w6_d4 - More & Ensemble Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  target  \n",
       "0 -0.002592  0.019908 -0.017646   151.0  \n",
       "1 -0.039493 -0.068330 -0.092204    75.0  \n",
       "2 -0.002592  0.002864 -0.025930   141.0  \n",
       "3  0.034309  0.022692 -0.009362   206.0  \n",
       "4 -0.002592 -0.031991 -0.046641   135.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_diabetes, load_wine\n",
    "\n",
    "data=load_diabetes()\n",
    "diabetes=pd.DataFrame(data.data, columns=data.feature_names)\n",
    "diabetes['target']=data.target\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  target  \n",
       "0                          3.92   1065.0       0  \n",
       "1                          3.40   1050.0       0  \n",
       "2                          3.17   1185.0       0  \n",
       "3                          3.45   1480.0       0  \n",
       "4                          2.93    735.0       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=load_wine()\n",
    "vino=pd.DataFrame(data.data, columns=data.feature_names)\n",
    "vino['target']=data.target\n",
    "vino.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_d, X_test_d, y_train_d, y_test_d=train_test_split(diabetes.drop('target', axis=1), diabetes.target)\n",
    "\n",
    "X_train_v, X_test_v, y_train_v, y_test_v=train_test_split(vino.drop('target', axis=1), vino.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
       "             eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
       "             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
       "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
       "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SGDR\n",
    "\n",
    "from sklearn.linear_model import SGDRegressor as SGDR\n",
    "sgdr=SGDR()\n",
    "sgdr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42625369049347084 0.37469493133461496\n"
     ]
    }
   ],
   "source": [
    "train_score=sgdr.score(X_train_d, y_train_d)\n",
    "test_score=sgdr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
       "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SGDC\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier as SGDC\n",
    "sgdc=SGDC()\n",
    "sgdc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5338345864661654 0.6\n"
     ]
    }
   ],
   "source": [
    "train_score=sgdc.score(X_train_v, y_train_v)\n",
    "test_score=sgdc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes\n",
    "# GNB\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB as GNB\n",
    "gnb=GNB()\n",
    "gnb.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9924812030075187 0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "train_score=gnb.score(X_train_v, y_train_v)\n",
    "test_score=gnb.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MNB\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB as MNB\n",
    "mnb=MNB(alpha=0.01)\n",
    "mnb.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8421052631578947 0.8222222222222222\n"
     ]
    }
   ],
   "source": [
    "train_score=mnb.score(X_train_v, y_train_v)\n",
    "test_score=mnb.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ComplementNB(alpha=0.01, class_prior=None, fit_prior=True, norm=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CNB\n",
    "\n",
    "from sklearn.naive_bayes import ComplementNB as CNB\n",
    "cnb=CNB(alpha=0.01)\n",
    "cnb.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6541353383458647 0.6\n"
     ]
    }
   ],
   "source": [
    "train_score=cnb.score(X_train_v, y_train_v)\n",
    "test_score=cnb.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BNB\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB as BNB\n",
    "bnb=BNB()\n",
    "bnb.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44360902255639095 0.26666666666666666\n"
     ]
    }
   ],
   "source": [
    "train_score=bnb.score(X_train_v, y_train_v)\n",
    "test_score=bnb.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNNR\n",
    "from sklearn.neighbors import KNeighborsRegressor as KNNR\n",
    "\n",
    "knnr=KNNR(n_neighbors=7)\n",
    "knnr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5803709123712041 0.3476672566286174\n"
     ]
    }
   ],
   "source": [
    "train_score=knnr.score(X_train_d, y_train_d)\n",
    "test_score=knnr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNNC\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNNC\n",
    "\n",
    "knnc=KNNC(n_neighbors=7)\n",
    "knnc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8045112781954887 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "train_score=knnc.score(X_train_v, y_train_v)\n",
    "test_score=knnc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grad Boost Regressor\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor as GBR\n",
    "\n",
    "gbr=GBR()\n",
    "gbr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8651255353807665 0.30958094283724225\n"
     ]
    }
   ],
   "source": [
    "train_score=gbr.score(X_train_d, y_train_d)\n",
    "test_score=gbr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grad Boost Classifier\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier as GBC\n",
    "\n",
    "gbc=GBC()\n",
    "gbc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9111111111111111\n"
     ]
    }
   ],
   "source": [
    "train_score=gbc.score(X_train_v, y_train_v)\n",
    "test_score=gbc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:52:02] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "             silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBR\n",
    "\n",
    "from xgboost import XGBRegressor as XGBR\n",
    "\n",
    "xgbr=XGBR()\n",
    "xgbr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8505089544530579 0.32668271490305556\n"
     ]
    }
   ],
   "source": [
    "train_score=xgbr.score(X_train_d, y_train_d)\n",
    "test_score=xgbr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='multi:softprob', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBC\n",
    "\n",
    "from xgboost import XGBClassifier as XGBC\n",
    "\n",
    "xgbc=XGBC()\n",
    "xgbc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "train_score=xgbc.score(X_train_v, y_train_v)\n",
    "test_score=xgbc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x12a054190>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CatBoost Regressor\n",
    "# https://catboost.ai/docs/concepts/python-reference_catboostregressor.html\n",
    "\n",
    "from catboost import CatBoostRegressor as CTR\n",
    "\n",
    "ctr=CTR()\n",
