analisis_datasets_paper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import itertools\n",
    "import warnings\n",
    "from typing import TypeVar, List, Tuple, Union, Dict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "from modules.random_forest import RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = TypeVar('T')\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para probar operaciones entre pares de nodos\n",
    "def aplicar_operaciones(X: np.ndarray) -> np.ndarray:\n",
    "    operaciones = [\n",
    "        lambda a, b: a + b,\n",
    "        lambda a, b: a - b,\n",
    "        lambda a, b: a * b,\n",
    "        lambda a, b: a / (b + 1e-5),  # Para evitar división por cero\n",
    "    ]\n",
    "    n_features = X.shape[1]\n",
    "    nuevas_caracteristicas = []\n",
    "\n",
    "    # Aplicar todas las operaciones entre pares de características\n",
    "    for i, j in itertools.combinations(range(n_features), 2):\n",
    "        for op in operaciones:\n",
    "            nueva_caracteristica = op(X[:, i], X[:, j])\n",
    "            nuevas_caracteristicas.append(nueva_caracteristica)\n",
    "\n",
    "    return np.column_stack(nuevas_caracteristicas)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def params_analysis(X: np.ndarray, y: np.ndarray, n_trees_arr: np.ndarray, br_arr: np.ndarray, weight_level_arr: np.ndarray, max_depth_arr: np.ndarray, debug: bool = False) -> Tuple[Dict[str, Union[float, Tuple[float, float]]], List[float]]:\n",
    "    best_params: dict[str, Union[int, float]] = {\n",
    "        'accuracy': 0,\n",
    "        'best_index': 0,\n",
    "        'n_trees': 0,\n",
    "        'br': 0,\n",
    "        'weight_level': 0,\n",
    "        'max_depth': 0,\n",
    "    }\n",
    "    accuracies = []\n",
    "    index = 0\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)\n",
    "    X_train_transformado = aplicar_operaciones(X_train)\n",
    "    X_test_transformado = aplicar_operaciones(X_test)\n",
    "\n",
    "    for n_trees in n_trees_arr:\n",
    "        for br in br_arr:\n",
    "            for weight_level in weight_level_arr:\n",
    "                for max_depth in max_depth_arr:\n",
    "                    rf = RandomForest(n_trees=n_trees, bootstrap_rate=br, nivel_peso=weight_level, profundidad_maxima=max_depth)\n",
    "                    rf.train([(x, label) for x, label in zip(X_train_transformado, y_train)])\n",
    "\n",
    "                    # Realizar predicciones en el conjunto de entrenamiento y prueba\n",
    "                    test_predictions = [rf.predict(instance) for instance in X_test_transformado]\n",
    "\n",
    "                    # Calcular precisión en conjunto de prueba\n",
    "                    test_accuracy = accuracy_score(y_test, test_predictions)\n",
    "                    accuracies.append(test_accuracy)\n",
    "\n",
    "                    if debug:\n",
    "                        print(f\"Params: n_trees={n_trees}, br={br}, weight_level={weight_level}, max_depth={max_depth}, accuracy={test_accuracy}\")\n",
    "\n",
    "                    if test_accuracy > best_params['accuracy']:\n",
    "                        best_params['accuracy'] = test_accuracy\n",
    "                        best_params['best_index'] = index\n",
    "                        best_params['n_trees'] = n_trees\n",
    "                        best_params['br'] = br\n",
    "                        best_params['weight_level'] = weight_level\n",
    "                        best_params['max_depth'] = max_depth\n",
    "\n",
    "                    index += 1\n",
    "\n",
    "    return best_params, accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def params_analysis_cat(X: np.ndarray, y: np.ndarray, n_trees_arr: np.ndarray, br_arr: np.ndarray, weight_level_arr: np.ndarray, max_depth_arr: np.ndarray, debug: bool = False) -> Tuple[Dict[str, Union[float, Tuple[float, float]]], List[float]]:\n",
    "    best_params: dict[str, Union[int, float]] = {\n",
    "        'accuracy': 0,\n",
    "        'best_index': 0,\n",
    "        'n_trees': 0,\n",
    "        'br': 0,\n",
    "        'weight_level': 0,\n",
    "        'max_depth': 0,\n",
    "    }\n",
    "    accuracies = []\n",
    "    index = 0\n",
    "    # Identify categorical columns\n",
    "    categorical_columns = X.select_dtypes(include=['object']).columns\n",
    "    \n",
    "    # Apply one-hot encoding to categorical columns\n",
    "    encoder = OneHotEncoder(sparse_output=False)\n",
    "    X_encoded = encoder.fit_transform(X[categorical_columns])\n",
    "    \n",
    "    # Drop original categorical columns and concatenate encoded columns\n",
    "    X = X.drop(categorical_columns, axis=1)\n",
    "    X = np.concatenate([X.to_numpy(), X_encoded], axis=1)\n",
    "    \n",
    "    # Split the data into training and testing sets\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)\n",
    "\n",
    "    X_train_transformado = aplicar_operaciones(X_train)\n",
    "    X_test_transformado = aplicar_operaciones(X_test)\n",
    "\n",
    "    for n_trees in n_trees_arr:\n",
    "        for br in br_arr:\n",
    "            for weight_level in weight_level_arr:\n",
    "                for max_depth in max_depth_arr:\n",
    "                    rf = RandomForest(n_trees=n_trees, bootstrap_rate=br, nivel_peso=weight_level, profundidad_maxima=max_depth)\n",
    "                    rf.train([(x, label) for x, label in zip(X_train_transformado, y_train)])\n",
    "\n",
    "                    # Realizar predicciones en el conjunto de entrenamiento y prueba\n",
    "                    test_predictions = [rf.predict(instance) for instance in X_test_transformado]\n",
    "\n",
    "                    # Calcular precisión en conjunto de prueba\n",
    "                    test_accuracy = accuracy_score(y_test, test_predictions)\n",
    "                    accuracies.append(test_accuracy)\n",
    "\n",
    "                    if debug:\n",
    "                        print(f\"Params: n_trees={n_trees}, br={br}, weight_level={weight_level}, max_depth={max_depth}, accuracy={test_accuracy}\")\n",
    "\n",
    "                    if test_accuracy > best_params['accuracy']:\n",
    "                        best_params['accuracy'] = test_accuracy\n",
    "                        best_params['best_index'] = index\n",
    "                        best_params['n_trees'] = n_trees\n",
    "                        best_params['br'] = br\n",
    "                        best_params['weight_level'] = weight_level\n",
    "                        best_params['max_depth'] = max_depth\n",
    "                    \n",
    "                    index += 1\n",
    "\n",
    "    return best_params, accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "br_arr = np.array([0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 2.0, 3.0, 4.0, 5.0])\n",
    "weight_level_arr = np.array([0.8, 0.9, 1.0, 1.1, 1.2])\n",
    "n_trees_arr = np.array([10, 50, 100, 250, 500])\n",
