chka-Kaggle实战/kk01-redhat-business/kk02-train-redhat-business.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import xgboost as xgb\n",
    "from preprocessing import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 获取训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read people.csv...\n",
      "Load train.csv...\n",
      "Load test.csv...\n",
      "Process tables...\n",
      "Merge...\n"
     ]
    }
   ],
   "source": [
    "train, test, features = read_test_train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 训练模型\n",
    "\n",
    "直接利用特征进行xgboost的构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost params. ETA: 0.3, MAX_DEPTH: 5, SUBSAMPLE: 0.5, COLSAMPLE_BY_TREE: 1\n"
     ]
    }
   ],
   "source": [
    "target = 'outcome' #目标\n",
    "random_state = 0 #随机数\n",
    "eta = 0.3 #学习率\n",
    "max_depth = 5 #最大深度\n",
    "subsample = 0.5 #子抽样\n",
    "colsample_bytree = 1 #列抽样\n",
    "start_time = time.time()\n",
    "\n",
    "print('XGBoost params. ETA: {}, MAX_DEPTH: {}, SUBSAMPLE: {}, COLSAMPLE_BY_TREE: {}'.format(eta, max_depth, subsample, colsample_bytree))\n",
    "params = {\n",
    "    \"objective\": \"binary:logistic\",\n",
    "    \"booster\" : \"gbtree\",\n",
    "    \"eval_metric\": \"auc\", #评价方式\n",
    "    \"eta\": eta,\n",
    "    \"tree_method\": 'exact',\n",
    "    \"max_depth\": max_depth,\n",
    "    \"subsample\": subsample,\n",
    "    \"colsample_bytree\": colsample_bytree,\n",
    "    \"silent\": 1, #静默输出\n",
    "    \"seed\": random_state,\n",
    "}\n",
    "\n",
    "#迭代轮次，多少课树\n",
    "num_boost_round = 1000 #最重要参数，调参\n",
    "early_stopping_rounds = 10 #10次都不提高，就停止了\n",
    "test_size = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length train: 1098645\n",
      "Length valid: 1098646\n",
      "[0]\ttrain-auc:0.914297\teval-auc:0.913981\n",
      "Multiple eval metrics have been passed: 'eval-auc' will be used for early stopping.\n",
      "\n",
      "Will train until eval-auc hasn't improved in 10 rounds.\n",
      "[1]\ttrain-auc:0.920316\teval-auc:0.920048\n",
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      "[739]\ttrain-auc:0.993882\teval-auc:0.991871\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-cb7cfe0d351c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#观察训练集、验证集上的表现\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mwatchlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdvalid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'eval'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mgbm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_boost_round\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwatchlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validating...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/xgboost-0.81-py3.6.egg/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[1;32m    214\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/xgboost-0.81-py3.6.egg/xgboost/training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\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[0;32m---> 74\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/xgboost-0.81-py3.6.egg/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   1060\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1061\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[0;32m-> 1062\u001b[0;31m                                                     dtrain.handle))\n\u001b[0m\u001b[1;32m   1063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1064\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "X_train, X_valid = train_test_split(train, test_size=test_size, random_state=random_state)\n",
    "X_train = X_train.sample(20000) # 数据量较大挑选部分数据\n",
    "X_valid = X_valid.sample(10000) # 数据量较大挑选部分数据\n",
    "print('Length train:', len(X_train.index))\n",
    "print('Length valid:', len(X_valid.index))\n",
    "y_train = X_train[target]\n",
    "y_valid = X_valid[target] #每一轮迭代验证\n",
    "dtrain = xgb.DMatrix(X_train[features], y_train)\n",
    "dvalid = xgb.DMatrix(X_valid[features], y_valid)\n",
    "\n",
    "#观察训练集、验证集上的表现\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
    "gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds, verbose_eval=True)\n",
    "\n",
    "print(\"Validating...\")\n",
    "check = gbm.predict(xgb.DMatrix(X_valid[features]), ntree_limit=gbm.best_iteration+1)\n",
    "score = roc_auc_score(X_valid[target].values, check)\n",
    "print('Check error value: {:.6f}'.format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用全量数据，合适参数进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost params. ETA: 0.3, MAX_DEPTH: 8, SUBSAMPLE: 0.5, COLSAMPLE_BY_TREE: 1\n",
      "Length train: 200000\n",
      "Length valid: 100000\n",
      "[0]\ttrain-auc:0.931049\n",
      "Will train until train-auc hasn't improved in 10 rounds.\n",
      "[1]\ttrain-auc:0.937465\n",
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      "[464]\ttrain-auc:0.999972\n"
     ]
    }
   ],
   "source": [
    "target = 'outcome'\n",
    "random_state = 0\n",
    "eta = 0.3\n",
    "max_depth = 8\n",
    "subsample = 0.5\n",
    "colsample_bytree = 1\n",
    "start_time = time.time()\n",
    "\n",
    "print('XGBoost params. ETA: {}, MAX_DEPTH: {}, SUBSAMPLE: {}, COLSAMPLE_BY_TREE: {}'.format(eta, max_depth, subsample, colsample_bytree))\n",
    "params = {\n",
    "    \"objective\": \"binary:logistic\",\n",
    "    \"booster\" : \"gbtree\",\n",
    "    \"eval_metric\": \"auc\",\n",
    "    \"eta\": eta,\n",
    "    \"tree_method\": 'exact',\n",
    "    \"max_depth\": max_depth,\n",
    "    \"subsample\": subsample,\n",
    "    \"colsample_bytree\": colsample_bytree,\n",
    "    \"silent\": 1,\n",
    "    \"seed\": random_state,\n",
    "}\n",
    "num_boost_round = 465\n",
    "early_stopping_rounds = 10\n",
    "test_size = 0.5\n",
    "\n",
    "# X_train, X_valid = train_test_split(train, test_size=test_size, random_state=random_state)\n",
    "X_train = X_train.sample(200000) # 数据量较大挑选部分数据\n",
    "X_valid = X_valid.sample(100000) # 数据量较大挑选部分数据\n",
    "print('Length train:', len(X_train.index))\n",
    "print('Length valid:', len(X_valid.index))\n",
    "y_train = X_train[target]\n",
    "y_valid = X_valid[target]\n",
    "dtrain = xgb.DMatrix(X_train[features], y_train)\n",
    "dvalid = xgb.DMatrix(X_valid[features], y_valid)\n",
    "\n",
    "watchlist = [(dtrain, 'train')]\n",
    "gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds, verbose_eval=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm.save_model('redhat_bussiness_verone_20181130.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc # one hot\n",
    "enc.fit_transform(Xtrain)\n",
    "enc.transform(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata\n",
    "xtrain\n",
    "xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc.fit(alldata)\n",
    "enc.transform(xtrain)\n",
    "enc.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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