240_features_dummy - 0.79.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/weiyuan/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (3,4,6,10,12,18,24,35,56,57,65,85,86,87,88,89,98,99,100,105,124,125,126,127,128,130,131,133,166,171,172,192,193,194,195,204,205,206,207,228,229,230,240,256,257,258,259,260,261,262) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 features with greater than 0.50 missing values.\n",
      "\n",
      "0 features with a single unique value.\n",
      "\n",
      "1 features with a correlation magnitude greater than 0.80.\n",
      "\n",
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[259]\tvalid_0's auc: 0.892899\tvalid_0's binary_logloss: 0.412797\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[163]\tvalid_0's auc: 0.891373\tvalid_0's binary_logloss: 0.416023\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[119]\tvalid_0's auc: 0.890652\tvalid_0's binary_logloss: 0.418424\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[229]\tvalid_0's auc: 0.887651\tvalid_0's binary_logloss: 0.421792\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[128]\tvalid_0's auc: 0.893794\tvalid_0's binary_logloss: 0.413373\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[176]\tvalid_0's auc: 0.892772\tvalid_0's binary_logloss: 0.413827\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[131]\tvalid_0's auc: 0.890325\tvalid_0's binary_logloss: 0.418409\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[130]\tvalid_0's auc: 0.885813\tvalid_0's binary_logloss: 0.425128\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[110]\tvalid_0's auc: 0.896553\tvalid_0's binary_logloss: 0.40922\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[145]\tvalid_0's auc: 0.895227\tvalid_0's binary_logloss: 0.409611\n",
      "\n",
      "4555 features with zero importance after one-hot encoding.\n",
      "\n",
      "1734 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "4962 features do not contribute to cumulative importance of 0.99.\n",
      "\n",
      "4715 total features out of 8121 identified for removal after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas  as pd\n",
    "from feature_selector import FeatureSelector\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "train_labels = data.satisfied\n",
    "train = data.drop(columns=['satisfied', 'id'])\n",
    "\n",
    "fs = FeatureSelector(data = train, labels = train_labels)\n",
    "\n",
    "fs.identify_all(selection_params = {'missing_threshold': 0.5,    \n",
    "                                    'correlation_threshold': 0.8, \n",
    "                                    'task': 'classification',    \n",
    "                                    'eval_metric': 'auc', \n",
    "                                    'cumulative_importance': 0.99})\n",
    "\n",
    "#test_data = pd.read_csv('test.csv')\n",
    "#test_data_select = test_data[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 6082 features including one-hot features.\n"
     ]
    }
   ],
   "source": [
    "train_removed_all_once = fs.remove(methods = 'all', keep_one_hot = False)\n",
    "\n",
    "train_new = train[list(train_removed_all_once.columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/weiyuan/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:6287: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "for column in train_new.columns:\n",
    "    train_new[column].fillna(train_new[column].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6044"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train_removed_with_dummy = pd.get_dummies(train_new)\n",
    "len(set(new_train_removed_with_dummy.columns))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "unique_features = list(new_train_removed_with_dummy.columns.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6044"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train_removed_with_unique_dummy = new_train_removed_with_dummy.loc[:,~new_train_removed_with_dummy.columns.duplicated()]\n",
    "\n",
    "len(new_train_removed_with_unique_dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_train_removed_with_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "#added some parameters\n",
