meinian.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import jieba\n",
    "%matplotlib inline\n",
    "pd.options.mode.chained_assignment = None\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strQ2B(ustring):\n",
    "    ss = []\n",
    "    for s in ustring:\n",
    "        rstring = \"\"\n",
    "        for uchar in s:\n",
    "            inside_code = ord(uchar)\n",
    "            if inside_code == 12288:  # 全角空格直接转换\n",
    "                inside_code = 32\n",
    "            elif (inside_code >= 65281 and inside_code <= 65374):  # 全角字符（除空格）根据关系转化\n",
    "                inside_code -= 65248\n",
    "            rstring += chr(inside_code)\n",
    "        ss.append(rstring)\n",
    "    return ss\n",
    "\n",
    "def getnum(string): \n",
    "    num = []\n",
    "    for s in jieba.lcut(string, cut_all=False):\n",
    "        l = []\n",
    "        for t in s.split():\n",
    "            try:\n",
    "                l.append(float(t))\n",
    "            except ValueError:\n",
    "                pass\n",
    "        if len(l) > 0:\n",
    "            num.append(l[0])\n",
    "    \n",
    "    return num\n",
    "\n",
    "def is_chinese(uchar):\n",
    "    \"\"\"判断一个unicode是否是汉字\"\"\"\n",
    "    if uchar >= u'\\u4e00' and uchar <= u'\\u9fa5':\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "    \n",
    "def cus(clf,x,y):\n",
    "    ypred = clf.predict(x)\n",
    "    return np.mean((np.log(np.abs((ypred+1)/(y+1)))**2))\n",
    "\n",
    "n_folds = 10\n",
    "def cv(model,X,y):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "    rmse= cross_val_score(model, X, y, scoring=cus, cv = kf)\n",
    "    return(rmse)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('meinian_round1_data_part1_20180408.txt',delimiter=\"$\")\n",
    "data2 = pd.read_csv('meinian_round1_data_part2_20180408.txt',delimiter=\"$\")\n",
    "train = pd.read_csv('meinian_round1_train_20180408.csv', encoding = \"ISO-8859-1\")\n",
    "test = pd.read_csv('meinian_round1_test_b_20180505.csv', encoding = \"ISO-8859-1\")\n",
    "\n",
    "\n",
    "data = data1.append(data2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data= pd.pivot_table(data, values='field_results', index='vid',columns='table_id', aggfunc=lambda x: ' '.join(str(v) for v in x))\n",
    "data.reset_index(inplace=True)\n",
    "data.set_index('vid',inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33086710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(rowlen,collen) = data.shape\n",
    "d = {}\n",
    "for i in range(collen):\n",
    "    d[i] = 1 - (data.iloc[:,i].isnull().sum()/57298)\n",
    "x = list(d.values())\n",
    "x = sorted(x, reverse=True)\n",
    "plt.plot(x)\n",
    "plt.show()\n",
    "\n",
    "index_keep = [k for k in d if d[k]>0.8]\n",
    "\n",
    "data_k = data.iloc[:,index_keep]\n",
    "\n",
    "col_0420_rep = {'未见异常':0, '正常':0, '未闻及异常':0, '正常 正常':0, '有力':1, '心音弱':2, '心音遥远':2, '较低':2,\n",
    "       '强弱不等':3, '心音强':1, '心音弱, 心音遥远':2, '心音强, 心音遥远':1, '右位心':0, '心音遥远, 心音弱':2,\n",
    "       '..........................................................................................0':0,\n",
    "       '主动脉第2心音强':1, '弱':2, '第二心音分裂':2, '低钝':2}\n",
    "data_k.replace({\"0420\": col_0420_rep},inplace=True)\n",
    "\n",
    "col_2302_rep = {'健康':0, '疾病':2, 'nan':0, '亚健康':1, ' 健康':0, '正常疲劳反应 健康':0,\n",
    "       '健康？？？？？？？？？？？？？？？':0, '健康 健康':0, '6C17002059健康':0, '.健康':0,\n",
    "       '6C17002185健康':0, '6C17002193健康':0, '6C17001723健康':0, '6C17002177健康':0,\n",
    "       '.+健康':0, '3健康':0, '、健康':0, '6C17002207健康':0, 'Z117114765健康':0,\n",
    "       'Z117078437健康':0,\n",
    "       '                                                          健康':0,\n",
    "       'I117110034健康':0, '                        健康':0, '261Quite34869健康':0,\n",
    "       'P617026382健康':0, 'I117105807健康':0, 'Q21701909%健康':0, '671809健康':0,\n",
    "       'Z117136428健康':0, '98健康':0, '1、289965健康':0, '5283698738881健康':0,\n",
    "       'Z117053064健康':0, 'y疾病':2, 'P617026234健康':0, '7817076622健康':0, '肥健康':1,\n",