    "max_depth_arr = np.array([5])\n",
    "\n",
    "def get_best_params(\n",
    "        X: np.ndarray,\n",
    "        y: np.ndarray,\n",
    "        n_trees_arr: np.ndarray = n_trees_arr,\n",
    "        br_arr: np.ndarray = br_arr,\n",
    "        weight_level_arr: np.ndarray = weight_level_arr,\n",
    "        max_depth_arr: np.ndarray = max_depth_arr,\n",
    "        contains_categorical: bool = False,\n",
    "        debug: bool = False\n",
    "    ) -> Tuple[Dict[str, Union[float, Tuple[float, float]]], List[float]]:\n",
    "    if contains_categorical:\n",
    "        return params_analysis_cat(X, y, n_trees_arr, br_arr, weight_level_arr, max_depth_arr, debug)\n",
    "    else:\n",
    "        return params_analysis(X, y, n_trees_arr, br_arr, weight_level_arr, max_depth_arr, debug)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abalon (id=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>name</th>\n",
       "      <th>role</th>\n",
       "      <th>type</th>\n",
       "      <th>demographic</th>\n",
       "      <th>description</th>\n",
       "      <th>units</th>\n",
       "      <th>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sex</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>M, F, and I (infant)</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Length</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>Longest shell measurement</td>\n",
       "      <td>mm</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Diameter</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>perpendicular to length</td>\n",
       "      <td>mm</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Height</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>with meat in shell</td>\n",
       "      <td>mm</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whole_weight</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>whole abalone</td>\n",
       "      <td>grams</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shucked_weight</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>weight of meat</td>\n",
       "      <td>grams</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Viscera_weight</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>gut weight (after bleeding)</td>\n",
       "      <td>grams</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shell_weight</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>after being dried</td>\n",
       "      <td>grams</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rings</td>\n",
       "      <td>Target</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>+1.5 gives the age in years</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name     role         type demographic  \\\n",
       "0             Sex  Feature  Categorical        None   \n",
       "1          Length  Feature   Continuous        None   \n",
       "2        Diameter  Feature   Continuous        None   \n",
       "3          Height  Feature   Continuous        None   \n",
       "4    Whole_weight  Feature   Continuous        None   \n",
       "5  Shucked_weight  Feature   Continuous        None   \n",
       "6  Viscera_weight  Feature   Continuous        None   \n",
       "7    Shell_weight  Feature   Continuous        None   \n",
       "8           Rings   Target      Integer        None   \n",
       "\n",
       "                   description  units missing_values  \n",
       "0         M, F, and I (infant)   None             no  \n",
       "1    Longest shell measurement     mm             no  \n",
       "2      perpendicular to length     mm             no  \n",
       "3           with meat in shell     mm             no  \n",
       "4                whole abalone  grams             no  \n",
       "5               weight of meat  grams             no  \n",
       "6  gut weight (after bleeding)  grams             no  \n",
       "7            after being dried  grams             no  \n",
       "8  +1.5 gives the age in years   None             no  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalon = fetch_ucirepo(id=1)\n",
    "\n",
    "X = abalon.data.features # datos\n",
    "y = abalon.data.targets # clases\n",
    "\n",
    "abalon.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole_weight</th>\n",
       "      <th>Shucked_weight</th>\n",
       "      <th>Viscera_weight</th>\n",
       "      <th>Shell_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>F</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Length  Diameter  Height  Whole_weight  Shucked_weight  \\\n",
       "0      M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1      M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2      F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3      M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4      I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ..     ...       ...     ...           ...             ...   \n",
       "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera_weight  Shell_weight  \n",
       "0             0.1010        0.1500  \n",
       "1             0.0485        0.0700  \n",
       "2             0.1415        0.2100  \n",
       "3             0.1140        0.1550  \n",
       "4             0.0395        0.0550  \n",
       "...              ...           ...  \n",
       "4172          0.2390        0.2490  \n",
       "4173          0.2145        0.2605  \n",
       "4174          0.2875        0.3080  \n",
       "4175          0.2610        0.2960  \n",
       "4176          0.3765        0.4950  \n",
       "\n",
       "[4177 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalon.data.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params, accuracies = get_best_params(X=X, y=y.to_numpy().flatten(), contains_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/abalon.npy', 'wb') as f:\n",
    "    np.save(f, best_params)\n",
    "    np.save(f, accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.29066985645933013,\n",
       " 'best_index': 230,\n",
       " 'n_trees': np.int64(500),\n",
       " 'br': np.float64(2.0),\n",
       " 'weight_level': np.float64(0.8),\n",
       " 'max_depth': np.int64(5)}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('results/abalon.npy', 'rb') as f:\n",
    "    best_params = np.load(f, allow_pickle=True).item()\n",
    "    accuracies = np.load(f, allow_pickle=True)\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=range(len(accuracies)), y=accuracies)\n",
    "plt.scatter(best_params['best_index'], best_params['accuracy'], color='red', label='Best accuracy')\n",
    "plt.annotate(\n",
    "    f\"Best accuracy: {best_params['accuracy']:.2f}\\nBR: {best_params['br']}\\nWeight level: {best_params['weight_level']}\\nMax depth: {best_params['max_depth']}\\nNro trees: {best_params['n_trees']}\", \n",
    "    (best_params['best_index'], best_params['accuracy']), textcoords=\"offset points\", xytext=(0, -200), ha='center', bbox=dict(\n",
    "        boxstyle='round,pad=0.5', fc='yellow', alpha=0.5\n",