    "kf = KFold(n_splits = 10, shuffle = True, random_state = 2)\n",
    "result = next(kf.split(X), None)\n",
    "result1 = next(kf.split(train_labels), None)\n",
    "\n",
    "\n",
    "X_train = X.iloc[result[0]]\n",
    "y_train =  train_labels.iloc[result1[0]]\n",
    "X_test = X.iloc[result[1]]\n",
    "y_test =  train_labels.iloc[result1[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.671253\tvalid_0's auc: 0.860653\n",
      "Training until validation scores don't improve for 10 rounds\n",
      "[2]\tvalid_0's binary_logloss: 0.654158\tvalid_0's auc: 0.869348\n",
      "[3]\tvalid_0's binary_logloss: 0.637341\tvalid_0's auc: 0.873536\n",
      "[4]\tvalid_0's binary_logloss: 0.621803\tvalid_0's auc: 0.875501\n",
      "[5]\tvalid_0's binary_logloss: 0.607199\tvalid_0's auc: 0.877375\n",
      "[6]\tvalid_0's binary_logloss: 0.593892\tvalid_0's auc: 0.878793\n",
      "[7]\tvalid_0's binary_logloss: 0.581822\tvalid_0's auc: 0.879429\n",
      "[8]\tvalid_0's binary_logloss: 0.571111\tvalid_0's auc: 0.881291\n",
      "[9]\tvalid_0's binary_logloss: 0.560723\tvalid_0's auc: 0.881486\n",
      "[10]\tvalid_0's binary_logloss: 0.551308\tvalid_0's auc: 0.881574\n",
      "[11]\tvalid_0's binary_logloss: 0.542568\tvalid_0's auc: 0.881707\n",
      "[12]\tvalid_0's binary_logloss: 0.534638\tvalid_0's auc: 0.881821\n",
      "[13]\tvalid_0's binary_logloss: 0.52748\tvalid_0's auc: 0.881759\n",
      "[14]\tvalid_0's binary_logloss: 0.520563\tvalid_0's auc: 0.881948\n",
      "[15]\tvalid_0's binary_logloss: 0.514397\tvalid_0's auc: 0.881932\n",
      "[16]\tvalid_0's binary_logloss: 0.508124\tvalid_0's auc: 0.882348\n",
      "[17]\tvalid_0's binary_logloss: 0.502986\tvalid_0's auc: 0.882477\n",
      "[18]\tvalid_0's binary_logloss: 0.497915\tvalid_0's auc: 0.882565\n",
      "[19]\tvalid_0's binary_logloss: 0.493338\tvalid_0's auc: 0.882817\n",
      "[20]\tvalid_0's binary_logloss: 0.489278\tvalid_0's auc: 0.883138\n",
      "[21]\tvalid_0's binary_logloss: 0.485519\tvalid_0's auc: 0.883182\n",
      "[22]\tvalid_0's binary_logloss: 0.481718\tvalid_0's auc: 0.883305\n",
      "[23]\tvalid_0's binary_logloss: 0.47827\tvalid_0's auc: 0.883699\n",
      "[24]\tvalid_0's binary_logloss: 0.474998\tvalid_0's auc: 0.883548\n",
      "[25]\tvalid_0's binary_logloss: 0.471355\tvalid_0's auc: 0.883991\n",
      "[26]\tvalid_0's binary_logloss: 0.468525\tvalid_0's auc: 0.883983\n",
      "[27]\tvalid_0's binary_logloss: 0.465773\tvalid_0's auc: 0.884015\n",
      "[28]\tvalid_0's binary_logloss: 0.463168\tvalid_0's auc: 0.884425\n",
      "[29]\tvalid_0's binary_logloss: 0.460527\tvalid_0's auc: 0.884715\n",
      "[30]\tvalid_0's binary_logloss: 0.457978\tvalid_0's auc: 0.885158\n",
      "[31]\tvalid_0's binary_logloss: 0.455724\tvalid_0's auc: 0.885826\n",
      "[32]\tvalid_0's binary_logloss: 0.453926\tvalid_0's auc: 0.885808\n",
      "[33]\tvalid_0's binary_logloss: 0.451992\tvalid_0's auc: 0.885807\n",
      "[34]\tvalid_0's binary_logloss: 0.450257\tvalid_0's auc: 0.885904\n",
      "[35]\tvalid_0's binary_logloss: 0.448533\tvalid_0's auc: 0.886218\n",
      "[36]\tvalid_0's binary_logloss: 0.446866\tvalid_0's auc: 0.886523\n",
      "[37]\tvalid_0's binary_logloss: 0.445438\tvalid_0's auc: 0.886797\n",
      "[38]\tvalid_0's binary_logloss: 0.444024\tvalid_0's auc: 0.887044\n",
      "[39]\tvalid_0's binary_logloss: 0.442526\tvalid_0's auc: 0.887386\n",
      "[40]\tvalid_0's binary_logloss: 0.441188\tvalid_0's auc: 0.887492\n",
      "[41]\tvalid_0's binary_logloss: 0.439755\tvalid_0's auc: 0.887931\n",
      "[42]\tvalid_0's binary_logloss: 0.438391\tvalid_0's auc: 0.888229\n",
      "[43]\tvalid_0's binary_logloss: 0.437569\tvalid_0's auc: 0.888201\n",
      "[44]\tvalid_0's binary_logloss: 0.436484\tvalid_0's auc: 0.888421\n",
      "[45]\tvalid_0's binary_logloss: 0.435428\tvalid_0's auc: 0.888508\n",
      "[46]\tvalid_0's binary_logloss: 0.434324\tvalid_0's auc: 0.88883\n",
      "[47]\tvalid_0's binary_logloss: 0.433369\tvalid_0's auc: 0.888953\n",
      "[48]\tvalid_0's binary_logloss: 0.432607\tvalid_0's auc: 0.889121\n",
      "[49]\tvalid_0's binary_logloss: 0.431648\tvalid_0's auc: 0.889366\n",
