dataming.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ucimlrepo import fetch_ucirepo \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "heart_disease = fetch_ucirepo(id=45) \n",
    "\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "\n",
    "X = heart_disease.data.features\n",
    "y = heart_disease.data.targets\n",
    "y = y.where(y == 0, 1)\n",
    "\n",
    "data = pd.concat([X, y], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0      164\n",
       "1      139\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
       "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
       "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
       "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
       "\n",
       "    ca  thal  \n",
       "0  0.0   6.0  \n",
       "1  3.0   3.0  \n",
       "2  2.0   7.0  \n",
       "3  0.0   3.0  \n",
       "4  0.0   3.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>4.734219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.467299</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>1.939706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
       "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  241.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  275.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  299.000000   \n",
       "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.672241   \n",
       "std      0.994971   22.875003    0.469794    1.161075    0.616226    0.937438   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal  \n",
       "count  301.000000  \n",
       "mean     4.734219  \n",
       "std      1.939706  \n",
       "min      3.000000  \n",
       "25%      3.000000  \n",
       "50%      3.000000  \n",
       "75%      7.000000  \n",
       "max      7.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = X['age']\n",
    "age.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex = X['sex']\n",
    "sex.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cp'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp = X['cp']\n",
    "\n",
    "cp.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tresbps = X['trestbps']\n",
    "tresbps.plot(kind='hist', bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chol = X['chol']\n",
    "chol.plot(kind='hist', bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fbs = X['fbs']\n",
    "fbs.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "restecg = X['restecg']\n",
    "restecg.value_counts().plot(kind='pie') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thalach = X['thalach']\n",
    "thalach.plot(kind='hist', bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exang = X['exang']\n",
    "exang.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oldpeak = X['oldpeak']\n",
    "oldpeak.plot(kind='hist', bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope = X['slope']\n",
    "slope.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca = X['ca']\n",
    "ca.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thal = X['thal']\n",
    "thal.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = X[['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']]\n",
    "num = X.drop(columns=cat.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          4\n",
       "thal        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_corr = pd.concat([num, y], axis=1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(num_corr, annot=True, fmt='.2g', cmap='coolwarm')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "p_values_matrix = pd.DataFrame(index=cat.columns, columns=cat.columns)\n",
    "cat = pd.concat([cat, y], axis=1)\n",
    "categorical_columns = cat.columns\n",
    "\n",
    "p_values_matrix = pd.DataFrame(index=categorical_columns, columns=categorical_columns, dtype=np.float64)\n",
    "\n",
    "# 对每对分类变量进行卡方检验并填充p值\n",
    "for i in range(len(categorical_columns)):\n",
    "    for j in range(i, len(categorical_columns)):\n",
    "        col1 = categorical_columns[i]\n",
    "        col2 = categorical_columns[j]\n",
    "        if i == j:\n",
    "            p_values_matrix.loc[col1, col2] = 1.0\n",
    "        else:\n",
    "            contingency_table = pd.crosstab(cat[col1], cat[col2])\n",
    "            _, p, _, _ = chi2_contingency(contingency_table)\n",
    "            p_values_matrix.loc[col1, col2] = p\n",
    "            p_values_matrix.loc[col2, col1] = p\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(p_values_matrix, annot=True, cmap='coolwarm', fmt='.2g')\n",
    "plt.show()\n",
    "\n",
    "cat = cat.drop(columns='num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>exang</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.885024e-02</td>\n",
