handle/dccup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/\n",
      "Collecting xgboost\n",
      "\u001b[33m  Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='mirrors.ustc.edu.cn', port=443): Read timed out. (read timeout=15)\",)': /pypi/web/simple/xgboost/\u001b[0m\n",
      "\u001b[?25l  Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c1/24/5fe7237b2eca13ee0cfb100bec8c23f4e69ce9df852a64b0493d49dae4e0/xgboost-0.90-py2.py3-none-manylinux1_x86_64.whl (142.8MB)\n",
      "\u001b[K    100% |████████████████████████████████| 142.8MB 252kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (from xgboost) (1.16.4)\n",
      "Requirement already satisfied: scipy in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (from xgboost) (1.3.0)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-0.90\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "from sklearn.model_selection import train_test_split\r\n",
    "from sklearn.metrics import mean_squared_error\r\n",
    "from sklearn.model_selection import GridSearchCV\r\n",
    "from sklearn.metrics import make_scorer\r\n",
    "from sklearn.model_selection import KFold\r\n",
    "import datetime\r\n",
    "import warnings\r\n",
    "import pickle as pkl\r\n",
    "import xgboost as xgb\r\n",
    "\r\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载完成 (374953, 110)\n"
     ]
    }
   ],
   "source": [
    "with open('/home/aistudio/data/data13514/feature_embed.p','rb') as source:\r\n",
    "    df = pkl.load(source)\r\n",
    "\r\n",
    "print('加载完成', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def logloss(y_true, y_pred):\r\n",
    "    deta = 3\r\n",
    "    eps=1e-15\r\n",
    "    # Prepare numpy array data\r\n",
    "    y_true = np.array(y_true)\r\n",
    "    y_pred = np.array(y_pred[:,1])\r\n",
    "    assert (len(y_true) and len(y_true) == len(y_pred))\r\n",
    "    # Clip y_pred between eps and 1-eps\r\n",
    "    p = np.clip(y_pred, eps, 1-eps)\r\n",
    "    loss = np.sum(- y_true * np.log(p) * deta - (1 - y_true) * np.log(1-p))\r\n",
    "    return loss/len(y_true)\r\n",
    "def singleloss(y_true, y_pred):\r\n",
    "    deta = 3\r\n",
    "    eps=1e-15\r\n",
    "    # Prepare numpy array data\r\n",
    "    y_true = np.array(y_true)\r\n",
    "    y_pred = np.array(y_pred)\r\n",
    "    assert (len(y_true) and len(y_true) == len(y_pred))\r\n",
    "    # Clip y_pred between eps and 1-eps\r\n",
    "    p = np.clip(y_pred, eps, 1-eps)\r\n",
    "    loss = np.sum(- y_true * np.log(p) * deta - (1 - y_true) * np.log(1-p))\r\n",
    "    return loss/len(y_true)\r\n",
    "def evalerror(preds, dtrain):\r\n",
    "    deta = 3\r\n",
    "    eps=1e-15\r\n",
    "    labels = dtrain.get_label()\r\n",
    "    y_true = np.array(labels)\r\n",
    "    y_pred = np.array(preds)\r\n",
    "    assert (len(y_true) and len(y_true) == len(y_pred))\r\n",
    "    # Clip y_pred between eps and 1-eps\r\n",
    "    p = np.clip(y_pred, eps, 1-eps)\r\n",
    "    loss = np.sum(- y_true * np.log(p) * deta - (1 - y_true) * np.log(1-p))\r\n",
    "    return 'error', loss/len(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "logScore = make_scorer(logloss, greater_is_better=True, needs_proba=True)\r\n",
    "scoring = {'logloss':logScore}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "划分训练测试集\n",
      "(374953, 109)\n",
      "(374953,)\n"
     ]
    }
   ],
   "source": [
    "print('划分训练测试集')\r\n",
    "feature = df.iloc[:,:-1].values\r\n",
    "label = df.iloc[:,-1].values\r\n",
    "label.reshape(-1,1)\r\n",
    "print(feature.shape)\r\n",
    "print(label.shape)\r\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(feature, label, test_size=0.2, stratify=label)\r\n",
    "train = xgb.DMatrix(data=feature, label=label)\r\n",
    "valid = xgb.DMatrix(data=x_valid, label=y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params = {\r\n",
    "    'booster': 'gbtree',\r\n",
    "    'objective':'binary:logistic',\r\n",
    "    'max_depth':12,\r\n",
    "    'eta':0.01,\r\n",
    "    'silent':1,\r\n",
    "    'gamma':0.2,\r\n",
    "    'min_child_weight':8,\r\n",