    "       '                                                                健康':0}\n",
    "data_k.replace({\"2302\": col_2302_rep},inplace=True)\n",
    "\n",
    "col_3190_rep = {'-':0, '阴性':0, '+-':1, '+':2, '++':3, '+++':4, '-        0mmol/L':0, '2+':3,\n",
    "       '0(-)':0, '阳性(+)':2, '++++':5, '2.8(+-)':1, '未做':0, '3+':4, '--':0, '阳性(1+)':2,\n",
    "       '+1     5.5mmol/L':2, '≥55(+4)':5, '阳性3+':4}\n",
    "data_k.replace({\"3190\": col_3190_rep},inplace=True)\n",
    "\n",
    "col_3191_rep = {'-':0, '阴性':0, '+':2, '-        0umol/L':0, '0(-)':0, '+++':4, '未做':0,\n",
    "       '阳性(+)':2, '8.6(+1)':2, '++':3, '+-':1, '2+':3, 'Normal':0}\n",
    "data_k.replace({\"3191\": col_3191_rep},inplace=True)\n",
    "\n",
    "col_3192_rep = {'-':0, '阴性':0, '+-':1, '+':2, '-        0mmol/L':0, '0(-)':0, '++':3, '未做':0,\n",
    "       '+++':4, '0.5(+-)':1, '1.5(+1)':2, '4.0(+2)':3, '1+':2, '--':0, '阳性(+)':2,\n",
    "       '+1     1.5mmol/L':2, '+-     0.5mmol/L':1}\n",
    "data_k.replace({\"3192\": col_3192_rep},inplace=True)\n",
    "\n",
    "col_3195_rep = {'-':0, '阴性':0, '+-':1, '+':2, '-           0g/L':0, '2+':3, '++':3, '0(-)':0, '阳性(+)':2, '+++':4, '未做':0, '3-5':4, '1+':2, '0.15(+-)':1, '+-       0.15g/L':1,   '0.3(+1)':2, '+1        0.3g/L':2, '1.0(+2)':3, '阳性(1+)':2, '+1':2}\n",
    "data_k.replace({\"3195\": col_3195_rep},inplace=True)\n",
    "\n",
    "col_3197_rep={'-':0, '阴性':0, '+':2, '未做':0, '+-':1, '--':0, '1+':2}\n",
    "data_k.replace({\"3197\": col_3197_rep},inplace=True)\n",
    "\n",
    "col_3196_rep = {'33(+1)':2, '4.9':6, '6.9':8, '阴性':0, '+++':4, '-':0, '3.4':5, '3.5':5, 'NormaL':0, '正常':0, '--':0, '2+':3, '5.7':7, '阳性(+)':2, '+':2, '6.8':8, '1+':2, '5.9':7, '++':3, '未做':0, '5.8':7, '+-':1, 'Normal':0}\n",
    "data_k.replace({\"3196\": col_3196_rep},inplace=True)\n",
    "\n",
    "col_3430_rep = {'血液':0, '+2':3, '阴性':0, '+++':4, '0-4':5, '-':0, '70(+1)':2, '125(+2)':3, '１＋':2, '15(+-)':1, '+1':2, '3':6, '+-    70CELL/uL':2, '未见':0, '阳性(+)':2, '0(-)':0, '+-     15CELL/uL':1, '+1     70CELL/uL':2, '阳性+':2, '+':2, '500(+3)':4, '0-1/HP':7, '3-5/hp':8, '1+':2, '++':3, '+/HP':2, '未做':0, '阳性(1+)':2, '-     15CELL/uL':1, '10-15':9, '+-':1, '-       0CELL/uL':0}\n",
    "data_k.replace({\"3430\": col_3430_rep},inplace=True)\n",
    "\n",
    "col_3730_rep ={'0-1':1,'1':1, '1-2':2, '2':2, '1-3':2,'2-5':3,'3-4':3,'5-10':4, '0-2':1, '3':3, '4':3, '0':0, '阴性':0,  '查见':1, '未见':0,  '未检出':0, '-':0,  '上皮细胞+':5,  '上皮细胞2+':6, '上皮细胞3+':7,'颗粒管型0-1':8,'颗粒管型0-3':9, '颗粒管型0-2':10,  '颗粒0-2/lp':11, '颗粒管型偶见':8, '透明管型0-2':12, '透明管型0-1':13,'见透明':14, '透明管型+':15, '查见透明管型':14, '0-1/HP':16,'1-3/HP':17,'3-4/HP':18,  '0-1/lp':19,    '0个/LP':0, '+':20,'白细胞管型：0-3/LP':21}\n",
    "data_k.replace({\"3730\": col_3730_rep},inplace=True)\n",
    "\n",
    "colname = list(data_k)\n",
    "\n",
    "trans2num = [12,15,17,18,19,20,21,22,24,25,26,31]\n",
    "for colind in trans2num:\n",
    "    a = list(data_k.iloc[:,colind])\n",
    "    b = []\n",
    "    for ele in a:\n",
    "        string = str(ele)\n",
    "        if string is None:\n",
    "            b.append(np.nan)\n",
    "        elif string=='nan':\n",
    "            b.append(np.nan)\n",
    "        else:\n",
    "            string = strQ2B(string)\n",
    "            string = ''.join(string)\n",
    "            l = getnum(string)\n",
    "            if len(l) == 0:\n",
    "                b.append(np.nan)\n",
    "            else:\n",
    "                meanl = sum(l) / float(len(l))\n",
    "                b.append(meanl)\n",
    "    data_k[colname[colind]+'new'] = b\n",
    "    \n",
    "data_k.loc[data_k['2403new'] == data_k['2403new'].max(),['2403new']] = 57.142\n",
    "data_k['BMI'] = data_k['2403new']/data_k['2404new']**2*10000\n",
    "\n",
    "xfeature = [colname[i]+'new' for i in trans2num] \n",
    "xfeature +=[\"0420\",\"2302\",\"3190\",\"3191\",\"3192\",\"3195\",\"3197\",'3196','3430','3730','BMI']\n",
    "\n",
    "data_f = data_k.loc[:,xfeature]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_f.loc[data_f['10004new']>1000,'10004new'] = 1.799\n",
    "data_f.loc[data_f['1814new']>250,'1814new'] = np.nan\n",