       "      <td>0.509260</td>\n",
       "      <td>0.166534</td>\n",
       "      <td>1.579410e-02</td>\n",
       "      <td>6.951351e-01</td>\n",
       "      <td>1.278866e-01</td>\n",
       "      <td>1.225282e-10</td>\n",
       "      <td>2.666712e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>7.885024e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.254363</td>\n",
       "      <td>0.140289</td>\n",
       "      <td>3.051565e-14</td>\n",
       "      <td>1.605360e-04</td>\n",
       "      <td>3.540681e-04</td>\n",
       "      <td>5.030028e-07</td>\n",
       "      <td>1.251711e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>5.092599e-01</td>\n",
       "      <td>2.543631e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336950</td>\n",
       "      <td>7.836774e-01</td>\n",
       "      <td>1.851322e-01</td>\n",
       "      <td>6.086592e-02</td>\n",
       "      <td>1.634654e-01</td>\n",
       "      <td>7.812734e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>1.665345e-01</td>\n",
       "      <td>1.402892e-01</td>\n",
       "      <td>0.336950</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.549978e-01</td>\n",
       "      <td>3.491630e-02</td>\n",
       "      <td>2.415234e-01</td>\n",
       "      <td>4.985387e-01</td>\n",
       "      <td>6.566524e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>1.579410e-02</td>\n",
       "      <td>3.051565e-14</td>\n",
       "      <td>0.783677</td>\n",
       "      <td>0.254998</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.490091e-06</td>\n",
       "      <td>5.529962e-03</td>\n",
       "      <td>7.757568e-08</td>\n",
       "      <td>1.413788e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>6.951351e-01</td>\n",
       "      <td>1.605360e-04</td>\n",
       "      <td>0.185132</td>\n",
       "      <td>0.034916</td>\n",
       "      <td>3.490091e-06</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.516625e-02</td>\n",
       "      <td>4.384547e-07</td>\n",
       "      <td>1.142885e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>1.278866e-01</td>\n",
       "      <td>3.540681e-04</td>\n",
       "      <td>0.060866</td>\n",
       "      <td>0.241523</td>\n",
       "      <td>5.529962e-03</td>\n",
       "      <td>8.516625e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.148854e-03</td>\n",
       "      <td>1.719952e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>1.225282e-10</td>\n",
       "      <td>5.030028e-07</td>\n",
       "      <td>0.163465</td>\n",
       "      <td>0.498539</td>\n",
       "      <td>7.757568e-08</td>\n",
       "      <td>4.384547e-07</td>\n",
       "      <td>1.148854e-03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.201820e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num</th>\n",
       "      <td>2.666712e-06</td>\n",
       "      <td>1.251711e-17</td>\n",
       "      <td>0.781273</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>1.413788e-13</td>\n",
       "      <td>1.142885e-10</td>\n",
       "      <td>1.719952e-15</td>\n",
       "      <td>8.201820e-19</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sex            cp       fbs   restecg         exang  \\\n",
       "sex      1.000000e+00  7.885024e-02  0.509260  0.166534  1.579410e-02   \n",
       "cp       7.885024e-02  1.000000e+00  0.254363  0.140289  3.051565e-14   \n",
       "fbs      5.092599e-01  2.543631e-01  1.000000  0.336950  7.836774e-01   \n",
       "restecg  1.665345e-01  1.402892e-01  0.336950  1.000000  2.549978e-01   \n",
       "exang    1.579410e-02  3.051565e-14  0.783677  0.254998  1.000000e+00   \n",
       "slope    6.951351e-01  1.605360e-04  0.185132  0.034916  3.490091e-06   \n",
       "ca       1.278866e-01  3.540681e-04  0.060866  0.241523  5.529962e-03   \n",
       "thal     1.225282e-10  5.030028e-07  0.163465  0.498539  7.757568e-08   \n",
       "num      2.666712e-06  1.251711e-17  0.781273  0.006567  1.413788e-13   \n",
       "\n",
       "                slope            ca          thal           num  \n",
       "sex      6.951351e-01  1.278866e-01  1.225282e-10  2.666712e-06  \n",
       "cp       1.605360e-04  3.540681e-04  5.030028e-07  1.251711e-17  \n",
       "fbs      1.851322e-01  6.086592e-02  1.634654e-01  7.812734e-01  \n",
       "restecg  3.491630e-02  2.415234e-01  4.985387e-01  6.566524e-03  \n",
       "exang    3.490091e-06  5.529962e-03  7.757568e-08  1.413788e-13  \n",
       "slope    1.000000e+00  8.516625e-02  4.384547e-07  1.142885e-10  \n",
       "ca       8.516625e-02  1.000000e+00  1.148854e-03  1.719952e-15  \n",
       "thal     4.384547e-07  1.148854e-03  1.000000e+00  8.201820e-19  \n",