      "[50]\tvalid_0's binary_logloss: 0.430919\tvalid_0's auc: 0.889508\n",
      "[51]\tvalid_0's binary_logloss: 0.430156\tvalid_0's auc: 0.88965\n",
      "[52]\tvalid_0's binary_logloss: 0.429292\tvalid_0's auc: 0.889792\n",
      "[53]\tvalid_0's binary_logloss: 0.428617\tvalid_0's auc: 0.889877\n",
      "[54]\tvalid_0's binary_logloss: 0.427869\tvalid_0's auc: 0.889941\n",
      "[55]\tvalid_0's binary_logloss: 0.427096\tvalid_0's auc: 0.890184\n",
      "[56]\tvalid_0's binary_logloss: 0.426324\tvalid_0's auc: 0.890461\n",
      "[57]\tvalid_0's binary_logloss: 0.425608\tvalid_0's auc: 0.890573\n",
      "[58]\tvalid_0's binary_logloss: 0.424851\tvalid_0's auc: 0.890802\n",
      "[59]\tvalid_0's binary_logloss: 0.424582\tvalid_0's auc: 0.890725\n",
      "[60]\tvalid_0's binary_logloss: 0.424229\tvalid_0's auc: 0.890776\n",
      "[61]\tvalid_0's binary_logloss: 0.423857\tvalid_0's auc: 0.890746\n",
      "[62]\tvalid_0's binary_logloss: 0.422952\tvalid_0's auc: 0.891106\n",
      "[63]\tvalid_0's binary_logloss: 0.422575\tvalid_0's auc: 0.891087\n",
      "[64]\tvalid_0's binary_logloss: 0.422135\tvalid_0's auc: 0.891265\n",
      "[65]\tvalid_0's binary_logloss: 0.421545\tvalid_0's auc: 0.891443\n",
      "[66]\tvalid_0's binary_logloss: 0.421186\tvalid_0's auc: 0.891513\n",
      "[67]\tvalid_0's binary_logloss: 0.420812\tvalid_0's auc: 0.89153\n",
      "[68]\tvalid_0's binary_logloss: 0.420561\tvalid_0's auc: 0.891505\n",
      "[69]\tvalid_0's binary_logloss: 0.420371\tvalid_0's auc: 0.891514\n",
      "[70]\tvalid_0's binary_logloss: 0.420243\tvalid_0's auc: 0.891424\n",
      "[71]\tvalid_0's binary_logloss: 0.420205\tvalid_0's auc: 0.891247\n",
      "[72]\tvalid_0's binary_logloss: 0.419952\tvalid_0's auc: 0.891275\n",
      "[73]\tvalid_0's binary_logloss: 0.419443\tvalid_0's auc: 0.891468\n",
      "[74]\tvalid_0's binary_logloss: 0.419033\tvalid_0's auc: 0.891585\n",
      "[75]\tvalid_0's binary_logloss: 0.418359\tvalid_0's auc: 0.891944\n",
      "[76]\tvalid_0's binary_logloss: 0.417933\tvalid_0's auc: 0.892103\n",
      "[77]\tvalid_0's binary_logloss: 0.41764\tvalid_0's auc: 0.892196\n",
      "[78]\tvalid_0's binary_logloss: 0.417578\tvalid_0's auc: 0.892128\n",
      "[79]\tvalid_0's binary_logloss: 0.417394\tvalid_0's auc: 0.892182\n",
      "[80]\tvalid_0's binary_logloss: 0.417285\tvalid_0's auc: 0.892152\n",
      "[81]\tvalid_0's binary_logloss: 0.417118\tvalid_0's auc: 0.892215\n",
      "[82]\tvalid_0's binary_logloss: 0.416782\tvalid_0's auc: 0.892356\n",
      "[83]\tvalid_0's binary_logloss: 0.41666\tvalid_0's auc: 0.892363\n",
      "[84]\tvalid_0's binary_logloss: 0.416457\tvalid_0's auc: 0.892479\n",
      "[85]\tvalid_0's binary_logloss: 0.416191\tvalid_0's auc: 0.892525\n",
      "[86]\tvalid_0's binary_logloss: 0.416121\tvalid_0's auc: 0.892472\n",
      "[87]\tvalid_0's binary_logloss: 0.415876\tvalid_0's auc: 0.892546\n",
      "[88]\tvalid_0's binary_logloss: 0.415857\tvalid_0's auc: 0.892479\n",
      "[89]\tvalid_0's binary_logloss: 0.415589\tvalid_0's auc: 0.892572\n",
      "[90]\tvalid_0's binary_logloss: 0.415465\tvalid_0's auc: 0.892581\n",
      "[91]\tvalid_0's binary_logloss: 0.415391\tvalid_0's auc: 0.892511\n",
      "[92]\tvalid_0's binary_logloss: 0.415018\tvalid_0's auc: 0.89264\n",
      "[93]\tvalid_0's binary_logloss: 0.414781\tvalid_0's auc: 0.892766\n",
      "[94]\tvalid_0's binary_logloss: 0.41451\tvalid_0's auc: 0.89291\n",
      "[95]\tvalid_0's binary_logloss: 0.414588\tvalid_0's auc: 0.892789\n",
      "[96]\tvalid_0's binary_logloss: 0.414475\tvalid_0's auc: 0.892838\n",
      "[97]\tvalid_0's binary_logloss: 0.414469\tvalid_0's auc: 0.892809\n",
      "[98]\tvalid_0's binary_logloss: 0.414197\tvalid_0's auc: 0.892964\n",
      "[99]\tvalid_0's binary_logloss: 0.414063\tvalid_0's auc: 0.893031\n",
      "[100]\tvalid_0's binary_logloss: 0.414189\tvalid_0's auc: 0.892916\n",
      "[101]\tvalid_0's binary_logloss: 0.414188\tvalid_0's auc: 0.892926\n",
      "[102]\tvalid_0's binary_logloss: 0.41414\tvalid_0's auc: 0.892828\n",
      "[103]\tvalid_0's binary_logloss: 0.414008\tvalid_0's auc: 0.892868\n",
      "[104]\tvalid_0's binary_logloss: 0.414003\tvalid_0's auc: 0.892897\n",