    "data_f.loc[data_f['1815new']>150,'1815new'] = np.nan\n",
    "data_f.loc[data_f['1850new']>30,'1850new'] = np.nan\n",
    "data_f.loc[data_f['190new']>300,'190new'] = np.nan\n",
    "data_f.loc[data_f['2404new']<125,'2404new'] = np.nan\n",
    "data_f.loc[data_f['2405new']>5000,'2405new'] = np.nan\n",
    "data_f.loc[data_f['3193new']>1.1,'3193new'] = np.nan\n",
    "\n",
    "data_f.replace('None', np.nan, inplace=True)\n",
    "for head in xfeature:\n",
    "    data_f[head].fillna(data_f[head].value_counts().index[0], inplace = True)\n",
    "\n",
    "for colind in list(train):\n",
    "    a = list(train.loc[:,colind])\n",
    "    b = []\n",
    "    for ele in a:\n",
    "        string = str(ele)\n",
    "        if string is None:\n",
    "            b.append(np.nan)\n",
    "        elif string=='nan':\n",
    "            b.append(np.nan)\n",
    "        else:\n",
    "            string = strQ2B(string)\n",
    "            string = ''.join(string)\n",
    "            l = getnum(string)\n",
    "            if len(l) == 0:\n",
    "                b.append(np.nan)\n",
    "            else:\n",
    "                meanl = sum(l) / float(len(l))\n",
    "                b.append(meanl)\n",
    "    train[colind+'new'] = b\n",
    "yfeature = ['Anew','Bnew','Cnew','Dnew','Enew']\n",
    "train_f = train.loc[:,yfeature]\n",
    "\n",
    "train_f.loc[train_f['Anew']==0,'Anew'] = np.nan\n",
    "train_f.loc[train_f['Bnew']==0,'Bnew'] = np.nan\n",
    "train_f.loc[train_f['Bnew']>1000,'Bnew'] = 100.164\n",
    "train_f.loc[train_f['Bnew']>900,'Bnew'] = 97.4\n",
    "\n",
    "\n",
    "for head in yfeature:\n",
    "    train_f[head].fillna(train_f[head].value_counts().index[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##### check the result after remove outlier\n",
    "# for head in xfeature:\n",
    "#     x = list(data_f[head])\n",
    "#     a = [float(el) for el in x if (str(el) != 'nan') and (el is not None)]\n",
    "#     plt.figure()\n",
    "#     plt.hist(a)\n",
    "#     print(max(a),min(a))\n",
    "#     plt.title(head)\n",
    "#     plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = train.vid\n",
    "Xtrain = data_f.loc[index,xfeature]\n",
    "Ytrain = train_f.copy()\n",
    "\n",
    "index2 = test.vid\n",
    "Xtest = data_f.loc[index2,xfeature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save train data\n",
    "# Xtrain.to_csv('Xtrain.csv',index = False, float_format= \"%.6f\")\n",
    "# Ytrain.to_csv('Ytrain.csv',index = False, float_format= \"%.6f\")\n",
    "# Xtest.to_csv('Xtest.csv',index = False, float_format= \"%.6f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38199, 23)\n",
      "linear\n",
      "score: 0.0175 (0.0006)\n",
      "\n",
      "score: 0.0208 (0.0009)\n",
      "\n",
      "score: 0.0948 (0.0020)\n",
      "\n",
      "score: 0.0164 (0.0014)\n",
      "\n",
      "score: 0.0469 (0.0042)\n",
      "\n",
      "****************************************************************************************************\n",
      "GBoost\n",
      "score: 0.0166 (0.0006)\n",
      "\n",
      "score: 0.0202 (0.0008)\n",
      "\n",
      "score: 0.0877 (0.0038)\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-128-abbfee5b40e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mhead\u001b[0m \u001b[1;32min\u001b[0m \u001b[0myfeature\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m         \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"score: {:.4f} ({:.4f})\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'*'\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-86-46ca662dafb9>\u001b[0m in \u001b[0;36mcv\u001b[1;34m(model, X, y)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_n_splits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m     \u001b[0mrmse\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[0;32m    340\u001b[0m                                 \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                                 pre_dispatch=pre_dispatch)\n\u001b[0m\u001b[0;32m    