    "    'subsample':1,\r\n",
    "    'colsample_bytree':1,\r\n",
    "    'colsample_bylevel':1,\r\n",
    "    'lambda':0.6,\r\n",
    "    'alpha':0,\r\n",
    "    'nthread':-1,\r\n",
    "    # 'eval_metric':logScore,\r\n",
    "    'seed':2018\r\n",
    "}\r\n",
    "test = [(valid,'eval')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-error:0.149458\teval-error:0.914799\n",
      "Multiple eval metrics have been passed: 'eval-error' will be used for early stopping.\n",
      "\n",
      "Will train until eval-error hasn't improved in 10 rounds.\n",
      "[1]\teval-error:0.148965\teval-error:0.91091\n",
      "[2]\teval-error:0.148778\teval-error:0.907121\n",
      "[3]\teval-error:0.148738\teval-error:0.903436\n",
      "[4]\teval-error:0.148338\teval-error:0.899839\n",
      "[5]\teval-error:0.148271\teval-error:0.896327\n",
      "[6]\teval-error:0.148218\teval-error:0.892912\n",
      "[7]\teval-error:0.147644\teval-error:0.889584\n",
      "[8]\teval-error:0.147644\teval-error:0.886339\n",
      "[9]\teval-error:0.147551\teval-error:0.883169\n",
      "[10]\teval-error:0.147471\teval-error:0.880084\n",
      "[11]\teval-error:0.147298\teval-error:0.877069\n",
      "[12]\teval-error:0.147031\teval-error:0.874132\n",
      "[13]\teval-error:0.146991\teval-error:0.871274\n",
      "[14]\teval-error:0.146764\teval-error:0.868483\n",
      "[15]\teval-error:0.146818\teval-error:0.865764\n",
      "[16]\teval-error:0.146818\teval-error:0.863124\n",
      "[17]\teval-error:0.146604\teval-error:0.860541\n",
      "[18]\teval-error:0.146458\teval-error:0.858016\n",
      "[19]\teval-error:0.146418\teval-error:0.855558\n",
      "[20]\teval-error:0.146164\teval-error:0.853166\n",
      "[21]\teval-error:0.146084\teval-error:0.850828\n",
      "[22]\teval-error:0.146058\teval-error:0.848549\n",
      "[23]\teval-error:0.146124\teval-error:0.84632\n",
      "[24]\teval-error:0.145884\teval-error:0.844152\n",
      "[25]\teval-error:0.145991\teval-error:0.84205\n",
      "[26]\teval-error:0.146111\teval-error:0.839992\n",
      "[27]\teval-error:0.145871\teval-error:0.837984\n",
      "[28]\teval-error:0.145844\teval-error:0.836036\n",
      "[29]\teval-error:0.145871\teval-error:0.834138\n",
      "[30]\teval-error:0.145871\teval-error:0.832287\n",
      "[31]\teval-error:0.145724\teval-error:0.830473\n",
      "[32]\teval-error:0.145777\teval-error:0.828713\n",
      "[33]\teval-error:0.145804\teval-error:0.827002\n",
      "[34]\teval-error:0.145497\teval-error:0.825324\n",
      "[35]\teval-error:0.145684\teval-error:0.823699\n",
      "[36]\teval-error:0.145617\teval-error:0.822122\n",
      "[37]\teval-error:0.145791\teval-error:0.820593\n",
      "[38]\teval-error:0.145871\teval-error:0.819099\n",
      "[39]\teval-error:0.145724\teval-error:0.817631\n",
      "[40]\teval-error:0.145591\teval-error:0.816215\n",
      "[41]\teval-error:0.145417\teval-error:0.814833\n",
      "[42]\teval-error:0.145471\teval-error:0.813489\n",
      "[43]\teval-error:0.145271\teval-error:0.812192\n",
      "[44]\teval-error:0.145271\teval-error:0.810928\n",
      "[45]\teval-error:0.145204\teval-error:0.809688\n",
      "[46]\teval-error:0.145204\teval-error:0.808498\n",
      "[47]\teval-error:0.144857\teval-error:0.807315\n",
      "[48]\teval-error:0.144911\teval-error:0.806178\n",
      "[49]\teval-error:0.145031\teval-error:0.80505\n",
      "[50]\teval-error:0.144951\teval-error:0.80396\n",
      "[51]\teval-error:0.144657\teval-error:0.80289\n",
      "[52]\teval-error:0.144737\teval-error:0.801886\n",
      "[53]\teval-error:0.144604\teval-error:0.800883\n",
      "[54]\teval-error:0.144537\teval-error:0.799921\n",
      "[55]\teval-error:0.144497\teval-error:0.798992\n",
      "[56]\teval-error:0.144324\teval-error:0.798094\n",
      "[57]\teval-error:0.144351\teval-error:0.797218\n",
      "[58]\teval-error:0.144257\teval-error:0.796365\n",
      "[59]\teval-error:0.144351\teval-error:0.795556\n",
      "[60]\teval-error:0.144204\teval-error:0.794754\n",
      "[61]\teval-error:0.144164\teval-error:0.793974\n",
      "[62]\teval-error:0.144151\teval-error:0.79322\n",