       "num      1.142885e-10  1.719952e-15  8.201820e-19  1.000000e+00  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_values_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "preprocessor = Pipeline([\n",
    "    ('robust', RobustScaler()),\n",
    "    ('standard', StandardScaler())\n",
    "])\n",
    "num = preprocessor.fit_transform(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = pd.DataFrame(num, columns=X.columns.drop(cat.columns))\n",
    "num.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "\n",
    "ca = cat['ca']\n",
    "ca = imputer.fit_transform(ca.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca = pd.Series(ca.flatten())\n",
    "\n",
    "def replace_close_values(val, target=0.672241, new_value=1, tolerance=0.3):\n",
    "    return new_value if np.isclose(val, target, atol=tolerance) else val\n",
    "\n",
    "ca = ca.apply(replace_close_values)\n",
    "ca = pd.get_dummies(ca, prefix='ca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "thal = X['thal']\n",
    "\n",
    "def replace_close_values(val, target=4.73, new_value=6, tolerance=0.3):\n",
    "    return new_value if np.isclose(val, target, atol=tolerance) else val\n",
    "\n",
    "thal = imputer.fit_transform(thal.values.reshape(-1, 1))\n",
    "thal = pd.Series(thal.flatten())\n",
    "thal = thal.apply(replace_close_values)\n",
    "thal = pd.get_dummies(thal, prefix='thal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "restecg = pd.get_dummies(restecg, prefix='restecg')\n",
    "slope = cat['slope']\n",
    "slope = pd.get_dummies(slope, prefix='slope')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = pd.get_dummies(cp, prefix='cp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = cat.drop(columns=['ca', 'thal', 'restecg', 'slope', 'cp'])\n",
    "cat = pd.concat([cat, ca, thal, restecg, slope, cp], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.hstack([num, cat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components='mle')\n",
    "X = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42, stratify=y)\n",
    "y_train = y_train.values.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析与建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer, precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, train_X, train_y, test_X, test_y):\n",
    "    train_X_pred, test_X_pred = model.predict(train_X), model.predict(test_X)\n",
    "    scores = {'accuracy': [accuracy_score(train_y, train_X_pred), accuracy_score(test_y, test_X_pred)],\n",
    "            'precision': [precision_score(train_y, train_X_pred), precision_score(test_y, test_X_pred)],\n",
    "            'recall': [recall_score(train_y, train_X_pred), recall_score(test_y, test_X_pred)],\n",
    "            'f1': [f1_score(y_true, y_pred) for y_true, y_pred in [(train_y, train_X_pred), (test_y, test_X_pred)]]}\n",
    "    return pd.DataFrame(scores, index=['train set', 'test set']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.847826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.847826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.843243</td>\n",
       "      <td>0.847826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy    0.857143  0.860000\n",
       "precision   0.847826  0.847826\n",
       "recall      0.838710  0.847826\n",
       "f1          0.843243  0.847826"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lgr = LogisticRegression()\n",
    "lgr.fit(X_train, y_train)\n",
    "y_pred = lgr.predict(X_test)\n",
    "evaluate(lgr, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.837438</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.795699</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.817680</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy    0.837438  0.880000\n",
       "precision   0.840909  0.869565\n",
       "recall      0.795699  0.869565\n",
       "f1          0.817680  0.869565"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "prameters = {'C':[0.1, 0.5, 1, 5, 10], 'gamma':[10, 1, 0.1, 0.01, 0.001, 1e-4]}\n",
    "svc = SVC(kernel='rbf')\n",
    "grid = GridSearchCV(svc, prameters, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "svc = grid.best_estimator_\n",
    "\n",
    "evaluate(svc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.812808</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.766990</td>\n",
       "      <td>0.745455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.849462</td>\n",
       "      <td>0.891304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.806122</td>\n",
       "      <td>0.811881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy    0.812808  0.810000\n",