343\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_train_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             return_times=True)\n\u001b[1;32m--> 206\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[0;32m    456\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[0;32m   1032\u001b[0m         \u001b[1;31m# fit the boosting stages\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1033\u001b[0m         n_stages = self._fit_stages(X, y, y_pred, sample_weight, random_state,\n\u001b[1;32m-> 1034\u001b[1;33m                                     begin_at_stage, monitor, X_idx_sorted)\n\u001b[0m\u001b[0;32m   1035\u001b[0m         \u001b[1;31m# change shape of arrays after fit (early-stopping or additional ests)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1036\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mn_stages\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[1;34m(self, X, y, y_pred, sample_weight, random_state, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1087\u001b[0m             y_pred = self._fit_stage(i, X, y, y_pred, sample_weight,\n\u001b[0;32m   1088\u001b[0m                                      \u001b[0msample_mask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1089\u001b[1;33m                                      X_csc, X_csr)\n\u001b[0m\u001b[0;32m   1090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1091\u001b[0m             \u001b[1;31m# track deviance (= loss)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[1;34m(self, i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[0;32m    786\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    787\u001b[0m                 tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[1;32m--> 788\u001b[1;33m                          check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m             \u001b[1;31m# update tree leaves\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1124\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1125\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    360\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "index = train.vid\n",
    "xfeature = [colname[i]+'new' for i in trans2num] \n",
    "xfeature +=[\"0420\",\"2302\",\"3190\",\"3191\",\"3192\",\"3195\",\"3197\",'3196','3430','3730','BMI']\n",
    "Xtrain = data_f.loc[index,xfeature]\n",
    "\n",
    "print(Xtrain.shape)\n",
    "\n",
    "#Base models\n",
    "linear = LinearRegression()\n",
    "nn=  MLPRegressor(hidden_layer_sizes=(20,10, 2), random_state=1)\n",
    "\n",
    "svr = SVR(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.00001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=True, max_iter=-1)\n",
    "\n",
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
    "#KRR = KernelRidge(alpha=1, kernel='linear', degree=2, coef0=2.5)\n",
    "GBoost = GradientBoostingRegressor(n_estimators=500, learning_rate=0.05,\n",
    "                                   max_depth=5, max_features='sqrt',\n",
    "                                   min_samples_leaf=50,min_samples_split=1000, \n",
    "                                   loss='huber', random_state =5)\n",
    "# model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "#                              learning_rate=0.05, max_depth=3, \n",
    "#                              min_child_weight=1.7817, n_estimators=2200,\n",
    "#                              reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "#                              subsample=0.5213, silent=1,\n",
    "#                              random_state =7, nthread = -1)\n",
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
    "\n",
    "model_all = [linear,GBoost]\n",
    "name = ['linear','GBoost']\n",
    "#Base models scores\n",
    "# for head in yfeature:\n",
    "#     score = cv(lasso, Xtrain, Ytrain[head])\n",
    "#     print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "# for head in yfeature:\n",
    "#     score = cv(ENet, Xtrain, Ytrain[head])\n",
    "#     print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "for model,n in zip(model_all,name):\n",