    "    ),\n",
    "    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='black', lw=2)\n",
    ")\n",
    "plt.xlabel('Parameter combinations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracies for different parameter combinations for Abalon dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Australian Credit Approval (Statlog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>name</th>\n",
       "      <th>role</th>\n",
       "      <th>type</th>\n",
       "      <th>demographic</th>\n",
       "      <th>description</th>\n",
       "      <th>units</th>\n",
       "      <th>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
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       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A6</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A7</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A8</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A9</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A10</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A11</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A12</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A13</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A14</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A15</td>\n",
       "      <td>Target</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name     role         type demographic description units missing_values\n",
       "0    A1  Feature  Categorical        None        None  None             no\n",
       "1    A2  Feature   Continuous        None        None  None             no\n",
       "2    A3  Feature   Continuous        None        None  None             no\n",
       "3    A4  Feature  Categorical        None        None  None             no\n",
       "4    A5  Feature  Categorical        None        None  None             no\n",
       "5    A6  Feature  Categorical        None        None  None             no\n",
       "6    A7  Feature   Continuous        None        None  None             no\n",
       "7    A8  Feature  Categorical        None        None  None             no\n",
       "8    A9  Feature  Categorical        None        None  None             no\n",
       "9   A10  Feature   Continuous        None        None  None             no\n",
       "10  A11  Feature  Categorical        None        None  None             no\n",
       "11  A12  Feature  Categorical        None        None  None             no\n",
       "12  A13  Feature   Continuous        None        None  None             no\n",
       "13  A14  Feature   Continuous        None        None  None             no\n",
       "14  A15   Target  Categorical        None        None  None             no"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statlog_australian_credit_approval = fetch_ucirepo(id=143)\n",
    "\n",
    "X = statlog_australian_credit_approval.data.features \n",
    "y = statlog_australian_credit_approval.data.targets\n",
    "\n",
    "statlog_australian_credit_approval.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     A1     A2      A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13   A14\n",
       "0     1  22.08  11.460   2   4   4  1.585   0   0    0    1    2  100  1213\n",
       "1     0  22.67   7.000   2   8   4  0.165   0   0    0    0    2  160     1\n",
       "2     0  29.58   1.750   1   4   4  1.250   0   0    0    1    2  280     1\n",
       "3     0  21.67  11.500   1   5   3  0.000   1   1   11    1    2    0     1\n",
       "4     1  20.17   8.170   2   6   4  1.960   1   1   14    0    2   60   159\n",
       "..   ..    ...     ...  ..  ..  ..    ...  ..  ..  ...  ...  ...  ...   ...\n",
       "685   1  31.57  10.500   2  14   4  6.500   1   0    0    0    2    0     1\n",
       "686   1  20.67   0.415   2   8   4  0.125   0   0    0    0    2    0    45\n",
       "687   0  18.83   9.540   2   6   4  0.085   1   0    0    0    2  100     1\n",
       "688   0  27.42  14.500   2  14   8  3.085   1   1    1    0    2  120    12\n",
       "689   1  41.00   0.040   2  10   4  0.040   0   1    1    0    1  560     1\n",
       "\n",
       "[690 rows x 14 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statlog_australian_credit_approval.data.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params, accuracies = get_best_params(X=X, y=y.to_numpy().flatten(), contains_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/statlog.npy', 'wb') as f:\n",
    "    np.save(f, best_params)\n",
    "    np.save(f, accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8768115942028986,\n",
       " 'best_index': 29,\n",
       " 'n_trees': np.int64(10),\n",
       " 'br': np.float64(1.2),\n",
       " 'weight_level': np.float64(1.2),\n",
       " 'max_depth': np.int64(5)}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('results/statlog.npy', 'rb') as f:\n",
    "    best_params = np.load(f, allow_pickle=True).item()\n",
    "    accuracies = np.load(f, allow_pickle=True)\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=range(len(accuracies)), y=accuracies)\n",
    "plt.scatter(best_params['best_index'], best_params['accuracy'], color='red', label='Best accuracy')\n",
    "plt.annotate(\n",
    "    f\"Best accuracy: {best_params['accuracy']:.2f}\\nBR: {best_params['br']}\\nWeight level: {best_params['weight_level']}\\nMax depth: {best_params['max_depth']}\\nNro trees: {best_params['n_trees']}\", \n",
    "    (best_params['best_index'], best_params['accuracy']), textcoords=\"offset points\", xytext=(250, -250), ha='center', bbox=dict(\n",
    "        boxstyle='round,pad=0.5', fc='yellow', alpha=0.5\n",
    "    ),\n",
    "    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='black', lw=2)\n",
    ")\n",
    "plt.xlabel('Parameter combinations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracies for different parameter combinations for Statlog Australian Credit Approval dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Horse Colic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>name</th>\n",
       "      <th>role</th>\n",
       "      <th>type</th>\n",
       "      <th>demographic</th>\n",
       "      <th>description</th>\n",
       "      <th>units</th>\n",