      "[63]\teval-error:0.144084\teval-error:0.792498\n",
      "[64]\teval-error:0.144244\teval-error:0.791782\n",
      "[65]\teval-error:0.144284\teval-error:0.791103\n",
      "[66]\teval-error:0.144271\teval-error:0.790438\n",
      "[67]\teval-error:0.144164\teval-error:0.7898\n",
      "[68]\teval-error:0.144284\teval-error:0.789196\n",
      "[69]\teval-error:0.144097\teval-error:0.788584\n",
      "[70]\teval-error:0.143871\teval-error:0.787994\n",
      "[71]\teval-error:0.143937\teval-error:0.787413\n",
      "[72]\teval-error:0.143991\teval-error:0.786865\n",
      "[73]\teval-error:0.143991\teval-error:0.786325\n",
      "[74]\teval-error:0.143897\teval-error:0.785824\n",
      "[75]\teval-error:0.143871\teval-error:0.785324\n",
      "[76]\teval-error:0.143897\teval-error:0.784849\n",
      "[77]\teval-error:0.143831\teval-error:0.784395\n",
      "[78]\teval-error:0.143791\teval-error:0.783948\n",
      "[79]\teval-error:0.143791\teval-error:0.783523\n",
      "[80]\teval-error:0.143751\teval-error:0.783119\n",
      "[81]\teval-error:0.143737\teval-error:0.78272\n",
      "[82]\teval-error:0.143751\teval-error:0.782349\n",
      "[83]\teval-error:0.143697\teval-error:0.782\n",
      "[84]\teval-error:0.143671\teval-error:0.781653\n",
      "[85]\teval-error:0.143617\teval-error:0.781325\n",
      "[86]\teval-error:0.143684\teval-error:0.781002\n",
      "[87]\teval-error:0.143511\teval-error:0.780696\n",
      "[88]\teval-error:0.143564\teval-error:0.780416\n",
      "[89]\teval-error:0.143564\teval-error:0.780143\n",
      "[90]\teval-error:0.143617\teval-error:0.779883\n",
      "[91]\teval-error:0.143577\teval-error:0.779633\n",
      "[92]\teval-error:0.143591\teval-error:0.779399\n",
      "[93]\teval-error:0.143564\teval-error:0.779163\n",
      "[94]\teval-error:0.143564\teval-error:0.778932\n",
      "[95]\teval-error:0.143484\teval-error:0.778736\n",
      "[96]\teval-error:0.143511\teval-error:0.778559\n",
      "[97]\teval-error:0.143524\teval-error:0.778383\n",
      "[98]\teval-error:0.143484\teval-error:0.778223\n",
      "[99]\teval-error:0.143537\teval-error:0.77807\n",
      "[100]\teval-error:0.143511\teval-error:0.777923\n",
      "[101]\teval-error:0.143404\teval-error:0.777802\n",
      "[102]\teval-error:0.143364\teval-error:0.777681\n",
      "[103]\teval-error:0.143337\teval-error:0.777567\n",
      "[104]\teval-error:0.143351\teval-error:0.777473\n",
      "[105]\teval-error:0.143284\teval-error:0.77737\n",
      "[106]\teval-error:0.143284\teval-error:0.777282\n",
      "[107]\teval-error:0.143377\teval-error:0.777217\n",
      "[108]\teval-error:0.143244\teval-error:0.777157\n",
      "[109]\teval-error:0.143257\teval-error:0.777099\n",
      "[110]\teval-error:0.143311\teval-error:0.777047\n",
      "[111]\teval-error:0.143217\teval-error:0.77701\n",
      "[112]\teval-error:0.143271\teval-error:0.776982\n",
      "[113]\teval-error:0.143231\teval-error:0.776957\n",
      "[114]\teval-error:0.143097\teval-error:0.776936\n",
      "[115]\teval-error:0.143057\teval-error:0.776929\n",
      "[116]\teval-error:0.14303\teval-error:0.776915\n",
      "[117]\teval-error:0.14295\teval-error:0.776933\n",
      "[118]\teval-error:0.14299\teval-error:0.776952\n",
      "[119]\teval-error:0.142977\teval-error:0.776948\n",
      "[120]\teval-error:0.142964\teval-error:0.776974\n",
      "[121]\teval-error:0.142924\teval-error:0.777012\n",
      "[122]\teval-error:0.143017\teval-error:0.777042\n",
      "[123]\teval-error:0.14299\teval-error:0.777071\n",
      "[124]\teval-error:0.143017\teval-error:0.777121\n",
      "[125]\teval-error:0.143004\teval-error:0.777192\n",
      "[126]\teval-error:0.14291\teval-error:0.777255\n",
      "Stopping. Best iteration:\n",
      "[116]\teval-error:0.14303\teval-error:0.776915\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = xgb.train(params=params, dtrain=train, num_boost_round=20000, feval=evalerror, evals=test, early_stopping_rounds=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "提交前预测结果（0.771093）\n",
      "[0.18703382 0.18690039 0.26751837 ... 0.35541648 0.21331905 0.19868453]\n",
