stacking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from hyperopt import fmin, tpe, hp\n",
    "import ipynb.fs.full.utils as utils\n",
    "import ipynb.fs.full.features as features\n",
    "import ipynb.fs.full.features_distancias as f_distancias\n",
    "# import ipynb.fs.full.featuresNLTK as f_nltk\n",
    "\n",
    "df_train = pd.read_csv('./data/train_filtrado.csv')\n",
    "# Para usarse con el submit a Kaggle\n",
    "df_test = pd.read_csv('./data/test.csv')\n",
    "\n",
    "df_train = features.llenar_nulls(df_train)\n",
    "df_test = features.llenar_nulls(df_test, hgb_mean=True, df_fill=df_train)\n",
    "\n",
    "df_clustering_train = pd.read_csv('data/clustering_train.csv')\n",
    "df_clustering_test = pd.read_csv('data/clustering_test.csv')\n",
    "\n",
    "def agregar_clustering(df, train=True):\n",
    "    df_cluster = df_clustering_train if train else df_clustering_test\n",
    "    df_merge = pd.merge(df, df_cluster, on='id')\n",
    "    return df_merge\n",
    "\n",
    "# df_train_f = agregar_clustering(df_train, True)\n",
    "# df_test_f = agregar_clustering(df_test, False)\n",
    "\n",
    "titulo_train = pd.read_csv('./data/titulo_ohe_train.csv')\n",
    "titulo_test = pd.read_csv('./data/titulo_ohe_test.csv')\n",
    "# train_DK = pd.read_csv('./data/df_train_KD.csv')\n",
    "# test_DK = pd.read_csv('./data/df_test_KD.csv')\n",
    "\n",
    "df_train_f = pd.merge(df_train, titulo_train, on= 'id', how= 'left')\n",
    "df_test_f = pd.merge(df_test, titulo_test, on= 'id', how= 'left')\n",
    "\n",
    "# df_train_f = pd.merge(df_train_f, train_DK, on= 'id', how= 'left')\n",
    "# df_test_f = pd.merge(df_test_f, test_DK, on= 'id', how= 'left')\n",
    "\n",
    "df_train_idf = pd.read_csv('./data/train_idf.csv')\n",
    "df_test_idf = pd.read_csv('./data/test_idf.csv')\n",
    "\n",
    "df_train_f = pd.merge(df_train_f, df_train_idf, on= 'id', how= 'left')\n",
    "df_test_f = pd.merge(df_test_f, df_test_idf, on= 'id', how= 'left')\n",
    "\n",
    "# df_train_f, df_test_f = utils.dividir_df_testeo(df_train_f, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_f = features.features_independientes_precio(df_test_f)\n",
    "df_test_f = features.features_dependientes_precio(df_test_f, df_train_f)\n",
    "\n",
    "df_train_f = features.features_independientes_precio(df_train_f)\n",
    "df_train_f = features.features_dependientes_precio(df_train_f, df_train_f)\n",
    "\n",
    "# df_test_f, cols_tipodepropiedad_ohe = features.columna_a_ohe(df_test_f, 'tipodepropiedad', N=100, df_aux=df_train, devolver_cols=True)\n",
    "# df_test_f, cols_provincia_ohe = features.columna_a_ohe(df_test_f, 'provincia', N=100, df_aux=df_train, devolver_cols=True)\n",
    "# df_test_f, cols_zona_ohe = features.columna_a_ohe(df_test_f, 'zona', df_aux=df_train_f, devolver_cols=True)\n",
    "\n",
    "# df_train_f = features.columna_a_ohe(df_train_f, 'tipodepropiedad', N=100, df_aux=df_test)\n",
    "# df_train_f = features.columna_a_ohe(df_train_f, 'provincia', N=100, df_aux=df_test)\n",
    "# df_train_f = features.columna_a_ohe(df_train_f, 'zona', df_aux=df_test_f)\n",
    "\n",
    "\n",
    "df_train_f['fecha'] = pd.to_datetime(df_train_f['fecha']).astype(int)\n",
    "df_test_f['fecha'] = pd.to_datetime(df_test_f['fecha']).astype(int)\n",
    "\n",
    "df_train_f = f_distancias.feature_distancias(df_train_f)\n",
    "df_test_f = f_distancias.feature_distancias(df_test_f, df_train_f)\n",
    "\n",
    "df_train_f = features.rankings(df_train_f, df_train_f)\n",
    "df_test_f = features.rankings(df_test_f, df_train_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_test_f = df_test_f.drop('Unnamed: 0', axis=1, errors='ignore')\n",
    "assert df_train_f.drop('precio', axis=1).columns.equals(df_test_f.drop('precio', axis=1, errors='ignore').columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selector de Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class FeatureSelector(BaseEstimator):\n",
    "    \n",
    "    base_features = list(df_train_f.drop('precio', axis=1, errors='ignore').columns)\n",
    "    \n",
    "    def __init__(self, features):\n",
    "        base_features = FeatureSelector.base_features\n",
    "\n",
    "        self.features = features\n",
    "        self.features_index = [i for i in range(len(base_features)) if base_features[i] in features]\n",
    "        \n",
    "    def fit(self, x, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, x):\n",
    "        return x[:, self.features_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "class LightGBMWrapper(lgb.LGBMRegressor):\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, test_size=0.01, random_state=0)\n",
    "        eval_set = [(x_eval, y_eval)]\n",
    "        if self.boosting_type == 'dart':\n",
    "            x_train = x\n",
    "            y_train = y\n",
    "            eval_set = None\n",
    "\n",
    "        return super(LightGBMWrapper, self).fit(x_train, y_train, \n",
    "                                                early_stopping_rounds=15, eval_set=eval_set)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return super(LightGBMWrapper, self).predict(X)\n",
    "\n",
    "params = {\n",
    " 'bagging_fraction': 0.21587742093682755,\n",
    " 'bagging_freq': 0,\n",
    " 'max_bin': 388,\n",
    " 'max_depth': 21,\n",
    " 'min_data_in_leaf': 19,\n",
    " 'min_split_gain': 0.21413040034431072,\n",
    " 'num_leaves': 640,\n",
    " 'reg_lambda': 9.336230074572562,\n",
    "#  'bagging_fraction': 0.5079900308614034,\n",
    "#  'bagging_freq': 0,\n",
    "#  'max_bin': 1462,\n",
    "#  'max_depth': 22,\n",
    "#  'min_data_in_leaf': 58,\n",
    "#  'min_split_gain': 0.6819500375795696,\n",
    "#  'num_leaves': 668,\n",
    "#  'reg_lambda': 48.786835826989865,\n",
    " 'boosting_type': 'gbdt',\n",
    " 'metric': 'mae',\n",
    " 'n_jobs': 4,\n",
    " 'objective': 'regression',\n",
    " 'n_estimators': 1500,\n",
    "}\n",
    "\n",
    "lgb_m = Pipeline(steps=[\n",
    "    ('feature_selector', FeatureSelector(['antiguedad', 'garages', 'banos', 'metroscubiertos', 'metrostotales',\n",
    "       'lat', 'lng', 'fecha', 'piscina', 'metroscubiertos_log',\n",
    "       'porcentaje_metros', 'diferencia_metros', 'metros_totales_normalizados',\n",
    "       'escomercial', 'promedio_metros_tipo_propiedad',\n",
    "       'promedio_metros_cub_tipo_propiedad', 'tipo_propiedad_compartida',\n",
    "       'prop_frecuente', 'top_provincia', 'es_ciudad_centrica',\n",
    "       'promedio_metros_totales_provincia',\n",
    "       'promedio_metros_cubiertos_provincia', 'dolar_promedio_mes',\n",
    "       'dolar_promedio_ult_mes', 'es_antigua', 'antiguedad_bins_unif',\n",
    "       'antiguedad_bins_perc', 'cantidad_inquilinos', 'tam_ambientes',\n",
    "       'promedio_precio_provincia', 'promedio_precio_ciudad', 'count_ciudad',\n",
    "       'promedio_id_zona', 'count_id_zona', 'promedio_precio_tipo_propiedad',\n",
    "       'count_tipo_propiedad', 'count_tipo_propiedad_ciudad', 'lat_norm',\n",
    "       'lng_norm', 'promedio_precio_booleanos', 'puntaje', 'idf_titulo',\n",
    "       'idf_descripcion', 'peso_titulo', 'peso_descripcion',\n",
    "       'distancia_ciudad_centrica', 'distancia_centro_mexico',\n",
    "       'distancia_ciudad_cara', 'ranking_en_provincia', 'ranking_en_ciudad',\n",
    "       'ranking_en_provincia_ciudad_precio',\n",
    "       'ranking_en_provincia_ciudad_cantidad',\n",
    "       'ranking_en_provincia_tipodepropiedad_precio',\n",
    "       'ranking_en_provincia_tipodepropiedad_cantidad', 'palabra_venta',\n",
    "       'palabra_casa', 'palabra_departamento', 'palabra_san',\n",
    "       'palabra_polanco', 'palabra_condominio', 'palabra_lomas',\n",
    "       'palabra_excelente', 'palabra_ciudad', 'palabra_méxico',\n",
    "       'similares_count', \n",
    "                                          'kmeans_basicos', 'kmeans_superficie',\n",
    "       'kmeans_metroscubiertos', 'kmeans_fechas', 'kmeans_latlng']\n",
    "        )),\n",
    "    ('lightgbm', LightGBMWrapper(**params))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "class XGBoostWrapper(xgb.XGBRegressor):\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, test_size=0.05, random_state=0)\n",
    "        eval_set = [(x_eval, y_eval)]\n",
    "\n",
    "        return super(xgb.XGBRegressor, self).fit(x_train, y_train, \n",
    "                                                early_stopping_rounds=10, eval_set=eval_set)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return super(xgb.XGBRegressor, self).predict(X)\n",
    "\n",
    "hps = {\n",
    " 'verbosity': 0,\n",
    " 'objective': 'reg:squarederror',\n",
    " 'eval_metric': 'mae',\n",
    " 'n_estimators': 1000,\n",
    " 'n_jobs': 4,\n",
    " 'colsample_bytree': 0.5754765853913819,\n",
    " 'max_depth': 14,\n",
    " 'reg_alpha': 21.057165648168983,\n",
    " 'scale_pos_weight': 9,\n",
    " 'subsample': 0.9968831535654209,\n",
    "#  'reg_alpha': 20.91434940058063,\n",
    "#  'colsample_bytree': 0.65,\n",
    " 'learning_rate': 0.14,\n",
    "#  'max_depth': int(16.0),\n",
    "#  'scale_pos_weight': 5,\n",
    "#  'subsample': 0.9569019167459879\n",
    "}\n",
    "\n",
    "xgb_m = Pipeline([\n",
    "    ('feature_selector', FeatureSelector(['antiguedad', 'garages', 'banos', 'metroscubiertos', 'metrostotales',\n",
    "       'lat', 'lng', 'fecha', 'gimnasio', 'usosmultiples', 'piscina',\n",
    "       'metrostotales_log', 'metroscubiertos_log', 'porcentaje_metros',\n",
    "       'diferencia_metros', 'metros_cubiertos_normalizados', 'escomercial',\n",
    "       'promedio_metros_tipo_propiedad', 'promedio_metros_cub_tipo_propiedad',\n",
    "       'tipo_propiedad_compartida', 'prop_frecuente', 'top_provincia',\n",
    "       'es_ciudad_centrica', 'promedio_metros_totales_provincia',\n",
    "       'promedio_metros_cubiertos_provincia', 'dias_desde_datos',\n",
    "       'meses_desde_datos', 'dolar_promedio_mes', 'dolar_promedio_ult_mes',\n",
    "       'dolar_diferencia_ult_mes', 'dolar_diferencia_mes_sum', 'delincuencia',\n",
    "       'turismo', 'es_antigua', 'antiguedad_bins_unif', 'antiguedad_bins_perc',\n",
    "       'cantidad_inquilinos', 'tam_ambientes', 'promedio_precio_provincia',\n",
    "       'promedio_precio_ciudad', 'count_ciudad', 'promedio_id_zona',\n",
    "       'count_id_zona', 'promedio_precio_tipo_propiedad',\n",
    "       'count_tipo_propiedad', 'count_tipo_propiedad_ciudad', 'lat_norm',\n",
    "       'lng_norm', 'promedio_precio_booleanos', 'puntaje', 'idf_titulo',\n",
    "       'idf_descripcion', 'peso_titulo', 'peso_descripcion',\n",
    "       'distancia_ciudad_centrica', 'distancia_centro_mexico',\n",
    "       'distancia_ciudad_cara', 'ranking_en_provincia', 'ranking_en_ciudad',\n",
    "       'ranking_en_provincia_ciudad_precio',\n",
    "       'ranking_en_provincia_ciudad_cantidad',\n",
    "       'ranking_en_provincia_tipodepropiedad_precio',\n",
    "       'ranking_en_provincia_tipodepropiedad_cantidad', \n",
    "                                          'similares_count',\n",
    "       'palabra_polanco', 'palabra_condominio', 'palabra_lomas',\n",
    "       'palabra_excelente', 'palabra_ciudad', 'palabra_méxico',\n",
    "       'palabra_terreno', 'kmeans_basicos', 'kmeans_superficie',\n",
    "       'kmeans_metroscubiertos', 'kmeans_fechas', 'kmeans_latlng'])),\n",
    "    ('xgboost', XGBoostWrapper(**hps))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "params = {'bootstrap': False,\n",
    "          'max_features': 'sqrt',\n",
    "          'min_samples_split': 4,\n",
    "          'n_jobs': 2,\n",
    "          'n_estimators': 100}\n",
    "\n",
    "forest_m = Pipeline([\n",
    "    ('feature_selector', FeatureSelector(['antiguedad',\n",
    "'count_ciudad',\n",
    "'metrostotales_log',\n",
    "'metroscubiertos_log',\n",
    "'porcentaje_metros',\n",
    "'diferencia_metros',\n",
    "'metros_totales_normalizados',\n",
    "'metros_cubiertos_normalizados',\n",
    "'lng_norm',\n",
    "'lat_norm',\n",
    "'top_provincia',\n",
    "'count_tipo_propiedad_ciudad',\n",
    "'dia',\n",
    "'dias_desde_datos',\n",
    "'meses_desde_datos',\n",
    "'count_tipo_propiedad',\n",
    "'promedio_precio_tipo_propiedad',\n",
    "'count_id_zona',\n",
    "'cantidad_inquilinos',\n",
    "'tam_ambientes',\n",
    "'promedio_precio_provincia',\n",
    "'promedio_precio_ciudad',\n",
    "'idf_descripcion',\n",
    "'idf_titulo',\n",
    "'promedio_id_zona',\n",
    "'puntaje',\n",
    "'lng',\n",
    "'fecha',\n",
    "'metroscubiertos',\n",
    "'banos',\n",
