Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d1af09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da09be3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education default  balance housing loan  contact  \\\n",
       "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
       "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
       "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
       "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
       "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0    5   may      1042         1     -1         0  unknown     yes  \n",
       "1    5   may      1467         1     -1         0  unknown     yes  \n",
       "2    5   may      1389         1     -1         0  unknown     yes  \n",
       "3    5   may       579         1     -1         0  unknown     yes  \n",
       "4    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('/Users/tonyjason/Downloads/bank.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bb06146",
   "metadata": {},
   "outputs": [],
   "source": [
    "category = ['job','marital','education','default','housing','loan','contact','month','poutcome']\n",
    "numeric = ['age','balance','day','duration','campaign','pdays','previous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24bec591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   59    0        1          1        0     2343        1     0        2   \n",
       "1   56    0        1          1        0       45        0     0        2   \n",
       "2   41    9        1          1        0     1270        1     0        2   \n",
       "3   55    7        1          1        0     2476        1     0        2   \n",
       "4   54    0        1          2        0      184        0     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome deposit  \n",
       "0    5      8      1042         1     -1         0         3     yes  \n",
       "1    5      8      1467         1     -1         0         3     yes  \n",
       "2    5      8      1389         1     -1         0         3     yes  \n",
       "3    5      8       579         1     -1         0         3     yes  \n",
       "4    5      8       673         2     -1         0         3     yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "for each in category:\n",
    "    dataset[each] = label_encoder.fit_transform(dataset[each])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97aaddff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   59    0        1          1        0     2343        1     0        2   \n",
       "1   56    0        1          1        0       45        0     0        2   \n",
       "2   41    9        1          1        0     1270        1     0        2   \n",
       "3   55    7        1          1        0     2476        1     0        2   \n",
       "4   54    0        1          2        0      184        0     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  deposit  \n",
       "0    5      8      1042         1     -1         0         3        1  \n",
       "1    5      8      1467         1     -1         0         3        1  \n",
       "2    5      8      1389         1     -1         0         3        1  \n",
       "3    5      8       579         1     -1         0         3        1  \n",
       "4    5      8       673         2     -1         0         3        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"deposit\"] = dataset['deposit'].map({'yes':1, 'no':0})\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe27b6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(['age','day','month','default','deposit'],axis=1).values\n",
    "Y = dataset['deposit'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04a4bcea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra \n",
    "import copy # deep copies of objects -> estimators\n",
    "\n",
    "#  estimator to ensemble with Adaboost:\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#  used for data handeling and visualisation\n",
    "import pandas as pd # loading and transforming data\n",
    "import matplotlib.pyplot as plt # visualisations\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay # creating & visualising confusion matrices\n",
    "from sklearn.model_selection import train_test_split # splitt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2917e4f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ff42888",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdaBoostClassifier():\n",
    "    def __init__(self, base_estimator=True, n_estimators=50, learning_rate=1):\n",
    "        if base_estimator:\n",
    "            self.base_estimator = DecisionTreeClassifier(max_depth=1, max_leaf_nodes=2)\n",
    "        else:\n",
    "            self.base_estimator = base_estimator\n",
    "            \n",
    "        self.n_estimators = n_estimators\n",