    "    print(n)\n",
    "    for head in yfeature:\n",
    "        score = cv(model, Xtrain, Ytrain[head])\n",
    "        print(\"score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "    print('*'*100)\n",
    "    \n",
    "# for head in yfeature:\n",
    "#     score = cv(model_xgb, Xtrain, Ytrain[head])\n",
    "#     print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "    \n",
    "# for head in yfeature:\n",
    "#     score = cv(model_lgb, Xtrain, Ytrain[head])\n",
    "#     print(\"LGBM score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getcolkeyword(colind):\n",
    "    test = [x for x in data_k.iloc[:,colind] if (x is not None) and (str(x) != 'nan')]\n",
    "    string = ' '.join(test)\n",
    "    li = jieba.lcut(string, cut_all=False)\n",
    "    setli = list(set(li))\n",
    "    allwordlist = [x for x in setli if is_chinese(x)]\n",
    "    fqcount = pd.DataFrame(np.zeros((len(allwordlist),1)),index=allwordlist,columns=['freq'])\n",
    "\n",
    "    for string in data_k.iloc[:,colind]: \n",
    "        if (string is not None) and (str(string) != 'nan'): \n",
    "            wordsli = jieba.lcut(string, cut_all=False)\n",
    "            for word in wordsli: \n",
    "                if word in allwordlist:\n",
    "                    fqcount.loc[word] += 1\n",
    "\n",
    "    fqcount.sort_values(by=['freq'],ascending=False,inplace=True)\n",
    "    fqcount /= fqcount.sum()\n",
    "    keyword = fqcount.index[fqcount['freq']>0.00001].tolist()\n",
    "    return keyword\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords9 = ['未见异常', '未查', '无', '未见', '饮食', '运动', '低盐', '血压','降压', '压痛', '睡眠', '充足', '浮肿', '术后', '待查', '突下', '胆囊', '下腹', '眼睑', '血糖', '糖尿病', '心脏', '降糖药', '彩超', '双下肢', '触痛', '戒烟', '腹部', '双肺']\n",
    "\n",
    "keywords14 = ['无','记载', '高血压', '偏高', '糖尿病', '脂肪肝', '血脂', '剖宫产', '胆囊', '未', '血压', '阑尾炎', '胃炎', '血糖', '肾结石', '子宫', '冠心病', '甲状腺', '尿酸', '乙肝', '腰椎间盘', '突出', '脑梗塞', '胆结石', '颈椎病', '冠状动脉', '肌瘤', '良性', '健康', '支气管炎', '支架', '胆囊炎', '植入术', '肺结核', '甲肝', '痛风', '胃溃疡', '减退', '支气管', '心脏病', '吸烟史', '亢进', '不足', '胆囊息肉', '先天性', '小三阳', '宫外孕', '哮喘', '早搏', '结石', '胃','十二指肠', '心肌', '溃疡', '球部', '尿路', '乳腺', '心肌梗塞', '卵巢囊肿', '胆石症', '甲状腺癌', '乳腺癌', '胸膜炎', '过敏性', '鼻炎', '微创', '骨折', '房颤', '前列腺', '肝', '结肠炎', '脾', '双侧', '贫血', '心肌炎', '增生', '剖腹产', '心脏起搏器', '脑溢血', '肺部', '阻塞性', '息肉', '搭桥术', '脑血栓', '慢性肾炎', '囊肿', '瓣膜', '高血糖', '未见异常', '返流性', '食道炎', '结节', '外伤', '取石', '心脏', '胃穿孔', '大三阳', '风湿性关节炎', '轻度', '肿瘤', '胃窦炎', '气胸', '保胆', '肠梗阻', '甲状腺炎', '射频', '阳性', '桥本', '下肢', '结肠', '表面抗原', '消融术', '脑血管', '肺', '扩张', '胰腺炎', '脑供', '孕', '腹部', '血吸虫病', '腰椎', '甲亢', '高尿酸', '癫痫病', '肾病', '疝', '血尿酸', '脂肪瘤', '血管瘤', '膝关节', '肥胖', '血粘度', '静脉曲张']\n",
    "\n",
    "keywords8 = ['异常', '未', '未见异常', '高血压', '偏高', '心律', '糖尿病', '未见', '心律不齐', '腹壁', '慢性', '脂肪肝', '心动','血脂', '过缓', '心率', '杂音', '剖宫产', '胆囊', '血压', '胃炎', '阑尾炎', '早搏', '不', '心脏', '心音', '血糖', '压痛', '呼吸', '肾结石', '子宫', '收缩期', '冠心病', '甲状腺', '二尖瓣', '饱和度', '血氧', '青霉素', '尿酸', '腹部', '正常', '乙肝', '偏高余', '腰椎间盘', '突出', '心动过速', '脑梗塞', '颈椎病', '胆结石', '冠状动脉', '弱', '肌瘤', '窦性', '支气管炎', '病变', '支架', '植入术', '主动脉瓣', '胆囊炎', '甲肝', '双肺', '肺结核', '肝脏', '痛风', '音粗', '肥胖', '下腹部', '运动', '饮食结构','中断', '频发', '胃溃疡', '绵阳', '大小', '三尖瓣', '触诊', '肺部', '减退', '磺胺类', '叩击', '不足', '支气管', '亢进', '胆囊息肉', '小三阳', '脾', '心脏病', '宫外孕', '减弱', '心肌', '供血', '肺罗音', '窦性心', '十二指肠', '尿路', '溃疡', '哮喘', '肾脏', '结石', '球部', '先天性', '胃', '粗糙', '脾脏', '动过', '缓', '房颤', '神经内科', '系统', '脐周', '心肌梗塞', '胸部', '卵巢囊肿', '乳腺', '肺动脉瓣', '花粉', '胆石症', '舒张',  '绝对', '肝', '肺', '乳腺癌', '胸膜炎', '甲状腺癌', '不齐', '鼻炎']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "colind = 8\n",
    "keyword = keywords8\n",
    "collen = data_k.iloc[:,colind].shape[0]\n",
    "df_from_col8 = pd.DataFrame(np.zeros((collen,len(keyword))),columns=keyword,index=data_k.index)\n",
    "\n",
    "for i in range(collen):\n",
    "    x = data_k.iloc[i,colind]\n",
    "    if (x is not None) and (str(x) != 'nan'):\n",
    "        cutli = jieba.lcut(x, cut_all=False)\n",
    "        for word in cutli:\n",
    "            if word in keyword:\n",
    "                df_from_col8.loc[df_from_col8.index[i],word] = 1\n",