    "ctr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9817724292532144 0.3129766866095862\n"
     ]
    }
   ],
   "source": [
    "train_score=ctr.score(X_train_d, y_train_d)\n",
    "test_score=ctr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x12bde7610>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CatBoost Classifier\n",
    "\n",
    "from catboost import CatBoostClassifier as CTC\n",
    "\n",
    "ctc=CTC()\n",
    "ctc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.2\n"
     ]
    }
   ],
   "source": [
    "train_score=ctc.score(X_train_v, y_train_v)\n",
    "test_score=ctc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "              random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LightGBM Regressor\n",
    "\n",
    "from lightgbm import LGBMRegressor as LGBMR\n",
    "\n",
    "lgbmr=LGBMR()\n",
    "lgbmr.fit(X_train_d, y_train_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9278182631582381 0.2864664539446736\n"
     ]
    }
   ],
   "source": [
    "train_score=lgbmr.score(X_train_d, y_train_d)\n",
    "test_score=lgbmr.score(X_test_d, y_test_d)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "               importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "               n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "               random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LightGBM Classifier\n",
    "\n",
    "from lightgbm import LGBMClassifier as LGBMC\n",
    "\n",
    "lgbmc=LGBMC()\n",
    "lgbmc.fit(X_train_v, y_train_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "train_score=lgbmc.score(X_train_v, y_train_v)\n",
    "test_score=lgbmc.score(X_test_v, y_test_v)\n",
    "print (train_score, test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H2O (ML for dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"12.0.2\" 2019-07-16; OpenJDK Runtime Environment (build 12.0.2+10); OpenJDK 64-Bit Server VM (build 12.0.2+10, mixed mode, sharing)\n",
      "  Starting server from /Users/data/Library/Python/3.7/lib/python/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/xx/_vmf5v917hb0dgxz9fw65cnh0000gn/T/tmpm2x5uj0c\n",
      "  JVM stdout: /var/folders/xx/_vmf5v917hb0dgxz9fw65cnh0000gn/T/tmpm2x5uj0c/h2o_data_started_from_python.out\n",
      "  JVM stderr: /var/folders/xx/_vmf5v917hb0dgxz9fw65cnh0000gn/T/tmpm2x5uj0c/h2o_data_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/Mexico_City</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.26.0.10</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>20 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_data_11itg4</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>4 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>{'http': None, 'https': None}</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.7.5 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/Mexico_City\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.26.0.10\n",
       "H2O cluster version age:    20 days\n",
       "H2O cluster name:           H2O_from_python_data_11itg4\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    4 Gb\n",
       "H2O cluster total cores:    12\n",
       "H2O cluster allowed cores:  12\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:       {'http': None, 'https': None}\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python version:             3.7.5 final\n",
       "--------------------------  ------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "url='https://s3.amazonaws.com/erin-data/higgs/'\n",
    "\n",
    "train=h2o.import_file(url+'higgs_train_10k.csv')\n",
    "test=h2o.import_file(url+'higgs_test_5k.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train.columns\n",
    "y='response'\n",
    "X.remove(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[y]=train[y].asfactor()\n",
    "test[y]=test[y].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# modelo\n",
    "\n",
    "aml=H2OAutoML(max_models=20, seed=1)\n",
    "aml.train(x=X, y=y, training_frame=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>StackedEnsemble_AllModels_AutoML_20191128_110618   </td><td style=\"text-align: right;\">0.79033 </td><td style=\"text-align: right;\"> 0.550676</td><td style=\"text-align: right;\">              0.313638</td><td style=\"text-align: right;\">0.431892</td><td style=\"text-align: right;\">0.18653 </td></tr>\n",
       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20191128_110618</td><td style=\"text-align: right;\">0.789041</td><td style=\"text-align: right;\"> 0.551361</td><td style=\"text-align: right;\">              0.325969</td><td style=\"text-align: right;\">0.432357</td><td style=\"text-align: right;\">0.186932</td></tr>\n",
       "<tr><td>XGBoost_grid_1_AutoML_20191128_110618_model_4      </td><td style=\"text-align: right;\">0.785011</td><td style=\"text-align: right;\"> 0.556344</td><td style=\"text-align: right;\">              0.318063</td><td style=\"text-align: right;\">0.434527</td><td style=\"text-align: right;\">0.188814</td></tr>\n",
       "<tr><td>XGBoost_3_AutoML_20191128_110618                   </td><td style=\"text-align: right;\">0.784844</td><td style=\"text-align: right;\"> 0.557121</td><td style=\"text-align: right;\">              0.311898</td><td style=\"text-align: right;\">0.434717</td><td style=\"text-align: right;\">0.188979</td></tr>\n",
       "<tr><td>XGBoost_2_AutoML_20191128_110618                   </td><td style=\"text-align: right;\">0.784043</td><td style=\"text-align: right;\"> 0.555627</td><td style=\"text-align: right;\">              0.312643</td><td style=\"text-align: right;\">0.434452</td><td style=\"text-align: right;\">0.188748</td></tr>\n",
       "<tr><td>XGBoost_grid_1_AutoML_20191128_110618_model_3      </td><td style=\"text-align: right;\">0.784037</td><td style=\"text-align: right;\"> 0.558918</td><td style=\"text-align: right;\">              0.327596</td><td style=\"text-align: right;\">0.435303</td><td style=\"text-align: right;\">0.189489</td></tr>\n",
       "<tr><td>XGBoost_grid_1_AutoML_20191128_110618_model_1      </td><td style=\"text-align: right;\">0.783577</td><td style=\"text-align: right;\"> 0.559298</td><td style=\"text-align: right;\">              0.336173</td><td style=\"text-align: right;\">0.435577</td><td style=\"text-align: right;\">0.189727</td></tr>\n",
       "<tr><td>XGBoost_1_AutoML_20191128_110618                   </td><td style=\"text-align: right;\">0.783156</td><td style=\"text-align: right;\"> 0.55631 </td><td style=\"text-align: right;\">              0.322884</td><td style=\"text-align: right;\">0.434894</td><td style=\"text-align: right;\">0.189132</td></tr>\n",