       "      <th>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surgery</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>Age</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hospital_number</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rectal_temperature</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pulse</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>respiratory_rate</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>temperature_of_extremities</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>peripheral_pulse</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mucous_membranes</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>capillary_refill_time</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pain</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>peristalsis</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abdominal_distension</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nasogastric_tube</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nasogastric_reflux</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nasogastric_reflux_ph</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>rectal_examination_feces</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abdomen</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>packed_cell_volume</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_protein</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abdominocentesis_appearance</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abdominocentesis_total_protein</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>outcome</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>surgical_lesion</td>\n",
       "      <td>Target</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lesion_site</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>lesion_type</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>lesion_subtype</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>cp_data</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name     role        type demographic  \\\n",
       "0                          surgery  Feature     Integer        None   \n",
       "1                              age  Feature     Integer         Age   \n",
       "2                  hospital_number  Feature     Integer        None   \n",
       "3               rectal_temperature  Feature  Continuous        None   \n",
       "4                            pulse  Feature     Integer        None   \n",
       "5                 respiratory_rate  Feature     Integer        None   \n",
       "6       temperature_of_extremities  Feature     Integer        None   \n",
       "7                 peripheral_pulse  Feature     Integer        None   \n",
       "8                 mucous_membranes  Feature     Integer        None   \n",
       "9            capillary_refill_time  Feature     Integer        None   \n",
       "10                            pain  Feature     Integer        None   \n",
       "11                     peristalsis  Feature     Integer        None   \n",
       "12            abdominal_distension  Feature     Integer        None   \n",
       "13                nasogastric_tube  Feature     Integer        None   \n",
       "14              nasogastric_reflux  Feature     Integer        None   \n",
       "15           nasogastric_reflux_ph  Feature  Continuous        None   \n",
       "16        rectal_examination_feces  Feature     Integer        None   \n",
       "17                         abdomen  Feature     Integer        None   \n",
       "18              packed_cell_volume  Feature     Integer        None   \n",
       "19                   total_protein  Feature     Integer        None   \n",
       "20     abdominocentesis_appearance  Feature     Integer        None   \n",
       "21  abdominocentesis_total_protein  Feature     Integer        None   \n",
       "22                         outcome  Feature     Integer        None   \n",
       "23                 surgical_lesion   Target     Integer        None   \n",
       "24                     lesion_site  Feature     Integer        None   \n",
       "25                     lesion_type  Feature     Integer        None   \n",
       "26                  lesion_subtype  Feature     Integer        None   \n",
       "27                         cp_data  Feature     Integer        None   \n",
       "\n",
       "   description units missing_values  \n",
       "0         None  None            yes  \n",
       "1         None  None             no  \n",
       "2         None  None             no  \n",
       "3         None  None            yes  \n",
       "4         None  None            yes  \n",
       "5         None  None            yes  \n",
       "6         None  None            yes  \n",
       "7         None  None            yes  \n",
       "8         None  None            yes  \n",
       "9         None  None            yes  \n",
       "10        None  None            yes  \n",
       "11        None  None            yes  \n",
       "12        None  None            yes  \n",
       "13        None  None            yes  \n",
       "14        None  None            yes  \n",
       "15        None  None            yes  \n",
       "16        None  None            yes  \n",
       "17        None  None            yes  \n",
       "18        None  None            yes  \n",
       "19        None  None            yes  \n",
       "20        None  None            yes  \n",
       "21        None  None            yes  \n",
       "22        None  None            yes  \n",
       "23        None  None             no  \n",
       "24        None  None             no  \n",
       "25        None  None             no  \n",
       "26        None  None             no  \n",
       "27        None  None             no  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_colic = fetch_ucirepo(id=47)\n",
    "\n",
    "X = horse_colic.data.features\n",
    "y = horse_colic.data.targets\n",
    "\n",
    "horse_colic.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>surgery</th>\n",
       "      <th>age</th>\n",
       "      <th>hospital_number</th>\n",
       "      <th>rectal_temperature</th>\n",
       "      <th>pulse</th>\n",
       "      <th>respiratory_rate</th>\n",
       "      <th>temperature_of_extremities</th>\n",
       "      <th>peripheral_pulse</th>\n",
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       "      <th>...</th>\n",
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       "      <th>packed_cell_volume</th>\n",
       "      <th>total_protein</th>\n",
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       "      <th>abdominocentesis_total_protein</th>\n",
       "      <th>outcome</th>\n",
       "      <th>lesion_site</th>\n",
       "      <th>lesion_type</th>\n",
       "      <th>lesion_subtype</th>\n",
       "      <th>cp_data</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>37.6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     surgery  age  hospital_number  rectal_temperature  pulse  \\\n",
       "0        2.0    1           530101                38.5   66.0   \n",
       "1        1.0    1           534817                39.2   88.0   \n",
       "2        2.0    1           530334                38.3   40.0   \n",
       "3        1.0    9          5290409                39.1  164.0   \n",
       "4        2.0    1           530255                37.3  104.0   \n",
       "..       ...  ...              ...                 ...    ...   \n",
       "363      2.0    1           529695                38.6   60.0   \n",
       "364      2.0    1           528452                37.8   42.0   \n",
       "365      1.0    1           534783                38.0   60.0   \n",