    "                \n",
    "colind = 9\n",
    "keyword = keywords9\n",
    "collen = data_k.iloc[:,colind].shape[0]\n",
    "df_from_col9 = pd.DataFrame(np.zeros((collen,len(keyword))),columns=keyword,index=data_k.index)\n",
    "\n",
    "for i in range(collen):\n",
    "    x = data_k.iloc[i,colind]\n",
    "    if (x is not None) and (str(x) != 'nan'):\n",
    "        cutli = jieba.lcut(x, cut_all=False)\n",
    "        for word in cutli:\n",
    "            if word in keyword:\n",
    "                df_from_col9.loc[df_from_col9.index[i],word] = 1\n",
    "                \n",
    "colind = 14\n",
    "keyword = keywords14\n",
    "collen = data_k.iloc[:,colind].shape[0]\n",
    "df_from_col14 = pd.DataFrame(np.zeros((collen,len(keyword))),columns=keyword,index=data_k.index)\n",
    "\n",
    "for i in range(collen):\n",
    "    x = data_k.iloc[i,colind]\n",
    "    if (x is not None) and (str(x) != 'nan'):\n",
    "        cutli = jieba.lcut(x, cut_all=False)\n",
    "        for word in cutli:\n",
    "            if word in keyword:\n",
    "                df_from_col14.loc[df_from_col14.index[i],word] = 1\n",
    "\n",
    "df_from_col = pd.concat([df_from_col8, df_from_col9,df_from_col14], axis=1)\n",
    "df_from_col = df_from_col.groupby(lambda x:x, axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38199, 231)\n",
      "(9532, 231)\n",
      "(38199, 5)\n"
     ]
    }
   ],
   "source": [
    "data_new = pd.concat([data_f, df_from_col], axis=1)\n",
    "\n",
    "xfeature = [colname[i]+'new' for i in trans2num] \n",
    "xfeature +=[\"0420\",\"2302\",\"3190\",\"3191\",\"3192\",\"3195\",\"3197\",'3196','3430','3730','BMI']\n",
    "xfeature = xfeature + list(set(keywords8+keywords9+keywords14))\n",
    "\n",
    "index = train.vid\n",
    "Xtrain = data_new.loc[index,xfeature]\n",
    "index2 = test.vid\n",
    "Xtest = data_new.loc[index2,xfeature]\n",
    "print(Xtrain.shape)\n",
    "print(Xtest.shape)\n",
    "print(Ytrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain.to_csv('Xtrain.csv',index = False, float_format= \"%.6f\")\n",
    "Ytrain.to_csv('Ytrain.csv',index = False, float_format= \"%.6f\")\n",
    "Xtest.to_csv('Xtest.csv',index = False, float_format= \"%.6f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38199, 231)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-189-83c517a1ad6b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodel_all\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mhead\u001b[0m \u001b[1;32min\u001b[0m \u001b[0myfeature\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"score: {:.4f} ({:.4f})\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'*'\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-86-46ca662dafb9>\u001b[0m in \u001b[0;36mcv\u001b[1;34m(model, X, y)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_n_splits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m     \u001b[0mrmse\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[0;32m    340\u001b[0m                                 \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                                 pre_dispatch=pre_dispatch)\n\u001b[0m\u001b[0;32m    343\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_train_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             return_times=True)\n\u001b[1;32m--> 206\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[0;32m    456\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[0;32m   1032\u001b[0m         \u001b[1;31m# fit the boosting stages\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1033\u001b[0m         n_stages = self._fit_stages(X, y, y_pred, sample_weight, random_state,\n\u001b[1;32m-> 1034\u001b[1;33m                                     begin_at_stage, monitor, X_idx_sorted)\n\u001b[0m\u001b[0;32m   1035\u001b[0m         \u001b[1;31m# change shape of arrays after fit (early-stopping or additional ests)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1036\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mn_stages\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[1;34m(self, X, y, y_pred, sample_weight, random_state, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1087\u001b[0m             