    "'ranking_en_provincia_tipodepropiedad_cantidad',\n",
    "'similares_count',\n",
    "'ranking_en_provincia_tipodepropiedad_precio',\n",
    "'lat',\n",
    "'ranking_en_provincia_ciudad_cantidad',\n",
    "'garages',\n",
    "'ranking_en_provincia_ciudad_precio',\n",
    "'ranking_en_ciudad',\n",
    "'ranking_en_provincia',\n",
    "'distancia_ciudad_cara',\n",
    "'distancia_centro_mexico',\n",
    "'distancia_ciudad_centrica',\n",
    "'metrostotales'])),\n",
    "    ('random_forest', RandomForestRegressor(**params))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExtraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "params = {'criterion': 'mse',\n",
    " 'max_features': 'sqrt',\n",
    " 'min_samples_split': 4}\n",
    "\n",
    "extratrees_m =  Pipeline([\n",
    "    ('feature_selector', FeatureSelector(['antiguedad', 'garages', 'banos', 'metroscubiertos', 'metrostotales',\n",
    "       'lat', 'lng', 'fecha', 'idf_titulo', 'idf_descripcion',\n",
    "       'peso_descripcion', 'porcentaje_metros', 'diferencia_metros',\n",
    "       'metroscubiertos_bins_unif', 'metroscubiertos_bins_perc',\n",
    "       'metros_totales_normalizados', 'metros_cubiertos_normalizados',\n",
    "       'promedio_metros_tipo_propiedad', 'promedio_metros_cub_tipo_propiedad',\n",
    "       'promedio_metros_cubiertos_provincia', 'mes', 'dia', 'dias_desde_datos',\n",
    "       'meses_desde_datos', 'antiguedad_bins_perc', 'tam_ambientes',\n",
    "       'promedio_precio_provincia', 'promedio_precio_ciudad',\n",
    "       'promedio_precio_ciudad_gen', 'varianza_precio_ciudad', 'count_ciudad',\n",
    "       'promedio_id_zona', 'promedio_id_zona_gen', 'varianza_id_zona',\n",
    "       'count_id_zona', 'promedio_precio_tipo_propiedad',\n",
    "       'promedio_precio_tipo_propiedad_ciudad',\n",
    "       'promedio_precio_tipo_propiedad_ciudad_gen',\n",
    "       'count_tipo_propiedad_ciudad', 'promedio_por_mes', 'varianza_por_mes',\n",
    "       'promedio_precio_habitaciones_banos_garages',\n",
    "       'promedio_precio_banos_garages', 'promedio_precio_hbg_tipo_propiedad',\n",
    "       'lat_norm', 'lng_norm', 'puntaje', 'distancia_ciudad_centrica',\n",
    "       'distancia_centro_mexico', 'distancia_ciudad_cara',\n",
    "       'ranking_en_provincia_tipodepropiedad_precio',\n",
    "       'ranking_en_provincia_tipodepropiedad_cantidad'])),\n",
    "    ('extra_trees', ExtraTreesRegressor(n_estimators=50, **params))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "\n",
    "\n",
    "class CatBoostWrapper(CatBoostRegressor):\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, test_size=0.05)\n",
    "        eval_set = [(x_eval, y_eval)]\n",
    "\n",
    "        # posiciones de features categoricas encontradas a mano\n",
    "        cat_features = [0, 1, 2]\n",
    "        return super(CatBoostWrapper, self).fit(x, y, cat_features=cat_features, eval_set=eval_set, early_stopping_rounds=25)\n",
    "\n",
    "\n",
    "f_s = FeatureSelector(['antiguedad',\n",
    "'metros_totales_normalizados',\n",
    "'metros_cubiertos_normalizados',\n",
    "'prop_frecuente',\n",
    "'anio',\n",
    "'dia',\n",
    "'dias_desde_datos',\n",
    "'meses_desde_datos',\n",
    "'antiguedad_bins_unif',\n",
    "'antiguedad_bins_perc',\n",
    "'cantidad_inquilinos',\n",
    "'tam_ambientes',\n",
    "'promedio_precio_provincia',\n",
    "'promedio_precio_ciudad',\n",
    "'count_ciudad',\n",
    "'promedio_id_zona',\n",
    "'count_id_zona',\n",
    "'promedio_precio_tipo_propiedad',\n",
    "'ranking_en_provincia_tipodepropiedad_cantidad',\n",
    "'ranking_en_provincia_tipodepropiedad_precio',\n",
    "'ranking_en_provincia_ciudad_cantidad',\n",
    "'ranking_en_provincia_ciudad_precio',\n",
    "'ranking_en_ciudad',\n",
    "'ranking_en_provincia',\n",
    "'metroscubiertos_bins_perc',\n",
    "'distancia_ciudad_cara',\n",
    "'distancia_ciudad_centrica',\n",
    "'puntaje',\n",
    "'lng_norm',\n",
    "'lat_norm',\n",
    "'count_tipo_propiedad_ciudad',\n",
    "'count_tipo_propiedad',\n",
    "'distancia_centro_mexico',\n",
    "'metroscubiertos_bins_unif',\n",
    "'top_provincia',\n",
    "'porcentaje_metros',\n",
    "'palabra_casa',\n",
    "'palabra_venta',\n",
    "'centroscomercialescercanos',\n",
    "'escuelascercanas',\n",
    "'piscina',\n",
    "'usosmultiples',\n",
    "'gimnasio',\n",
    "'fecha',\n",
    "'lng',\n",
    "'lat',\n",
    "'metrostotales',\n",
    "'metroscubiertos',\n",
    "'banos',\n",
    "'garages',\n",
    "'habitaciones',\n",
    "'palabra_departamento',\n",
    "'diferencia_metros',\n",
    "'palabra_san',\n",
    "'metroscubiertos_log',\n",
    "'metrostotales_log',\n",
    "'similares_count',\n",
    "'peso_descripcion',\n",
    "'idf_titulo'] + ['provincia', 'ciudad', 'tipodepropiedad'])\n",
    "\n",
    "params = {\n",
    "    'od_type': 'Iter',\n",
    "    'border_count': 176,\n",
    "    'colsample_bylevel': 0.2092942139131877,\n",
    "    'depth': 14,\n",
    "    'l2_leaf_reg': 0.20165655817712402,\n",
    "    'od_wait': 4,\n",
    "    'learning_rate': 0.15,\n",
    "    'iterations': 300,\n",
    "    'silent': True,\n",
    "    'eval_metric': 'MAE'\n",
    "}\n",
    "\n",
    "\n",
    "catboost_m = Pipeline([\n",
    "    ('feature_selector', f_s),\n",
    "    ('catboost', CatBoostWrapper(**params))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, BatchNormalization, Activation\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def keras_modelo():    \n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=512, activation='relu'))\n",
    "    model.add(Dropout(0.13))\n",
    "    model.add(Dense(units=250, activation='relu'))\n",
    "    model.add(Dropout(0.13))\n",
    "    model.add(Dense(units=128, activation='relu'))\n",
    "    model.add(Dropout(0.13))\n",
    "    model.add(Dense(units=1, activation='linear'))\n",
    "\n",
    "    model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_squared_error'])  \n",
    "    return model\n",
    "\n",
    "keras_m = Pipeline([\n",
    "    ('feature_selector', FeatureSelector(['antiguedad', 'habitaciones', 'garages', 'banos', 'metroscubiertos',\n",
    "       'metrostotales', 'lat', 'lng', 'gimnasio', 'usosmultiples',\n",
    "       'piscina', 'clustering_label', 'idf_descripcion', 'porcentaje_metros', 'diferencia_metros',\n",
    "       'promedio_metros_tipo_propiedad', 'prop_frecuente', 'top_provincia',\n",
    "       'promedio_metros_totales_provincia', 'anio', 'mes',\n",
    "       'trimestre', 'dias_desde_datos', 'cantidad_inquilinos', 'tam_ambientes', 'promedio_precio_provincia',\n",
    "       'promedio_precio_ciudad', 'count_ciudad', 'promedio_id_zona',\n",
    "       'count_id_zona','promedio_precio_tipo_propiedad',\n",
    "       'promedio_precio_tipo_propiedad_ciudad', 'count_tipo_propiedad',\n",
    "       'count_tipo_propiedad_ciudad', 'promedio_por_mes', \n",
    "       'promedio_precio_banos_garages', 'promedio_precio_hbg_tipo_propiedad',\n",
    "       'promedio_precio_booleanos', 'puntaje',\n",
    "       'distancia_ciudad_centrica', 'distancia_centro_mexico',\n",
    "       'ranking_en_provincia_tipodepropiedad_precio',\n",
    "       'ranking_en_provincia_tipodepropiedad_cantidad'])),\n",
    "    ('scaler', MinMaxScaler()),\n",
    "    ('keras', KerasRegressor(build_fn=keras_modelo, epochs=5))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task:         [regression]\n",
      "metric:       [mean_absolute_error]\n",
      "variant:      [A]\n",
      "n_estimators: [4]\n",
      "\n",
      "estimator  0: [catboost: Pipeline]\n",
      "    fold  0:  [488975.01123029]\n",
      "    fold  1:  [488419.92430948]\n",
      "    fold  2:  [492307.16223316]\n",
      "    fold  3:  [491535.91937291]\n",
      "    fold  4:  [487021.22850153]\n",
      "    ----\n",
      "    MEAN:     [489651.84912947] + [1974.65549162]\n",
      "\n",
      "estimator  1: [xgboost: Pipeline]\n",
      "[0]\tvalidation_0-mae:2.19737e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.89912e+06\n",
      "[2]\tvalidation_0-mae:1.64503e+06\n",
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      "[163]\tvalidation_0-mae:483710\n",
      "Stopping. Best iteration:\n",
      "[153]\tvalidation_0-mae:483654\n",
      "\n",
      "    fold  0:  [477796.46063152]\n",
      "[0]\tvalidation_0-mae:2.21864e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.91854e+06\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Stopping. Best iteration:\n",
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      "\n",
      "    fold  1:  [475751.77181833]\n",
      "[0]\tvalidation_0-mae:2.19831e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.89974e+06\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "[148]\tvalidation_0-mae:479928\n",
      "Stopping. Best iteration:\n",
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      "\n",
      "    fold  2:  [482104.35326961]\n",
      "[0]\tvalidation_0-mae:2.22378e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.92314e+06\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Stopping. Best iteration:\n",
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      "\n",
      "    fold  3:  [478427.08040626]\n",
      "[0]\tvalidation_0-mae:2.21013e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[102]\tvalidation_0-mae:485202\n",
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      "[209]\tvalidation_0-mae:482932\n",
      "Stopping. Best iteration:\n",
      "[199]\tvalidation_0-mae:482867\n",
      "\n",
      "    fold  4:  [477786.70693074]\n",
      "    ----\n",
      "    MEAN:     [478373.27461129] + [2072.33664846]\n",
      "\n",
      "estimator  2: [randomforest: Pipeline]\n",
      "    fold  0:  [471388.51897051]\n",
      "    fold  1:  [473467.57896366]\n",
      "    fold  2:  [478969.93864073]\n",
      "    fold  3:  [476447.89328343]\n",
      "    fold  4:  [471970.44797502]\n",
      "    ----\n",
      "    MEAN:     [474448.87556667] + [2860.32907188]\n",
      "\n",
      "estimator  3: [lightgbm: Pipeline]\n",
      "[1]\tvalid_0's l1: 1.43703e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.32426e+06\n",
      "[3]\tvalid_0's l1: 1.22406e+06\n",
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      "[146]\tvalid_0's l1: 472759\n",
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      "[158]\tvalid_0's l1: 473164\n",
      "Early stopping, best iteration is:\n",
      "[143]\tvalid_0's l1: 472228\n",
      "    fold  0:  [474141.44319424]\n",
      "[1]\tvalid_0's l1: 1.45612e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.34413e+06\n",
      "[3]\tvalid_0's l1: 1.24364e+06\n",
      "[4]\tvalid_0's l1: 1.15592e+06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\tvalid_0's l1: 1.07708e+06\n",
      "[6]\tvalid_0's l1: 1.0072e+06\n",
      "[7]\tvalid_0's l1: 944694\n",
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      "[77]\tvalid_0's l1: 482276\n",
      "[78]\tvalid_0's l1: 482246\n",
      "[79]\tvalid_0's l1: 481978\n",
      "[80]\tvalid_0's l1: 481873\n",
      "[81]\tvalid_0's l1: 481921\n",
      "[82]\tvalid_0's l1: 481651\n",
      "[83]\tvalid_0's l1: 481292\n",
      "[84]\tvalid_0's l1: 481206\n",
      "[85]\tvalid_0's l1: 480796\n",
      "[86]\tvalid_0's l1: 480959\n",
      "[87]\tvalid_0's l1: 480497\n",
      "[88]\tvalid_0's l1: 480138\n",
      "[89]\tvalid_0's l1: 479971\n",
      "[90]\tvalid_0's l1: 479558\n",
      "[91]\tvalid_0's l1: 479237\n",
      "[92]\tvalid_0's l1: 479025\n",
      "[93]\tvalid_0's l1: 478873\n",
      "[94]\tvalid_0's l1: 478201\n",
      "[95]\tvalid_0's l1: 478051\n",
      "[96]\tvalid_0's l1: 477764\n",
      "[97]\tvalid_0's l1: 477410\n",
      "[98]\tvalid_0's l1: 476870\n",
      "[99]\tvalid_0's l1: 476456\n",
      "[100]\tvalid_0's l1: 476107\n",
      "[101]\tvalid_0's l1: 475798\n",
      "[102]\tvalid_0's l1: 475525\n",
      "[103]\tvalid_0's l1: 475006\n",
      "[104]\tvalid_0's l1: 474786\n",
      "[105]\tvalid_0's l1: 474537\n",
      "[106]\tvalid_0's l1: 474440\n",
      "[107]\tvalid_0's l1: 474253\n",
      "[108]\tvalid_0's l1: 474062\n",
      "[109]\tvalid_0's l1: 473762\n",
      "[110]\tvalid_0's l1: 474127\n",
      "[111]\tvalid_0's l1: 473917\n",
      "[112]\tvalid_0's l1: 473718\n",
      "[113]\tvalid_0's l1: 473075\n",
      "[114]\tvalid_0's l1: 472852\n",
      "[115]\tvalid_0's l1: 472803\n",
      "[116]\tvalid_0's l1: 472871\n",
      "[117]\tvalid_0's l1: 472859\n",
      "[118]\tvalid_0's l1: 472748\n",
      "[119]\tvalid_0's l1: 472668\n",
      "[120]\tvalid_0's l1: 472509\n",
