Elo - ML model - 3.695.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Load-imports-and-dfs\" data-toc-modified-id=\"Load-imports-and-dfs-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Load imports and dfs</a></span></li><li><span><a href=\"#Using-LGBM\" data-toc-modified-id=\"Using-LGBM-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Using LGBM</a></span></li><li><span><a href=\"#Using-XGboost-(not-helping)\" data-toc-modified-id=\"Using-XGboost-(not-helping)-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Using XGboost (not helping)</a></span></li><li><span><a href=\"#Stacking-(not-helping)\" data-toc-modified-id=\"Stacking-(not-helping)-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Stacking (not helping)</a></span></li><li><span><a href=\"#Use-LGBM-without-kfold-on-outliers-for-blending\" data-toc-modified-id=\"Use-LGBM-without-kfold-on-outliers-for-blending-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Use LGBM without kfold on outliers for blending</a></span></li><li><span><a href=\"#Stacking-using-Bayesian-Ridge\" data-toc-modified-id=\"Stacking-using-Bayesian-Ridge-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Stacking using Bayesian Ridge</a></span></li><li><span><a href=\"#File-link\" data-toc-modified-id=\"File-link-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>File link</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(120000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 120 seconds\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 4\n",
    "%autosave 120"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Load imports & test, train DFs\n",
    "2. Apply stratified kfold & traing with lgbm and submit to kaggle\n",
    "3. Check for missing data feature engineering parts missed if score > 3.7\n",
    "4. Split the training set to train & validation\n",
    "5. Train over RF to get a benchmark of the dataset\n",
    "6. Drop irrelevant features by sorting on feature importance\n",
    "7. Drop redundant features by plotting a Dendogram and removing the redundancy\n",
    "8. Use a feature interpreter to see which feature is contributing lot to prediction\n",
    "9. Use extrapolation to see if we can remove feature which add bias of training & validation set\n",
    "10. Use partial dependence plot to see outliers/missed features\n",
    "11. Submit to kaggle the RF model and see the score\n",
    "11. Use stratified kfold to train the RF model and submit to kaggle\n",
    "12. Repeat again with LGBM/XGBoost/Adaboost. Stack the model and submit to kaggle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load imports and dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.io import *\n",
    "from fastai.structured import *\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from pandas_summary import DataFrameSummary\n",
    "from IPython.display import display\n",
    "from sklearn import metrics\n",
    "import feather\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold, RepeatedKFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df2 = feather.read_dataframe('train_df')\n",
    "test_df2 = feather.read_dataframe('test_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [train_df2, test_df2]:\n",
    "    for f in ['purchase_date_max','purchase_date_min','purchase_date_max_old',\\\n",
    "                     'purchase_date_min_old']:\n",
    "        df[f] = df[f].astype(np.int64) * 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "del_cols = ['first_active_monthIs_month_start', 'authorized_flag_1_mean', 'first_active_monthIs_year_start',\n",
    "       'first_active_monthIs_year_end', 'first_active_monthIs_quarter_end', 'avg_trans_per_merchant',\n",
    "       'first_active_monthIs_month_end', 'first_active_monthDay', 'card_id_size',\n",
    "       'authorized_flag_1_amount_mean_mean', 'authorized_flag_1_amount_sum_sum','card_id', 'first_active_month','target','outliers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode_fe(df):\n",
    "    return pd.get_dummies(df, columns=['feature_1', 'feature_2', 'feature_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [train_df2, test_df2] = [one_hot_encode_fe(d) for d in [train_df2, test_df2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_columns = [c for c in train_df2.columns if c not in ['card_id', 'first_active_month','target','outliers']]\n",