       "<tr><td>GBM_5_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.780344</td><td style=\"text-align: right;\"> 0.558723</td><td style=\"text-align: right;\">              0.334826</td><td style=\"text-align: right;\">0.436138</td><td style=\"text-align: right;\">0.190216</td></tr>\n",
       "<tr><td>GBM_1_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.780286</td><td style=\"text-align: right;\"> 0.560701</td><td style=\"text-align: right;\">              0.316822</td><td style=\"text-align: right;\">0.43642 </td><td style=\"text-align: right;\">0.190462</td></tr>\n",
       "<tr><td>GBM_2_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.77971 </td><td style=\"text-align: right;\"> 0.560845</td><td style=\"text-align: right;\">              0.332059</td><td style=\"text-align: right;\">0.436651</td><td style=\"text-align: right;\">0.190664</td></tr>\n",
       "<tr><td>GBM_3_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.774661</td><td style=\"text-align: right;\"> 0.56568 </td><td style=\"text-align: right;\">              0.33475 </td><td style=\"text-align: right;\">0.43918 </td><td style=\"text-align: right;\">0.192879</td></tr>\n",
       "<tr><td>XGBoost_grid_1_AutoML_20191128_110618_model_2      </td><td style=\"text-align: right;\">0.773   </td><td style=\"text-align: right;\"> 0.577489</td><td style=\"text-align: right;\">              0.34726 </td><td style=\"text-align: right;\">0.443367</td><td style=\"text-align: right;\">0.196575</td></tr>\n",
       "<tr><td>GBM_4_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.769793</td><td style=\"text-align: right;\"> 0.571616</td><td style=\"text-align: right;\">              0.338649</td><td style=\"text-align: right;\">0.441869</td><td style=\"text-align: right;\">0.195248</td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_20191128_110618_model_1          </td><td style=\"text-align: right;\">0.769752</td><td style=\"text-align: right;\"> 0.572583</td><td style=\"text-align: right;\">              0.344331</td><td style=\"text-align: right;\">0.442452</td><td style=\"text-align: right;\">0.195764</td></tr>\n",
       "<tr><td>DRF_1_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.76334 </td><td style=\"text-align: right;\"> 0.580105</td><td style=\"text-align: right;\">              0.342391</td><td style=\"text-align: right;\">0.445477</td><td style=\"text-align: right;\">0.198449</td></tr>\n",
       "<tr><td>XRT_1_AutoML_20191128_110618                       </td><td style=\"text-align: right;\">0.76257 </td><td style=\"text-align: right;\"> 0.582324</td><td style=\"text-align: right;\">              0.347   </td><td style=\"text-align: right;\">0.446207</td><td style=\"text-align: right;\">0.199101</td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_20191128_110618_model_2          </td><td style=\"text-align: right;\">0.752753</td><td style=\"text-align: right;\"> 0.931836</td><td style=\"text-align: right;\">              0.345093</td><td style=\"text-align: right;\">0.496916</td><td style=\"text-align: right;\">0.246926</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20191128_110618_model_2 </td><td style=\"text-align: right;\">0.74168 </td><td style=\"text-align: right;\"> 0.600275</td><td style=\"text-align: right;\">              0.371788</td><td style=\"text-align: right;\">0.454351</td><td style=\"text-align: right;\">0.206435</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20191128_110618_model_1 </td><td style=\"text-align: right;\">0.697665</td><td style=\"text-align: right;\"> 0.66084 </td><td style=\"text-align: right;\">              0.410474</td><td style=\"text-align: right;\">0.475588</td><td style=\"text-align: right;\">0.226184</td></tr>\n",
       "<tr><td>GLM_grid_1_AutoML_20191128_110618_model_1          </td><td style=\"text-align: right;\">0.682648</td><td style=\"text-align: right;\"> 0.63852 </td><td style=\"text-align: right;\">              0.397234</td><td style=\"text-align: right;\">0.472683</td><td style=\"text-align: right;\">0.223429</td></tr>\n",
       "<tr><td>DeepLearning_1_AutoML_20191128_110618              </td><td style=\"text-align: right;\">0.677452</td><td style=\"text-align: right;\"> 0.643055</td><td style=\"text-align: right;\">              0.413804</td><td style=\"text-align: right;\">0.474501</td><td style=\"text-align: right;\">0.225151</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb=aml.leaderboard\n",
    "lb.head(rows=lb.nrows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OStackedEnsembleEstimator :  Stacked Ensemble\n",
      "Model Key:  StackedEnsemble_AllModels_AutoML_20191128_110618\n",
      "\n",
      "No model summary for this model\n",
      "\n",
      "ModelMetricsBinomialGLM: stackedensemble\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.10528564093956509\n",
      "RMSE: 0.3244774891106702\n",
      "LogLoss: 0.36133768216085166\n",
      "Null degrees of freedom: 9999\n",
      "Residual degrees of freedom: 9987\n",
      "Null deviance: 13828.11338742432\n",
      "Residual deviance: 7226.753643217034\n",
      "AIC: 7252.753643217034\n",
      "AUC: 0.9555637373697842\n",
      "pr_auc: 0.9587053187808241\n",
      "Gini: 0.9111274747395683\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.49624085112722405: \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>(734.0/4705.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>460.0</td>\n",
       "      <td>4835.0</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>(460.0/5295.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Total</td>\n",
       "      <td>4431.0</td>\n",
       "      <td>5569.0</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>(1194.0/10000.0)</td>\n",
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      "text/plain": [
       "               0       1   Error               Rate\n",
       "0      0  3971.0   734.0   0.156     (734.0/4705.0)\n",
       "1      1   460.0  4835.0  0.0869     (460.0/5295.0)\n",
       "2  Total  4431.0  5569.0  0.1194   (1194.0/10000.0)"
      ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
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       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>max f1</td>\n",
       "      <td>0.496241</td>\n",
       "      <td>0.890096</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>max f2</td>\n",
       "      <td>0.383641</td>\n",
       "      <td>0.930258</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.615770</td>\n",
       "      <td>0.901538</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.516569</td>\n",