y_pred = self._fit_stage(i, X, y, y_pred, sample_weight,\n\u001b[0;32m   1088\u001b[0m                                      \u001b[0msample_mask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1089\u001b[1;33m                                      X_csc, X_csr)\n\u001b[0m\u001b[0;32m   1090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1091\u001b[0m             \u001b[1;31m# track deviance (= loss)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[1;34m(self, i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[0;32m    786\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    787\u001b[0m                 tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[1;32m--> 788\u001b[1;33m                          check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m             \u001b[1;31m# update tree leaves\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1124\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1125\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    360\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Base models\n",
    "linear = LinearRegression()\n",
    "nn=  MLPRegressor(hidden_layer_sizes=(20, 10,5, 2), random_state=1)\n",
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
    "#KRR = KernelRidge(alpha=1, kernel='linear', degree=2, coef0=2.5)\n",
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.02,\n",
    "                                   max_depth=5, max_features='sqrt',\n",
    "                                   min_samples_leaf=50,min_samples_split=1000, \n",
    "                                   loss='huber', random_state =5)\n",
    "\n",
    "knn = KNeighborsRegressor(n_neighbors=10, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=-1)\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=50, n_jobs=-1)\n",
    "\n",
    "# model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "#                              learning_rate=0.05, max_depth=3, \n",
    "#                              min_child_weight=1.7817, n_estimators=2200,\n",
    "#                              reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "#                              subsample=0.5213, silent=1,\n",
    "#                              random_state =7, nthread = -1)\n",
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
    "\n",
    "model_all = [GBoost]\n",
    "for model in model_all:\n",
    "    for head in yfeature:\n",
    "        score = cv(model, Xtrain, Ytrain[head])\n",
    "        print(\"score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "    print('*'*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####  generate results to submit\n",
    "index2 = test.vid\n",
    "Xtest = data_new.loc[index2,xfeature]\n",
    "ypred = pd.DataFrame()\n",
    "ypred['vid'] = test.vid\n",
    "GBoost = GradientBoostingRegressor(n_estimators=1000, learning_rate=0.05,\n",
    "                                   max_depth=5, max_features='sqrt',\n",
    "                                   min_samples_leaf=50,min_samples_split=1000, \n",
    "                                   loss='huber', random_state =5)\n",
    "for head in yfeature:\n",
    "    GBoost.fit(Xtrain, Ytrain[head])\n",
    "    ypred[head] = GBoost.predict(Xtest)\n",
    "    \n",
    "ypred.to_csv('submit.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38199, 196)\n",
      "score: 0.0160 (0.0005)\n",
      "\n",
      "score: 0.0199 (0.0004)\n",
      "\n",
      "score: 0.0960 (0.0014)\n",
      "\n",
      "score: 0.0166 (0.0013)\n",
      "\n",
      "score: 0.0472 (0.0020)\n",
      "\n",
      "****************************************************************************************************\n",
      "score: 0.0153 (0.0005)\n",
      "\n",
      "score: 0.0192 (0.0005)\n",
      "\n",
      "score: 0.0874 (0.0031)\n",
      "\n",
      "score: 0.0159 (0.0013)\n",
      "\n",
      "score: 0.0451 (0.0023)\n",
      "\n",