    "target = train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((201917, 135), (123623, 133))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df2.shape, test_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201917,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 131/131 [00:03<00:00, 41.54it/s]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp\n",
    "list_p_value =[]\n",
    "\n",
    "for i in tqdm(df_train_columns):\n",
    "    list_p_value.append(ks_2samp(test_df2[i] , train_df2[i])[1])\n",
    "\n",
    "Se = pd.Series(list_p_value, index = df_train_columns).sort_values() \n",
    "list_discarded = list(Se[Se < .1].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['avg_spend_per_transaction',\n",
       " 'purchase_amount_mean',\n",
       " 'avg_spend_per_merchant',\n",
       " 'purchase_amount_max_old',\n",
       " 'purchase_amount_total_mean',\n",
       " 'category_3_mean_mean_old',\n",
       " 'purchase_amount_sum']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_discarded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train_columns = [c for c in df_train_columns if c not in list_discarded]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\ttraining's rmse: 3.66487\tvalid_1's rmse: 3.72105\n",
      "[200]\ttraining's rmse: 3.58667\tvalid_1's rmse: 3.68688\n",
      "[300]\ttraining's rmse: 3.53977\tvalid_1's rmse: 3.67242\n",
      "[400]\ttraining's rmse: 3.5035\tvalid_1's rmse: 3.66458\n",
      "[500]\ttraining's rmse: 3.47327\tvalid_1's rmse: 3.65957\n",
      "[600]\ttraining's rmse: 3.44785\tvalid_1's rmse: 3.65695\n",
      "[700]\ttraining's rmse: 3.42615\tvalid_1's rmse: 3.65539\n",
      "[800]\ttraining's rmse: 3.40644\tvalid_1's rmse: 3.65447\n",
      "[900]\ttraining's rmse: 3.38785\tvalid_1's rmse: 3.65337\n",
      "[1000]\ttraining's rmse: 3.37083\tvalid_1's rmse: 3.65292\n",
      "Early stopping, best iteration is:\n",
      "[971]\ttraining's rmse: 3.3757\tvalid_1's rmse: 3.65287\n",
      "fold 1\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\ttraining's rmse: 3.6669\tvalid_1's rmse: 3.71284\n",
      "[200]\ttraining's rmse: 3.58858\tvalid_1's rmse: 3.68074\n",
      "[300]\ttraining's rmse: 3.53967\tvalid_1's rmse: 3.67003\n",
      "[400]\ttraining's rmse: 3.50217\tvalid_1's rmse: 3.66373\n",
      "[500]\ttraining's rmse: 3.47164\tvalid_1's rmse: 3.66012\n",
      "[600]\ttraining's rmse: 3.44721\tvalid_1's rmse: 3.65788\n",
      "[700]\ttraining's rmse: 3.42463\tvalid_1's rmse: 3.65687\n",
      "[800]\ttraining's rmse: 3.40424\tvalid_1's rmse: 3.65599\n",
      "[900]\ttraining's rmse: 3.38583\tvalid_1's rmse: 3.65501\n",
      "[1000]\ttraining's rmse: 3.36746\tvalid_1's rmse: 3.65487\n",
      "[1100]\ttraining's rmse: 3.35072\tvalid_1's rmse: 3.65488\n",
      "[1200]\ttraining's rmse: 3.3345\tvalid_1's rmse: 3.65458\n",
      "[1300]\ttraining's rmse: 3.31883\tvalid_1's rmse: 3.6542\n",
      "[1400]\ttraining's rmse: 3.3033\tvalid_1's rmse: 3.65438\n",
      "Early stopping, best iteration is:\n",
      "[1355]\ttraining's rmse: 3.31057\tvalid_1's rmse: 3.65411\n",
      "fold 2\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\ttraining's rmse: 3.66373\tvalid_1's rmse: 3.71929\n",
      "[200]\ttraining's rmse: 3.58437\tvalid_1's rmse: 3.68845\n",
      "[300]\ttraining's rmse: 3.53585\tvalid_1's rmse: 3.67591\n",
      "[400]\ttraining's rmse: 3.50005\tvalid_1's rmse: 3.66811\n",
      "[500]\ttraining's rmse: 3.47019\tvalid_1's rmse: 3.66255\n",
      "[600]\ttraining's rmse: 3.44491\tvalid_1's rmse: 3.65861\n",
      "[700]\ttraining's rmse: 3.42271\tvalid_1's rmse: 3.65724\n",
      "[800]\ttraining's rmse: 3.40328\tvalid_1's rmse: 3.65594\n",
      "[900]\ttraining's rmse: 3.38471\tvalid_1's rmse: 3.6546\n",
      "[1000]\ttraining's rmse: 3.36784\tvalid_1's rmse: 3.65433\n",
      "[1100]\ttraining's rmse: 3.35094\tvalid_1's rmse: 3.65327\n",
      "[1200]\ttraining's rmse: 3.33537\tvalid_1's rmse: 3.65287\n",
      "[1300]\ttraining's rmse: 3.31984\tvalid_1's rmse: 3.65278\n",
      "[1400]\ttraining's rmse: 3.30439\tvalid_1's rmse: 3.65215\n",
      "[1500]\ttraining's rmse: 3.28878\tvalid_1's rmse: 3.65194\n",
      "[1600]\ttraining's rmse: 3.27392\tvalid_1's rmse: 3.65214\n",
      "Early stopping, best iteration is:\n",
      "[1509]\ttraining's rmse: 3.2874\tvalid_1's rmse: 3.65184\n",
      "fold 3\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\ttraining's rmse: 3.66336\tvalid_1's rmse: 3.72247\n",
      "[200]\ttraining's rmse: 3.58314\tvalid_1's rmse: 3.69244\n",