      "[121]\tvalid_0's l1: 472302\n",
      "[122]\tvalid_0's l1: 471864\n",
      "[123]\tvalid_0's l1: 471622\n",
      "[124]\tvalid_0's l1: 471432\n",
      "[125]\tvalid_0's l1: 471230\n",
      "[126]\tvalid_0's l1: 470961\n",
      "[127]\tvalid_0's l1: 470429\n",
      "[128]\tvalid_0's l1: 470335\n",
      "[129]\tvalid_0's l1: 470008\n",
      "[130]\tvalid_0's l1: 469830\n",
      "[131]\tvalid_0's l1: 469660\n",
      "[132]\tvalid_0's l1: 469564\n",
      "[133]\tvalid_0's l1: 469561\n",
      "[134]\tvalid_0's l1: 469274\n",
      "[135]\tvalid_0's l1: 469055\n",
      "[136]\tvalid_0's l1: 468990\n",
      "[137]\tvalid_0's l1: 469052\n",
      "[138]\tvalid_0's l1: 469131\n",
      "[139]\tvalid_0's l1: 469023\n",
      "[140]\tvalid_0's l1: 468757\n",
      "[141]\tvalid_0's l1: 468631\n",
      "[142]\tvalid_0's l1: 468541\n",
      "[143]\tvalid_0's l1: 468459\n",
      "[144]\tvalid_0's l1: 468301\n",
      "[145]\tvalid_0's l1: 468258\n",
      "[146]\tvalid_0's l1: 468183\n",
      "[147]\tvalid_0's l1: 467845\n",
      "[148]\tvalid_0's l1: 467880\n",
      "[149]\tvalid_0's l1: 467808\n",
      "[150]\tvalid_0's l1: 467767\n",
      "[151]\tvalid_0's l1: 467610\n",
      "[152]\tvalid_0's l1: 467313\n",
      "[153]\tvalid_0's l1: 467162\n",
      "[154]\tvalid_0's l1: 466906\n",
      "[155]\tvalid_0's l1: 466673\n",
      "[156]\tvalid_0's l1: 466807\n",
      "[157]\tvalid_0's l1: 466677\n",
      "[158]\tvalid_0's l1: 466583\n",
      "[159]\tvalid_0's l1: 466422\n",
      "[160]\tvalid_0's l1: 466375\n",
      "[161]\tvalid_0's l1: 466595\n",
      "[162]\tvalid_0's l1: 466574\n",
      "[163]\tvalid_0's l1: 466517\n",
      "[164]\tvalid_0's l1: 466266\n",
      "[165]\tvalid_0's l1: 466191\n",
      "[166]\tvalid_0's l1: 466540\n",
      "[167]\tvalid_0's l1: 466563\n",
      "[168]\tvalid_0's l1: 466683\n",
      "[169]\tvalid_0's l1: 466534\n",
      "[170]\tvalid_0's l1: 466401\n",
      "[171]\tvalid_0's l1: 466326\n",
      "[172]\tvalid_0's l1: 466310\n",
      "[173]\tvalid_0's l1: 466291\n",
      "[174]\tvalid_0's l1: 466598\n",
      "[175]\tvalid_0's l1: 466486\n",
      "[176]\tvalid_0's l1: 466404\n",
      "[177]\tvalid_0's l1: 466326\n",
      "[178]\tvalid_0's l1: 466271\n",
      "[179]\tvalid_0's l1: 466040\n",
      "[180]\tvalid_0's l1: 466116\n",
      "[181]\tvalid_0's l1: 466049\n",
      "[182]\tvalid_0's l1: 465864\n",
      "[183]\tvalid_0's l1: 465661\n",
      "[184]\tvalid_0's l1: 465605\n",
      "[185]\tvalid_0's l1: 465520\n",
      "[186]\tvalid_0's l1: 465539\n",
      "[187]\tvalid_0's l1: 465775\n",
      "[188]\tvalid_0's l1: 466032\n",
      "[189]\tvalid_0's l1: 465875\n",
      "[190]\tvalid_0's l1: 465897\n",
      "[191]\tvalid_0's l1: 465892\n",
      "[192]\tvalid_0's l1: 465868\n",
      "[193]\tvalid_0's l1: 465739\n",
      "[194]\tvalid_0's l1: 465542\n",
      "[195]\tvalid_0's l1: 465512\n",
      "[196]\tvalid_0's l1: 465428\n",
      "[197]\tvalid_0's l1: 465612\n",
      "[198]\tvalid_0's l1: 465757\n",
      "[199]\tvalid_0's l1: 465698\n",
      "[200]\tvalid_0's l1: 465520\n",
      "[201]\tvalid_0's l1: 465583\n",
      "[202]\tvalid_0's l1: 465699\n",
      "[203]\tvalid_0's l1: 465789\n",
      "[204]\tvalid_0's l1: 465914\n",
      "[205]\tvalid_0's l1: 465949\n",
      "[206]\tvalid_0's l1: 465756\n",
      "[207]\tvalid_0's l1: 465788\n",
      "[208]\tvalid_0's l1: 465854\n",
      "[209]\tvalid_0's l1: 465851\n",
      "[210]\tvalid_0's l1: 465954\n",
      "[211]\tvalid_0's l1: 465812\n",
      "Early stopping, best iteration is:\n",
      "[196]\tvalid_0's l1: 465428\n",
      "    fold  1:  [471011.98452219]\n",
      "[1]\tvalid_0's l1: 1.46651e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.35175e+06\n",
      "[3]\tvalid_0's l1: 1.24827e+06\n",
      "[4]\tvalid_0's l1: 1.15667e+06\n",
      "[5]\tvalid_0's l1: 1.07519e+06\n",
      "[6]\tvalid_0's l1: 1.00348e+06\n",
      "[7]\tvalid_0's l1: 940448\n",
      "[8]\tvalid_0's l1: 885174\n",
      "[9]\tvalid_0's l1: 835668\n",
      "[10]\tvalid_0's l1: 791334\n",
      "[11]\tvalid_0's l1: 753112\n",
      "[12]\tvalid_0's l1: 716854\n",
      "[13]\tvalid_0's l1: 687987\n",
      "[14]\tvalid_0's l1: 662965\n",
      "[15]\tvalid_0's l1: 641525\n",
      "[16]\tvalid_0's l1: 621652\n",
      "[17]\tvalid_0's l1: 604426\n",
      "[18]\tvalid_0's l1: 589374\n",
      "[19]\tvalid_0's l1: 576822\n",
      "[20]\tvalid_0's l1: 565922\n",
      "[21]\tvalid_0's l1: 555278\n",
      "[22]\tvalid_0's l1: 545684\n",
      "[23]\tvalid_0's l1: 538100\n",
      "[24]\tvalid_0's l1: 531466\n",
      "[25]\tvalid_0's l1: 524912\n",
      "[26]\tvalid_0's l1: 519512\n",
      "[27]\tvalid_0's l1: 515138\n",
      "[28]\tvalid_0's l1: 510661\n",
      "[29]\tvalid_0's l1: 506662\n",
      "[30]\tvalid_0's l1: 503526\n",
      "[31]\tvalid_0's l1: 500113\n",
      "[32]\tvalid_0's l1: 497645\n",
      "[33]\tvalid_0's l1: 495549\n",
      "[34]\tvalid_0's l1: 493073\n",
      "[35]\tvalid_0's l1: 490679\n",
      "[36]\tvalid_0's l1: 489495\n",
      "[37]\tvalid_0's l1: 487917\n",
      "[38]\tvalid_0's l1: 486375\n",
      "[39]\tvalid_0's l1: 484908\n",
      "[40]\tvalid_0's l1: 483946\n",
      "[41]\tvalid_0's l1: 483135\n",
      "[42]\tvalid_0's l1: 482308\n",
      "[43]\tvalid_0's l1: 481206\n",
      "[44]\tvalid_0's l1: 480252\n",
      "[45]\tvalid_0's l1: 479446\n",
      "[46]\tvalid_0's l1: 477447\n",
      "[47]\tvalid_0's l1: 476632\n",
      "[48]\tvalid_0's l1: 476277\n",
      "[49]\tvalid_0's l1: 476010\n",
      "[50]\tvalid_0's l1: 475667\n",
      "[51]\tvalid_0's l1: 475288\n",
      "[52]\tvalid_0's l1: 474702\n",
      "[53]\tvalid_0's l1: 474626\n",
      "[54]\tvalid_0's l1: 473823\n",
      "[55]\tvalid_0's l1: 473024\n",
      "[56]\tvalid_0's l1: 472679\n",
      "[57]\tvalid_0's l1: 471800\n",
      "[58]\tvalid_0's l1: 471746\n",
      "[59]\tvalid_0's l1: 471847\n",
      "[60]\tvalid_0's l1: 471888\n",
      "[61]\tvalid_0's l1: 471489\n",
      "[62]\tvalid_0's l1: 471414\n",
      "[63]\tvalid_0's l1: 471155\n",
      "[64]\tvalid_0's l1: 471030\n",
      "[65]\tvalid_0's l1: 470699\n",
      "[66]\tvalid_0's l1: 470457\n",
      "[67]\tvalid_0's l1: 470725\n",
      "[68]\tvalid_0's l1: 470558\n",
      "[69]\tvalid_0's l1: 470412\n",
      "[70]\tvalid_0's l1: 470047\n",
      "[71]\tvalid_0's l1: 470050\n",
      "[72]\tvalid_0's l1: 469738\n",
      "[73]\tvalid_0's l1: 469260\n",
      "[74]\tvalid_0's l1: 469511\n",
      "[75]\tvalid_0's l1: 469454\n",
      "[76]\tvalid_0's l1: 469088\n",
      "[77]\tvalid_0's l1: 469066\n",
      "[78]\tvalid_0's l1: 469045\n",
      "[79]\tvalid_0's l1: 468376\n",
      "[80]\tvalid_0's l1: 468366\n",
      "[81]\tvalid_0's l1: 467885\n",
      "[82]\tvalid_0's l1: 467336\n",
      "[83]\tvalid_0's l1: 467047\n",
      "[84]\tvalid_0's l1: 466816\n",
      "[85]\tvalid_0's l1: 466699\n",
      "[86]\tvalid_0's l1: 466635\n",
      "[87]\tvalid_0's l1: 466357\n",
      "[88]\tvalid_0's l1: 466207\n",
      "[89]\tvalid_0's l1: 465891\n",
      "[90]\tvalid_0's l1: 465349\n",
      "[91]\tvalid_0's l1: 464905\n",
      "[92]\tvalid_0's l1: 465307\n",
      "[93]\tvalid_0's l1: 465232\n",
      "[94]\tvalid_0's l1: 465320\n",
      "[95]\tvalid_0's l1: 464821\n",
      "[96]\tvalid_0's l1: 464935\n",
      "[97]\tvalid_0's l1: 464411\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[98]\tvalid_0's l1: 464360\n",
      "[99]\tvalid_0's l1: 464753\n",
      "[100]\tvalid_0's l1: 464659\n",
      "[101]\tvalid_0's l1: 464640\n",
      "[102]\tvalid_0's l1: 464720\n",
      "[103]\tvalid_0's l1: 464447\n",
      "[104]\tvalid_0's l1: 464479\n",
      "[105]\tvalid_0's l1: 464171\n",
      "[106]\tvalid_0's l1: 463968\n",
      "[107]\tvalid_0's l1: 463565\n",
      "[108]\tvalid_0's l1: 463677\n",
      "[109]\tvalid_0's l1: 463901\n",
      "[110]\tvalid_0's l1: 463720\n",
      "[111]\tvalid_0's l1: 463316\n",
      "[112]\tvalid_0's l1: 463272\n",
      "[113]\tvalid_0's l1: 463164\n",
      "[114]\tvalid_0's l1: 463095\n",
      "[115]\tvalid_0's l1: 463253\n",
      "[116]\tvalid_0's l1: 462908\n",
      "[117]\tvalid_0's l1: 463030\n",
      "[118]\tvalid_0's l1: 463111\n",
      "[119]\tvalid_0's l1: 463240\n",
      "[120]\tvalid_0's l1: 463175\n",
      "[121]\tvalid_0's l1: 463283\n",
      "[122]\tvalid_0's l1: 463149\n",
      "[123]\tvalid_0's l1: 462900\n",
      "[124]\tvalid_0's l1: 462938\n",
      "[125]\tvalid_0's l1: 462825\n",
      "[126]\tvalid_0's l1: 462820\n",
      "[127]\tvalid_0's l1: 462674\n",
      "[128]\tvalid_0's l1: 462299\n",
      "[129]\tvalid_0's l1: 462144\n",
      "[130]\tvalid_0's l1: 462274\n",
      "[131]\tvalid_0's l1: 462328\n",
      "[132]\tvalid_0's l1: 462172\n",
      "[133]\tvalid_0's l1: 462122\n",
      "[134]\tvalid_0's l1: 461796\n",
      "[135]\tvalid_0's l1: 461504\n",
      "[136]\tvalid_0's l1: 461363\n",
      "[137]\tvalid_0's l1: 461231\n",
      "[138]\tvalid_0's l1: 461246\n",
      "[139]\tvalid_0's l1: 460987\n",
      "[140]\tvalid_0's l1: 460487\n",
      "[141]\tvalid_0's l1: 460591\n",
      "[142]\tvalid_0's l1: 460954\n",
      "[143]\tvalid_0's l1: 460985\n",
      "[144]\tvalid_0's l1: 460848\n",
      "[145]\tvalid_0's l1: 460911\n",
      "[146]\tvalid_0's l1: 460784\n",
      "[147]\tvalid_0's l1: 460721\n",
      "[148]\tvalid_0's l1: 460529\n",
      "[149]\tvalid_0's l1: 460652\n",
      "[150]\tvalid_0's l1: 460272\n",
      "[151]\tvalid_0's l1: 459899\n",
      "[152]\tvalid_0's l1: 459817\n",
      "[153]\tvalid_0's l1: 459734\n",
      "[154]\tvalid_0's l1: 459808\n",
      "[155]\tvalid_0's l1: 459579\n",
      "[156]\tvalid_0's l1: 459332\n",
      "[157]\tvalid_0's l1: 459342\n",
      "[158]\tvalid_0's l1: 459002\n",
      "[159]\tvalid_0's l1: 459138\n",
      "[160]\tvalid_0's l1: 459040\n",
      "[161]\tvalid_0's l1: 458958\n",
      "[162]\tvalid_0's l1: 458828\n",
      "[163]\tvalid_0's l1: 459148\n",
      "[164]\tvalid_0's l1: 459245\n",
      "[165]\tvalid_0's l1: 459357\n",
      "[166]\tvalid_0's l1: 459300\n",
      "[167]\tvalid_0's l1: 459184\n",
      "[168]\tvalid_0's l1: 459041\n",
      "[169]\tvalid_0's l1: 459052\n",
      "[170]\tvalid_0's l1: 458951\n",
      "[171]\tvalid_0's l1: 458926\n",
      "[172]\tvalid_0's l1: 459027\n",
      "[173]\tvalid_0's l1: 458989\n",
      "[174]\tvalid_0's l1: 459096\n",
      "[175]\tvalid_0's l1: 459096\n",
      "[176]\tvalid_0's l1: 458974\n",
      "[177]\tvalid_0's l1: 458878\n",
      "Early stopping, best iteration is:\n",
      "[162]\tvalid_0's l1: 458828\n",
      "    fold  2:  [474197.63451169]\n",
      "[1]\tvalid_0's l1: 1.49122e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.37494e+06\n",
      "[3]\tvalid_0's l1: 1.27022e+06\n",
      "[4]\tvalid_0's l1: 1.17871e+06\n",
      "[5]\tvalid_0's l1: 1.09686e+06\n",
      "[6]\tvalid_0's l1: 1.02349e+06\n",
      "[7]\tvalid_0's l1: 959039\n",
      "[8]\tvalid_0's l1: 901856\n",
      "[9]\tvalid_0's l1: 852464\n",
      "[10]\tvalid_0's l1: 808417\n",
      "[11]\tvalid_0's l1: 770300\n",
      "[12]\tvalid_0's l1: 736401\n",
      "[13]\tvalid_0's l1: 707161\n",
      "[14]\tvalid_0's l1: 682263\n",
      "[15]\tvalid_0's l1: 658753\n",
      "[16]\tvalid_0's l1: 638705\n",
      "[17]\tvalid_0's l1: 621459\n",
      "[18]\tvalid_0's l1: 606632\n",
      "[19]\tvalid_0's l1: 593076\n",
      "[20]\tvalid_0's l1: 580867\n",
      "[21]\tvalid_0's l1: 570892\n",
      "[22]\tvalid_0's l1: 562200\n",
      "[23]\tvalid_0's l1: 554911\n",
      "[24]\tvalid_0's l1: 547303\n",
      "[25]\tvalid_0's l1: 540899\n",
      "[26]\tvalid_0's l1: 535939\n",
      "[27]\tvalid_0's l1: 531796\n",
      "[28]\tvalid_0's l1: 528534\n",
      "[29]\tvalid_0's l1: 524445\n",
      "[30]\tvalid_0's l1: 521065\n",
      "[31]\tvalid_0's l1: 517394\n",
      "[32]\tvalid_0's l1: 514119\n",
      "[33]\tvalid_0's l1: 511546\n",
      "[34]\tvalid_0's l1: 509340\n",
      "[35]\tvalid_0's l1: 507406\n",
      "[36]\tvalid_0's l1: 505259\n",
      "[37]\tvalid_0's l1: 503467\n",
      "[38]\tvalid_0's l1: 502151\n",
      "[39]\tvalid_0's l1: 500686\n",
      "[40]\tvalid_0's l1: 499156\n",
      "[41]\tvalid_0's l1: 498303\n",
      "[42]\tvalid_0's l1: 497044\n",
      "[43]\tvalid_0's l1: 495733\n",
      "[44]\tvalid_0's l1: 494373\n",
      "[45]\tvalid_0's l1: 493452\n",
      "[46]\tvalid_0's l1: 492167\n",
      "[47]\tvalid_0's l1: 490882\n",
      "[48]\tvalid_0's l1: 489636\n",
      "[49]\tvalid_0's l1: 488988\n",
      "[50]\tvalid_0's l1: 488439\n",
      "[51]\tvalid_0's l1: 487618\n",
      "[52]\tvalid_0's l1: 486729\n",
      "[53]\tvalid_0's l1: 486569\n",