      "[105]\tvalid_0's binary_logloss: 0.413913\tvalid_0's auc: 0.892882\n",
      "[106]\tvalid_0's binary_logloss: 0.413851\tvalid_0's auc: 0.892886\n",
      "[107]\tvalid_0's binary_logloss: 0.413995\tvalid_0's auc: 0.892769\n",
      "[108]\tvalid_0's binary_logloss: 0.41386\tvalid_0's auc: 0.892908\n",
      "[109]\tvalid_0's binary_logloss: 0.414186\tvalid_0's auc: 0.892707\n",
      "Early stopping, best iteration is:\n",
      "[99]\tvalid_0's binary_logloss: 0.414063\tvalid_0's auc: 0.893031\n",
      "lightgbm运行时间为27.0秒\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import lightgbm as lgb\n",
    "\n",
    "train_data = lgb.Dataset(X_train, label=y_train)\n",
    "test_data = lgb.Dataset(X_test, label=y_test)\n",
    "\n",
    "\n",
    "params = {'boosting_type': 'gbdt',\n",
    "          'num_leaves': 80, #结果对最终效果影响较大，越大值越好，太大会出现过拟合\n",
    "          'min_data_in_leaf': 30,\n",
    "          'objective': 'binary', #定义的目标函数\n",
    "          'max_depth': -1,\n",
    "          'learning_rate': 0.05,\n",
    "          \"min_sum_hessian_in_leaf\": 6,\n",
    "          \"boosting\": \"gbdt\",\n",
    "          \"feature_fraction\": 0.9,  #提取的特征比率\n",
    "          \"bagging_freq\": 1,\n",
    "          \"bagging_fraction\": 0.8,\n",
    "          \"bagging_seed\": 11,\n",
    "          \"lambda_l1\": 0.1,             #l1正则\n",
    "          # 'lambda_l2': 0.001,     #l2正则\n",
    "          \"verbosity\": -1,\n",
    "          \"nthread\": -1,                #线程数量，-1表示全部线程，线程越多，运行的速度越快\n",
    "          'metric': {'binary_logloss', 'auc'},  ##评价函数选择\n",
    "          \"random_state\": 2019, #随机数种子，可以防止每次运行的结果不一致\n",
    "          # 'device': 'gpu' ##如果安装的事gpu版本的lightgbm,可以加快运算\n",
    "          }\n",
    "\n",
    "start = time.time()\n",
    "lgb2 = lgb.train(train_set=train_data,\n",
    "                      early_stopping_rounds=10,\n",
    "                      num_boost_round=1000,\n",
    "                      params=params,\n",
    "                      valid_sets=test_data)\n",
    "end = time.time()\n",
    "print('lightgbm运行时间为{}秒'.format(round(end-start,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "lgb2_preds=lgb2.predict(X_test)\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, lgb2_preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Validation ROC')\n",
    "plt.plot(fpr, tpr, 'b', label = 'Val AUC = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "    \n",
    "def fbeta(y_true, pred):\n",
    "    # 调整阈值\n",
    "    best_acc=0\n",
    "    precision_recall = []\n",
    "    best_thershold=0\n",
    "    for thershold in [0.15,0.2, 0.25,0.3,0.4,0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49,0.5,0.52125, 0.525, 0.55, 0.6, 0.65, 0.7]:\n",
    "        preds = [1 if i > thershold else 0 for i in pred]\n",
    "        cur_acc = accuracy_score(y_test, preds)\n",
    "        if cur_acc > best_acc:\n",
    "            best_thershold = thershold\n",
    "            best_acc = cur_acc\n",
    "            print(best_acc)\n",
    "    return 'best_thershold', best_thershold,True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7237367021276596\n",
      "0.754654255319149\n",
      "0.7769281914893617\n",
      "0.7962101063829787\n",
      "0.8121675531914894\n",
      "0.8181515957446809\n",
      "0.8184840425531915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('best_thershold', 0.44, True)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fbeta(y_test, lgb2_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/weiyuan/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (3,6,10,12,21,24,35,85,87,88,89,100,124,126,127,128,130,131,133,166,172,177,193,194,195,205,206,207,220,222,228,229,230,240,257,258,260,261,262) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['v30', 'v31', 'v36', 'v38', 'v42', 'v46', 'v48', 'v49', 'v51', 'v54',\n",
       "       ...\n",
       "       'cntry_HU', 'cntry_IE', 'cntry_IL', 'cntry_LT', 'cntry_NL', 'cntry_NO',\n",
       "       'cntry_PL', 'cntry_PT', 'cntry_SE', 'cntry_SI'],\n",
       "      dtype='object', length=6643)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_dummy = pd.get_dummies(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "         id  v15  v16  v26  v27  v28  v29  v30  v31  v32  ...  cntry_HU  \\\n",