      "0.7772550531941499\n"
     ]
    }
   ],
   "source": [
    "model.save_model('work/my_model.model')\n",
    "model = xgb.Booster()\n",
    "model.load_model('work/my_model.model')\n",
    "print('提交前预测结果（0.771093）')\n",
    "res = model.predict(xgb.DMatrix(x_valid))\n",
    "print(res)\n",
    "print(singleloss(y_valid,res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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<ipython-input-68-b780eb430b73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_boost_round\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnfold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevalerror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best round：'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best score：'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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/opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/xgboost/training.py\u001b[0m in \u001b[0;36mcv\u001b[0;34m(params, dtrain, num_boost_round, nfold, stratified, folds, metrics, obj, feval, maximize, early_stopping_rounds, fpreproc, as_pandas, verbose_eval, show_stdv, seed, callbacks, shuffle)\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0mresults\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    414\u001b[0m     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc,\n\u001b[0;32m--> 415\u001b[0;31m                       stratified, folds, shuffle)\n\u001b[0m\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;31m# setup callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/xgboost/training.py\u001b[0m in \u001b[0;36mmknfold\u001b[0;34m(dall, nfold, param, seed, evals, fpreproc, stratified, folds, shuffle)\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0mret\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    276\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         \u001b[0mdtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0min_idset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\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    278\u001b[0m         \u001b[0mdtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_idset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;31m# run preprocessing on the data set if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36mslice\u001b[0;34m(self, rindex)\u001b[0m\n\u001b[1;32m    815\u001b[0m                                                \u001b[0mc_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m                                                \u001b[0mc_bst_ulong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrindex\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--> 817\u001b[0;31m                                                ctypes.byref(res.handle)))\n\u001b[0m\u001b[1;32m    818\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cv = xgb.cv(params, train, num_boost_round=1000, nfold=5, feval=evalerror, early_stopping_rounds=10, seed=0)\r\n",
    "print('Best round：',cv.shape[0])\r\n",
    "print('Best score：',cv.values[-1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.80034995 0.19965006]\n",
      " [0.8144449  0.18555509]\n",
      " [0.6479087  0.3520913 ]\n",
      " ...\n",
      " [0.5023437  0.4976563 ]\n",
      " [0.70095223 0.29904777]\n",
      " [0.778038   0.221962  ]]\n",
      "0.7150601975202411\n"
     ]
    }
   ],
   "source": [
    "model = xgb.XGBClassifier(max_depth=12, learning_rate=0.1, n_estimators=19, silent=True, \r\n",
    "                         objective='binary:logistic', nthread=-1, gamma=0.2, min_child_weight=8, \r\n",
    "                         max_delta_step=0, subsample=0.9, colsample_bytree=0.9, colsample_bylevel=0.9, \r\n",
    "                         reg_alpha=0., reg_lambda=0.6, scale_pos_weight=2.5, base_score=0.5, seed=0, \r\n",
    "                         missing=None)\r\n",
    "model.fit(feature,label)\r\n",
    "res = model.predict_proba(x_valid)\r\n",
    "print(res)\r\n",
    "print(logloss(y_valid,res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:  3.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8497743090809315\n",