      "[54]\tvalid_0's l1: 486588\n",
      "[55]\tvalid_0's l1: 486204\n",
      "[56]\tvalid_0's l1: 485817\n",
      "[57]\tvalid_0's l1: 485880\n",
      "[58]\tvalid_0's l1: 485639\n",
      "[59]\tvalid_0's l1: 485443\n",
      "[60]\tvalid_0's l1: 484422\n",
      "[61]\tvalid_0's l1: 483960\n",
      "[62]\tvalid_0's l1: 483611\n",
      "[63]\tvalid_0's l1: 482855\n",
      "[64]\tvalid_0's l1: 482222\n",
      "[65]\tvalid_0's l1: 481423\n",
      "[66]\tvalid_0's l1: 481265\n",
      "[67]\tvalid_0's l1: 481438\n",
      "[68]\tvalid_0's l1: 481115\n",
      "[69]\tvalid_0's l1: 480519\n",
      "[70]\tvalid_0's l1: 480279\n",
      "[71]\tvalid_0's l1: 480285\n",
      "[72]\tvalid_0's l1: 480180\n",
      "[73]\tvalid_0's l1: 480542\n",
      "[74]\tvalid_0's l1: 479929\n",
      "[75]\tvalid_0's l1: 479584\n",
      "[76]\tvalid_0's l1: 479631\n",
      "[77]\tvalid_0's l1: 478967\n",
      "[78]\tvalid_0's l1: 478839\n",
      "[79]\tvalid_0's l1: 478505\n",
      "[80]\tvalid_0's l1: 477911\n",
      "[81]\tvalid_0's l1: 477335\n",
      "[82]\tvalid_0's l1: 477190\n",
      "[83]\tvalid_0's l1: 476752\n",
      "[84]\tvalid_0's l1: 476676\n",
      "[85]\tvalid_0's l1: 476102\n",
      "[86]\tvalid_0's l1: 476042\n",
      "[87]\tvalid_0's l1: 474884\n",
      "[88]\tvalid_0's l1: 474271\n",
      "[89]\tvalid_0's l1: 473979\n",
      "[90]\tvalid_0's l1: 473846\n",
      "[91]\tvalid_0's l1: 473607\n",
      "[92]\tvalid_0's l1: 473551\n",
      "[93]\tvalid_0's l1: 473737\n",
      "[94]\tvalid_0's l1: 473278\n",
      "[95]\tvalid_0's l1: 473149\n",
      "[96]\tvalid_0's l1: 472869\n",
      "[97]\tvalid_0's l1: 472278\n",
      "[98]\tvalid_0's l1: 471870\n",
      "[99]\tvalid_0's l1: 471800\n",
      "[100]\tvalid_0's l1: 471519\n",
      "[101]\tvalid_0's l1: 471547\n",
      "[102]\tvalid_0's l1: 471654\n",
      "[103]\tvalid_0's l1: 471423\n",
      "[104]\tvalid_0's l1: 471473\n",
      "[105]\tvalid_0's l1: 471165\n",
      "[106]\tvalid_0's l1: 470792\n",
      "[107]\tvalid_0's l1: 470373\n",
      "[108]\tvalid_0's l1: 470574\n",
      "[109]\tvalid_0's l1: 470479\n",
      "[110]\tvalid_0's l1: 470303\n",
      "[111]\tvalid_0's l1: 470205\n",
      "[112]\tvalid_0's l1: 470268\n",
      "[113]\tvalid_0's l1: 470436\n",
      "[114]\tvalid_0's l1: 470244\n",
      "[115]\tvalid_0's l1: 470012\n",
      "[116]\tvalid_0's l1: 469920\n",
      "[117]\tvalid_0's l1: 469941\n",
      "[118]\tvalid_0's l1: 469444\n",
      "[119]\tvalid_0's l1: 469673\n",
      "[120]\tvalid_0's l1: 469150\n",
      "[121]\tvalid_0's l1: 468782\n",
      "[122]\tvalid_0's l1: 468682\n",
      "[123]\tvalid_0's l1: 468755\n",
      "[124]\tvalid_0's l1: 468465\n",
      "[125]\tvalid_0's l1: 468458\n",
      "[126]\tvalid_0's l1: 468563\n",
      "[127]\tvalid_0's l1: 468397\n",
      "[128]\tvalid_0's l1: 468276\n",
      "[129]\tvalid_0's l1: 468311\n",
      "[130]\tvalid_0's l1: 468494\n",
      "[131]\tvalid_0's l1: 468226\n",
      "[132]\tvalid_0's l1: 468004\n",
      "[133]\tvalid_0's l1: 468268\n",
      "[134]\tvalid_0's l1: 468367\n",
      "[135]\tvalid_0's l1: 468272\n",
      "[136]\tvalid_0's l1: 468180\n",
      "[137]\tvalid_0's l1: 468163\n",
      "[138]\tvalid_0's l1: 468218\n",
      "[139]\tvalid_0's l1: 468332\n",
      "[140]\tvalid_0's l1: 468073\n",
      "[141]\tvalid_0's l1: 467999\n",
      "[142]\tvalid_0's l1: 467681\n",
      "[143]\tvalid_0's l1: 467585\n",
      "[144]\tvalid_0's l1: 467528\n",
      "[145]\tvalid_0's l1: 467806\n",
      "[146]\tvalid_0's l1: 467431\n",
      "[147]\tvalid_0's l1: 467467\n",
      "[148]\tvalid_0's l1: 467450\n",
      "[149]\tvalid_0's l1: 467604\n",
      "[150]\tvalid_0's l1: 467509\n",
      "[151]\tvalid_0's l1: 467378\n",
      "[152]\tvalid_0's l1: 467546\n",
      "[153]\tvalid_0's l1: 467714\n",
      "[154]\tvalid_0's l1: 467519\n",
      "[155]\tvalid_0's l1: 467453\n",
      "[156]\tvalid_0's l1: 467351\n",
      "[157]\tvalid_0's l1: 467141\n",
      "[158]\tvalid_0's l1: 466910\n",
      "[159]\tvalid_0's l1: 466519\n",
      "[160]\tvalid_0's l1: 466598\n",
      "[161]\tvalid_0's l1: 466711\n",
      "[162]\tvalid_0's l1: 466797\n",
      "[163]\tvalid_0's l1: 466876\n",
      "[164]\tvalid_0's l1: 466914\n",
      "[165]\tvalid_0's l1: 467054\n",
      "[166]\tvalid_0's l1: 466930\n",
      "[167]\tvalid_0's l1: 466840\n",
      "[168]\tvalid_0's l1: 466833\n",
      "[169]\tvalid_0's l1: 466835\n",
      "[170]\tvalid_0's l1: 466772\n",
      "[171]\tvalid_0's l1: 466860\n",
      "[172]\tvalid_0's l1: 466805\n",
      "[173]\tvalid_0's l1: 466839\n",
      "[174]\tvalid_0's l1: 466790\n",
      "Early stopping, best iteration is:\n",
      "[159]\tvalid_0's l1: 466519\n",
      "    fold  3:  [474537.37871721]\n",
      "[1]\tvalid_0's l1: 1.53137e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.41119e+06\n",
      "[3]\tvalid_0's l1: 1.30346e+06\n",
      "[4]\tvalid_0's l1: 1.20664e+06\n",
      "[5]\tvalid_0's l1: 1.12203e+06\n",
      "[6]\tvalid_0's l1: 1.04718e+06\n",
      "[7]\tvalid_0's l1: 980686\n",
      "[8]\tvalid_0's l1: 925678\n",
      "[9]\tvalid_0's l1: 875401\n",
      "[10]\tvalid_0's l1: 831030\n",
      "[11]\tvalid_0's l1: 791490\n",
      "[12]\tvalid_0's l1: 757042\n",
      "[13]\tvalid_0's l1: 726259\n",
      "[14]\tvalid_0's l1: 699351\n",
      "[15]\tvalid_0's l1: 675058\n",
      "[16]\tvalid_0's l1: 654008\n",
      "[17]\tvalid_0's l1: 636648\n",
      "[18]\tvalid_0's l1: 621468\n",
      "[19]\tvalid_0's l1: 607800\n",
      "[20]\tvalid_0's l1: 595666\n",
      "[21]\tvalid_0's l1: 585827\n",
      "[22]\tvalid_0's l1: 577024\n",
      "[23]\tvalid_0's l1: 567681\n",
      "[24]\tvalid_0's l1: 560622\n",
      "[25]\tvalid_0's l1: 553905\n",
      "[26]\tvalid_0's l1: 547395\n",
      "[27]\tvalid_0's l1: 542422\n",
      "[28]\tvalid_0's l1: 537916\n",
      "[29]\tvalid_0's l1: 534413\n",
      "[30]\tvalid_0's l1: 530414\n",
      "[31]\tvalid_0's l1: 527948\n",
      "[32]\tvalid_0's l1: 525569\n",
      "[33]\tvalid_0's l1: 523375\n",
      "[34]\tvalid_0's l1: 521146\n",
      "[35]\tvalid_0's l1: 519381\n",
      "[36]\tvalid_0's l1: 518098\n",
      "[37]\tvalid_0's l1: 516487\n",
      "[38]\tvalid_0's l1: 514915\n",
      "[39]\tvalid_0's l1: 513263\n",
      "[40]\tvalid_0's l1: 511071\n",
      "[41]\tvalid_0's l1: 510195\n",
      "[42]\tvalid_0's l1: 509922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[43]\tvalid_0's l1: 509252\n",
      "[44]\tvalid_0's l1: 508521\n",
      "[45]\tvalid_0's l1: 507440\n",
      "[46]\tvalid_0's l1: 506161\n",
      "[47]\tvalid_0's l1: 505649\n",
      "[48]\tvalid_0's l1: 505478\n",
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      "[50]\tvalid_0's l1: 503854\n",
      "[51]\tvalid_0's l1: 503216\n",
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      "[53]\tvalid_0's l1: 500957\n",
      "[54]\tvalid_0's l1: 500625\n",
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      "[56]\tvalid_0's l1: 499910\n",
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      "[58]\tvalid_0's l1: 499463\n",
      "[59]\tvalid_0's l1: 499497\n",
      "[60]\tvalid_0's l1: 498867\n",
      "[61]\tvalid_0's l1: 498944\n",
      "[62]\tvalid_0's l1: 498827\n",
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      "[64]\tvalid_0's l1: 497696\n",
      "[65]\tvalid_0's l1: 496621\n",
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      "[68]\tvalid_0's l1: 494785\n",
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      "[88]\tvalid_0's l1: 486548\n",
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      "[90]\tvalid_0's l1: 486142\n",
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      "[95]\tvalid_0's l1: 485754\n",
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      "[100]\tvalid_0's l1: 484428\n",
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      "[114]\tvalid_0's l1: 482881\n",
      "[115]\tvalid_0's l1: 482541\n",
      "[116]\tvalid_0's l1: 482351\n",
      "[117]\tvalid_0's l1: 482025\n",
      "[118]\tvalid_0's l1: 482018\n",
      "[119]\tvalid_0's l1: 481790\n",
      "[120]\tvalid_0's l1: 481763\n",
      "[121]\tvalid_0's l1: 481648\n",
      "[122]\tvalid_0's l1: 481549\n",
      "[123]\tvalid_0's l1: 481283\n",
      "[124]\tvalid_0's l1: 481064\n",
      "[125]\tvalid_0's l1: 480910\n",
      "[126]\tvalid_0's l1: 480713\n",
      "[127]\tvalid_0's l1: 480617\n",
      "[128]\tvalid_0's l1: 480062\n",
      "[129]\tvalid_0's l1: 480096\n",
      "[130]\tvalid_0's l1: 480078\n",
      "[131]\tvalid_0's l1: 480080\n",
      "[132]\tvalid_0's l1: 480188\n",
      "[133]\tvalid_0's l1: 480172\n",
      "[134]\tvalid_0's l1: 479957\n",
      "[135]\tvalid_0's l1: 479754\n",
      "[136]\tvalid_0's l1: 479692\n",
      "[137]\tvalid_0's l1: 479451\n",
      "[138]\tvalid_0's l1: 479278\n",
      "[139]\tvalid_0's l1: 478921\n",
      "[140]\tvalid_0's l1: 479112\n",
      "[141]\tvalid_0's l1: 479011\n",
      "[142]\tvalid_0's l1: 479028\n",
      "[143]\tvalid_0's l1: 479033\n",
      "[144]\tvalid_0's l1: 478412\n",
      "[145]\tvalid_0's l1: 478371\n",
      "[146]\tvalid_0's l1: 478489\n",
      "[147]\tvalid_0's l1: 478507\n",
      "[148]\tvalid_0's l1: 478336\n",
      "[149]\tvalid_0's l1: 478662\n",
      "[150]\tvalid_0's l1: 478467\n",
      "[151]\tvalid_0's l1: 478744\n",
      "[152]\tvalid_0's l1: 478338\n",
      "[153]\tvalid_0's l1: 478488\n",
      "[154]\tvalid_0's l1: 478322\n",
      "[155]\tvalid_0's l1: 478231\n",
      "[156]\tvalid_0's l1: 478040\n",
      "[157]\tvalid_0's l1: 478147\n",
      "[158]\tvalid_0's l1: 478146\n",
      "[159]\tvalid_0's l1: 478149\n",
      "[160]\tvalid_0's l1: 478149\n",
      "[161]\tvalid_0's l1: 478162\n",
      "[162]\tvalid_0's l1: 478179\n",
      "[163]\tvalid_0's l1: 478275\n",
      "[164]\tvalid_0's l1: 478113\n",
      "[165]\tvalid_0's l1: 477949\n",
      "[166]\tvalid_0's l1: 477830\n",
      "[167]\tvalid_0's l1: 477889\n",
      "[168]\tvalid_0's l1: 477938\n",
      "[169]\tvalid_0's l1: 477805\n",
      "[170]\tvalid_0's l1: 477599\n",
      "[171]\tvalid_0's l1: 477506\n",
      "[172]\tvalid_0's l1: 477407\n",
      "[173]\tvalid_0's l1: 477456\n",
      "[174]\tvalid_0's l1: 477268\n",
      "[175]\tvalid_0's l1: 477409\n",
      "[176]\tvalid_0's l1: 477121\n",
      "[177]\tvalid_0's l1: 477164\n",
      "[178]\tvalid_0's l1: 476977\n",
      "[179]\tvalid_0's l1: 476982\n",
      "[180]\tvalid_0's l1: 476861\n",
      "[181]\tvalid_0's l1: 476702\n",
      "[182]\tvalid_0's l1: 476553\n",
      "[183]\tvalid_0's l1: 476489\n",
      "[184]\tvalid_0's l1: 476320\n",
      "[185]\tvalid_0's l1: 476357\n",
      "[186]\tvalid_0's l1: 476532\n",
      "[187]\tvalid_0's l1: 476517\n",
      "[188]\tvalid_0's l1: 476649\n",
      "[189]\tvalid_0's l1: 476808\n",
      "[190]\tvalid_0's l1: 476717\n",
      "[191]\tvalid_0's l1: 476786\n",
      "[192]\tvalid_0's l1: 477001\n",
      "[193]\tvalid_0's l1: 477083\n",
      "[194]\tvalid_0's l1: 477144\n",
      "[195]\tvalid_0's l1: 477107\n",
      "[196]\tvalid_0's l1: 477188\n",
      "[197]\tvalid_0's l1: 477121\n",
      "[198]\tvalid_0's l1: 477112\n",
      "[199]\tvalid_0's l1: 476929\n",
      "Early stopping, best iteration is:\n",
      "[184]\tvalid_0's l1: 476320\n",
      "    fold  4:  [469147.37909590]\n",
      "    ----\n",
      "    MEAN:     [472607.16400825] + [2150.53700726]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StackingTransformer(estimators=[('catboost',\n",
       "                                 Pipeline(memory=None,\n",
       "                                          steps=[('feature_selector',\n",
       "                                                  FeatureSelector(features=['antiguedad',\n",
       "                                                                            'metros_totales_normalizados',\n",
       "                                                                            'metros_cubiertos_normalizados',\n",
       "                                                                            'prop_frecuente',\n",
       "                                                                            'anio',\n",
       "                                                                            'dia',\n",
       "                                                                            'dias_desde_datos',\n",
       "                                                                            'meses_desde_datos',\n",
       "                                                                            'antiguedad_bins_unif',\n",