       "      <td>0.881400</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>max precision</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>max recall</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.514113</td>\n",
       "      <td>0.761854</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.536544</td>\n",
       "      <td>0.878215</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.516569</td>\n",
       "      <td>0.880429</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max tns</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>4705.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>max fns</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>5285.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>max fps</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>4705.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>max tps</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>5295.000000</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.943788</td>\n",
       "      <td>0.998111</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold        value    idx\n",
       "0                        max f1   0.496241     0.890096  207.0\n",
       "1                        max f2   0.383641     0.930258  254.0\n",
       "2                  max f0point5   0.615770     0.901538  158.0\n",
       "3                  max accuracy   0.516569     0.881400  199.0\n",
       "4                 max precision   0.943788     1.000000    0.0\n",
       "5                    max recall   0.141282     1.000000  360.0\n",
       "6               max specificity   0.943788     1.000000    0.0\n",
       "7              max absolute_mcc   0.514113     0.761854  200.0\n",
       "8    max min_per_class_accuracy   0.536544     0.878215  190.0\n",
       "9   max mean_per_class_accuracy   0.516569     0.880429  199.0\n",
       "10                      max tns   0.943788  4705.000000    0.0\n",
       "11                      max fns   0.943788  5285.000000    0.0\n",
       "12                      max fps   0.045647  4705.000000  399.0\n",
       "13                      max tps   0.141282  5295.000000  360.0\n",
       "14                      max tnr   0.943788     1.000000    0.0\n",
       "15                      max fnr   0.943788     0.998111    0.0\n",
       "16                      max fpr   0.045647     1.000000  399.0\n",
       "17                      max tpr   0.141282     1.000000  360.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 52,95 %, avg score: 53,71 %\n"
     ]
    },
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       "      <th>group</th>\n",
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       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.923949</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.931682</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931682</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
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       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
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       "      <td>0.916958</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.920591</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926137</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.037771</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.912057</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.914344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.922206</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.056657</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.907811</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.909924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919135</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.075543</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.904071</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.905858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916480</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.094429</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.882922</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.888574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.893371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.904925</td>\n",
       "      <td>0.094429</td>\n",
       "      <td>0.188857</td>\n",
       "      <td>88.857413</td>\n",
       "      <td>88.857413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.859312</td>\n",
       "      <td>1.884797</td>\n",
       "      <td>1.887315</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.871125</td>\n",
       "      <td>0.999333</td>\n",
       "      <td>0.893658</td>\n",
       "      <td>0.094240</td>\n",
       "      <td>0.283097</td>\n",
       "      <td>88.479698</td>\n",
       "      <td>88.731508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.830289</td>\n",
       "      <td>1.865911</td>\n",
       "      <td>1.881964</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.845125</td>\n",
       "      <td>0.996500</td>\n",
       "      <td>0.881525</td>\n",
       "      <td>0.093296</td>\n",
       "      <td>0.376393</td>\n",
       "      <td>86.591124</td>\n",
       "      <td>88.196412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.761887</td>\n",
       "      <td>1.799811</td>\n",
       "      <td>1.854580</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.798224</td>\n",
       "      <td>0.982000</td>\n",
       "      <td>0.853758</td>\n",
       "      <td>0.179981</td>\n",
       "      <td>0.556374</td>\n",
       "      <td>79.981114</td>\n",
       "      <td>85.457979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.674336</td>\n",
       "      <td>1.637394</td>\n",
       "      <td>1.800283</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.719439</td>\n",
       "      <td>0.953250</td>\n",
       "      <td>0.820178</td>\n",
       "      <td>0.163739</td>\n",
       "      <td>0.720113</td>\n",
       "      <td>63.739377</td>\n",
       "      <td>80.028329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.559147</td>\n",
       "      <td>1.335222</td>\n",
       "      <td>1.707271</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.618007</td>\n",
       "      <td>0.904000</td>\n",
       "      <td>0.779744</td>\n",
       "      <td>0.133522</td>\n",
       "      <td>0.853636</td>\n",
       "      <td>33.522191</td>\n",
       "      <td>70.727101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.442055</td>\n",
       "      <td>0.932956</td>\n",
       "      <td>1.578218</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.502416</td>\n",
