += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('mse_reg_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49bb6c3",
   "metadata": {},
   "source": [
    "# r2 plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "1bc4c2ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 750x450 with 0 Axes>"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9), dpi = 50)\n",
    "g = sns.barplot(x = 'key', y = 'value', data=r2_df, capsize=.2)\n",
    "_ = g.set_xticks(range(len(r2_df)))\n",
    "_ = g.set_xticklabels(g.get_xticklabels(), rotation=35, fontsize = 20)\n",
    "_ = plt.setp(g.get_yticklabels(), fontsize =18)\n",
    "_ = g.set_xlabel(\"regression models\", fontsize = 28)\n",
    "_ = g.set_ylabel(\"R^2 Scores\", fontsize = 25)\n",
    "_ = g.set_ylim([-0.001, 0.001]) # ax.set(ylim=(0.4, 0.6))\n",
    "r2_dummy_mean = r2_df[r2_df.key=='dummy']['value'].values\n",
    "_ = g.plot([-1,len(r2_df)], [r2_dummy_mean, r2_dummy_mean], color = 'red', linewidth = 2.3)\n",
    "_ = g.plot([-1,len(cv_df)], [0, 0], color = 'gray', linestyle = '--', linewidth = 1)\n",
    "_ = g.annotate(text = f'dummy\\n: {r2_dummy_mean[0]:.4f}', xy = (6, r2_dummy_mean), xytext = (7.1, -0.0008),\n",
    "               arrowprops = dict(facecolor = 'red', shrink = 0), fontsize = 18)\n",
    "\n",
    "\n",
    "val = r2_df['value']\n",
    "cnt = 0\n",
    "for i in range(len(r2_df)):\n",
    "    if f'{val[i]:.2f}'[-1] != '0' or f'{val[i]:.2f}'[-2] != '0':\n",
    "        _ = g.text(cnt-0.28, -0.00007, f'{val[i]:.2f}', fontsize = 15)\n",
    "    \n",
    "    elif f'{val[i]:.2f}'[-1] == '0' and val[i] < 0:\n",
    "#         tmp_val = f'{val[i]:.4f}'[-4:]\n",
    "#         _ = g.text(cnt-0.47, -0.0001, f'-{tmp_val}'[:3]+'\\n'+f'-{tmp_val}'[3:], fontsize = 15)\n",
    "#         _ = g.text(cnt-0.47, -0.000125, f'{val[i]:.4f}'[:5]+'\\n'+'    '+f'{val[i]:.4f}'[5:], fontsize = 15)\n",
    "        _ = g.text(cnt-0.40, -0.00007, f'{val[i]:.4f}', fontsize = 15)\n",
    "\n",
    "\n",
    "    elif val[i] > 0:\n",
    "#         _ = g.text(cnt-0.4, 0.0, f'{val[i]:.4f}'[:4]+'\\n'+'   '+f'{val[i]:.4f}'[4:], fontsize = 15)\n",
    "        _ = g.text(cnt-0.37, 0.00003, f'{val[i]:.4f}', fontsize = 15)\n",
    "    cnt += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('r2_reg_plot.png')\n",
    "\n",
    "# [r2 info](https://vitalflux.com/r-squared-explained-machine-learning/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537c4a3e",
   "metadata": {},
   "source": [
    "# cv plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "9a49087c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 750x450 with 0 Axes>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9), dpi = 50)\n",
    "g = sns.barplot(x = 'key', y = 'value', data=cv_df, capsize=.2)\n",
    "_ = g.set_xticks(range(len(cv_df)))\n",
    "_ = g.set_xticklabels(g.get_xticklabels(), rotation=35, fontsize = 20)\n",
    "# _ = g.set_yticklabels(g.get_yticklabels(), fontsize = 18)\n",
    "_ = plt.setp(g.get_yticklabels(), fontsize =18)\n",
    "_ = g.set_xlabel(\"regression models\", fontsize = 28)\n",
    "_ = g.set_ylabel(\"CV Scores\", fontsize = 25)\n",
    "_ = g.set_ylim([-0.010, 0.0125]) # ax.set(ylim=(0.4, 0.6))\n",
    "cv_dummy_mean = cv_df[cv_df.key=='dummy']['value'].values\n",
    "_ = g.plot([-1,len(cv_df)], [cv_dummy_mean, cv_dummy_mean], color = 'red', linewidth = 2.3)\n",
    "_ = g.plot([-1,len(cv_df)], [0, 0], color = 'gray', linestyle = '--', linewidth = 1)\n",
    "_ = g.annotate(text = f'dummy\\n: {cv_dummy_mean[0]:.4f}', xy = (6, cv_dummy_mean), xytext = (7.1, -0.0035),\n",
    "               arrowprops = dict(facecolor = 'red', shrink = 0), fontsize = 18)\n",
    "\n",
    "\n",
    "val = cv_df['value']\n",
    "cnt = 0\n",
    "for i in range(len(cv_df)):\n",
    "    if (f'{val[i]:.2f}'[-1] != '0' or f'{val[i]:.2f}'[-2] != '0') and len(f'{val[i]:.2f}')>5:\n",
    "        tmp_fval = f'{val[i]:.2f}'\n",
    "        if len(tmp_fval[:tmp_fval.index('.')]) > 4:\n",
    "#             _ = g.text(cnt-0.3, -0.002, tmp_fval[:tmp_fval.index('.')]+'\\n'+'    '+tmp_fval[tmp_fval.index('.'):], fontsize = 15)\n",
    "            _ = g.text(cnt-0.45, -0.0012, tmp_fval, fontsize = 15)\n",
    "        else:\n",
    "#             _ = g.text(cnt-0.3, -0.002, ' '+tmp_fval[:tmp_fval.index('.')]+'\\n'+'   '+tmp_fval[tmp_fval.index('.'):], fontsize = 15)\n",
    "            _ = g.text(cnt-0.4, -0.0012, tmp_fval, fontsize = 15)\n",
    "            \n",
    "    elif (f'{val[i]:.2f}'[-1] != '0' or f'{val[i]:.2f}'[-2] != '0') and len(f'{val[i]:.2f}')<=5 and val[i] < 0:\n",
    "        _ = g.text(cnt-0.28, -0.0012, f'{val[i]:.2f}', fontsize = 15)\n",
    "    \n",
    "    elif f'{val[i]:.2f}'[-1] == '0' and val[i] < 0:\n",
    "#         _ = g.text(cnt-0.25, -0.0019, f'{val[i]:.4f}'[:5]+'\\n'+'    '+f'{val[i]:.4f}'[5:], fontsize = 15)\n",
    "        _ = g.text(cnt-0.4, -0.0012, f'{val[i]:.4f}', fontsize = 15)\n",
    "        \n",
    "    elif val[i] > 0:\n",
    "#         _ = g.text(cnt-0.22, 0.0004, f'{val[i]:.4f}'[:4]+'\\n'+'   '+f'{val[i]:.4f}'[4:], fontsize = 15)\n",
    "        _ = g.text(cnt-0.34, 0.0004, f'{val[i]:.4f}', fontsize = 15)\n",
    "    cnt += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cv_reg_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148e27a9",
   "metadata": {},
   "source": [
    "[틱 글꼴 크기](https://www.delftstack.com/ko/howto/matplotlib/how-to-set-tick-labels-font-size-in-matplotlib/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8e6ad55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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