       "                                                                            'antiguedad_bins_perc',\n",
       "                                                                            'cantidad_inquilinos',\n",
       "                                                                            'tam_ambientes',\n",
       "                                                                            'pro...\n",
       "                                                                  num_leaves=640,\n",
       "                                                                  objective='regression',\n",
       "                                                                  random_state=None,\n",
       "                                                                  reg_alpha=0.0,\n",
       "                                                                  reg_lambda=9.336230074572562,\n",
       "                                                                  silent=True,\n",
       "                                                                  subsample=1.0,\n",
       "                                                                  subsample_for_bin=200000,\n",
       "                                                                  subsample_freq=0))],\n",
       "                                          verbose=False))],\n",
       "                    metric=None, n_folds=5, needs_proba=False, random_state=0,\n",
       "                    regression=True, shuffle=False, stratified=False,\n",
       "                    transform_pred=None, transform_target=None, variant='A',\n",
       "                    verbose=2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from vecstack import stacking\n",
    "from vecstack_sk import StackingTransformer\n",
    "\n",
    "modelos = [\n",
    "#            ('keras', keras_m),\n",
    "           ('catboost', catboost_m),\n",
    "           ('xgboost', xgb_m),\n",
    "           ('randomforest', forest_m),\n",
    "           ('lightgbm', lgb_m), \n",
    "#            ('extratrees', extratrees_m),\n",
    "          ]\n",
    "\n",
    "stack = StackingTransformer(modelos, \n",
    "                          regression=True, verbose=2, n_folds=5)\n",
    "\n",
    "stack.fit(df_train_f.drop('precio', axis=1).values, \n",
    "                          df_train_f['precio'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set was detected.\n",
      "Transforming...\n",
      "\n",
      "estimator  0: [catboost: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  1: [xgboost: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  2: [randomforest: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  3: [lightgbm: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "Transforming...\n",
      "\n",
      "estimator  0: [catboost: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  1: [xgboost: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  2: [randomforest: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n",
      "estimator  3: [lightgbm: Pipeline]\n",
      "    model from fold  0: done\n",
      "    model from fold  1: done\n",
      "    model from fold  2: done\n",
      "    model from fold  3: done\n",
      "    model from fold  4: done\n",
      "    ----\n",
      "    DONE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "s_train = stack.transform(df_train_f.drop('precio', axis=1).values)\n",
    "s_test = stack.transform(df_test_f.drop('precio', axis=1, errors='ignore').values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion con todos los features + stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_s = df_train_f.copy()\n",
    "df_test_s = df_test_f.copy()\n",
    "\n",
    "df_train_s['stack01'], df_train_s['stack02'], df_train_s['stack03'], df_train_s['stack04'] = zip(*s_train)\n",
    "df_test_s['stack01'], df_test_s['stack02'], df_test_s['stack03'], df_test_s['stack04'] = zip(*s_test)\n",
    "\n",
    "features_stacking = ['stack01', 'stack02', 'stack03', 'stack04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_s['id'] = df_train['id']\n",
    "df_test_s['id'] = df_test['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from hyperopt import fmin, tpe, hp\n",
    "from hyperopt.pyll.base import scope\n",
    "import ipynb.fs.full.utils as utils\n",
    "import ipynb.fs.full.features as features\n",
    "import ipynb.fs.full.features_distancias as f_distancias\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from hyperopt import STATUS_OK\n",
    "\n",
    "\n",
    "features = ['antiguedad', 'garages', 'banos', 'metroscubiertos', 'metrostotales',\n",
    "       'lat', 'lng', 'fecha', 'piscina', 'metroscubiertos_log',\n",
    "       'porcentaje_metros', 'diferencia_metros', 'metros_totales_normalizados',\n",
    "       'escomercial', 'promedio_metros_tipo_propiedad',\n",
    "       'promedio_metros_cub_tipo_propiedad', 'tipo_propiedad_compartida',\n",
    "       'prop_frecuente', 'top_provincia', 'es_ciudad_centrica',\n",
    "       'promedio_metros_totales_provincia',\n",
    "       'promedio_metros_cubiertos_provincia', 'dolar_promedio_mes',\n",
    "       'dolar_promedio_ult_mes', 'es_antigua', 'antiguedad_bins_unif',\n",
    "       'antiguedad_bins_perc', 'cantidad_inquilinos', 'tam_ambientes',\n",
    "       'promedio_precio_provincia', 'promedio_precio_ciudad', 'count_ciudad',\n",
    "       'promedio_id_zona', 'count_id_zona', 'promedio_precio_tipo_propiedad',\n",
    "       'count_tipo_propiedad', 'count_tipo_propiedad_ciudad', 'lat_norm',\n",
    "       'lng_norm', 'promedio_precio_booleanos', 'puntaje', 'idf_titulo',\n",
    "       'idf_descripcion', 'peso_titulo', 'peso_descripcion',\n",
    "       'distancia_ciudad_centrica', 'distancia_centro_mexico',\n",
    "       'distancia_ciudad_cara', 'ranking_en_provincia', 'ranking_en_ciudad',\n",
    "       'ranking_en_provincia_ciudad_precio',\n",
    "       'ranking_en_provincia_ciudad_cantidad',\n",
    "       'ranking_en_provincia_tipodepropiedad_precio',\n",
    "       'ranking_en_provincia_tipodepropiedad_cantidad', 'palabra_venta',\n",
    "       'palabra_casa', 'palabra_departamento', 'palabra_san',\n",
    "       'palabra_polanco', 'palabra_condominio', 'palabra_lomas',\n",
    "       'palabra_excelente', 'palabra_ciudad', 'palabra_méxico',\n",
    "       'similares_count', \n",
    "            'kmeans_basicos', 'kmeans_superficie',\n",
    "       'kmeans_metroscubiertos', 'kmeans_fechas', 'kmeans_latlng']\n",
    "\n",
    "\n",
    "params_2nd = {\n",
    "    \n",
    " 'bagging_fraction': 0.552992696225161,\n",
    " 'bagging_freq': 0,\n",
    " 'max_bin': 397,\n",
    " 'max_depth': 20,\n",
    " 'min_data_in_leaf': 83,\n",
    " 'min_split_gain': 0.9034743446375575,\n",
    " 'num_leaves': 734,\n",
    " 'reg_lambda': 72.68622962042346,\n",
    "#  'bagging_freq': 0,\n",
    "#  'max_bin': 1643,\n",
    "#  'max_depth': 6,\n",
    "#  'min_data_in_leaf': 62,\n",
    "#  'min_split_gain': 0.8965566444331939,\n",
    "#  'num_leaves': 592,\n",
    "#  'reg_lambda': 35.74667217035101,\n",
    "#  'bagging_freq': 0\n",
    "#  'max_bin': 1012,\n",
    "#  'max_depth': 5,\n",
    "#  'min_data_in_leaf': 40,\n",
    "#  'min_split_gain': 0.2934964048344291,\n",
    "#  'num_leaves': 469,\n",
    "#  'reg_lambda': 36.47905035024398,\n",
    " 'boosting_type': 'dart',\n",
    " 'num_boost_round': 1500,\n",
    " 'objective': 'regression',\n",
    " 'metric': 'mae'\n",
    "}\n",
    "\n",
    "\n",
    "lgb_m_2nd = LightGBMWrapper(**params_2nd)\n",
    "lgb_m_2nd.fit(utils.filtrar_features(df_train_s, features + features_stacking).values, df_train_s['precio'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_s['target'] = lgb_m_2nd.predict(utils.filtrar_features(df_test_s, features + features_stacking))\n",
    "# df_test_s = utils.pesificar_df(df_test_s, col_precio_in='target', col_precio_out='target')\n",
    "df_test_s[['id', 'target']].to_csv('respuesta66.csv', index = False)\n",
    "\n",
    "# 65: Full stacking, todos modelos, LightGBM DART de 1500\n",
    "# 66: Full stacking, todos modelos, LightGBM GBDT de 1500\n",
    "\n",
    "# print(f'MAE Stacking-full: {utils.MAE(df_test_s[\"precio\"].values, df_test_s[\"target\"].values)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sin DART:   MAE Stacking-full: 495768.2576267224"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion solo con features de stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_2nd = {'bagging_fraction': 0.8243831977099841,\n",
    " 'bagging_freq': int(10.0),\n",
    " 'feature_fraction': 0.9228324501365147,\n",
    " 'learning_rate': 0.050664243951241736,\n",
    " 'max_depth': int(3.0),\n",
    " 'num_leaves': int(78.0),\n",
    " 'objective': 'regression',\n",
    " 'boosting_type': 'dart',\n",
    " 'num_boost_round': 1200,\n",
    " 'metric': 'mae'}\n",
    "\n",
    "\n",
    "lgb_m_2nd = LightGBMWrapper(**params_2nd)\n",
    "lgb_m_2nd.fit(s_train, df_train_f['precio'].values)\n",
    "\n",
    "df_test_f['target'] = lgb_m_2nd.predict(s_test)\n",
    "df_test_f[['id', 'target']].to_csv('respuesta47.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion con promedios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_f['target'] = s_test.T[1].T\n",
    "df_test_f[['id', 'target']].to_csv('respuesta61.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "\n",
    "y_pred_test = mode(s_test, axis=1)[0]\n",
    "print(f\"MAE Stacking only: {utils.MAE(y_pred_test, df_test_f['precio'].values)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_pred_test = np.average(s_test, axis=1)\n",
    "print(f\"MAE Stacking only: {utils.MAE(y_pred_test, df_test_f['precio'].values)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "def mae_res(weights):\n",
    "    y_pred_test = np.average(s_test, weights=weights, axis=1)\n",
    "    return utils.MAE(y_pred_test, df_test_f['precio'].values)\n",
    "\n",
    "x0 = [1] * len(s_test.T)\n",
    "minimize(mae_res, x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize, differential_evolution\n",
    "\n",
    "def mae_res(weights):\n",
    "    y_pred_test = np.average(s_test, weights=weights, axis=1)\n",
    "    return utils.MAE(y_pred_test, df_test_f['precio'].values)\n",
    "\n",
    "x0 = [(-3, 4)] * len(s_test.T)\n",
    "differential_evolution(mae_res, bounds=x0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "class BlendingTransformer():\n",
    "\n",
    "    def __init__(self, models, model_blend, val_split=0.10, verbose=True):\n",
    "        self.models = models\n",
    "        self.val_split = val_split\n",
    "        self.model_blend = model_blend\n",
    "        self.verbose = verbose\n",
    "\n",
    "        \n",
    "    def simple_fit(self, x, y):\n",
    "        x_cat = FeatureSelector(['provincia']).transform(x)\n",
    "        \n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, \n",
    "                                                            test_size=self.val_split,\n",
    "                                                            random_state=1,\n",
    "                                                            stratify=x_cat)\n",
    "\n",
    "        self._fit_first(x_train, y_train)\n",
    "        self._fit_second(x_eval, y_eval)\n",
    "        \n",
    "\n",
    "\n",
    "    def fit(self, x, y, x_test):\n",
    "\n",
    "        x_cat = FeatureSelector(['provincia']).transform(x)\n",
    "        \n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, \n",
    "                                                            test_size=self.val_split,\n",
    "                                                            random_state=0)\n",
    "#                                                             stratify=x_cat)\n",
    "\n",
    "        self._fit_first(x_train, y_train)\n",
    "        self._fit_second(x_eval, y_eval)\n",
    "\n",
    "        self._fit_first(x, y)\n",
    "        y_test = self._predict_second(x_test)\n",
    "\n",
    "        x = np.concatenate((x, x_test))\n",
    "        y = np.concatenate((y, y_test))\n",
    "        x_cat = FeatureSelector(['provincia', 'intervalo_metros_cubiertos']).transform(x)\n",
    "        \n",
    "        x_train, x_eval, y_train, y_eval = train_test_split(x, y, \n",
    "                                                            test_size=self.val_split, \n",
    "                                                            random_state=0)\n",
    "#                                                             stratify=x_cat)\n",
    "        self._fit_first(x_train, y_train)\n",