      "[300]\ttraining's rmse: 3.53309\tvalid_1's rmse: 3.68004\n",
      "[400]\ttraining's rmse: 3.49792\tvalid_1's rmse: 3.67263\n",
      "[500]\ttraining's rmse: 3.46838\tvalid_1's rmse: 3.6677\n",
      "[600]\ttraining's rmse: 3.44281\tvalid_1's rmse: 3.66465\n",
      "[700]\ttraining's rmse: 3.4208\tvalid_1's rmse: 3.66294\n",
      "[800]\ttraining's rmse: 3.4005\tvalid_1's rmse: 3.66129\n",
      "[900]\ttraining's rmse: 3.38168\tvalid_1's rmse: 3.6604\n",
      "[1000]\ttraining's rmse: 3.36432\tvalid_1's rmse: 3.65996\n",
      "Early stopping, best iteration is:\n",
      "[981]\ttraining's rmse: 3.36783\tvalid_1's rmse: 3.65982\n",
      "fold 4\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\ttraining's rmse: 3.66458\tvalid_1's rmse: 3.70857\n",
      "[200]\ttraining's rmse: 3.58415\tvalid_1's rmse: 3.67985\n",
      "[300]\ttraining's rmse: 3.53409\tvalid_1's rmse: 3.66941\n",
      "[400]\ttraining's rmse: 3.49708\tvalid_1's rmse: 3.66295\n",
      "[500]\ttraining's rmse: 3.46674\tvalid_1's rmse: 3.65968\n",
      "[600]\ttraining's rmse: 3.44082\tvalid_1's rmse: 3.65789\n",
      "[700]\ttraining's rmse: 3.4184\tvalid_1's rmse: 3.6569\n",
      "[800]\ttraining's rmse: 3.3979\tvalid_1's rmse: 3.65656\n",
      "Early stopping, best iteration is:\n",
      "[767]\ttraining's rmse: 3.40442\tvalid_1's rmse: 3.65634\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.6549983949875506"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param = {'num_leaves': 31,\n",
    "         'min_data_in_leaf': 30, \n",
    "         'objective':'regression',\n",
    "         'max_depth': -1,\n",
    "         'learning_rate': 0.01,\n",
    "         \"min_child_samples\": 20,\n",
    "         \"boosting\": \"gbdt\",\n",
    "         \"feature_fraction\": 0.9,\n",
    "         \"bagging_freq\": 1,\n",
    "         \"bagging_fraction\": 0.9 ,\n",
    "         \"bagging_seed\": 11,\n",
    "         \"metric\": 'rmse',\n",
    "         \"lambda_l1\": 0.1,\n",
    "         \"verbosity\": -1,\n",
    "         \"nthread\": 4,\n",
    "         \"random_state\": 4590}\n",
    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=4590)\n",
    "oof = np.zeros(len(train_df2))\n",
    "predictions = np.zeros(len(test_df2))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df2,train_df2['outliers'].values)):\n",
    "    print(\"fold {}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train_df2.iloc[trn_idx][df_train_columns], label=target.iloc[trn_idx])#, categorical_feature=categorical_feats)\n",
    "    val_data = lgb.Dataset(train_df2.iloc[val_idx][df_train_columns], label=target.iloc[val_idx])#, categorical_feature=categorical_feats)\n",
    "\n",
    "    num_round = 10000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=100, early_stopping_rounds = 100)\n",
    "    oof[val_idx] = clf.predict(train_df2.iloc[val_idx][df_train_columns], num_iteration=clf.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"Feature\"] = df_train_columns\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance()\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    \n",
    "    predictions += clf.predict(test_df2[df_train_columns], num_iteration=clf.best_iteration) / folds.n_splits\n",
    "\n",
    "np.sqrt(mean_squared_error(oof, target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('oof_lgb_3', oof)\n",
    "np.save('predictions_lgb_3', predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Score for submission-2019-01-11 10:18:09.909061.csv - 3.6597596743401666, LB - 3.707 (included cat_1)\n",
    "2. Score for submission-2019-01-12 13:53:27.215115.csv - 3.656726211308068, LB - 3.696 (jst refactored)\n",
    "3. No changes what so ever - 3.6627995460091523\n",
    "4. Replaced with kernels code - 3.6623128394907103\n",
    "5. Replaced with kernels code along with cat_2 mean - 3.6609564684554594\n",
    "6. Added extra feature - 3.6559302073797637, LB - 3.696 (No grouping by auth flag and all)\n",
    "7. Added aggs on cat_1, auth - 3.6554801486969906, LB- 3.696\n",
    "8. Removed unnecessary cols - 3.6556548946965446, LB - 3.696\n",
    "9. Removed aggs on cat_1 - 3.654208034404632, LB - 3.697\n",
    "10. Removed unnecessary del_cols - 3.65493381575084, LB - 3.697\n",
    "11. One hot encoded features - 3.6558286566186897\n",
    "12. Added monthly aggregates - 3.653533666238162, LB - 3.716\n",
    "13. Removed some monthly aggregates - 3.654769374794982, LB - 3.707\n",