       "precision   0.766990  0.745455\n",
       "recall      0.849462  0.891304\n",
       "f1          0.806122  0.811881"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "evaluate(gnb, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy         1.0  0.810000\n",
       "precision        1.0  0.800000\n",
       "recall           1.0  0.782609\n",
       "f1               1.0  0.791209"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "prameters = {'learning_rate':[0.1, 0.5, 1], 'max_iter':[100, 200, 300]}\n",
    "hgb = HistGradientBoostingClassifier()\n",
    "grid = GridSearchCV(hgb, prameters, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "hgb = grid.best_estimator_\n",
    "evaluate(hgb, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/numpy/ma/core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.990148</td>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.989247</td>\n",
       "      <td>0.735849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.989247</td>\n",
       "      <td>0.847826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.989247</td>\n",
       "      <td>0.787879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy    0.990148  0.790000\n",
       "precision   0.989247  0.735849\n",
       "recall      0.989247  0.847826\n",
       "f1          0.989247  0.787879"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "parameters = {\n",
    "    'n_estimators': [100, 300, 500],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]}\n",
    "\n",
    "rfc = RandomForestClassifier()\n",
    "grid = GridSearchCV(rfc, parameters, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "rfc = grid.best_estimator_\n",
    "evaluate(rfc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>train set</th>\n",
       "      <th>test set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.842365</td>\n",
       "      <td>0.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.850575</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.795699</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.822222</td>\n",
       "      <td>0.879121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           train set  test set\n",
       "accuracy    0.842365  0.890000\n",
       "precision   0.850575  0.888889\n",
       "recall      0.795699  0.869565\n",
       "f1          0.822222  0.879121"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "parameters = {\n",
    "    'hidden_layer_sizes': [(100,), (50, 50), (100, 100)],\n",
    "    'alpha': [0.0001, 0.001, 0.01],\n",
    "    'max_iter': [100, 200, 300]}\n",
    "mlp = MLPClassifier()\n",
    "grid = GridSearchCV(mlp, parameters, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "mlp = grid.best_estimator_\n",
    "evaluate(mlp, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/onatter/miniconda3/envs/ag312/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20241221_133257\"\n",
      "Verbosity: 2 (Standard Logging)\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.2\n",
      "Python Version:     3.12.8\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP Tue Nov 5 00:21:55 UTC 2024\n",
      "CPU Count:          16\n",
      "Memory Avail:       23.96 GB / 27.27 GB (87.9%)\n",
      "Disk Space Avail:   758.67 GB / 1006.85 GB (75.4%)\n",
      "===================================================\n",
      "No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...\n",
      "\tRecommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):\n",
      "\tpresets='experimental' : New in v1.2: Pre-trained foundation model + parallel fits. The absolute best accuracy without consideration for inference speed. Does not support GPU.\n",
      "\tpresets='best'         : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.\n",
      "\tpresets='high'         : Strong accuracy with fast inference speed.\n",
      "\tpresets='good'         : Good accuracy with very fast inference speed.\n",
      "\tpresets='medium'       : Fast training time, ideal for initial prototyping.\n",
      "Beginning AutoGluon training ...\n",
      "AutoGluon will save models to \"/home/onatter/ml/datamining/AutogluonModels/ag-20241221_133257\"\n",
      "Train Data Rows:    203\n",
      "Train Data Columns: 20\n",
      "Label Column:       20\n",
      "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
      "\t2 unique label values:  [1, 0]\n",
      "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])\n",
      "Problem Type:       binary\n",
      "Preprocessing data ...\n",
      "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    24535.68 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.03 MB (0.0% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tStage 5 Generators:\n",
