AS_5.3_LightGBM_model_2.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import validation as v\n",
    "import pandas as pd\n",
    "\n",
    "path_to_train = './data/train.csv'\n",
    "train = pd.read_csv(path_to_train, encoding='utf-8')\n",
    "\n",
    "train_index, validation_index = v.split_train_validation_by_last_record_and_flow(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "params = {\n",
    "    'application':'binary',\n",
    "    'num_leaves': 8,\n",
    "    'max_depth': 4,\n",
    "    'bagging_fraction': 0.65,\n",
    "    'feature_fraction': 0.7,\n",
    "    'max_bin': 10,\n",
    "    'metric': 'auc',\n",
    "    'verbose': 1,\n",
    "    'lambda_l1': 1.0\n",
    "}\n",
    "cols = ['context_type',\n",
    "       'release_date', 'platform_name', 'platform_family', 'media_duration',\n",
    "       'listen_type', 'user_gender', 'user_age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's auc: 0.650594\tvalid_1's auc: 0.569079\n",
      "[2]\ttraining's auc: 0.655589\tvalid_1's auc: 0.53363\n",
      "[3]\ttraining's auc: 0.657491\tvalid_1's auc: 0.550546\n",
      "[4]\ttraining's auc: 0.660066\tvalid_1's auc: 0.524842\n",
      "[5]\ttraining's auc: 0.660675\tvalid_1's auc: 0.541648\n",
      "[6]\ttraining's auc: 0.661646\tvalid_1's auc: 0.542855\n",
      "[7]\ttraining's auc: 0.66174\tvalid_1's auc: 0.528321\n",
      "[8]\ttraining's auc: 0.661941\tvalid_1's auc: 0.535728\n",
      "[9]\ttraining's auc: 0.662341\tvalid_1's auc: 0.537817\n",
      "[10]\ttraining's auc: 0.662871\tvalid_1's auc: 0.540158\n",
      "[11]\ttraining's auc: 0.663035\tvalid_1's auc: 0.538227\n",
      "[12]\ttraining's auc: 0.663362\tvalid_1's auc: 0.538266\n",
      "[13]\ttraining's auc: 0.663788\tvalid_1's auc: 0.535958\n",
      "[14]\ttraining's auc: 0.664066\tvalid_1's auc: 0.537372\n",
      "[15]\ttraining's auc: 0.664149\tvalid_1's auc: 0.540723\n",
      "[16]\ttraining's auc: 0.664214\tvalid_1's auc: 0.540889\n",
      "[17]\ttraining's auc: 0.664344\tvalid_1's auc: 0.541746\n",
      "[18]\ttraining's auc: 0.664454\tvalid_1's auc: 0.544087\n",
      "[19]\ttraining's auc: 0.66449\tvalid_1's auc: 0.542744\n",
      "[20]\ttraining's auc: 0.664395\tvalid_1's auc: 0.544706\n",
      "[21]\ttraining's auc: 0.664738\tvalid_1's auc: 0.545276\n",
      "[22]\ttraining's auc: 0.66487\tvalid_1's auc: 0.548673\n",
      "[23]\ttraining's auc: 0.665402\tvalid_1's auc: 0.546887\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-4-a3afbc1e3fc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodel_lgbm\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmlgbm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlgbm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrossvalidate_model_by_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_on_both\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[0;32m/media/windows_d/DRIVE/MS CS UCU/DataScienceGame/model_lgbm.py\u001b[0m in \u001b[0;36mcrossvalidate_model_by_index\u001b[0;34m(df, train_index, validation_index, cols, params, verbose, iterations, validate_on_both)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mvalidation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlgb_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlgb_test\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     model_lgm = lgb.train(params, lgb_train, iterations, valid_sets=validation, verbose_eval=verbose,\n\u001b[0;32m---> 38\u001b[0;31m                           evals_result=evals)\n\u001b[0m\u001b[1;32m     39\u001b[0m     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"valid_1\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvalidate_on_both\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"valid_0\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mevs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"auc\"\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/tsdaemon/anaconda3/lib/python3.6/site-packages/lightgbm-0.1-py3.6.egg/lightgbm/engine.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, callbacks)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid_sets\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_valid_contain_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m                 \u001b[0mevaluation_result_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeval\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    187\u001b[0m             \u001b[0mevaluation_result_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_valid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/tsdaemon/anaconda3/lib/python3.6/site-packages/lightgbm-0.1-py3.6.egg/lightgbm/basic.py\u001b[0m in \u001b[0;36meval_train\u001b[0;34m(self, feval)\u001b[0m\n\u001b[1;32m   1453\u001b[0m             \u001b[0mEvaluation\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1454\u001b[0m         \"\"\"\n\u001b[0;32m-> 1455\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__inner_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__train_data_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\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[0m\u001b[1;32m   1456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1457\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0meval_valid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/tsdaemon/anaconda3/lib/python3.6/site-packages/lightgbm-0.1-py3.6.egg/lightgbm/basic.py\u001b[0m in \u001b[0;36m__inner_eval\u001b[0;34m(self, data_name, data_idx, feval)\u001b[0m\n\u001b[1;32m   1680\u001b[0m                 \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1681\u001b[0m                 \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_out_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1682\u001b[0;31m                 result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))\n\u001b[0m\u001b[1;32m   1683\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtmp_out_len\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__num_inner_eval\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1684\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Wrong length of eval results\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import model_lgbm as mlgbm\n",
    "_, fi = mlgbm.crossvalidate_model_by_index(train, train_index, validation_index, cols, params, iterations=200, validate_on_both=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import model_lgbm as mlgbm\n",
    "\n",
    "path_to_train = './data/train.csv'\n",
    "path_to_test = './data/test.csv'\n",
    "path_to_sample = './data/sample_submission_kaggle.csv'\n",
    "path_to_submission = './data/submission.csv'\n",
    "\n",
    "train = pd.read_csv(path_to_train, encoding='utf-8')\n",
    "test = pd.read_csv(path_to_test, encoding='utf-8')\n",
    "\n",
    "fs, pred = mlgbm.create_submission(train, test, path_to_sample, params, cols, path_to_submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_score(fs, cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_to_submission_correlation = './data/vitalik_submission.csv'\n",
    "previous_submission = pd.read_csv(path_to_submission_correlation, encoding='utf-8')['is_listened']\n",
    "\n",
    "from scipy.stats.stats import pearsonr\n",
    "pearsonr(pred,previous_submission)"
   ]
  }
 ],
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