       "0     14061    0    0    0    0    0    0    1    0    0  ...         0   \n",
       "1     16467    0    0    0    0    0    0    1    0    0  ...         0   \n",
       "2     25725    0    0    0    0    0    0    1    0    0  ...         0   \n",
       "3      9100    0    0    0    0    0    0    0    0    0  ...         0   \n",
       "4     32597    0    0    0    0    0    0    1    0    0  ...         0   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
       "9240  31430    0    0    0    0    0    0    0    1    1  ...         0   \n",
       "9241   3876    0    0    0    0    0    0    0    0    0  ...         0   \n",
       "9242  20710    0    0    0    0    0    0    0    0    0  ...         0   \n",
       "9243  26015    0    0    0    0    0    0    1    0    0  ...         0   \n",
       "9244  12970    0    0    0    0    0    0    0    0    0  ...         0   \n",
       "\n",
       "      cntry_IE  cntry_IL  cntry_LT  cntry_NL  cntry_NO  cntry_PL  cntry_PT  \\\n",
       "0            0         0         0         0         0         0         0   \n",
       "1            0         0         0         0         0         0         0   \n",
       "2            0         0         0         0         0         0         0   \n",
       "3            0         0         0         0         0         0         0   \n",
       "4            0         0         0         0         0         0         0   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "9240         0         0         0         0         0         0         0   \n",
       "9241         0         0         0         0         0         0         0   \n",
       "9242         0         0         0         0         0         0         0   \n",
       "9243         0         0         0         0         0         0         0   \n",
       "9244         0         0         0         0         0         0         0   \n",
       "\n",
       "      cntry_SE  cntry_SI  \n",
       "0            0         0  \n",
       "1            0         0  \n",
       "2            0         0  \n",
       "3            0         0  \n",
       "4            0         0  \n",
       "...        ...       ...  \n",
       "9240         0         1  \n",
       "9241         0         1  \n",
       "9242         0         1  \n",
       "9243         0         1  \n",
       "9244         0         1  \n",
       "\n",
       "[9245 rows x 5840 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27072, 6643)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9245, 6643)\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros(shape=(test_data_dummy.shape[0],X_train.shape[1]))\n",
    "\n",
    "test_df = pd.DataFrame(data=a, columns = X_train.columns)\n",
    "\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "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-173-16d2b482e3bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_df_cols\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      7\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_data_dummy_cols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mtest_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_data_dummy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\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      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mnot_listed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3470\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3471\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3472\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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   3473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3474\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3548\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3549\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3550\u001b[0;31m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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   