       "      <td>0.835667</td>\n",
       "      <td>0.733523</td>\n",
       "      <td>0.093296</td>\n",
       "      <td>0.946931</td>\n",
       "      <td>-6.704438</td>\n",
       "      <td>57.821845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.329772</td>\n",
       "      <td>0.404155</td>\n",
       "      <td>1.410495</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.385277</td>\n",
       "      <td>0.746857</td>\n",
       "      <td>0.683773</td>\n",
       "      <td>0.040415</td>\n",
       "      <td>0.987347</td>\n",
       "      <td>-59.584514</td>\n",
       "      <td>41.049508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.236893</td>\n",
       "      <td>0.103872</td>\n",
       "      <td>1.247167</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.282930</td>\n",
       "      <td>0.660375</td>\n",
       "      <td>0.633668</td>\n",
       "      <td>0.010387</td>\n",
       "      <td>0.997734</td>\n",
       "      <td>-89.612842</td>\n",
       "      <td>24.716714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.154046</td>\n",
       "      <td>0.020774</td>\n",
       "      <td>1.110901</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.196403</td>\n",
       "      <td>0.588222</td>\n",
       "      <td>0.585083</td>\n",
       "      <td>0.002077</td>\n",
       "      <td>0.999811</td>\n",
       "      <td>-97.922568</td>\n",
       "      <td>11.090127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>0.001889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.105321</td>\n",
       "      <td>0.529500</td>\n",
       "      <td>0.537107</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-99.811143</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                      0.01         0.923949  1.888574   \n",
       "1         2                      0.02         0.916958  1.888574   \n",
       "2         3                      0.03         0.912057  1.888574   \n",
       "3         4                      0.04         0.907811  1.888574   \n",
       "4         5                      0.05         0.904071  1.888574   \n",
       "5         6                      0.10         0.882922  1.888574   \n",
       "6         7                      0.15         0.859312  1.884797   \n",
       "7         8                      0.20         0.830289  1.865911   \n",
       "8         9                      0.30         0.761887  1.799811   \n",
       "9        10                      0.40         0.674336  1.637394   \n",
       "10       11                      0.50         0.559147  1.335222   \n",
       "11       12                      0.60         0.442055  0.932956   \n",
       "12       13                      0.70         0.329772  0.404155   \n",
       "13       14                      0.80         0.236893  0.103872   \n",
       "14       15                      0.90         0.154046  0.020774   \n",
       "15       16                      1.00         0.045647  0.001889   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          1.888574          1.000  0.931682                  1.000000   \n",
       "1          1.888574          1.000  0.920591                  1.000000   \n",
       "2          1.888574          1.000  0.914344                  1.000000   \n",
       "3          1.888574          1.000  0.909924                  1.000000   \n",
       "4          1.888574          1.000  0.905858                  1.000000   \n",
       "5          1.888574          1.000  0.893371                  1.000000   \n",
       "6          1.887315          0.998  0.871125                  0.999333   \n",
       "7          1.881964          0.988  0.845125                  0.996500   \n",
       "8          1.854580          0.953  0.798224                  0.982000   \n",
       "9          1.800283          0.867  0.719439                  0.953250   \n",
       "10         1.707271          0.707  0.618007                  0.904000   \n",
       "11         1.578218          0.494  0.502416                  0.835667   \n",
       "12         1.410495          0.214  0.385277                  0.746857   \n",
       "13         1.247167          0.055  0.282930                  0.660375   \n",
       "14         1.110901          0.011  0.196403                  0.588222   \n",
       "15         1.000000          0.001  0.105321                  0.529500   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate       gain  \\\n",
       "0           0.931682      0.018886                 0.018886  88.857413   \n",
       "1           0.926137      0.018886                 0.037771  88.857413   \n",
       "2           0.922206      0.018886                 0.056657  88.857413   \n",
       "3           0.919135      0.018886                 0.075543  88.857413   \n",
       "4           0.916480      0.018886                 0.094429  88.857413   \n",
       "5           0.904925      0.094429                 0.188857  88.857413   \n",
       "6           0.893658      0.094240                 0.283097  88.479698   \n",
       "7           0.881525      0.093296                 0.376393  86.591124   \n",
       "8           0.853758      0.179981                 0.556374  79.981114   \n",
       "9           0.820178      0.163739                 0.720113  63.739377   \n",
       "10          0.779744      0.133522                 0.853636  33.522191   \n",
       "11          0.733523      0.093296                 0.946931  -6.704438   \n",
       "12          0.683773      0.040415                 0.987347 -59.584514   \n",
       "13          0.633668      0.010387                 0.997734 -89.612842   \n",
       "14          0.585083      0.002077                 0.999811 -97.922568   \n",
       "15          0.537107      0.000189                 1.000000 -99.811143   \n",
       "\n",
       "    cumulative_gain  \n",
       "0         88.857413  \n",
       "1         88.857413  \n",
       "2         88.857413  \n",
       "3         88.857413  \n",
       "4         88.857413  \n",
       "5         88.857413  \n",
       "6         88.731508  \n",
       "7         88.196412  \n",
       "8         85.457979  \n",
       "9         80.028329  \n",
       "10        70.727101  \n",
       "11        57.821845  \n",
       "12        41.049508  \n",
       "13        24.716714  \n",
       "14        11.090127  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomialGLM: stackedensemble\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 0.18653030653176983\n",
      "RMSE: 0.4318915448718229\n",