    "14. reverting to earlier checkpoing - 3.65377489489624\n",
    "15. Benchmarks - LGBM - 3.654910478618945, DL - 3.6971527526968155\n",
    "16. Just ran the whole FE page -submission-2019-01-20 01:46:39.187580.csv - 3.6546249070742816 - 3.696\n",
    "17. Changed hyperparams of LGBM - 3.654728019446817\n",
    "18. With changed hyperparams - 7 fold - 3.6532023662229682 - 3.696\n",
    "19. Changed random seed to 2019 - 3.6591621358131183\n",
    "20. Added extra features with old hyper params - 3.6549729015873496, submission-2019-01-20 19:11:58.593189.csv -3.696\n",
    "21. Added days*features column - 3.6549983949875506\n",
    "\n",
    "**Stacking:**\n",
    "1. LGBM-3.6550013784231608, XGBoost-3.6594364661840606, stacked-3.6517291440175144. LB - 3.697\n",
    "2. 3.6550013784231608, 3.656226013110323, 3.652125180502318. LB -  3.698"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n",
    "        .groupby(\"Feature\")\n",
    "        .mean()\n",
    "        .sort_values(by=\"importance\", ascending=False)[:1000].index)\n",
    "\n",
    "best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n",
    "\n",
    "plt.figure(figsize=(14,25))\n",
    "sns.barplot(x=\"importance\",\n",
    "            y=\"Feature\",\n",
    "            data=best_features.sort_values(by=\"importance\",\n",
    "                                           ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('lgbm_importances.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['authorized_flag_mean', 'first_active_monthIs_year_end', 'first_active_monthIs_month_start',\n",
       "       'first_active_monthIs_year_start', 'first_active_monthIs_quarter_end', 'card_id_size',\n",
       "       'first_active_monthDay', 'first_active_monthIs_month_end', 'avg_trans_per_merchant',\n",
       "       'authorized_flag_sum', 'repurchase_merchant_rate', 'purchase_Year_nunique_old'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features.sort_values(by='importance', ascending=True)[:50]['Feature'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Using XGboost (not helping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-\n",
      "Fold 1\n",
      "xgb 0--------------------------------------------------\n",
      "[17:07:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 464 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[0]\ttrain-rmse:3.94515\tvalid-rmse:3.95135\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[17:07:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 396 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 420 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 436 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 434 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 392 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 430 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 390 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 414 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 414 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 382 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 436 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 420 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 414 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 408 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 406 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 440 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 386 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 426 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 398 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 412 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 390 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 396 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 446 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 398 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 416 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 404 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 378 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 412 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:07:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 388 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "[17:11:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 262 extra nodes, 0 pruned nodes, max_depth=8\n",
      "Stopping. Best iteration:\n",
      "[587]\ttrain-rmse:3.12312\tvalid-rmse:3.6598\n",
      "\n",