3551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3552\u001b[0m         \u001b[0;31m# check if we are modifying a copy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3380\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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-> 3381\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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   3382\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\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   3383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, item, value)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blklocs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\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[0m\n\u001b[0;32m-> 1100\u001b[0;31m                     \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblk_locs\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   1101\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blklocs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblk\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   1102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mdelete\u001b[0;34m(self, loc)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0mDelete\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\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[0m\u001b[1;32m    387\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdelete\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mdelete\u001b[0;34m(arr, obj, axis)\u001b[0m\n\u001b[1;32m   4422\u001b[0m         \u001b[0mkeep\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4423\u001b[0m         \u001b[0mslobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4424\u001b[0;31m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4426\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "test_data_dummy_cols = list(test_data_dummy.columns)\n",
    "test_df_cols = list(test_df.columns)\n",
    "\n",
    "not_listed = []\n",
    "\n",
    "for col in tqdm(test_df_cols):\n",
    "    if col in test_data_dummy_cols:\n",
    "        test_df[col] = test_data_dummy[col]\n",
    "    else:\n",
    "        not_listed.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "876eabb28b414e19863a792619ac2ad4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6643), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v3_100\n",
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      "v268_1974\n",
      "v268_1978\n",
      "v268_1980\n",
      "v268_1981\n",
      "v268_1982\n",
      "v268_1986\n",
      "v268_1991\n",
      "v268_1992\n",
      "v269_.b\n",
      "v269_.c\n",
      "v269_1954\n",
      "v269_1986\n",
      "v269_1987\n",
      "v269_1988\n",
      "v269_1990\n",
      "v269_1991\n",
      "v269_1993\n",
      "v269_1994\n",
      "v269_2000\n",
      "v269_2001\n",
      "v269_2004\n",
      "v269_2015\n",
      "v270_.b\n",
      "v270_.c\n",
      "v270_1981\n",
      "v270_1988\n",
      "v270_1992\n",
      "v270_1993\n",
      "v270_1995\n",
      "v270_1996\n",
      "v270_2000\n",
      "v270_2002\n",
      "v270_2003\n",
      "v270_2006\n",
      "v270_2008\n",
      "v270_2009\n",
      "v270_2012\n",
      "v270_2015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "\n",
    "for col in tqdm_notebook(test_df.columns):\n",
    "    if col in test_data_dummy.columns:\n",
    "        test_df[col] = test_data_dummy[col]\n",
    "    else:\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds=lgb2.predict(test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "thershold = 0.44\n",
    "preds = [1 if i > thershold else 0 for i in test_preds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9245"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.insert(1,'Predicted', preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/weiyuan/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Predicted'] = preds\n",
    "\n",
    "id_ = test_data['id']\n",
    "test_data.insert(0,'Id', id_)\n",
    "df = test_data[['Id', 'Predicted']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('submission.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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