    "        self._fit_second(x_eval, y_eval)\n",
    "\n",
    "        self._fit_first(x, y)\n",
    "\n",
    "\n",
    "    def _fit_first(self, x, y):\n",
    "\n",
    "        for i in range(len(self.models)):   \n",
    "            if self.verbose:\n",
    "                print(f'Fitting {i}...')\n",
    "            self.models[i] = clone(self.models[i])\n",
    "            self.models[i].fit(x, y)\n",
    "\n",
    "\n",
    "    def _fit_second(self, x, y):\n",
    "        if self.verbose:\n",
    "            print(f'Fitting blender...')\n",
    "        self.model_blend = clone(self.model_blend)\n",
    "        self.model_blend.fit(self._predict_first(x), y)\n",
    "\n",
    "\n",
    "            \n",
    "    def _predict_first(self, x):\n",
    "        preds = []\n",
    "        for i in range(len(self.models)):\n",
    "            y_pred = self.models[i].predict(x)\n",
    "            preds.append(y_pred)\n",
    "\n",
    "        return np.array(preds).T\n",
    "\n",
    "\n",
    "    def _predict_second(self, x):\n",
    "        return self.model_blend.predict(self._predict_first(x))\n",
    "\n",
    "\n",
    "    def predict(self, x):\n",
    "        return self._predict_second(x)\n",
    "    \n",
    "    \n",
    "modelos = [\n",
    "           lgb_m,\n",
    "           catboost_m,\n",
    "           xgb_m,\n",
    "           forest_m\n",
    "          ]\n",
    "\n",
    "params_2nd = {\n",
    " 'bagging_freq': 0,\n",
    " 'max_bin': 861,\n",
    " 'max_depth': 4,\n",
    " 'min_data_in_leaf': 95,\n",
    " 'min_split_gain': 0.13726766148127256,\n",
    " 'num_leaves': 798,\n",
    " 'reg_lambda': 74.22833470483843,\n",
    " 'feature_fraction': 0.85,\n",
    " 'learning_rate': 0.15,\n",
    " 'objective': 'regression',\n",
    " 'boosting_type': 'dart',\n",
    " 'num_boost_round': 1000,\n",
    " 'metric': 'mae'}\n",
    "\n",
    "# params_2nd = {'bagging_fraction': 0.8243831977099841,\n",
    "#  'bagging_freq': int(10.0),\n",
    "#  'feature_fraction': 0.9228324501365147,\n",
    "#  'learning_rate': 0.050664243951241736,\n",
    "#  'max_depth': int(3.0),\n",
    "#  'num_leaves': int(78.0),\n",
    "#  'objective': 'regression',\n",
    "#  'boosting_type': 'dart',\n",
    "#  'num_boost_round': 1200,\n",
    "#  'metric': 'mae'}\n",
    "\n",
    "lgb_m_2nd = LightGBMWrapper(**params_2nd)\n",
    "\n",
    "blend = BlendingTransformer(modelos, lgb_m_2nd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple blend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "blend.simple_fit(df_train_f.drop('precio', axis=1).values, df_train_f['precio'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.MAE(blend.predict(df_test_f.drop('precio', axis=1).values), df_test_f['precio'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long blend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 0...\n",
      "[1]\tvalid_0's l1: 1.48682e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.36904e+06\n",
      "[3]\tvalid_0's l1: 1.26466e+06\n",
      "[4]\tvalid_0's l1: 1.17256e+06\n",
      "[5]\tvalid_0's l1: 1.09105e+06\n",
      "[6]\tvalid_0's l1: 1.01946e+06\n",
      "[7]\tvalid_0's l1: 956309\n",
      "[8]\tvalid_0's l1: 900921\n",
      "[9]\tvalid_0's l1: 853020\n",
      "[10]\tvalid_0's l1: 810861\n",
      "[11]\tvalid_0's l1: 772613\n",
      "[12]\tvalid_0's l1: 738978\n",
      "[13]\tvalid_0's l1: 710386\n",
      "[14]\tvalid_0's l1: 684068\n",
      "[15]\tvalid_0's l1: 662252\n",
      "[16]\tvalid_0's l1: 642139\n",
      "[17]\tvalid_0's l1: 624821\n",
      "[18]\tvalid_0's l1: 610373\n",
      "[19]\tvalid_0's l1: 597791\n",
      "[20]\tvalid_0's l1: 585636\n",
      "[21]\tvalid_0's l1: 574655\n",
      "[22]\tvalid_0's l1: 566305\n",
      "[23]\tvalid_0's l1: 557239\n",
      "[24]\tvalid_0's l1: 549744\n",
      "[25]\tvalid_0's l1: 543549\n",
      "[26]\tvalid_0's l1: 537631\n",
      "[27]\tvalid_0's l1: 532955\n",
      "[28]\tvalid_0's l1: 528318\n",
      "[29]\tvalid_0's l1: 524851\n",
      "[30]\tvalid_0's l1: 521363\n",
      "[31]\tvalid_0's l1: 518898\n",
      "[32]\tvalid_0's l1: 515962\n",
      "[33]\tvalid_0's l1: 513136\n",
      "[34]\tvalid_0's l1: 509793\n",
      "[35]\tvalid_0's l1: 508535\n",
      "[36]\tvalid_0's l1: 507035\n",
      "[37]\tvalid_0's l1: 505049\n",
      "[38]\tvalid_0's l1: 502737\n",
      "[39]\tvalid_0's l1: 501506\n",
      "[40]\tvalid_0's l1: 500704\n",
      "[41]\tvalid_0's l1: 500011\n",
      "[42]\tvalid_0's l1: 498484\n",
      "[43]\tvalid_0's l1: 497131\n",
      "[44]\tvalid_0's l1: 496090\n",
      "[45]\tvalid_0's l1: 495283\n",
      "[46]\tvalid_0's l1: 494875\n",
      "[47]\tvalid_0's l1: 494194\n",
      "[48]\tvalid_0's l1: 493355\n",
      "[49]\tvalid_0's l1: 492844\n",
      "[50]\tvalid_0's l1: 492311\n",
      "[51]\tvalid_0's l1: 491747\n",
      "[52]\tvalid_0's l1: 491383\n",
      "[53]\tvalid_0's l1: 490969\n",
      "[54]\tvalid_0's l1: 490795\n",
      "[55]\tvalid_0's l1: 490114\n",
      "[56]\tvalid_0's l1: 489976\n",
      "[57]\tvalid_0's l1: 489323\n",
      "[58]\tvalid_0's l1: 489144\n",
      "[59]\tvalid_0's l1: 488864\n",
      "[60]\tvalid_0's l1: 488014\n",
      "[61]\tvalid_0's l1: 487843\n",
      "[62]\tvalid_0's l1: 487296\n",
      "[63]\tvalid_0's l1: 486723\n",
      "[64]\tvalid_0's l1: 486392\n",
      "[65]\tvalid_0's l1: 485774\n",
      "[66]\tvalid_0's l1: 485043\n",
      "[67]\tvalid_0's l1: 484744\n",
      "[68]\tvalid_0's l1: 484608\n",
      "[69]\tvalid_0's l1: 483944\n",
      "[70]\tvalid_0's l1: 483424\n",
      "[71]\tvalid_0's l1: 483512\n",
      "[72]\tvalid_0's l1: 483321\n",
      "[73]\tvalid_0's l1: 482389\n",
      "[74]\tvalid_0's l1: 482071\n",
      "[75]\tvalid_0's l1: 481738\n",
      "[76]\tvalid_0's l1: 481116\n",
      "[77]\tvalid_0's l1: 480613\n",
      "[78]\tvalid_0's l1: 479867\n",
      "[79]\tvalid_0's l1: 479475\n",
      "[80]\tvalid_0's l1: 479088\n",
      "[81]\tvalid_0's l1: 478430\n",
      "[82]\tvalid_0's l1: 478145\n",
      "[83]\tvalid_0's l1: 477813\n",
      "[84]\tvalid_0's l1: 477154\n",
      "[85]\tvalid_0's l1: 476882\n",
      "[86]\tvalid_0's l1: 476779\n",
      "[87]\tvalid_0's l1: 476338\n",
      "[88]\tvalid_0's l1: 476188\n",
      "[89]\tvalid_0's l1: 476184\n",
      "[90]\tvalid_0's l1: 475955\n",
      "[91]\tvalid_0's l1: 475723\n",
      "[92]\tvalid_0's l1: 475585\n",
      "[93]\tvalid_0's l1: 475214\n",
      "[94]\tvalid_0's l1: 475020\n",
      "[95]\tvalid_0's l1: 475103\n",
      "[96]\tvalid_0's l1: 475164\n",
      "[97]\tvalid_0's l1: 475286\n",
      "[98]\tvalid_0's l1: 474725\n",
      "[99]\tvalid_0's l1: 474586\n",
      "[100]\tvalid_0's l1: 474965\n",
      "[101]\tvalid_0's l1: 474743\n",
      "[102]\tvalid_0's l1: 474607\n",
      "[103]\tvalid_0's l1: 474444\n",
      "[104]\tvalid_0's l1: 473965\n",
      "[105]\tvalid_0's l1: 473590\n",
      "[106]\tvalid_0's l1: 473435\n",
      "[107]\tvalid_0's l1: 473444\n",
      "[108]\tvalid_0's l1: 473223\n",
      "[109]\tvalid_0's l1: 473071\n",
      "[110]\tvalid_0's l1: 472723\n",
      "[111]\tvalid_0's l1: 472180\n",
      "[112]\tvalid_0's l1: 471673\n",
      "[113]\tvalid_0's l1: 471285\n",
      "[114]\tvalid_0's l1: 471252\n",
      "[115]\tvalid_0's l1: 471117\n",
      "[116]\tvalid_0's l1: 470865\n",
      "[117]\tvalid_0's l1: 470912\n",
      "[118]\tvalid_0's l1: 470927\n",
      "[119]\tvalid_0's l1: 470963\n",
      "[120]\tvalid_0's l1: 470803\n",
      "[121]\tvalid_0's l1: 470694\n",
      "[122]\tvalid_0's l1: 470675\n",
      "[123]\tvalid_0's l1: 470543\n",
      "[124]\tvalid_0's l1: 470288\n",
      "[125]\tvalid_0's l1: 470409\n",
      "[126]\tvalid_0's l1: 470352\n",
      "[127]\tvalid_0's l1: 470223\n",
      "[128]\tvalid_0's l1: 470275\n",
      "[129]\tvalid_0's l1: 470073\n",
      "[130]\tvalid_0's l1: 470104\n",
      "[131]\tvalid_0's l1: 470098\n",
      "[132]\tvalid_0's l1: 469805\n",
      "[133]\tvalid_0's l1: 469829\n",
      "[134]\tvalid_0's l1: 469800\n",
      "[135]\tvalid_0's l1: 469911\n",
      "[136]\tvalid_0's l1: 469795\n",
      "[137]\tvalid_0's l1: 469460\n",
      "[138]\tvalid_0's l1: 469487\n",
      "[139]\tvalid_0's l1: 469443\n",
      "[140]\tvalid_0's l1: 469384\n",
      "[141]\tvalid_0's l1: 469249\n",
      "[142]\tvalid_0's l1: 468917\n",
      "[143]\tvalid_0's l1: 468548\n",
      "[144]\tvalid_0's l1: 468429\n",
      "[145]\tvalid_0's l1: 468296\n",
      "[146]\tvalid_0's l1: 468419\n",
      "[147]\tvalid_0's l1: 468236\n",
      "[148]\tvalid_0's l1: 468356\n",
      "[149]\tvalid_0's l1: 468265\n",
      "[150]\tvalid_0's l1: 468453\n",
      "[151]\tvalid_0's l1: 468623\n",
      "[152]\tvalid_0's l1: 468645\n",
      "[153]\tvalid_0's l1: 468697\n",
      "[154]\tvalid_0's l1: 468635\n",
      "[155]\tvalid_0's l1: 468537\n",
      "[156]\tvalid_0's l1: 468208\n",
      "[157]\tvalid_0's l1: 468154\n",
      "[158]\tvalid_0's l1: 467895\n",
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      "[160]\tvalid_0's l1: 467648\n",
      "[161]\tvalid_0's l1: 467703\n",
      "[162]\tvalid_0's l1: 467469\n",
      "[163]\tvalid_0's l1: 467296\n",
      "[164]\tvalid_0's l1: 467179\n",
      "[165]\tvalid_0's l1: 467231\n",
      "[166]\tvalid_0's l1: 467439\n",
      "[167]\tvalid_0's l1: 467299\n",
      "[168]\tvalid_0's l1: 467115\n",
      "[169]\tvalid_0's l1: 467084\n",
      "[170]\tvalid_0's l1: 467009\n",
      "[171]\tvalid_0's l1: 467042\n",
      "[172]\tvalid_0's l1: 466924\n",
      "[173]\tvalid_0's l1: 466407\n",
      "[174]\tvalid_0's l1: 466406\n",
      "[175]\tvalid_0's l1: 466178\n",
      "[176]\tvalid_0's l1: 466036\n",
      "[177]\tvalid_0's l1: 466091\n",
      "[178]\tvalid_0's l1: 466129\n",
      "[179]\tvalid_0's l1: 466066\n",
      "[180]\tvalid_0's l1: 465872\n",
      "[181]\tvalid_0's l1: 465772\n",
      "[182]\tvalid_0's l1: 465755\n",
      "[183]\tvalid_0's l1: 465721\n",
      "[184]\tvalid_0's l1: 465780\n",
      "[185]\tvalid_0's l1: 465896\n",
      "[186]\tvalid_0's l1: 465831\n",
      "[187]\tvalid_0's l1: 465895\n",
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      "[189]\tvalid_0's l1: 465525\n",
      "[190]\tvalid_0's l1: 465401\n",
      "[191]\tvalid_0's l1: 465267\n",
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      "[193]\tvalid_0's l1: 465388\n",
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      "[240]\tvalid_0's l1: 461269\n",
      "[241]\tvalid_0's l1: 461151\n",
      "[242]\tvalid_0's l1: 461131\n",
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      "[244]\tvalid_0's l1: 461232\n",
      "[245]\tvalid_0's l1: 461146\n",
      "[246]\tvalid_0's l1: 461173\n",
      "[247]\tvalid_0's l1: 461190\n",
      "[248]\tvalid_0's l1: 461082\n",
      "[249]\tvalid_0's l1: 460973\n",
      "[250]\tvalid_0's l1: 460761\n",
      "[251]\tvalid_0's l1: 460876\n",
      "[252]\tvalid_0's l1: 460941\n",
      "[253]\tvalid_0's l1: 460713\n",
      "[254]\tvalid_0's l1: 460526\n",
      "[255]\tvalid_0's l1: 460533\n",
      "[256]\tvalid_0's l1: 460615\n",
      "[257]\tvalid_0's l1: 460441\n",
      "[258]\tvalid_0's l1: 460425\n",
      "[259]\tvalid_0's l1: 460561\n",
      "[260]\tvalid_0's l1: 460523\n",
      "[261]\tvalid_0's l1: 460568\n",
      "[262]\tvalid_0's l1: 460540\n",
      "[263]\tvalid_0's l1: 460503\n",
      "[264]\tvalid_0's l1: 460540\n",
      "[265]\tvalid_0's l1: 460692\n",
      "[266]\tvalid_0's l1: 460798\n",
      "[267]\tvalid_0's l1: 460939\n",
      "[268]\tvalid_0's l1: 460981\n",
      "[269]\tvalid_0's l1: 461129\n",
      "[270]\tvalid_0's l1: 461258\n",
      "[271]\tvalid_0's l1: 461266\n",
      "[272]\tvalid_0's l1: 461023\n",
      "[273]\tvalid_0's l1: 461077\n",
      "Early stopping, best iteration is:\n",
      "[258]\tvalid_0's l1: 460425\n",
      "Fitting 1...\n",
      "Fitting 2...\n",
      "[0]\tvalidation_0-mae:2.18134e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.88465e+06\n",
      "[2]\tvalidation_0-mae:1.63349e+06\n",
      "[3]\tvalidation_0-mae:1.41966e+06\n",
      "[4]\tvalidation_0-mae:1.24237e+06\n",
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      "[21]\tvalidation_0-mae:501193\n",
      "[22]\tvalidation_0-mae:498868\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23]\tvalidation_0-mae:496612\n",
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      "[270]\tvalidation_0-mae:476197\n",
      "[271]\tvalidation_0-mae:476174\n",
      "[272]\tvalidation_0-mae:476198\n",
      "[273]\tvalidation_0-mae:476210\n",
      "[274]\tvalidation_0-mae:476221\n",
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      "[278]\tvalidation_0-mae:476202\n",