      "-\n",
      "Fold 2\n",
      "xgb 1--------------------------------------------------\n",
      "[17:11:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 448 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[0]\ttrain-rmse:3.94516\tvalid-rmse:3.95124\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[17:11:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 428 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "[17:14:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 384 extra nodes, 0 pruned nodes, max_depth=8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:14:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 326 extra nodes, 0 pruned nodes, max_depth=8\n",
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      "[17:14:07] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 306 extra nodes, 0 pruned nodes, max_depth=8\n",
      "Stopping. Best iteration:\n",
      "[488]\ttrain-rmse:3.16635\tvalid-rmse:3.66418\n",
      "\n",
      "-\n",
      "Fold 3\n",
      "xgb 2--------------------------------------------------\n",
      "[17:14:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 412 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[0]\ttrain-rmse:3.94803\tvalid-rmse:3.93899\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[17:14:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 404 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:17:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 136 extra nodes, 0 pruned nodes, max_depth=8\n",
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      "[17:17:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 328 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:17:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 208 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:17:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 240 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:17:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 242 extra nodes, 0 pruned nodes, max_depth=8\n",
      "Stopping. Best iteration:\n",
      "[633]\ttrain-rmse:3.10935\tvalid-rmse:3.64531\n",
      "\n",
      "-\n",
      "Fold 4\n",
      "xgb 3--------------------------------------------------\n",
      "[17:18:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 432 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[0]\ttrain-rmse:3.94611\tvalid-rmse:3.94617\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[17:18:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 430 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:20:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=8\n",
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      "[17:20:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 144 extra nodes, 0 pruned nodes, max_depth=8\n",
      "Stopping. Best iteration:\n",
      "[440]\ttrain-rmse:3.20186\tvalid-rmse:3.65193\n",
      "\n",
      "-\n",
      "Fold 5\n",
      "xgb 4--------------------------------------------------\n",
      "[17:20:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 370 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[0]\ttrain-rmse:3.94542\tvalid-rmse:3.95059\n",
      "Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until valid-rmse hasn't improved in 50 rounds.\n",
      "[17:20:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 436 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[17:23:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 230 extra nodes, 0 pruned nodes, max_depth=8\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[17:24:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 262 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:24:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 276 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:24:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 276 extra nodes, 0 pruned nodes, max_depth=8\n",
      "[17:24:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 272 extra nodes, 0 pruned nodes, max_depth=8\n",
      "Stopping. Best iteration:\n",
      "[632]\ttrain-rmse:3.10114\tvalid-rmse:3.65802\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.656226013110323"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_params = {'eta': 0.01, 'max_depth': 8, 'subsample': 0.8, 'colsample_bytree': 0.8, \n",
    "          'objective': 'reg:linear', 'eval_metric': 'rmse', 'silent': True, 'scale_pos_weight': 1, \n",
    "              'min_child_weight': 1 }\n",