      "Best param: {'min_child_weight': 8}\n"
     ]
    }
   ],
   "source": [
    "params = {\r\n",
    "    # 'learning_rate':[0.1,0.05,0.03,0.01],\r\n",
    "    # 'n_estimatores':np.arange(100,400,100),\r\n",
    "    # 'min_child_weight':np.arange(7,14,1),\r\n",
    "    # 'max_depth':np.arange(8,15,1),\r\n",
    "    # 'gamma':np.arange(0,0.5,.1),\r\n",
    "    # 'subsample':[0.5,0.6,0.7,0.8,0.9,1],# np.arange(0.5,1.1,.1),\r\n",
    "    # 'colsample_bytree':[0.5,0.6,0.7,0.8,0.9,1],# np.arange(0.5,1.1,.1),\r\n",
    "    # 'colsample_bylevel':[0.5,0.6,0.7,0.8,0.9,1],# np.arange(0.5,1.1,.1),\r\n",
    "    # 'reg_alpha':[0.,0.1,0.2], # np.arange(0,0.5,.1),\r\n",
    "    # 'reg_lambda':[0.5,0.6,0.7,0.8,0.9,1], # np.arange(0.5,1.1,.1),\r\n",
    "}\r\n",
    "\r\n",
    "cv = GridSearchCV(model, param_grid=params, scoring=logScore, n_jobs=-1, cv=3, verbose=1)\r\n",
    "\r\n",
    "cv.fit(feature, label)\r\n",
    "print('Best score:',cv.best_score_)\r\n",
    "print('Best param:',cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('/home/aistudio/data/data13514/eature_embed_all.','rb') as source:\r\n",
    "    features = pkl.load(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929807, 109)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>785</td>\n",
       "      <td>785</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>63</td>\n",
       "      <td>68</td>\n",
       "      <td>20042.9</td>\n",
       "      <td>19299.8</td>\n",
       "      <td>0.0149549</td>\n",
       "      <td>0.0155307</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2        3        4          5          6    7   8   9    ... 99   \\\n",
       "0  0.8   2   2      680      680          0          0  510   0   0  ...   0   \n",
       "1  0.4   2   2      500      500          0          0  330   0   0  ...   0   \n",
       "2  0.4   1   1      420      420          0          0  420   0   0  ...   0   \n",
       "3  0.4   3   3      785      785          0          0   85   0   0  ...   0   \n",
       "4  0.4  63  68  20042.9  19299.8  0.0149549  0.0155307  300   0   0  ...   1   \n",
       "\n",
       "  100 101 102 103 104 105 106 107 108  \n",
       "0   0   0   1   0   1   0   0   0   1  \n",
       "1   0   0   1   1   0   0   0   1   0  \n",
       "2   0   0   1   1   0   0   0   1   0  \n",
       "3   0   0   0   1   0   0   0   1   0  \n",
       "4   0   0   0   1   0   0   0   1   0  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features['predict'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features['predict'] = model.predict(xgb.DMatrix(features.iloc[:,:-1].values))\r\n",
    "# features['predict'] = model.predict_proba(features.iloc[:,:-1].values)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = features[[64,'predict']]\r\n",
    "output = output.rename({64:'customer_id','predict':'result'},axis=1)\r\n",
    "output.to_csv('reuslt.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929807, 2)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133</td>\n",
       "      <td>0.322463</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>413</td>\n",
       "      <td>0.234745</td>\n",
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       "    <tr>\n",
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       "      <td>0.214413</td>\n",
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       "      <th>3</th>\n",
       "      <td>502</td>\n",
       "      <td>0.291103</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.698398</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.623874</td>\n",
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       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.623874</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>0.337641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>0.191385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>89</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>107</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>131</td>\n",