       "366      2.0    1           528926                38.0   42.0   \n",
       "367      2.0    1           530670                37.6   88.0   \n",
       "\n",
       "     respiratory_rate  temperature_of_extremities  peripheral_pulse  \\\n",
       "0                28.0                         3.0               3.0   \n",
       "1                20.0                         NaN               NaN   \n",
       "2                24.0                         1.0               1.0   \n",
       "3                84.0                         4.0               1.0   \n",
       "4                35.0                         NaN               NaN   \n",
       "..                ...                         ...               ...   \n",
       "363              30.0                         1.0               1.0   \n",
       "364              40.0                         1.0               1.0   \n",
       "365              12.0                         1.0               1.0   \n",
       "366              12.0                         3.0               NaN   \n",
       "367              36.0                         3.0               1.0   \n",
       "\n",
       "     mucous_membranes  capillary_refill_time  ...  abdomen  \\\n",
       "0                 NaN                    2.0  ...      5.0   \n",
       "1                 4.0                    1.0  ...      2.0   \n",
       "2                 3.0                    1.0  ...      1.0   \n",
       "3                 6.0                    2.0  ...      NaN   \n",
       "4                 6.0                    2.0  ...      NaN   \n",
       "..                ...                    ...  ...      ...   \n",
       "363               3.0                    1.0  ...      NaN   \n",
       "364               1.0                    1.0  ...      3.0   \n",
       "365               2.0                    1.0  ...      4.0   \n",
       "366               3.0                    1.0  ...      1.0   \n",
       "367               1.0                    1.0  ...      NaN   \n",
       "\n",
       "     packed_cell_volume  total_protein  abdominocentesis_appearance  \\\n",
       "0                  45.0            8.4                          NaN   \n",
       "1                  50.0           85.0                          2.0   \n",
       "2                  33.0            6.7                          NaN   \n",
       "3                  48.0            7.2                          3.0   \n",
       "4                  74.0            7.4                          NaN   \n",
       "..                  ...            ...                          ...   \n",
       "363                40.0            6.0                          1.0   \n",
       "364                36.0            6.2                          NaN   \n",
       "365                44.0           65.0                          3.0   \n",
       "366                37.0            5.8                          NaN   \n",
       "367                44.0            6.0                          NaN   \n",
       "\n",
       "     abdominocentesis_total_protein  outcome  lesion_site  lesion_type  \\\n",
       "0                               NaN      2.0        11300            0   \n",
       "1                               2.0      3.0         2208            0   \n",
       "2                               NaN      1.0            0            0   \n",
       "3                               5.3      2.0         2208            0   \n",
       "4                               NaN      2.0         4300            0   \n",
       "..                              ...      ...          ...          ...   \n",
       "363                             NaN      1.0         3205            0   \n",
       "364                             NaN      1.0         4124            0   \n",
       "365                             2.0      3.0         2209            0   \n",
       "366                             NaN      1.0         3111            0   \n",
       "367                             NaN      2.0         2112            0   \n",
       "\n",
       "     lesion_subtype  cp_data  \n",
       "0                 0        2  \n",
       "1                 0        2  \n",
       "2                 0        1  \n",
       "3                 0        1  \n",
       "4                 0        2  \n",
       "..              ...      ...  \n",
       "363               0        2  \n",
       "364               0        2  \n",
       "365               0        2  \n",
       "366               0        2  \n",
       "367               0        2  \n",
       "\n",
       "[368 rows x 27 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_colic.data.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surgery                           0\n",
       "age                               0\n",
       "hospital_number                   0\n",
       "rectal_temperature                0\n",
       "pulse                             0\n",
       "respiratory_rate                  0\n",
       "temperature_of_extremities        0\n",
       "peripheral_pulse                  0\n",
       "mucous_membranes                  0\n",
       "capillary_refill_time             0\n",
       "pain                              0\n",
       "peristalsis                       0\n",
       "abdominal_distension              0\n",
       "nasogastric_tube                  0\n",
       "nasogastric_reflux                0\n",
       "nasogastric_reflux_ph             0\n",
       "rectal_examination_feces          0\n",
       "abdomen                           0\n",
       "packed_cell_volume                0\n",
       "total_protein                     0\n",
       "abdominocentesis_appearance       0\n",
       "abdominocentesis_total_protein    0\n",
       "outcome                           0\n",
       "lesion_site                       0\n",
       "lesion_type                       0\n",
       "lesion_subtype                    0\n",
       "cp_data                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize KNN Imputer\n",
    "imputer = KNNImputer(n_neighbors= 5) #Fills missing values based on the values of the nearest neighbors.\n",
    "# Fit & Transform the data \n",
    "numeric_df = X.select_dtypes(include='float64')\n",
    "imputed_Hepatits = imputer.fit_transform(numeric_df)\n",
    "# Create a DataFrame from the imputed Data\n",
    "imputed_df = pd.DataFrame(imputed_Hepatits ,columns=numeric_df.columns) \n",
    "# Replace the original numeric columns with the imputed  data\n",
    "X[numeric_df.columns] = imputed_df\n",
    "# Check Null Values\n",
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params, accuracies = get_best_params(X=X.to_numpy(), y=y.to_numpy().flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/horse_colic.npy', 'wb') as f:\n",
    "    np.save(f, best_params)\n",
    "    np.save(f, accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7837837837837838,\n",
       " 'best_index': 30,\n",
       " 'n_trees': np.int64(10),\n",