      "LogLoss: 0.5506762349913022\n",
      "Null degrees of freedom: 9999\n",
      "Residual degrees of freedom: 9986\n",
      "Null deviance: 13829.71731382349\n",
      "Residual deviance: 11013.524699826045\n",
      "AIC: 11041.524699826045\n",
      "AUC: 0.7903299987255638\n",
      "pr_auc: 0.8062919719245256\n",
      "Gini: 0.5806599974511275\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.36119493061345753: \n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2381.0</td>\n",
       "      <td>2324.0</td>\n",
       "      <td>0.4939</td>\n",
       "      <td>(2324.0/4705.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>706.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>0.1333</td>\n",
       "      <td>(706.0/5295.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Total</td>\n",
       "      <td>3087.0</td>\n",
       "      <td>6913.0</td>\n",
       "      <td>0.303</td>\n",
       "      <td>(3030.0/10000.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0       1   Error               Rate\n",
       "0      0  2381.0  2324.0  0.4939    (2324.0/4705.0)\n",
       "1      1   706.0  4589.0  0.1333     (706.0/5295.0)\n",
       "2  Total  3087.0  6913.0   0.303   (3030.0/10000.0)"
      ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>max f1</td>\n",
       "      <td>0.361195</td>\n",
       "      <td>0.751802</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>max f2</td>\n",
       "      <td>0.153129</td>\n",
       "      <td>0.861122</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.588439</td>\n",
       "      <td>0.739196</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.520302</td>\n",
       "      <td>0.715000</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>max precision</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>max recall</td>\n",
       "      <td>0.064382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.553151</td>\n",
       "      <td>0.431172</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.525721</td>\n",
       "      <td>0.713921</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.553151</td>\n",
       "      <td>0.715877</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max tns</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>4705.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>max fns</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>5287.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>max fps</td>\n",
       "      <td>0.052341</td>\n",
       "      <td>4705.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>max tps</td>\n",
       "      <td>0.064382</td>\n",
       "      <td>5295.000000</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.941153</td>\n",
       "      <td>0.998489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.052341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.064382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold        value    idx\n",
       "0                        max f1   0.361195     0.751802  265.0\n",
       "1                        max f2   0.153129     0.861122  361.0\n",
       "2                  max f0point5   0.588439     0.739196  165.0\n",
       "3                  max accuracy   0.520302     0.715000  195.0\n",
       "4                 max precision   0.941153     1.000000    0.0\n",
       "5                    max recall   0.064382     1.000000  395.0\n",
       "6               max specificity   0.941153     1.000000    0.0\n",
       "7              max absolute_mcc   0.553151     0.431172  181.0\n",
       "8    max min_per_class_accuracy   0.525721     0.713921  193.0\n",
       "9   max mean_per_class_accuracy   0.553151     0.715877  181.0\n",
       "10                      max tns   0.941153  4705.000000    0.0\n",
       "11                      max fns   0.941153  5287.000000    0.0\n",
       "12                      max fps   0.052341  4705.000000  399.0\n",
       "13                      max tps   0.064382  5295.000000  395.0\n",
       "14                      max tnr   0.941153     1.000000    0.0\n",
       "15                      max fnr   0.941153     0.998489    0.0\n",
       "16                      max fpr   0.052341     1.000000  399.0\n",
       "17                      max tpr   0.064382     1.000000  395.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 52,95 %, avg score: 52,93 %\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>cumulative_data_fraction</th>\n",
       "      <th>lower_threshold</th>\n",
       "      <th>lift</th>\n",
       "      <th>cumulative_lift</th>\n",
       "      <th>response_rate</th>\n",
       "      <th>score</th>\n",
       "      <th>cumulative_response_rate</th>\n",
       "      <th>cumulative_score</th>\n",
       "      <th>capture_rate</th>\n",
       "      <th>cumulative_capture_rate</th>\n",
       "      <th>gain</th>\n",
       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.920197</td>\n",
       "      <td>1.831917</td>\n",
       "      <td>1.831917</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.927906</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.927906</td>\n",
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       "      <td>0.018319</td>\n",
       "      <td>83.191690</td>\n",
       "      <td>83.191690</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.911380</td>\n",
       "      <td>1.775260</td>\n",
       "      <td>1.803588</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.915512</td>\n",
       "      <td>0.955000</td>\n",
       "      <td>0.921709</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>77.525968</td>\n",
       "      <td>80.358829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.903764</td>\n",
       "      <td>1.850803</td>\n",
       "      <td>1.819326</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.907780</td>\n",
       "      <td>0.963333</td>\n",
       "      <td>0.917066</td>\n",
       "      <td>0.018508</td>\n",
       "      <td>0.054580</td>\n",
       "      <td>85.080264</td>\n",
       "      <td>81.932641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.897695</td>\n",
       "      <td>1.775260</td>\n",
       "      <td>1.808310</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.900570</td>\n",
       "      <td>0.957500</td>\n",
       "      <td>0.912942</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>0.072332</td>\n",
       "      <td>77.525968</td>\n",
       "      <td>80.830973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.892494</td>\n",
       "      <td>1.775260</td>\n",