      "[279]\tvalidation_0-mae:476202\n",
      "[280]\tvalidation_0-mae:476198\n",
      "[281]\tvalidation_0-mae:476184\n",
      "Stopping. Best iteration:\n",
      "[271]\tvalidation_0-mae:476174\n",
      "\n",
      "Fitting 3...\n",
      "Fitting blender...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/lightgbm/engine.py:148: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/lightgbm/callback.py:192: UserWarning: Early stopping is not available in dart mode\n",
      "  warnings.warn('Early stopping is not available in dart mode')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 0...\n",
      "[1]\tvalid_0's l1: 1.46846e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.35449e+06\n",
      "[3]\tvalid_0's l1: 1.25369e+06\n",
      "[4]\tvalid_0's l1: 1.16354e+06\n",
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      "Early stopping, best iteration is:\n",
      "[268]\tvalid_0's l1: 473207\n",
      "Fitting 1...\n",
      "Fitting 2...\n",
      "[0]\tvalidation_0-mae:2.18788e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.89129e+06\n",
      "[2]\tvalidation_0-mae:1.63768e+06\n",
      "[3]\tvalidation_0-mae:1.42291e+06\n",
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      "[13]\tvalidation_0-mae:577029\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14]\tvalidation_0-mae:557774\n",
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      "[215]\tvalidation_0-mae:477604\n",
      "[216]\tvalidation_0-mae:477635\n",
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      "[218]\tvalidation_0-mae:477620\n",
      "[219]\tvalidation_0-mae:477636\n",
      "[220]\tvalidation_0-mae:477635\n",
      "Stopping. Best iteration:\n",
      "[210]\tvalidation_0-mae:477569\n",
      "\n",
      "Fitting 3...\n",
      "Fitting 0...\n",
      "[1]\tvalid_0's l1: 1.46373e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.34261e+06\n",
      "[3]\tvalid_0's l1: 1.23618e+06\n",
      "[4]\tvalid_0's l1: 1.14191e+06\n",
      "[5]\tvalid_0's l1: 1.0589e+06\n",
      "[6]\tvalid_0's l1: 985893\n",
      "[7]\tvalid_0's l1: 920034\n",
      "[8]\tvalid_0's l1: 862938\n",
      "[9]\tvalid_0's l1: 811667\n",
      "[10]\tvalid_0's l1: 767309\n",
      "[11]\tvalid_0's l1: 727342\n",
      "[12]\tvalid_0's l1: 692594\n",
      "[13]\tvalid_0's l1: 661873\n",
      "[14]\tvalid_0's l1: 636032\n",
      "[15]\tvalid_0's l1: 612867\n",
      "[16]\tvalid_0's l1: 593153\n",
      "[17]\tvalid_0's l1: 575159\n",
      "[18]\tvalid_0's l1: 559346\n",
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      "[20]\tvalid_0's l1: 533775\n",
      "[21]\tvalid_0's l1: 523392\n",
      "[22]\tvalid_0's l1: 513144\n",
      "[23]\tvalid_0's l1: 505003\n",
      "[24]\tvalid_0's l1: 497648\n",
      "[25]\tvalid_0's l1: 490928\n",
      "[26]\tvalid_0's l1: 485702\n",
      "[27]\tvalid_0's l1: 480883\n",
      "[28]\tvalid_0's l1: 476199\n",
      "[29]\tvalid_0's l1: 472617\n",
      "[30]\tvalid_0's l1: 468962\n",
      "[31]\tvalid_0's l1: 465316\n",
      "[32]\tvalid_0's l1: 462289\n",
      "[33]\tvalid_0's l1: 459975\n",
      "[34]\tvalid_0's l1: 457372\n",
      "[35]\tvalid_0's l1: 455172\n",
      "[36]\tvalid_0's l1: 453380\n",
      "[37]\tvalid_0's l1: 451624\n",
      "[38]\tvalid_0's l1: 449832\n",
      "[39]\tvalid_0's l1: 448275\n",
      "[40]\tvalid_0's l1: 446925\n",
      "[41]\tvalid_0's l1: 445749\n",
      "[42]\tvalid_0's l1: 444208\n",
      "[43]\tvalid_0's l1: 442806\n",
      "[44]\tvalid_0's l1: 441787\n",
      "[45]\tvalid_0's l1: 440468\n",
      "[46]\tvalid_0's l1: 439335\n",
      "[47]\tvalid_0's l1: 438543\n",
      "[48]\tvalid_0's l1: 437773\n",
      "[49]\tvalid_0's l1: 436707\n",
      "[50]\tvalid_0's l1: 435680\n",
      "[51]\tvalid_0's l1: 435024\n",
      "[52]\tvalid_0's l1: 434343\n",
      "[53]\tvalid_0's l1: 434002\n",
      "[54]\tvalid_0's l1: 434087\n",
      "[55]\tvalid_0's l1: 433166\n",
      "[56]\tvalid_0's l1: 432867\n",
      "[57]\tvalid_0's l1: 431964\n",
      "[58]\tvalid_0's l1: 431741\n",
      "[59]\tvalid_0's l1: 431406\n",
      "[60]\tvalid_0's l1: 431002\n",
      "[61]\tvalid_0's l1: 430146\n",
      "[62]\tvalid_0's l1: 429777\n",
      "[63]\tvalid_0's l1: 429493\n",
      "[64]\tvalid_0's l1: 428592\n",
      "[65]\tvalid_0's l1: 428108\n",
      "[66]\tvalid_0's l1: 427611\n",
      "[67]\tvalid_0's l1: 427001\n",
      "[68]\tvalid_0's l1: 426544\n",
      "[69]\tvalid_0's l1: 425793\n",
      "[70]\tvalid_0's l1: 425316\n",
      "[71]\tvalid_0's l1: 424608\n",
      "[72]\tvalid_0's l1: 424499\n",
      "[73]\tvalid_0's l1: 423754\n",
      "[74]\tvalid_0's l1: 423619\n",
      "[75]\tvalid_0's l1: 423163\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[76]\tvalid_0's l1: 422628\n",
      "[77]\tvalid_0's l1: 422044\n",
      "[78]\tvalid_0's l1: 421388\n",
      "[79]\tvalid_0's l1: 421119\n",
      "[80]\tvalid_0's l1: 420785\n",
      "[81]\tvalid_0's l1: 420371\n",
      "[82]\tvalid_0's l1: 419915\n",
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      "[85]\tvalid_0's l1: 419441\n",
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      "[87]\tvalid_0's l1: 418612\n",
      "[88]\tvalid_0's l1: 418666\n",
      "[89]\tvalid_0's l1: 418243\n",
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      "[91]\tvalid_0's l1: 418019\n",
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      "[105]\tvalid_0's l1: 414325\n",
      "[106]\tvalid_0's l1: 414055\n",
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      "[119]\tvalid_0's l1: 411520\n",
      "[120]\tvalid_0's l1: 411333\n",
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      "[123]\tvalid_0's l1: 411035\n",
      "[124]\tvalid_0's l1: 410830\n",
      "[125]\tvalid_0's l1: 410579\n",
      "[126]\tvalid_0's l1: 410377\n",
      "[127]\tvalid_0's l1: 410554\n",
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      "[137]\tvalid_0's l1: 409834\n",
      "[138]\tvalid_0's l1: 409678\n",
      "[139]\tvalid_0's l1: 409690\n",
      "[140]\tvalid_0's l1: 409825\n",
      "[141]\tvalid_0's l1: 409789\n",
      "[142]\tvalid_0's l1: 409611\n",
      "[143]\tvalid_0's l1: 409351\n",
      "[144]\tvalid_0's l1: 409208\n",
      "[145]\tvalid_0's l1: 409256\n",
      "[146]\tvalid_0's l1: 409160\n",
      "[147]\tvalid_0's l1: 409015\n",
      "[148]\tvalid_0's l1: 408819\n",
      "[149]\tvalid_0's l1: 408899\n",
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      "[151]\tvalid_0's l1: 408832\n",
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      "[153]\tvalid_0's l1: 409050\n",
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      "[155]\tvalid_0's l1: 409211\n",
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      "[157]\tvalid_0's l1: 408805\n",
      "[158]\tvalid_0's l1: 408662\n",
      "[159]\tvalid_0's l1: 408238\n",
      "[160]\tvalid_0's l1: 408222\n",
      "[161]\tvalid_0's l1: 408399\n",
      "[162]\tvalid_0's l1: 408417\n",
      "[163]\tvalid_0's l1: 408205\n",
      "[164]\tvalid_0's l1: 408239\n",
      "[165]\tvalid_0's l1: 408172\n",
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      "[167]\tvalid_0's l1: 408198\n",
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      "[177]\tvalid_0's l1: 407141\n",
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      "[207]\tvalid_0's l1: 405656\n",
      "[208]\tvalid_0's l1: 405641\n",
      "[209]\tvalid_0's l1: 405400\n",
      "[210]\tvalid_0's l1: 405409\n",
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      "[260]\tvalid_0's l1: 402161\n",
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      "[262]\tvalid_0's l1: 401995\n",
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      "[266]\tvalid_0's l1: 401654\n",
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      "[275]\tvalid_0's l1: 401471\n",
      "[276]\tvalid_0's l1: 401498\n",
      "[277]\tvalid_0's l1: 401377\n",
      "[278]\tvalid_0's l1: 401178\n",
      "[279]\tvalid_0's l1: 401017\n",
      "[280]\tvalid_0's l1: 400907\n",
      "[281]\tvalid_0's l1: 400947\n",
      "[282]\tvalid_0's l1: 400760\n",
      "[283]\tvalid_0's l1: 400793\n",
      "[284]\tvalid_0's l1: 400810\n",
      "[285]\tvalid_0's l1: 400754\n",
      "[286]\tvalid_0's l1: 400718\n",
      "[287]\tvalid_0's l1: 400577\n",
      "[288]\tvalid_0's l1: 400626\n",
      "[289]\tvalid_0's l1: 400444\n",
      "[290]\tvalid_0's l1: 400346\n",
      "[291]\tvalid_0's l1: 400325\n",
      "[292]\tvalid_0's l1: 400339\n",
      "[293]\tvalid_0's l1: 400386\n",
      "[294]\tvalid_0's l1: 400344\n",
      "[295]\tvalid_0's l1: 400413\n",
      "[296]\tvalid_0's l1: 400425\n",
      "[297]\tvalid_0's l1: 400311\n",
      "[298]\tvalid_0's l1: 400278\n",
      "[299]\tvalid_0's l1: 400285\n",
      "[300]\tvalid_0's l1: 400233\n",
      "[301]\tvalid_0's l1: 400197\n",
      "[302]\tvalid_0's l1: 400166\n",
      "[303]\tvalid_0's l1: 400165\n",
      "[304]\tvalid_0's l1: 400227\n",
      "[305]\tvalid_0's l1: 400271\n",
      "[306]\tvalid_0's l1: 400240\n",
      "[307]\tvalid_0's l1: 400279\n",
      "[308]\tvalid_0's l1: 400176\n",
      "[309]\tvalid_0's l1: 400163\n",
      "[310]\tvalid_0's l1: 400203\n",
      "[311]\tvalid_0's l1: 400206\n",
      "[312]\tvalid_0's l1: 400174\n",
      "[313]\tvalid_0's l1: 400164\n",
      "[314]\tvalid_0's l1: 400143\n",
      "[315]\tvalid_0's l1: 400098\n",
      "[316]\tvalid_0's l1: 400155\n",
      "[317]\tvalid_0's l1: 400173\n",
      "[318]\tvalid_0's l1: 400179\n",
      "[319]\tvalid_0's l1: 400056\n",
      "[320]\tvalid_0's l1: 399956\n",
      "[321]\tvalid_0's l1: 400011\n",
      "[322]\tvalid_0's l1: 400086\n",
      "[323]\tvalid_0's l1: 400156\n",
      "[324]\tvalid_0's l1: 400028\n",
      "[325]\tvalid_0's l1: 400003\n",
      "[326]\tvalid_0's l1: 400057\n",
      "[327]\tvalid_0's l1: 400065\n",
      "[328]\tvalid_0's l1: 400129\n",
      "[329]\tvalid_0's l1: 399993\n",
      "[330]\tvalid_0's l1: 399937\n",
      "[331]\tvalid_0's l1: 399837\n",
      "[332]\tvalid_0's l1: 399855\n",
      "[333]\tvalid_0's l1: 399872\n",
      "[334]\tvalid_0's l1: 399867\n",
      "[335]\tvalid_0's l1: 399869\n",
      "[336]\tvalid_0's l1: 399765\n",
      "[337]\tvalid_0's l1: 399816\n",
      "[338]\tvalid_0's l1: 399737\n",
      "[339]\tvalid_0's l1: 399747\n",
      "[340]\tvalid_0's l1: 399668\n",
      "[341]\tvalid_0's l1: 399549\n",
      "[342]\tvalid_0's l1: 399567\n",
      "[343]\tvalid_0's l1: 399531\n",
      "[344]\tvalid_0's l1: 399435\n",
      "[345]\tvalid_0's l1: 399429\n",
      "[346]\tvalid_0's l1: 399402\n",
      "[347]\tvalid_0's l1: 399370\n",
      "[348]\tvalid_0's l1: 399312\n",
      "[349]\tvalid_0's l1: 399280\n",
      "[350]\tvalid_0's l1: 399303\n",
      "[351]\tvalid_0's l1: 399279\n",
      "[352]\tvalid_0's l1: 399178\n",
      "[353]\tvalid_0's l1: 399115\n",
      "[354]\tvalid_0's l1: 398968\n",
      "[355]\tvalid_0's l1: 398922\n",
      "[356]\tvalid_0's l1: 398883\n",
      "[357]\tvalid_0's l1: 398884\n",
      "[358]\tvalid_0's l1: 398808\n",
      "[359]\tvalid_0's l1: 398878\n",
      "[360]\tvalid_0's l1: 398738\n",
      "[361]\tvalid_0's l1: 398732\n",
      "[362]\tvalid_0's l1: 398648\n",
      "[363]\tvalid_0's l1: 398574\n",
      "[364]\tvalid_0's l1: 398584\n",
      "[365]\tvalid_0's l1: 398561\n",
      "[366]\tvalid_0's l1: 398484\n",
      "[367]\tvalid_0's l1: 398393\n",
      "[368]\tvalid_0's l1: 398353\n",
      "[369]\tvalid_0's l1: 398362\n",
      "[370]\tvalid_0's l1: 398429\n",
      "[371]\tvalid_0's l1: 398479\n",
      "[372]\tvalid_0's l1: 398361\n",
      "[373]\tvalid_0's l1: 398455\n",
      "[374]\tvalid_0's l1: 398416\n",
      "[375]\tvalid_0's l1: 398410\n",
      "[376]\tvalid_0's l1: 398474\n",
      "[377]\tvalid_0's l1: 398372\n",
      "[378]\tvalid_0's l1: 398363\n",
      "[379]\tvalid_0's l1: 398240\n",
      "[380]\tvalid_0's l1: 398196\n",
      "[381]\tvalid_0's l1: 398145\n",
      "[382]\tvalid_0's l1: 398225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[383]\tvalid_0's l1: 398122\n",
      "[384]\tvalid_0's l1: 398138\n",
      "[385]\tvalid_0's l1: 398131\n",
      "[386]\tvalid_0's l1: 398084\n",
      "[387]\tvalid_0's l1: 398037\n",
      "[388]\tvalid_0's l1: 397985\n",
      "[389]\tvalid_0's l1: 397945\n",
      "[390]\tvalid_0's l1: 397991\n",
      "[391]\tvalid_0's l1: 398057\n",
      "[392]\tvalid_0's l1: 397934\n",
      "[393]\tvalid_0's l1: 397937\n",
      "[394]\tvalid_0's l1: 398006\n",
      "[395]\tvalid_0's l1: 398022\n",
      "[396]\tvalid_0's l1: 397956\n",
      "[397]\tvalid_0's l1: 397843\n",
      "[398]\tvalid_0's l1: 397828\n",