       " 'br': np.float64(2.0),\n",
       " 'weight_level': np.float64(0.8),\n",
       " 'max_depth': np.int64(5)}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('results/horse_colic.npy', 'rb') as f:\n",
    "    best_params = np.load(f, allow_pickle=True).item()\n",
    "    accuracies = np.load(f, allow_pickle=True)\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=range(len(accuracies)), y=accuracies)\n",
    "plt.scatter(best_params['best_index'], best_params['accuracy'], color='red', label='Best accuracy')\n",
    "plt.annotate(\n",
    "    f\"Best accuracy: {best_params['accuracy']:.2f}\\nBR: {best_params['br']}\\nWeight level: {best_params['weight_level']}\\nMax depth: {best_params['max_depth']}\\nNro trees: {best_params['n_trees']}\", \n",
    "    (best_params['best_index'], best_params['accuracy']), textcoords=\"offset points\", xytext=(250, -80), ha='center', bbox=dict(\n",
    "        boxstyle='round,pad=0.5', fc='yellow', alpha=0.5\n",
    "    ),\n",
    "    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='black', lw=2)\n",
    ")\n",
    "plt.xlabel('Parameter combinations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracies for different parameter combinations for Horse Colic dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hepatitis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "      <td>None</td>\n",
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       "      <th>10</th>\n",
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       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spiders</td>\n",
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       "      <th>12</th>\n",
       "      <td>Ascites</td>\n",
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       "      <td>Varices</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bilirubin</td>\n",
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      ],
      "text/plain": [
       "               name     role         type demographic description units  \\\n",
       "0             Class   Target  Categorical        None        None  None   \n",
       "1               Age  Feature      Integer        None        None  None   \n",
       "2               Sex  Feature  Categorical        None        None  None   \n",
       "3           Steroid  Feature  Categorical        None        None  None   \n",
       "4        Antivirals  Feature  Categorical        None        None  None   \n",
       "5           Fatigue  Feature  Categorical        None        None  None   \n",
       "6           Malaise  Feature  Categorical        None        None  None   \n",
       "7          Anorexia  Feature  Categorical        None        None  None   \n",
       "8         Liver Big  Feature  Categorical        None        None  None   \n",
       "9        Liver Firm  Feature  Categorical        None        None  None   \n",
       "10  Spleen Palpable  Feature  Categorical        None        None  None   \n",
       "11          Spiders  Feature  Categorical        None        None  None   \n",
       "12          Ascites  Feature  Categorical        None        None  None   \n",
       "13          Varices  Feature  Categorical        None        None  None   \n",
       "14        Bilirubin  Feature   Continuous        None        None  None   \n",
       "15    Alk Phosphate  Feature      Integer        None        None  None   \n",
       "16             Sgot  Feature      Integer        None        None  None   \n",
       "17          Albumin  Feature      Integer        None        None  None   \n",
       "18          Protime  Feature      Integer        None        None  None   \n",
       "19        Histology  Feature      Integer        None        None  None   \n",
       "\n",
       "   missing_values  \n",
       "0              no  \n",
       "1              no  \n",
       "2              no  \n",
       "3             yes  \n",
       "4              no  \n",
       "5             yes  \n",
       "6             yes  \n",
       "7             yes  \n",
       "8             yes  \n",
       "9             yes  \n",
       "10            yes  \n",
       "11            yes  \n",
       "12            yes  \n",
       "13            yes  \n",
       "14            yes  \n",
       "15            yes  \n",
       "16            yes  \n",
       "17            yes  \n",
       "18            yes  \n",
       "19             no  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hepatitis = fetch_ucirepo(id=46)\n",
    "\n",
    "X = hepatitis.data.features # datos\n",
    "y = hepatitis.data.targets # clases\n",
    "\n",
    "hepatitis.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Steroid  Antivirals  Fatigue  Malaise  Anorexia  Liver Big  \\\n",
       "0     30    2      1.0           2      2.0      2.0       2.0        1.0   \n",
       "1     50    1      1.0           2      1.0      2.0       2.0        1.0   \n",
       "2     78    1      2.0           2      1.0      2.0       2.0        2.0   \n",
       "3     31    1      NaN           1      2.0      2.0       2.0        2.0   \n",
       "4     34    1      2.0           2      2.0      2.0       2.0        2.0   \n",
       "..   ...  ...      ...         ...      ...      ...       ...        ...   \n",
       "150   46    1      2.0           2      1.0      1.0       1.0        2.0   \n",
       "151   44    1      2.0           2      1.0      2.0       2.0        2.0   \n",
       "152   61    1      1.0           2      1.0      1.0       2.0        1.0   \n",
       "153   53    2      1.0           2      1.0      2.0       2.0        2.0   \n",
       "154   43    1      2.0           2      1.0      2.0       2.0        2.0   \n",
       "\n",
       "     Liver Firm  Spleen Palpable  Spiders  Ascites  Varices  Bilirubin  \\\n",
       "0           2.0              2.0      2.0      2.0      2.0        1.0   \n",
       "1           2.0              2.0      2.0      2.0      2.0        0.9   \n",
       "2           2.0              2.0      2.0      2.0      2.0        0.7   \n",
       "3           2.0              2.0      2.0      2.0      2.0        0.7   \n",
       "4           2.0              2.0      2.0      2.0      2.0        1.0   \n",
       "..          ...              ...      ...      ...      ...        ...   \n",
       "150         2.0              2.0      1.0      1.0      1.0        7.6   \n",
       "151         1.0              2.0      2.0      2.0      2.0        0.9   \n",
       "152         1.0              2.0      1.0      2.0      2.0        0.8   \n",
       "153         2.0              1.0      1.0      2.0      1.0        1.5   \n",
       "154         2.0              1.0      1.0      1.0      2.0        1.2   \n",
       "\n",
       "     Alk Phosphate   Sgot  Albumin  Protime  Histology  \n",
       "0             85.0   18.0      4.0      NaN          1  \n",
       "1            135.0   42.0      3.5      NaN          1  \n",