       "      <td>1.801700</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.895152</td>\n",
       "      <td>0.954000</td>\n",
       "      <td>0.909384</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>0.090085</td>\n",
       "      <td>77.525968</td>\n",
       "      <td>80.169972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.865620</td>\n",
       "      <td>1.711048</td>\n",
       "      <td>1.756374</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.879316</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.894350</td>\n",
       "      <td>0.085552</td>\n",
       "      <td>0.175637</td>\n",
       "      <td>71.104816</td>\n",
       "      <td>75.637394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.833481</td>\n",
       "      <td>1.624174</td>\n",
       "      <td>1.712307</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.849965</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.879555</td>\n",
       "      <td>0.081209</td>\n",
       "      <td>0.256846</td>\n",
       "      <td>62.417375</td>\n",
       "      <td>71.230721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.799758</td>\n",
       "      <td>1.548631</td>\n",
       "      <td>1.671388</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.816171</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>0.863709</td>\n",
       "      <td>0.077432</td>\n",
       "      <td>0.334278</td>\n",
       "      <td>54.863078</td>\n",
       "      <td>67.138810</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.720163</td>\n",
       "      <td>1.376771</td>\n",
       "      <td>1.573182</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.761657</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>0.829692</td>\n",
       "      <td>0.137677</td>\n",
       "      <td>0.471955</td>\n",
       "      <td>37.677054</td>\n",
       "      <td>57.318225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.632492</td>\n",
       "      <td>1.203022</td>\n",
       "      <td>1.480642</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.676919</td>\n",
       "      <td>0.784000</td>\n",
       "      <td>0.791498</td>\n",
       "      <td>0.120302</td>\n",
       "      <td>0.592257</td>\n",
       "      <td>20.302172</td>\n",
       "      <td>48.064212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.537840</td>\n",
       "      <td>1.102927</td>\n",
       "      <td>1.405099</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.586553</td>\n",
       "      <td>0.744000</td>\n",
       "      <td>0.750509</td>\n",
       "      <td>0.110293</td>\n",
       "      <td>0.702550</td>\n",
       "      <td>10.292729</td>\n",
       "      <td>40.509915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.445712</td>\n",
       "      <td>0.874410</td>\n",
       "      <td>1.316651</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.490779</td>\n",
       "      <td>0.697167</td>\n",
       "      <td>0.707221</td>\n",
       "      <td>0.087441</td>\n",
       "      <td>0.789991</td>\n",
       "      <td>-12.559018</td>\n",
       "      <td>31.665093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.351513</td>\n",
       "      <td>0.817753</td>\n",
       "      <td>1.245380</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.399565</td>\n",
       "      <td>0.659429</td>\n",
       "      <td>0.663270</td>\n",
       "      <td>0.081775</td>\n",
       "      <td>0.871766</td>\n",
       "      <td>-18.224740</td>\n",
       "      <td>24.537974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.267160</td>\n",
       "      <td>0.613787</td>\n",
       "      <td>1.166431</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.308156</td>\n",
       "      <td>0.617625</td>\n",
       "      <td>0.618881</td>\n",
       "      <td>0.061379</td>\n",
       "      <td>0.933144</td>\n",
       "      <td>-38.621341</td>\n",
       "      <td>16.643059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.177068</td>\n",
       "      <td>0.474032</td>\n",
       "      <td>1.089497</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.223438</td>\n",
       "      <td>0.576889</td>\n",
       "      <td>0.574943</td>\n",
       "      <td>0.047403</td>\n",
       "      <td>0.980548</td>\n",
       "      <td>-52.596789</td>\n",
       "      <td>8.949743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.048423</td>\n",
       "      <td>0.194523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.118718</td>\n",
       "      <td>0.529500</td>\n",
       "      <td>0.529320</td>\n",
       "      <td>0.019452</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-80.547686</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                      0.01         0.920197  1.831917   \n",
       "1         2                      0.02         0.911380  1.775260   \n",
       "2         3                      0.03         0.903764  1.850803   \n",
       "3         4                      0.04         0.897695  1.775260   \n",
       "4         5                      0.05         0.892494  1.775260   \n",
       "5         6                      0.10         0.865620  1.711048   \n",
       "6         7                      0.15         0.833481  1.624174   \n",
       "7         8                      0.20         0.799758  1.548631   \n",
       "8         9                      0.30         0.720163  1.376771   \n",
       "9        10                      0.40         0.632492  1.203022   \n",
       "10       11                      0.50         0.537840  1.102927   \n",
       "11       12                      0.60         0.445712  0.874410   \n",
       "12       13                      0.70         0.351513  0.817753   \n",
       "13       14                      0.80         0.267160  0.613787   \n",
       "14       15                      0.90         0.177068  0.474032   \n",
       "15       16                      1.00         0.048423  0.194523   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          1.831917          0.970  0.927906                  0.970000   \n",
       "1          1.803588          0.940  0.915512                  0.955000   \n",
       "2          1.819326          0.980  0.907780                  0.963333   \n",
       "3          1.808310          0.940  0.900570                  0.957500   \n",
       "4          1.801700          0.940  0.895152                  0.954000   \n",
       "5          1.756374          0.906  0.879316                  0.930000   \n",
       "6          1.712307          0.860  0.849965                  0.906667   \n",