       "      <td>0.353556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>136</td>\n",
       "      <td>0.610412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>146</td>\n",
       "      <td>0.392727</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>153</td>\n",
       "      <td>0.641131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>159</td>\n",
       "      <td>0.745780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>179</td>\n",
       "      <td>0.232552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>191</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>192</td>\n",
       "      <td>0.351143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>199</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>203</td>\n",
       "      <td>0.230331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>214</td>\n",
       "      <td>0.343428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>225</td>\n",
       "      <td>0.310970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>245</td>\n",
       "      <td>0.318390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>256</td>\n",
       "      <td>0.332471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>286</td>\n",
       "      <td>0.252964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>307</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>317</td>\n",
       "      <td>0.258458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>324</td>\n",
       "      <td>0.222739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929777</th>\n",
       "      <td>936721</td>\n",
       "      <td>0.185012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929778</th>\n",
       "      <td>936739</td>\n",
       "      <td>0.182228</td>\n",
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       "    <tr>\n",
       "      <th>929779</th>\n",
       "      <td>936858</td>\n",
       "      <td>0.183724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929780</th>\n",
       "      <td>938200</td>\n",
       "      <td>0.220816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929781</th>\n",
       "      <td>947597</td>\n",
       "      <td>0.224057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929782</th>\n",
       "      <td>936484</td>\n",
       "      <td>0.280425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929783</th>\n",
       "      <td>936677</td>\n",
       "      <td>0.291717</td>\n",
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       "    <tr>\n",
       "      <th>929784</th>\n",
       "      <td>936888</td>\n",
       "      <td>0.268035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929785</th>\n",
       "      <td>936509</td>\n",
       "      <td>0.406955</td>\n",
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       "    <tr>\n",
       "      <th>929786</th>\n",
       "      <td>936468</td>\n",
       "      <td>0.302401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929787</th>\n",
       "      <td>936671</td>\n",
       "      <td>0.289618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929788</th>\n",
       "      <td>936892</td>\n",
       "      <td>0.298634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929789</th>\n",
       "      <td>936505</td>\n",
       "      <td>0.278865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929790</th>\n",
       "      <td>936805</td>\n",
       "      <td>0.286614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929791</th>\n",
       "      <td>936681</td>\n",
       "      <td>0.268035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929792</th>\n",
       "      <td>936818</td>\n",
       "      <td>0.255431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929793</th>\n",
       "      <td>936857</td>\n",
       "      <td>0.296621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929794</th>\n",
       "      <td>936901</td>\n",
       "      <td>0.327529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929795</th>\n",
       "      <td>936769</td>\n",
       "      <td>0.360679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929796</th>\n",