    "        self.learning_rate = learning_rate\n",
    "        \n",
    "        self.estimators = None\n",
    "        self.estimator_weights = None\n",
    "        \n",
    "        #  to track performance, not nesseccary for the algorithm\n",
    "        self.total_errors = None\n",
    "        self.training_error = None\n",
    "        self.validation_error = None\n",
    "        \n",
    "    def fit(self, X, y, X_val=None, y_val=None):\n",
    "        #  resetting lists \n",
    "        self.estimators = []\n",
    "        self.estimator_weights = []\n",
    "        self.total_errors = []\n",
    "        self.training_error = []\n",
    "        self.validation_error = []\n",
    "        lr = self.learning_rate\n",
    "        \n",
    "        #  0) initialise equal weights\n",
    "        sample_weights = np.full(len(X), 1/len(X))\n",
    "        \n",
    "        for est_i in range(self.n_estimators):\n",
    "            #  1) fit weak learner\n",
    "            estimator = copy.copy(self.base_estimator)\n",
    "            estimator.fit(X, y, sample_weights)\n",
    "            \n",
    "            #  2) calculate total error\n",
    "            prediction = estimator.predict(X)\n",
    "            total_error = np.where(prediction != y, sample_weights, 0).sum() \n",
    "            \n",
    "            #  3) determine weight / amount of say in final prediction\n",
    "            amount_of_say = lr * 0.5 * np.log((1 - total_error)/(total_error + 1e-10))\n",
    "\n",
    "            #  3.5) save estimator and it's weight before going into the next iteration\n",
    "            self.estimators.append(estimator)\n",
    "            self.estimator_weights.append(amount_of_say)\n",
    "            \n",
    "            #  4) update weights\n",
    "            sample_weights = np.where(prediction != y, sample_weights * np.exp(amount_of_say), sample_weights * np.exp(-1 * amount_of_say))\n",
    "            \n",
    "            #  5) renormalize weights\n",
    "            sample_weights = sample_weights / sample_weights.sum()\n",
    "            \n",
    "            #  5.5) keep track of total- and training-error over iterations for documentation purposes\n",
    "            self.total_errors.append(total_error)\n",
    "            self.training_error.append(np.where(self.predict(X) != y, 1, 0).sum()/len(X))\n",
    "            if type(X_val) != \"NoneType\":\n",
    "                self.validation_error.append(np.where(self.predict(X_val) != y_val, 1, 0).sum()/len(X_val))\n",
    "    \n",
    "    def predict(self, X, verbose=False):\n",
    "        \"\"\"\n",
    "        * every estimator makes his predictions in the shape (len(X)) -> [a, b, ..., len(X)]\n",
    "        * stack prediction of estimators to have them row wise(each row corresponds to a sample) -> [[a1, a2], [b1, b2], ..., len(X)]\n",
    "        * at each row apply the weighted majority vote previously discussed\n",
    "        \"\"\"\n",
    "        predictions = np.stack([estimator.predict(X) for estimator in self.estimators], axis=1) \n",
    "        weighted_majority_vote = lambda x: np.unique(x)[np.argmax([np.where(x==categ, self.estimator_weights, 0).sum() for categ in np.unique(x)])]\n",
    "        return np.apply_along_axis(weighted_majority_vote, axis=1, arr=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2aa217a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.8140451451092798,\n",
      "recall = 0.797360248447205,\n",
      "precision = 0.7992217898832685,\n",
      "f1-score = 0.7982899339292654\n"
     ]
    }
   ],
   "source": [
    "base = DecisionTreeClassifier(max_depth=2, max_leaf_nodes=4)\n",
    "\n",
    "#  fit Adaboost classifier with 100 estimators\n",
    "adaboost = AdaBoostClassifier(base_estimator=base, n_estimators=500, learning_rate=1)\n",
    "adaboost.fit(X_train, y_train, X_val, y_val)\n",
    "\n",
    "#  make predictions:\n",
    "predictions = adaboost.predict(X_val)\n",
    "\n",
    "#  confusion matrix\n",
    "cm = confusion_matrix(y_val, predictions, labels=[0, 1])\n",
    "cm_displ = ConfusionMatrixDisplay(cm)\n",
    "cm_displ.plot()\n",
    "plt.show()\n",
    "\n",
    "#  calculate accuracy:\n",
    "accuracy = np.mean(predictions==y_val)\n",
    "\n",
    "#  calculate recall:\n",
    "recall = cm[1, 1]/cm[1, :].sum() # of the total actual positives, how much were classified correctly\n",
    "\n",
    "#  calculate precision:\n",
    "precision = cm[1, 1]/cm[:, 1].sum() # of all predicted positives, how much were True positives\n",
    "\n",
    "#  not that neccessary for this problem, but for the completeness:\n",
    "f1 = 2 * ((recall * precision)/(recall + precision)) \n",
    "\n",
    "print(f\"accuracy = {accuracy},\\nrecall = {recall},\\nprecision = {precision},\\nf1-score = {f1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "098968fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}