       "2             96.0   32.0      4.0      NaN          1  \n",
       "3             46.0   52.0      4.0     80.0          1  \n",
       "4              NaN  200.0      4.0      NaN          1  \n",
       "..             ...    ...      ...      ...        ...  \n",
       "150            NaN  242.0      3.3     50.0          2  \n",
       "151          126.0  142.0      4.3      NaN          2  \n",
       "152           75.0   20.0      4.1      NaN          2  \n",
       "153           81.0   19.0      4.1     48.0          2  \n",
       "154          100.0   19.0      3.1     42.0          2  \n",
       "\n",
       "[155 rows x 19 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hepatitis.data.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                0\n",
       "Sex                0\n",
       "Steroid            0\n",
       "Antivirals         0\n",
       "Fatigue            0\n",
       "Malaise            0\n",
       "Anorexia           0\n",
       "Liver Big          0\n",
       "Liver Firm         0\n",
       "Spleen Palpable    0\n",
       "Spiders            0\n",
       "Ascites            0\n",
       "Varices            0\n",
       "Bilirubin          0\n",
       "Alk Phosphate      0\n",
       "Sgot               0\n",
       "Albumin            0\n",
       "Protime            0\n",
       "Histology          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize KNN Imputer\n",
    "imputer = KNNImputer(n_neighbors= 5) #Fills missing values based on the values of the nearest neighbors.\n",
    "# Fit & Transform the data \n",
    "numeric_df = X.select_dtypes(include='float64')\n",
    "imputed_Hepatits = imputer.fit_transform(numeric_df)\n",
    "# Create a DataFrame from the imputed Data\n",
    "imputed_df = pd.DataFrame(imputed_Hepatits ,columns=numeric_df.columns) \n",
    "# Replace the original numeric columns with the imputed  data\n",
    "X[numeric_df.columns] = imputed_df\n",
    "# Check Null Values\n",
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params, accuracies = get_best_params(X=X, y=y.to_numpy().flatten(), contains_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/hepatitis.npy', 'wb') as f:\n",
    "    np.save(f, best_params)\n",
    "    np.save(f, accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7741935483870968,\n",
       " 'best_index': 0,\n",
       " 'n_trees': np.int64(10),\n",
       " 'br': np.float64(0.2),\n",
       " 'weight_level': np.float64(0.8),\n",
       " 'max_depth': np.int64(5)}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('results/hepatitis.npy', 'rb') as f:\n",
    "    best_params = np.load(f, allow_pickle=True).item()\n",
    "    accuracies = np.load(f, allow_pickle=True)\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=range(len(accuracies)), y=accuracies)\n",
    "plt.scatter(best_params['best_index'], best_params['accuracy'], color='red', label='Best accuracy')\n",
    "plt.annotate(\n",
    "    f\"Best accuracy: {best_params['accuracy']:.2f}\\nBR: {best_params['br']}\\nWeight level: {best_params['weight_level']}\\nMax depth: {best_params['max_depth']}\\nNro trees: {best_params['n_trees']}\", \n",
    "    (best_params['best_index'], best_params['accuracy']), textcoords=\"offset points\", xytext=(250, -80), ha='center', bbox=dict(\n",
    "        boxstyle='round,pad=0.5', fc='yellow', alpha=0.5\n",
    "    ),\n",
    "    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='black', lw=2)\n",
    ")\n",
    "plt.xlabel('Parameter combinations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracies for different parameter combinations for Hepatitis dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>name</th>\n",
       "      <th>role</th>\n",
       "      <th>type</th>\n",
       "      <th>demographic</th>\n",
       "      <th>description</th>\n",
       "      <th>units</th>\n",
       "      <th>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>Age</td>\n",
       "      <td>N/A</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workclass</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Income</td>\n",
       "      <td>Private, Self-emp-not-inc, Self-emp-inc, Feder...</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Education Level</td>\n",
       "      <td>Bachelors, Some-college, 11th, HS-grad, Prof-...</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>education-num</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>Education Level</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>marital-status</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Other</td>\n",
       "      <td>Married-civ-spouse, Divorced, Never-married, S...</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occupation</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Other</td>\n",
       "      <td>Tech-support, Craft-repair, Other-service, Sal...</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>relationship</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Other</td>\n",
       "      <td>Wife, Own-child, Husband, Not-in-family, Other...</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>race</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Race</td>\n",
       "      <td>White, Asian-Pac-Islander, Amer-Indian-Eskimo,...</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sex</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Binary</td>\n",
       "      <td>Sex</td>\n",
       "      <td>Female, Male.</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>capital-gain</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>capital-loss</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hours-per-week</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Integer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>native-country</td>\n",
       "      <td>Feature</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>Other</td>\n",
       "      <td>United-States, Cambodia, England, Puerto-Rico,...</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>income</td>\n",
       "      <td>Target</td>\n",
       "      <td>Binary</td>\n",
       "      <td>Income</td>\n",
       "      <td>&gt;50K, &lt;=50K.</td>\n",
       "      <td>None</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name     role         type      demographic  \\\n",
       "0              age  Feature      Integer              Age   \n",
       "1        workclass  Feature  Categorical           Income   \n",
       "2           fnlwgt  Feature      Integer             None   \n",
       "3        education  Feature  Categorical  Education Level   \n",
       "4    education-num  Feature      Integer  Education Level   \n",
       "5   marital-status  Feature  Categorical            Other   \n",
       "6       occupation  Feature  Categorical            Other   \n",
       "7     relationship  Feature  Categorical            Other   \n",
       "8             race  Feature  Categorical             Race   \n",
       "9              sex  Feature       Binary              Sex   \n",
