chka-Kaggle实战/kk01-redhat-business/kk03-predict-redhat-business.ipynb

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 "cells": [
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   "cell_type": "code",
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   "metadata": {},
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   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Read people.csv...\n",
      "Load train.csv...\n",
      "Load test.csv...\n",
      "Process tables...\n",
      "Merge...\n",
      "Writing submission:  submission_0.98_2019-01-05-20-30.csv\n"
     ]
    }
   ],
   "source": [
    "def create_submission(score, test, prediction):\n",
    "    now = datetime.datetime.now()\n",
    "    sub_file = 'submission_' + str(score) + '_' + str(now.strftime(\"%Y-%m-%d-%H-%M\")) + '.csv'\n",
    "    print('Writing submission: ', sub_file)\n",
    "    f = open(sub_file, 'w')\n",
    "    f.write('activity_id,outcome\\n')\n",
    "    total = 0\n",
    "    for id in test['activity_id']:\n",
    "        str1 = str(id) + ',' + str(prediction[total])\n",
    "        str1 += '\\n'\n",
    "        total += 1\n",
    "        f.write(str1)\n",
    "    f.close()\n",
    "\n",
    "## 读取数据\n",
    "train, test, features = read_test_train()\n",
    "\n",
    "gbm = xgb.Booster(model_file='redhat_bussiness_verone_20181130.model')\n",
    "\n",
    "score = '0.98'\n",
    "test_prediction = gbm.predict(xgb.DMatrix(test[features]))\n",
    "\n",
    "create_submission(score, test, test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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