      "[399]\tvalid_0's l1: 397745\n",
      "[400]\tvalid_0's l1: 397767\n",
      "[401]\tvalid_0's l1: 397830\n",
      "[402]\tvalid_0's l1: 397854\n",
      "[403]\tvalid_0's l1: 397874\n",
      "[404]\tvalid_0's l1: 397962\n",
      "[405]\tvalid_0's l1: 397938\n",
      "[406]\tvalid_0's l1: 397903\n",
      "[407]\tvalid_0's l1: 397718\n",
      "[408]\tvalid_0's l1: 397664\n",
      "[409]\tvalid_0's l1: 397571\n",
      "[410]\tvalid_0's l1: 397546\n",
      "[411]\tvalid_0's l1: 397599\n",
      "[412]\tvalid_0's l1: 397543\n",
      "[413]\tvalid_0's l1: 397463\n",
      "[414]\tvalid_0's l1: 397411\n",
      "[415]\tvalid_0's l1: 397266\n",
      "[416]\tvalid_0's l1: 397214\n",
      "[417]\tvalid_0's l1: 397199\n",
      "[418]\tvalid_0's l1: 397222\n",
      "[419]\tvalid_0's l1: 397182\n",
      "[420]\tvalid_0's l1: 397213\n",
      "[421]\tvalid_0's l1: 397108\n",
      "[422]\tvalid_0's l1: 397136\n",
      "[423]\tvalid_0's l1: 397129\n",
      "[424]\tvalid_0's l1: 397116\n",
      "[425]\tvalid_0's l1: 397066\n",
      "[426]\tvalid_0's l1: 397035\n",
      "[427]\tvalid_0's l1: 397031\n",
      "[428]\tvalid_0's l1: 397071\n",
      "[429]\tvalid_0's l1: 397044\n",
      "[430]\tvalid_0's l1: 397066\n",
      "[431]\tvalid_0's l1: 397033\n",
      "[432]\tvalid_0's l1: 397023\n",
      "[433]\tvalid_0's l1: 397094\n",
      "[434]\tvalid_0's l1: 397002\n",
      "[435]\tvalid_0's l1: 396988\n",
      "[436]\tvalid_0's l1: 396876\n",
      "[437]\tvalid_0's l1: 396949\n",
      "[438]\tvalid_0's l1: 396922\n",
      "[439]\tvalid_0's l1: 396850\n",
      "[440]\tvalid_0's l1: 396728\n",
      "[441]\tvalid_0's l1: 396665\n",
      "[442]\tvalid_0's l1: 396655\n",
      "[443]\tvalid_0's l1: 396591\n",
      "[444]\tvalid_0's l1: 396543\n",
      "[445]\tvalid_0's l1: 396533\n",
      "[446]\tvalid_0's l1: 396420\n",
      "[447]\tvalid_0's l1: 396418\n",
      "[448]\tvalid_0's l1: 396342\n",
      "[449]\tvalid_0's l1: 396335\n",
      "[450]\tvalid_0's l1: 396202\n",
      "[451]\tvalid_0's l1: 396246\n",
      "[452]\tvalid_0's l1: 396191\n",
      "[453]\tvalid_0's l1: 396118\n",
      "[454]\tvalid_0's l1: 396064\n",
      "[455]\tvalid_0's l1: 396025\n",
      "[456]\tvalid_0's l1: 396126\n",
      "[457]\tvalid_0's l1: 396004\n",
      "[458]\tvalid_0's l1: 396010\n",
      "[459]\tvalid_0's l1: 395984\n",
      "[460]\tvalid_0's l1: 395927\n",
      "[461]\tvalid_0's l1: 395993\n",
      "[462]\tvalid_0's l1: 395926\n",
      "[463]\tvalid_0's l1: 395841\n",
      "[464]\tvalid_0's l1: 395740\n",
      "[465]\tvalid_0's l1: 395679\n",
      "[466]\tvalid_0's l1: 395674\n",
      "[467]\tvalid_0's l1: 395678\n",
      "[468]\tvalid_0's l1: 395630\n",
      "[469]\tvalid_0's l1: 395709\n",
      "[470]\tvalid_0's l1: 395664\n",
      "[471]\tvalid_0's l1: 395657\n",
      "[472]\tvalid_0's l1: 395677\n",
      "[473]\tvalid_0's l1: 395661\n",
      "[474]\tvalid_0's l1: 395693\n",
      "[475]\tvalid_0's l1: 395660\n",
      "[476]\tvalid_0's l1: 395688\n",
      "[477]\tvalid_0's l1: 395718\n",
      "[478]\tvalid_0's l1: 395657\n",
      "[479]\tvalid_0's l1: 395672\n",
      "[480]\tvalid_0's l1: 395630\n",
      "[481]\tvalid_0's l1: 395666\n",
      "[482]\tvalid_0's l1: 395580\n",
      "[483]\tvalid_0's l1: 395588\n",
      "[484]\tvalid_0's l1: 395507\n",
      "[485]\tvalid_0's l1: 395505\n",
      "[486]\tvalid_0's l1: 395512\n",
      "[487]\tvalid_0's l1: 395461\n",
      "[488]\tvalid_0's l1: 395405\n",
      "[489]\tvalid_0's l1: 395462\n",
      "[490]\tvalid_0's l1: 395452\n",
      "[491]\tvalid_0's l1: 395449\n",
      "[492]\tvalid_0's l1: 395435\n",
      "[493]\tvalid_0's l1: 395418\n",
      "[494]\tvalid_0's l1: 395373\n",
      "[495]\tvalid_0's l1: 395377\n",
      "[496]\tvalid_0's l1: 395284\n",
      "[497]\tvalid_0's l1: 395217\n",
      "[498]\tvalid_0's l1: 395221\n",
      "[499]\tvalid_0's l1: 395224\n",
      "[500]\tvalid_0's l1: 395158\n",
      "[501]\tvalid_0's l1: 395073\n",
      "[502]\tvalid_0's l1: 395142\n",
      "[503]\tvalid_0's l1: 395027\n",
      "[504]\tvalid_0's l1: 395098\n",
      "[505]\tvalid_0's l1: 395147\n",
      "[506]\tvalid_0's l1: 395198\n",
      "[507]\tvalid_0's l1: 395233\n",
      "[508]\tvalid_0's l1: 395148\n",
      "[509]\tvalid_0's l1: 395253\n",
      "[510]\tvalid_0's l1: 395269\n",
      "[511]\tvalid_0's l1: 395285\n",
      "[512]\tvalid_0's l1: 395284\n",
      "[513]\tvalid_0's l1: 395337\n",
      "[514]\tvalid_0's l1: 395295\n",
      "[515]\tvalid_0's l1: 395156\n",
      "[516]\tvalid_0's l1: 395121\n",
      "[517]\tvalid_0's l1: 395120\n",
      "[518]\tvalid_0's l1: 395054\n",
      "Early stopping, best iteration is:\n",
      "[503]\tvalid_0's l1: 395027\n",
      "Fitting 1...\n",
      "Fitting 2...\n",
      "[0]\tvalidation_0-mae:2.19147e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.89167e+06\n",
      "[2]\tvalidation_0-mae:1.63433e+06\n",
      "[3]\tvalidation_0-mae:1.41725e+06\n",
      "[4]\tvalidation_0-mae:1.23536e+06\n",
      "[5]\tvalidation_0-mae:1.08314e+06\n",
      "[6]\tvalidation_0-mae:954850\n",
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      "[147]\tvalidation_0-mae:417998\n",
      "[148]\tvalidation_0-mae:417971\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[149]\tvalidation_0-mae:417877\n",
      "[150]\tvalidation_0-mae:417874\n",
      "[151]\tvalidation_0-mae:417850\n",
      "[152]\tvalidation_0-mae:417843\n",
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      "[154]\tvalidation_0-mae:417802\n",
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      "Stopping. Best iteration:\n",
      "[279]\tvalidation_0-mae:416450\n",
      "\n",
      "Fitting 3...\n",
      "Fitting blender...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/lightgbm/engine.py:148: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n",
      "/home/javier/.thumbnails/datos/datos-tp2/.venv/lib/python3.6/site-packages/lightgbm/callback.py:192: UserWarning: Early stopping is not available in dart mode\n",
      "  warnings.warn('Early stopping is not available in dart mode')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 0...\n",
      "[1]\tvalid_0's l1: 1.45142e+06\n",
      "Training until validation scores don't improve for 15 rounds\n",
      "[2]\tvalid_0's l1: 1.33264e+06\n",
      "[3]\tvalid_0's l1: 1.22686e+06\n",
      "[4]\tvalid_0's l1: 1.13197e+06\n",
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      "[275]\tvalid_0's l1: 416173\n",
      "[276]\tvalid_0's l1: 416201\n",
      "[277]\tvalid_0's l1: 416167\n",
      "[278]\tvalid_0's l1: 416256\n",
      "[279]\tvalid_0's l1: 416255\n",
      "[280]\tvalid_0's l1: 416265\n",
      "[281]\tvalid_0's l1: 416137\n",
      "[282]\tvalid_0's l1: 416171\n",
      "[283]\tvalid_0's l1: 416052\n",
      "[284]\tvalid_0's l1: 415987\n",
      "[285]\tvalid_0's l1: 415917\n",
      "[286]\tvalid_0's l1: 415857\n",
      "[287]\tvalid_0's l1: 415790\n",
      "[288]\tvalid_0's l1: 415696\n",
      "[289]\tvalid_0's l1: 415789\n",
      "[290]\tvalid_0's l1: 415770\n",
      "[291]\tvalid_0's l1: 415733\n",
      "[292]\tvalid_0's l1: 415647\n",
      "[293]\tvalid_0's l1: 415590\n",
      "[294]\tvalid_0's l1: 415467\n",
      "[295]\tvalid_0's l1: 415446\n",
      "[296]\tvalid_0's l1: 415440\n",
      "[297]\tvalid_0's l1: 415417\n",
      "[298]\tvalid_0's l1: 415364\n",
      "[299]\tvalid_0's l1: 415351\n",
      "[300]\tvalid_0's l1: 415262\n",
      "[301]\tvalid_0's l1: 415315\n",
      "[302]\tvalid_0's l1: 415356\n",
      "[303]\tvalid_0's l1: 415365\n",
      "[304]\tvalid_0's l1: 415357\n",
      "[305]\tvalid_0's l1: 415452\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[306]\tvalid_0's l1: 415427\n",
      "[307]\tvalid_0's l1: 415405\n",
      "[308]\tvalid_0's l1: 415448\n",
      "[309]\tvalid_0's l1: 415365\n",
      "[310]\tvalid_0's l1: 415224\n",
      "[311]\tvalid_0's l1: 415233\n",
      "[312]\tvalid_0's l1: 415219\n",
      "[313]\tvalid_0's l1: 415184\n",
      "[314]\tvalid_0's l1: 415094\n",
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      "[319]\tvalid_0's l1: 414751\n",
      "[320]\tvalid_0's l1: 414690\n",
      "[321]\tvalid_0's l1: 414691\n",
      "[322]\tvalid_0's l1: 414807\n",
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      "[325]\tvalid_0's l1: 414725\n",
      "[326]\tvalid_0's l1: 414729\n",
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      "[332]\tvalid_0's l1: 414160\n",
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      "[334]\tvalid_0's l1: 414110\n",
      "[335]\tvalid_0's l1: 414074\n",
      "[336]\tvalid_0's l1: 414047\n",
      "[337]\tvalid_0's l1: 413951\n",
      "[338]\tvalid_0's l1: 413996\n",
      "[339]\tvalid_0's l1: 414042\n",
      "[340]\tvalid_0's l1: 413937\n",
      "[341]\tvalid_0's l1: 413968\n",
      "[342]\tvalid_0's l1: 413823\n",
      "[343]\tvalid_0's l1: 413765\n",
      "[344]\tvalid_0's l1: 413652\n",
      "[345]\tvalid_0's l1: 413679\n",
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      "[347]\tvalid_0's l1: 413612\n",
      "[348]\tvalid_0's l1: 413569\n",
      "[349]\tvalid_0's l1: 413492\n",
      "[350]\tvalid_0's l1: 413478\n",
      "[351]\tvalid_0's l1: 413402\n",
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      "[353]\tvalid_0's l1: 413415\n",
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      "[360]\tvalid_0's l1: 413438\n",
      "[361]\tvalid_0's l1: 413489\n",
      "[362]\tvalid_0's l1: 413476\n",
      "[363]\tvalid_0's l1: 413419\n",
      "[364]\tvalid_0's l1: 413306\n",
      "[365]\tvalid_0's l1: 413287\n",
      "[366]\tvalid_0's l1: 413439\n",
      "[367]\tvalid_0's l1: 413399\n",
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      "[370]\tvalid_0's l1: 413271\n",
      "[371]\tvalid_0's l1: 413286\n",
      "[372]\tvalid_0's l1: 413184\n",
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      "[374]\tvalid_0's l1: 413007\n",
      "[375]\tvalid_0's l1: 412978\n",
      "[376]\tvalid_0's l1: 412912\n",
      "[377]\tvalid_0's l1: 412874\n",
      "[378]\tvalid_0's l1: 412919\n",
      "[379]\tvalid_0's l1: 412986\n",
      "[380]\tvalid_0's l1: 413037\n",
      "[381]\tvalid_0's l1: 413074\n",
      "[382]\tvalid_0's l1: 413076\n",
      "[383]\tvalid_0's l1: 413206\n",
      "[384]\tvalid_0's l1: 413105\n",
      "[385]\tvalid_0's l1: 413140\n",
      "[386]\tvalid_0's l1: 413135\n",
      "[387]\tvalid_0's l1: 413111\n",
      "[388]\tvalid_0's l1: 413094\n",
      "[389]\tvalid_0's l1: 413217\n",
      "[390]\tvalid_0's l1: 413185\n",
      "[391]\tvalid_0's l1: 413204\n",
      "[392]\tvalid_0's l1: 413101\n",
      "Early stopping, best iteration is:\n",
      "[377]\tvalid_0's l1: 412874\n",
      "Fitting 1...\n",
      "Fitting 2...\n",
      "[0]\tvalidation_0-mae:2.16221e+06\n",
      "Will train until validation_0-mae hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mae:1.864e+06\n",
      "[2]\tvalidation_0-mae:1.61037e+06\n",
      "[3]\tvalidation_0-mae:1.39504e+06\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[193]\tvalidation_0-mae:399601\n",
      "[194]\tvalidation_0-mae:399609\n",
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      "[236]\tvalidation_0-mae:398843\n",
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      "[239]\tvalidation_0-mae:398849\n",
      "[240]\tvalidation_0-mae:398857\n",
      "[241]\tvalidation_0-mae:398881\n",
      "[242]\tvalidation_0-mae:398894\n",
      "[243]\tvalidation_0-mae:398889\n",
      "[244]\tvalidation_0-mae:398879\n",
      "[245]\tvalidation_0-mae:398869\n",
      "[246]\tvalidation_0-mae:398870\n",
      "[247]\tvalidation_0-mae:398865\n",
      "Stopping. Best iteration:\n",
      "[237]\tvalidation_0-mae:398811\n",
      "\n",
      "Fitting 3...\n"
     ]
    }
   ],
   "source": [
    "blend.fit(df_train_f.drop('precio', axis=1).values, \n",
    "                          df_train_f['precio'].values, df_test_f.drop('precio', axis=1, errors='ignore').values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_s = df_test_f.copy()\n",
    "df_test_s['target'] = blend.predict(df_test_s.drop('precio', axis=1, errors='ignore').values)\n",
    "# utils.MAE(df_test_s['target'], df_test_s['precio'])\n",
    "# df_test_s = utils.pesificar_df(df_test_s, col_precio_in='target', col_precio_out='target')\n",
    "df_test_s[['id', 'target']].to_csv('respuesta68.csv', index = False)\n",
    "\n",
    "# 67: LightGBM con DART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_f['precio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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