    "\n",
    "oof_xgb_3 = np.zeros(len(train_df2))\n",
    "predictions_xgb_3 = np.zeros(len(test_df2))\n",
    "\n",
    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=15)\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df2,train_df2['outliers'].values)):    \n",
    "    print('-')\n",
    "    print(\"Fold {}\".format(fold_ + 1))\n",
    "    trn_data = xgb.DMatrix(data=train_df2.iloc[trn_idx][df_train_columns], label=target.iloc[trn_idx])\n",
    "    val_data = xgb.DMatrix(data=train_df2.iloc[val_idx][df_train_columns], label=target.iloc[val_idx])\n",
    "    watchlist = [(trn_data, 'train'), (val_data, 'valid')]\n",
    "    print(\"xgb \" + str(fold_) + \"-\" * 50)\n",
    "    num_round = 10000\n",
    "    xgb_model = xgb.train(xgb_params, trn_data, num_round, watchlist, early_stopping_rounds=50, verbose_eval=1000)\n",
    "    oof_xgb_3[val_idx] = xgb_model.predict(xgb.DMatrix(train_df2.iloc[val_idx][df_train_columns]), ntree_limit=xgb_model.best_ntree_limit+50)\n",
    "\n",
    "    predictions_xgb_3 += xgb_model.predict(xgb.DMatrix(test_df2[df_train_columns]), ntree_limit=xgb_model.best_ntree_limit+50) / folds.n_splits\n",
    "    \n",
    "np.save('oof_xgb_3', oof_xgb_3)\n",
    "np.save('predictions_xgb_3', predictions_xgb_3)\n",
    "np.sqrt(mean_squared_error(target.values, oof_xgb_3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Stacking (not helping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold n°0\n",
      "fold n°1\n",
      "fold n°2\n",
      "fold n°3\n",
      "fold n°4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.652125180502318"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stack = np.vstack([oof, oof_xgb_3]).transpose()\n",
    "test_stack = np.vstack([predictions, predictions_xgb_3]).transpose()\n",
    "\n",
    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=15)\n",
    "oof = np.zeros(train_stack.shape[0])\n",
    "predictions = np.zeros(test_stack.shape[0])\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_stack, train_df2['outliers'].values)):\n",
    "    print(\"fold n°{}\".format(fold_))\n",
    "    trn_data, trn_y = train_stack[trn_idx], target.iloc[trn_idx].values\n",
    "    val_data, val_y = train_stack[val_idx], target.iloc[val_idx].values\n",
    "\n",
    "    clf = Ridge(alpha=1)\n",
    "    clf.fit(trn_data, trn_y)\n",
    "    \n",
    "    oof[val_idx] = clf.predict(val_data)\n",
    "    predictions += clf.predict(test_stack) / folds.n_splits\n",
    "\n",
    "\n",
    "np.sqrt(mean_squared_error(target.values, oof))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use LGBM without kfold on outliers for blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features = [c for c in df_train_columns if 'feature_' in c] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_1', 'feature_2', 'feature_3']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[1054]\ttraining's rmse: 3.26717\tvalid_1's rmse: 3.66168\n",
      "fold 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[712]\ttraining's rmse: 3.31186\tvalid_1's rmse: 3.81229\n",
      "fold 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[754]\ttraining's rmse: 3.35715\tvalid_1's rmse: 3.56932\n",
      "fold 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[881]\ttraining's rmse: 3.30772\tvalid_1's rmse: 3.62979\n",
      "fold 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[846]\ttraining's rmse: 3.32707\tvalid_1's rmse: 3.61651\n",
      "fold 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[948]\ttraining's rmse: 3.27317\tvalid_1's rmse: 3.73203\n",
      "fold 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[646]\ttraining's rmse: 3.36856\tvalid_1's rmse: 3.63769\n",
      "fold 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[609]\ttraining's rmse: 3.38148\tvalid_1's rmse: 3.64302\n",
      "fold 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[1263]\ttraining's rmse: 3.21901\tvalid_1's rmse: 3.66376\n",
      "fold 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:1184: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/lightgbm/basic.py:742: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[597]\ttraining's rmse: 3.39153\tvalid_1's rmse: 3.60188\n",
      "CV score: 3.65593 \n"
     ]
    }
   ],
   "source": [
    "param = {'num_leaves': 31,\n",
    "         'min_data_in_leaf': 27, \n",
    "         'objective':'regression',\n",
    "         'max_depth': -1,\n",