      "****************************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "data_new = pd.concat([data_f, df_from_col], axis=1)\n",
    "index = train.vid\n",
    "xfeature = [colname[i]+'new' for i in trans2num] \n",
    "xfeature +=[\"0420\",\"2302\",\"3190\",\"3191\",\"3192\",\"3195\",\"3197\",'BMI']\n",
    "xfeature = xfeature + keyword\n",
    "Xtrain = data_new .loc[index,xfeature]\n",
    "\n",
    "print(Xtrain.shape)\n",
    "\n",
    "#Base models\n",
    "linear = LinearRegression()\n",
    "nn=  MLPRegressor(hidden_layer_sizes=(20, 10,5, 2), random_state=1)\n",
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
    "#KRR = KernelRidge(alpha=1, kernel='linear', degree=2, coef0=2.5)\n",
    "GBoost = GradientBoostingRegressor(n_estimators=500, learning_rate=0.05,\n",
    "                                   max_depth=5, max_features='sqrt',\n",
    "                                   min_samples_leaf=50,min_samples_split=1000, \n",
    "                                   loss='huber', random_state =5)\n",
    "# model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "#                              learning_rate=0.05, max_depth=3, \n",
    "#                              min_child_weight=1.7817, n_estimators=2200,\n",
    "#                              reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "#                              subsample=0.5213, silent=1,\n",
    "#                              random_state =7, nthread = -1)\n",
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
    "\n",
    "model_all = [linear,GBoost]\n",
    "#Base models scores\n",
    "# for head in yfeature:\n",
    "#     score = cv(lasso, Xtrain, Ytrain[head])\n",
    "#     print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "# for head in yfeature:\n",
    "#     score = cv(ENet, Xtrain, Ytrain[head])\n",
    "#     print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n",
    "for model in model_all:\n",
    "    for head in yfeature:\n",
    "        score = cv(model, Xtrain, Ytrain[head])\n",
    "        print(\"score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "    print('*'*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## previous resutls\n",
    "### linear\n",
    "score: 0.0175 (0.0005)\n",
    "\n",
    "score: 0.0208 (0.0005)\n",
    "\n",
    "score: 0.0951 (0.0017)\n",
    "\n",
    "score: 0.0165 (0.0013)\n",
    "\n",
    "score: 0.0470 (0.0022)\n",
    "  \n",
    "### GB  \n",
    "score: 0.0167 (0.0005)\n",
    "\n",
    "score: 0.0203 (0.0004)\n",
    "\n",
    "score: 0.0879 (0.0032)\n",
    "\n",
    "score: 0.0159 (0.0012)\n",
    "\n",
    "score: 0.0458 (0.0023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####  generate results to submit\n",
    "index2 = test.vid\n",
    "Xtest = data_new.loc[index2,xfeature]\n",
    "ypred = pd.DataFrame()\n",
    "ypred['vid'] = test.vid\n",
    "GBoost = GradientBoostingRegressor(n_estimators=1000, learning_rate=0.05,\n",
    "                                   max_depth=5, max_features='sqrt',\n",
    "                                   min_samples_leaf=50,min_samples_split=1000, \n",
    "                                   loss='huber', random_state =5)\n",
    "for head in yfeature:\n",
    "    GBoost.fit(Xtrain, Ytrain[head])\n",
    "    ypred[head] = GBoost.predict(Xtest)\n",
    "    \n",
    "ypred.to_csv('submit.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba\n",
    "text = \"肝脏大小、形态正常，包膜光整，肝内血管走行较清晰，回声均匀。\"\n",
    "seg_list = jieba.cut(text, cut_all=True)\n",
    "print(\"Full Mode: \" + \"/\" .join(seg_list))  # 全模式\n",
    "\n",
    "seg_list = jieba.cut(text, cut_all=False)\n",
    "print(\"Default Mode: \" + \"/ \".join(seg_list))  # 精确模式\n",
    "\n",
    "seg_list = jieba.cut(text)  # 默认是精确模式\n",
    "print(\", \".join(seg_list))\n",
    "\n",
    "seg_list = jieba.cut_for_search(text)  # 搜索引擎模式\n",
    "print(\", \".join(seg_list))\n",
    "\n",
    "# import jieba.posseg as pseg\n",
    "# #查看词性\n",
    "# words = pseg.cut(\"我爱北京天安门\")\n",
    "# for word, flag in words:\n",
    "#     print('%s %s' % (word, flag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}