      "\t\tFitting DropDuplicatesFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('float', []) : 20 | ['0', '1', '2', '3', '4', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', []) : 20 | ['0', '1', '2', '3', '4', ...]\n",
      "\t0.0s = Fit runtime\n",
      "\t20 features in original data used to generate 20 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.05s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 162, Val Rows: 41\n",
      "User-specified model hyperparameters to be fit:\n",
      "{\n",
      "\t'NN_TORCH': [{}],\n",
      "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
      "\t'CAT': [{}],\n",
      "\t'XGB': [{}],\n",
      "\t'FASTAI': [{}],\n",
      "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n",
      "}\n",
      "Fitting 13 L1 models, fit_strategy=\"sequential\" ...\n",
      "Fitting model: KNeighborsUnif ...\n",
      "\t0.8293\t = Validation score   (accuracy)\n",
      "\t1.82s\t = Training   runtime\n",
      "\t0.03s\t = Validation runtime\n",
      "Fitting model: KNeighborsDist ...\n",
      "\t0.8293\t = Validation score   (accuracy)\n",
      "\t0.0s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBMXT ...\n",
      "\t0.8049\t = Validation score   (accuracy)\n",
      "\t0.25s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: LightGBM ...\n",
      "\t0.8537\t = Validation score   (accuracy)\n",
      "\t0.19s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: RandomForestGini ...\n",
      "\t0.7805\t = Validation score   (accuracy)\n",
      "\t0.68s\t = Training   runtime\n",
      "\t0.07s\t = Validation runtime\n",
      "Fitting model: RandomForestEntr ...\n",
      "\t0.7561\t = Validation score   (accuracy)\n",
      "\t0.67s\t = Training   runtime\n",
      "\t0.06s\t = Validation runtime\n",
      "Fitting model: CatBoost ...\n",
      "\t0.7805\t = Validation score   (accuracy)\n",
      "\t0.52s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: ExtraTreesGini ...\n",
      "\t0.8049\t = Validation score   (accuracy)\n",
      "\t0.69s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: ExtraTreesEntr ...\n",
      "\t0.8049\t = Validation score   (accuracy)\n",
      "\t0.68s\t = Training   runtime\n",
      "\t0.07s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI ...\n",
      "No improvement since epoch 3: early stopping\n",
      "\t0.7317\t = Validation score   (accuracy)\n",
      "\t1.1s\t = Training   runtime\n",
      "\t0.02s\t = Validation runtime\n",
      "Fitting model: XGBoost ...\n",
      "\t0.7805\t = Validation score   (accuracy)\n",
      "\t0.16s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: NeuralNetTorch ...\n",
      "\t0.8049\t = Validation score   (accuracy)\n",
      "\t1.36s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBMLarge ...\n",
      "\t0.7805\t = Validation score   (accuracy)\n",
      "\t0.3s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ...\n",
      "\tEnsemble Weights: {'LightGBM': 1.0}\n",
      "\t0.8537\t = Validation score   (accuracy)\n",
      "\t0.06s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 9.05s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 34705.6 rows/s (41 batch size)\n",
      "Disabling decision threshold calibration for metric `accuracy` due to having fewer than 10000 rows of validation data for calibration, to avoid overfitting (41 rows).\n",
      "\t`accuracy` is generally not improved through threshold calibration. Force calibration via specifying `calibrate_decision_threshold=True`.\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/home/onatter/ml/datamining/AutogluonModels/ag-20241221_133257\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.78,\n",
       " 'balanced_accuracy': 0.7753623188405797,\n",
       " 'mcc': 0.5561240279482094,\n",
       " 'roc_auc': 0.8880837359098229,\n",
       " 'f1': 0.75,\n",
       " 'precision': 0.7857142857142857,\n",
       " 'recall': 0.717391304347826}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autogluon.tabular import TabularDataset, TabularPredictor\n",
    "\n",
    "train_data = TabularDataset(pd.concat([pd.DataFrame(X_train).reset_index(drop=True), \n",
    "                                       pd.DataFrame(y_train, columns=['target']).reset_index(drop=True)], axis=1, ignore_index=True))\n",
    "test_data = TabularDataset(pd.concat([pd.DataFrame(X_test).reset_index(drop=True), y_test.reset_index(drop=True)], axis=1, ignore_index=True))\n",
    "\n",
    "predictor = TabularPredictor(label=20).fit(train_data)\n",
    "\n",
    "predictor.evaluate(test_data, silent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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