    "         'learning_rate': 0.015,\n",
    "         \"boosting\": \"gbdt\",\n",
    "         \"feature_fraction\": 0.9,\n",
    "         \"bagging_freq\": 1,\n",
    "         \"bagging_fraction\": 0.9,\n",
    "         \"bagging_seed\": 11,\n",
    "         \"metric\": 'rmse',\n",
    "         \"lambda_l1\": 0.1,\n",
    "         \"verbosity\": -1,\n",
    "         \"nthread\": 4,\n",
    "         \"random_state\": 4950}\n",
    "\n",
    "folds = RepeatedKFold(n_splits=5, n_repeats=2, random_state=4950)\n",
    "oof_2 = np.zeros(len(train_df2))\n",
    "predictions_2 = np.zeros(len(test_df2))\n",
    "feature_importance_df_2 = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df2.values, target.values)):\n",
    "    print(\"fold {}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train_df2.iloc[trn_idx][df_train_columns], label=target.iloc[trn_idx], categorical_feature=cat_features)\n",
    "    val_data = lgb.Dataset(train_df2.iloc[val_idx][df_train_columns], label=target.iloc[val_idx], categorical_feature=cat_features)\n",
    "\n",
    "    num_round = 10000\n",
    "    clf_r = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=-1, early_stopping_rounds = 200)\n",
    "    oof_2[val_idx] = clf_r.predict(train_df2.iloc[val_idx][df_train_columns], num_iteration=clf_r.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"Feature\"] = df_train_columns\n",
    "    fold_importance_df[\"importance\"] = clf_r.feature_importance()\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df_2 = pd.concat([feature_importance_df_2, fold_importance_df], axis=0)\n",
    "    \n",
    "    predictions_2 += clf_r.predict(test_df2[df_train_columns], num_iteration=clf_r.best_iteration) / (5 * 2)\n",
    "\n",
    "print(\"CV score: {:<8.5f}\".format(mean_squared_error(oof_2, target)**0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking using Bayesian Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0\n",
      "fold 1\n",
      "fold 2\n",
      "fold 3\n",
      "fold 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.6526672995445217"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "train_stack = np.vstack([oof,oof_2]).transpose()\n",
    "test_stack = np.vstack([predictions, predictions_2]).transpose()\n",
    "\n",
    "folds_stack = RepeatedKFold(n_splits=5, n_repeats=1, random_state=4950)\n",
    "oof_stack = np.zeros(train_stack.shape[0])\n",
    "predictions_3 = np.zeros(test_stack.shape[0])\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack,target)):\n",
    "    print(\"fold {}\".format(fold_))\n",
    "    trn_data, trn_y = train_stack[trn_idx], target.iloc[trn_idx].values\n",
    "    val_data, val_y = train_stack[val_idx], target.iloc[val_idx].values\n",
    "    \n",
    "    clf_3 = BayesianRidge()\n",
    "    clf_3.fit(trn_data, trn_y)\n",
    "    \n",
    "    oof_stack[val_idx] = clf_3.predict(val_data)\n",
    "    predictions_3 += clf_3.predict(test_stack) / 5\n",
    "    \n",
    "np.sqrt(mean_squared_error(target.values, oof_stack))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. LGBM Skfold-3.6549729015873496 (3.696 on LB) Rkfold - 3.65591, Stacked - 3.652611210821926, LB-3.696 -submission-2019-01-20 19:35:24.892043.csv\n",
    "2. LGBM SKFold- 3.6549983949875506, RKfold - 3.65593 , Stacked - 3.6526672995445217, LB - 3.695 : submission-2019-01-20 20:47:32.743729.csv👏 - added days*features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = pd.DataFrame({\"card_id\":test_df2[\"card_id\"].values})\n",
    "sub_df[\"target\"] = predictions_3\n",
    "sub_df.to_csv(f'submission-{pd.to_datetime(\"today\")}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = pd.DataFrame({\"card_id\":test_df2[\"card_id\"].values})\n",
    "sub_df[\"target\"] = predictions\n",
    "sub_df.to_csv(f'submission-{pd.to_datetime(\"today\")}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Elo - ML model - 3.696 beta.ipynb'\r\n",
      "'Elo - ML model - 3.696.ipynb'\r\n",
      "'Elo - ML model - cat aggregates.ipynb'\r\n",
      "'Elo - ML model clean.ipynb'\r\n",
      "'Elo - ML model proc_df.ipynb'\r\n",
      "'Elo - ML model-RF copy.ipynb'\r\n",
      "'Elo - ML model-backup.ipynb'\r\n",
      "'Elo - ML model-new.ipynb'\r\n",
      "'Elo RF- New.ipynb'\r\n",
      "'Elo RF.ipynb'\r\n",
      "'Elo end 2 end.ipynb'\r\n",
      "'Elo feature engineering - 3.696.ipynb'\r\n",
      "'Elo feature engineering 3.696 - beta.ipynb'\r\n",