       "10    capital-gain  Feature      Integer             None   \n",
       "11    capital-loss  Feature      Integer             None   \n",
       "12  hours-per-week  Feature      Integer             None   \n",
       "13  native-country  Feature  Categorical            Other   \n",
       "14          income   Target       Binary           Income   \n",
       "\n",
       "                                          description units missing_values  \n",
       "0                                                 N/A  None             no  \n",
       "1   Private, Self-emp-not-inc, Self-emp-inc, Feder...  None            yes  \n",
       "2                                                None  None             no  \n",
       "3    Bachelors, Some-college, 11th, HS-grad, Prof-...  None             no  \n",
       "4                                                None  None             no  \n",
       "5   Married-civ-spouse, Divorced, Never-married, S...  None             no  \n",
       "6   Tech-support, Craft-repair, Other-service, Sal...  None            yes  \n",
       "7   Wife, Own-child, Husband, Not-in-family, Other...  None             no  \n",
       "8   White, Asian-Pac-Islander, Amer-Indian-Eskimo,...  None             no  \n",
       "9                                       Female, Male.  None             no  \n",
       "10                                               None  None             no  \n",
       "11                                               None  None             no  \n",
       "12                                               None  None             no  \n",
       "13  United-States, Cambodia, England, Puerto-Rico,...  None            yes  \n",
       "14                                       >50K, <=50K.  None             no  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = fetch_ucirepo(id=2) \n",
    "\n",
    "X = adult.data.features # datos\n",
    "y = adult.data.targets # clases\n",
    "\n",
    "adult.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
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       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt  education  education-num  \\\n",
       "0       39         State-gov   77516  Bachelors             13   \n",
       "1       50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2       38           Private  215646    HS-grad              9   \n",
       "3       53           Private  234721       11th              7   \n",
       "4       28           Private  338409  Bachelors             13   \n",
       "...    ...               ...     ...        ...            ...   \n",
       "48837   39           Private  215419  Bachelors             13   \n",
       "48838   64               NaN  321403    HS-grad              9   \n",
       "48839   38           Private  374983  Bachelors             13   \n",
       "48840   44           Private   83891  Bachelors             13   \n",
       "48841   35      Self-emp-inc  182148  Bachelors             13   \n",
       "\n",
       "           marital-status         occupation    relationship  \\\n",
       "0           Never-married       Adm-clerical   Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial         Husband   \n",
       "2                Divorced  Handlers-cleaners   Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "4      Married-civ-spouse     Prof-specialty            Wife   \n",
       "...                   ...                ...             ...   \n",
       "48837            Divorced     Prof-specialty   Not-in-family   \n",
       "48838             Widowed                NaN  Other-relative   \n",
       "48839  Married-civ-spouse     Prof-specialty         Husband   \n",
       "48840            Divorced       Adm-clerical       Own-child   \n",
       "48841  Married-civ-spouse    Exec-managerial         Husband   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "48837               White  Female             0             0              36   \n",
       "48838               Black    Male             0             0              40   \n",
       "48839               White    Male             0             0              50   \n",
       "48840  Asian-Pac-Islander    Male          5455             0              40   \n",
       "48841               White    Male             0             0              60   \n",
       "\n",
       "      native-country  \n",
       "0      United-States  \n",
       "1      United-States  \n",
       "2      United-States  \n",
       "3      United-States  \n",
       "4               Cuba  \n",
       "...              ...  \n",
       "48837  United-States  \n",
       "48838  United-States  \n",
       "48839  United-States  \n",
       "48840  United-States  \n",
       "48841  United-States  \n",
       "\n",
       "[48842 rows x 14 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.data.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education-num     0\n",
       "marital-status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital-gain      0\n",
       "capital-loss      0\n",
       "hours-per-week    0\n",
       "native-country    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.occupation.fillna('?', inplace=True)\n",
    "X.workclass.fillna('?', inplace=True)\n",
    "X['native-country'].fillna('?', inplace=True)\n",
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params, accuracies = get_best_params(X=X, y=y.to_numpy().flatten(), contains_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results/adult.npy', 'wb') as f:\n",
    "    np.save(f, best_params)\n",
    "    np.save(f, accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.5052717780734978,\n",
       " 'best_index': 0,\n",
       " 'n_trees': np.int64(10),\n",
       " 'br': np.float64(0.2),\n",
       " 'weight_level': np.float64(0.8),\n",
       " 'max_depth': np.int64(5)}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('results/adult.npy', 'rb') as f:\n",
    "    best_params = np.load(f, allow_pickle=True).item()\n",
    "    accuracies = np.load(f, allow_pickle=True)\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=range(len(accuracies)), y=accuracies)\n",
    "plt.scatter(best_params['best_index'], best_params['accuracy'], color='red', label='Best accuracy')\n",
    "plt.annotate(\n",
    "    f\"Best accuracy: {best_params['accuracy']:.2f}\\nBR: {best_params['br']}\\nWeight level: {best_params['weight_level']}\\nMax depth: {best_params['max_depth']}\\nNro trees: {best_params['n_trees']}\", \n",
    "    (best_params['best_index'], best_params['accuracy']), textcoords=\"offset points\", xytext=(250, -80), ha='center', bbox=dict(\n",
    "        boxstyle='round,pad=0.5', fc='yellow', alpha=0.5\n",
    "    ),\n",
    "    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='black', lw=2)\n",
    ")\n",
    "plt.xlabel('Parameter combinations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracies for different parameter combinations for Adult dataset')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}