       "      <td>936625</td>\n",
       "      <td>0.291631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929797</th>\n",
       "      <td>936822</td>\n",
       "      <td>0.336831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929798</th>\n",
       "      <td>936636</td>\n",
       "      <td>0.272935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929799</th>\n",
       "      <td>936757</td>\n",
       "      <td>0.267025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929800</th>\n",
       "      <td>936545</td>\n",
       "      <td>0.545101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929801</th>\n",
       "      <td>936612</td>\n",
       "      <td>0.237687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929802</th>\n",
       "      <td>938104</td>\n",
       "      <td>0.271272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929803</th>\n",
       "      <td>936461</td>\n",
       "      <td>0.533813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929804</th>\n",
       "      <td>936527</td>\n",
       "      <td>0.533875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929805</th>\n",
       "      <td>936920</td>\n",
       "      <td>0.535583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929806</th>\n",
       "      <td>936954</td>\n",
       "      <td>0.385646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>929807 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id    result\n",
       "0              133  0.322463\n",
       "1              413  0.234745\n",
       "2              113  0.214413\n",
       "3              502  0.291103\n",
       "4                0  0.698398\n",
       "5                7  0.623874\n",
       "6                8  0.623874\n",
       "7               17  0.337641\n",
       "8               55  0.191385\n",
       "9               73  0.222739\n",
       "10              89  0.222739\n",
       "11             107  0.222739\n",
       "12             131  0.353556\n",
       "13             136  0.610412\n",
       "14             146  0.392727\n",
       "15             153  0.641131\n",
       "16             159  0.745780\n",
       "17             179  0.232552\n",
       "18             191  0.222739\n",
       "19             192  0.351143\n",
       "20             199  0.222739\n",
       "21             203  0.230331\n",
       "22             214  0.343428\n",
       "23             225  0.310970\n",
       "24             245  0.318390\n",
       "25             256  0.332471\n",
       "26             286  0.252964\n",
       "27             307  0.222739\n",
       "28             317  0.258458\n",
       "29             324  0.222739\n",
       "...            ...       ...\n",
       "929777      936721  0.185012\n",
       "929778      936739  0.182228\n",
       "929779      936858  0.183724\n",
       "929780      938200  0.220816\n",
       "929781      947597  0.224057\n",
       "929782      936484  0.280425\n",
       "929783      936677  0.291717\n",
       "929784      936888  0.268035\n",
       "929785      936509  0.406955\n",
       "929786      936468  0.302401\n",
       "929787      936671  0.289618\n",
       "929788      936892  0.298634\n",
       "929789      936505  0.278865\n",
       "929790      936805  0.286614\n",
       "929791      936681  0.268035\n",
       "929792      936818  0.255431\n",
       "929793      936857  0.296621\n",
       "929794      936901  0.327529\n",
       "929795      936769  0.360679\n",
       "929796      936625  0.291631\n",
       "929797      936822  0.336831\n",
       "929798      936636  0.272935\n",
       "929799      936757  0.267025\n",
       "929800      936545  0.545101\n",
       "929801      936612  0.237687\n",
       "929802      938104  0.271272\n",
       "929803      936461  0.533813\n",
       "929804      936527  0.533875\n",
       "929805      936920  0.535583\n",
       "929806      936954  0.385646\n",
       "\n",
       "[929807 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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