      "'Elo feature engineering using agg.ipynb'\r\n",
      "'Elo feature engineering-backup.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new-Copy1.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new.ipynb'\r\n",
      "'Elo feature engineering-proc_df-OLD.ipynb'\r\n",
      "'Elo feature engineering-proc_df.ipynb'\r\n",
      "'Elo merchant category reco - using RF.ipynb'\r\n",
      " README.md\r\n",
      "'Scrap pad.ipynb'\r\n",
      "'Scrap pad.py'\r\n",
      "'TS Fresh.ipynb'\r\n",
      "'TS Fresh.py'\r\n",
      "'Train & test clean.ipynb'\r\n",
      "'Using DL.ipynb'\r\n",
      "'Using Feature tools.ipynb'\r\n",
      " adf\r\n",
      " authorised_trans_proc_agg\r\n",
      " cols_keep.npy\r\n",
      " data\r\n",
      " df_auth\r\n",
      " df_hot_dropped_all\r\n",
      " df_hot_ext\r\n",
      " df_hot_test\r\n",
      " df_hot_test_ext\r\n",
      " df_new\r\n",
      " df_non_auth\r\n",
      " df_proc_dfed\r\n",
      " df_test_n_o\r\n",
      " df_xo\r\n",
      " fastai\r\n",
      " fm_new_hist\r\n",
      " hist_trans\r\n",
      " hist_trans_3_696\r\n",
      " hist_trans_agg\r\n",
      " hist_trans_agg_3_696\r\n",
      " hist_trans_agg_n\r\n",
      " hist_trans_agg_proc\r\n",
      " lgbm_importances.png\r\n",
      " new_hist_prc\r\n",
      " new_hist_trans\r\n",
      " new_hist_trans_3_696\r\n",
      " new_hist_trans_agg\r\n",
      " new_hist_trans_agg_3_696\r\n",
      " new_hist_trans_agg_n\r\n",
      " new_trans_agg_proc\r\n",
      " new_trans_proc_agg\r\n",
      " non_authorised_trans_proc\r\n",
      " non_authorised_trans_proc_agg\r\n",
      " oof_lgb_3.npy\r\n",
      " oof_xgb_3.npy\r\n",
      " predictions_lgb_3.npy\r\n",
      " predictions_xgb_3.npy\r\n",
      "'scrap pad 2.ipynb'\r\n",
      "'submission-2019-01-11 09:49:53.716164.csv'\r\n",
      "'submission-2019-01-11 10:18:09.909061.csv'\r\n",
      "'submission-2019-01-12 13:53:27.215115.csv'\r\n",
      "'submission-2019-01-12 16:27:31.239044.csv'\r\n",
      "'submission-2019-01-12 17:25:56.678553.csv'\r\n",
      "'submission-2019-01-12 17:33:29.475835.csv'\r\n",
      "'submission-2019-01-12 17:42:01.350278.csv'\r\n",
      "'submission-2019-01-12 22:12:59.947458.csv'\r\n",
      "'submission-2019-01-13 15:59:45.792347.csv'\r\n",
      "'submission-2019-01-13 18:24:51.237991.csv'\r\n",
      "'submission-2019-01-14 19:04:33.028609.csv'\r\n",
      "'submission-2019-01-14 19:56:09.906235.csv'\r\n",
      "'submission-2019-01-15 22:18:34.294443.csv'\r\n",
      "'submission-2019-01-15 22:24:38.071972.csv'\r\n",
      "'submission-2019-01-15 22:29:20.144417.csv'\r\n",
      "'submission-2019-01-16 00:35:46.490517.csv'\r\n",
      "'submission-2019-01-16 00:36:26.004443.csv'\r\n",
      "'submission-2019-01-16 12:20:00.957329.csv'\r\n",
      "'submission-2019-01-16 21:37:16.421927.csv'\r\n",
      "'submission-2019-01-17 10:20:06.724992.csv'\r\n",
      "'submission-2019-01-17 11:09:17.495216.csv'\r\n",
      "'submission-2019-01-17 23:23:57.743505.csv'\r\n",
      "'submission-2019-01-18 01:18:08.182115.csv'\r\n",
      "'submission-2019-01-18 01:42:20.587834.csv'\r\n",
      "'submission-2019-01-18 01:47:04.024727.csv'\r\n",
      "'submission-2019-01-18 16:16:45.475456.csv'\r\n",
      "'submission-2019-01-18 16:27:58.560480.csv'\r\n",
      "'submission-2019-01-18 17:22:46.188675.csv'\r\n",
      "'submission-2019-01-20 00:30:10.643280.csv'\r\n",
      "'submission-2019-01-20 01:28:12.077657.csv'\r\n",
      "'submission-2019-01-20 01:46:39.187580.csv'\r\n",
      "'submission-2019-01-20 02:32:16.439302.csv'\r\n",
      "'submission-2019-01-20 17:01:54.207459.csv'\r\n",
      "'submission-2019-01-20 17:27:46.797180.csv'\r\n",
      "'submission-2019-01-20 18:29:37.773532.csv'\r\n",
      "'submission-2019-01-20 19:11:58.593189.csv'\r\n",
      "'submission-2019-01-20 19:35:24.892043.csv'\r\n",
      "'submission-2019-01-20 20:47:32.743729.csv'\r\n",
      " target_all\r\n",
      " target_outliers_removed\r\n",
      " test_clean.csv\r\n",
      " test_clean.csv.zip\r\n",
      " test_df\r\n",
      " test_df_3_696\r\n",
      " test_df_proc\r\n",
      " test_df_proc_new\r\n",
      " test_df_proc_new_m\r\n",
      " train_clean.csv\r\n",
      " train_clean.csv.zip\r\n",
      " train_clean0\r\n",
      " train_df\r\n",
      " train_df_3_696\r\n",
      " train_df_proc\r\n",
      " train_df_proc_new\r\n",
      " train_df_proc_new_m\r\n"
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