       "7          1.671388          0.820  0.816171                  0.885000   \n",
       "8          1.573182          0.729  0.761657                  0.833000   \n",
       "9          1.480642          0.637  0.676919                  0.784000   \n",
       "10         1.405099          0.584  0.586553                  0.744000   \n",
       "11         1.316651          0.463  0.490779                  0.697167   \n",
       "12         1.245380          0.433  0.399565                  0.659429   \n",
       "13         1.166431          0.325  0.308156                  0.617625   \n",
       "14         1.089497          0.251  0.223438                  0.576889   \n",
       "15         1.000000          0.103  0.118718                  0.529500   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate       gain  \\\n",
       "0           0.927906      0.018319                 0.018319  83.191690   \n",
       "1           0.921709      0.017753                 0.036072  77.525968   \n",
       "2           0.917066      0.018508                 0.054580  85.080264   \n",
       "3           0.912942      0.017753                 0.072332  77.525968   \n",
       "4           0.909384      0.017753                 0.090085  77.525968   \n",
       "5           0.894350      0.085552                 0.175637  71.104816   \n",
       "6           0.879555      0.081209                 0.256846  62.417375   \n",
       "7           0.863709      0.077432                 0.334278  54.863078   \n",
       "8           0.829692      0.137677                 0.471955  37.677054   \n",
       "9           0.791498      0.120302                 0.592257  20.302172   \n",
       "10          0.750509      0.110293                 0.702550  10.292729   \n",
       "11          0.707221      0.087441                 0.789991 -12.559018   \n",
       "12          0.663270      0.081775                 0.871766 -18.224740   \n",
       "13          0.618881      0.061379                 0.933144 -38.621341   \n",
       "14          0.574943      0.047403                 0.980548 -52.596789   \n",
       "15          0.529320      0.019452                 1.000000 -80.547686   \n",
       "\n",
       "    cumulative_gain  \n",
       "0         83.191690  \n",
       "1         80.358829  \n",
       "2         81.932641  \n",
       "3         80.830973  \n",
       "4         80.169972  \n",
       "5         75.637394  \n",
       "6         71.230721  \n",
       "7         67.138810  \n",
       "8         57.318225  \n",
       "9         48.064212  \n",
       "10        40.509915  \n",
       "11        31.665093  \n",
       "12        24.537974  \n",
       "13        16.643059  \n",
       "14         8.949743  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
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       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.736608</td><td style=\"text-align: right;\">0.263392</td></tr>\n",
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": []
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds=aml.leader.predict(test)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  response</th></tr>\n",
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       "<tr><td style=\"text-align: right;\">         0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[y]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  \n",
       "2  2.31  \n",
       "3  2.63  \n",
       "4  2.75  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data/diamonds.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=['Unnamed: 0', 'price', 'cut', 'color', 'clarity'])\n",
    "y=df.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:26:27] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "n_df['xgbr']=xgbr.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df['lbgmr']=lgbmr.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "n_df['ctr']=ctr.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_df['gbr']=gbr.fit(X_train, y_train).predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xgbr</th>\n",
       "      <th>lbgmr</th>\n",
       "      <th>ctr</th>\n",
       "      <th>gbr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6125.557129</td>\n",
       "      <td>5883.477257</td>\n",
       "      <td>5994.724594</td>\n",
       "      <td>6099.274687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6546.608398</td>\n",
       "      <td>5292.906895</td>\n",
       "      <td>5930.915799</td>\n",
       "      <td>6535.302704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9787.240234</td>\n",
       "      <td>8937.473552</td>\n",
       "      <td>9463.683778</td>\n",
       "      <td>9517.920971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2695.185303</td>\n",
       "      <td>2748.855638</td>\n",
       "      <td>2765.759316</td>\n",
       "      <td>2690.203673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>937.427368</td>\n",
       "      <td>922.045530</td>\n",
       "      <td>914.525655</td>\n",
       "      <td>930.800844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          xgbr        lbgmr          ctr          gbr\n",
       "0  6125.557129  5883.477257  5994.724594  6099.274687\n",
       "1  6546.608398  5292.906895  5930.915799  6535.302704\n",
       "2  9787.240234  8937.473552  9463.683778  9517.920971\n",
       "3  2695.185303  2748.855638  2765.759316  2690.203673\n",
       "4   937.427368   922.045530   914.525655   930.800844"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression as LR\n",
    "\n",
    "linreg=LR()\n",
    "linreg.fit(n_df.values, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8919048585941831"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.score(n_df.values, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8905276311138302"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8908474256011555"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbmr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8915512427555063"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8904642649956624"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:33:56] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8798817547354505"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mlxtend\n",
    "\n",
    "from mlxtend.regressor import StackingRegressor\n",
    "\n",
    "metamodelo=StackingRegressor(regressors=[xgbr, gbr, lgbmr, ctr], meta_regressor=linreg)\n",
    "metamodelo.fit(X_train, y_train)\n",
    "metamodelo.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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