linear-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b3d5dd96",
   "metadata": {},
   "source": [
    "## Application of Week 2  - Linear Models - Classification\n",
    "\n",
    "by Haifa Altlili"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "368b90a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/haifamohamed/opt/anaconda3/envs/haifaenv/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18773adb",
   "metadata": {},
   "source": [
    "**Hinge Loss**\n",
    "\n",
    "$Loss_{h} (X, y, \\theta) = \\max (1-y*(\\theta.X) , 0)$\n",
    "\n",
    "Where $X$ = [1,x] is the feature vector\n",
    "\n",
    "y is the true label\n",
    " $\\theta$ is the weight vector including the offset parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7143acd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hinge_loss_one_step(feature_vector, label, theta_vector):\n",
    "    \"\"\"\n",
    "    This function calculates the hinge loss on a single data point\n",
    "    Arguments:\n",
    "    feature_vector: numpy array describing the data point\n",
    "    label: the real classification value of the data point\n",
    "    Theta and theta_0 describe the linear classifier\n",
    "    \n",
    "    Return:\n",
    "    The hinge loss value\n",
    "    hinge_loss(z) = 0 if z >= 1\n",
    "    hinge_loss(z) = 1 - z if z < 1s\n",
    "    z = label*(theta.feature_vector + theta_0) (agreement)\n",
    "    \n",
    "    theta_vector includes the offset parameter theta_0 = [theta_0, theta]\n",
    "    feature_vector = [1, X]\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.max(1 - label*(theta_vector.dot(feature_vector)), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393e479a",
   "metadata": {},
   "source": [
    "**TrainLoss Formula**\n",
    "\n",
    "$TrainLoss(\\theta) = (1/|Dtrain|)*\\sum \\limits_{(X,y)}^{} hingLoss(X,y,\\theta)$\n",
    "\n",
    "where $\\theta$ is the weight vector including the bias term (offset parameter)\n",
    "\n",
    "$X$ is the feature vector = [1, x]\n",
    "\n",
    "Dtrain is the size of the training set. If we have n samples then Dtrain = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcbc2e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_loss(feature_matrix, labels, theta_vector):\n",
    "    \"\"\"\n",
    "    D = dimension of feature_matrix\n",
    "    TrainLoss = (1/D)*sum(hinge_loss_one_step(feature_vector, label, theta_vector))\n",
    "    feature_matrix dimension = nx(d+1)\n",
    "    feature_vector dimension = (d+1)x1\n",
    "    theta_vector dimension = (d+1)x1\n",
    "    labels dimesion = nx1\n",
    "    \n",
    "    d+1 becuase we're additing a column of ones to the feature_matrix and that's becuase we are including the offset parameter theta_0\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    N, D = feature_matrix.shape\n",
    "    train_loss = 0\n",
    "    \n",
    "    for i in range(N):\n",
    "        train_loss += hinge_loss_one_step(feature_matrix[:,i], labels[i], theta_vector)\n",
    "        \n",
    "    return train_loss/N\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce91664",
   "metadata": {},
   "source": [
    "**Gradient of the hinge loss**\n",
    "\n",
    "$Gradient(X, y, \\theta) = -X*y$ **if** $1 - (\\theta.X)*y > 0$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d75a84ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hinge_loss_gradient(feature_vector, label, theta_vector):\n",
    "    \"\"\"\n",
    "    Calculates the gradient of the hinge loss\n",
    "    \"\"\"\n",
    "    if 1 - (theta_vector.dot(feature_vector))*label > 0:\n",
    "        return -feature_vector*label\n",
    "    else:\n",
    "        return np.zeros(len(feature_vector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47ad2e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_loss_gradient(feature_matrix, labels, theta_vector):\n",
    "    \"\"\"\n",
    "    calculates the gradient of the train_loss\n",
    "    \"\"\"\n",
    "    N, D = feature_matrix.shape\n",
    "    train_loss_gradient = np.zeros(D)\n",
    "    for i in range(N):\n",
    "        train_loss_gradient += hinge_loss_gradient(feature_matrix[i], labels[i], theta_vector)\n",
    "    \n",
    "    return train_loss_gradient/N\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d53404",
   "metadata": {},
   "source": [
    "**Implementing Gradient Descent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d0194fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(feature_matrix, labels, T, eta, lambda_):\n",
    "    \"\"\"\n",
    "    This function implements gradient descent algorithm\n",
    "    feature_matrix \\: [1, X] where X represents the samples\n",
    "    labels: True target values\n",
    "    T: number of epochs\n",
    "    eta: learning rate\n",
    "    lambda_: reguslarization coefficient\n",
    "    \"\"\"\n",
    "    N, D = feature_matrix.shape\n",
    "    # Initialize theta_vector\n",
    "    theta_vector = np.zeros(D)\n",
    "    for t in range(T):\n",
    "        theta_vector = theta_vector - eta*(train_loss_gradient(feature_matrix, labels, theta_vector) + lambda_*theta_vector)\n",
    "        #print(f'theta: {theta_vector} and epoch: {t}')\n",
    "    return theta_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6086381",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(features, theta_vec):\n",
    "    \"\"\"\n",
    "    theta_vec is the optimized theta after applying the gradient descent algorithm\n",
    "    \"\"\"\n",
    "    N, D = features.shape\n",
    "    y_pred = np.zeros(N)\n",
    "    for i in range(N):\n",
    "        y_pred[i] = np.sign(theta_vec.dot(features[i]))\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dc24093",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_classifier_accuracy(targets, predictions):\n",
    "    \"\"\"\n",
    "    returns the accuracy rate\n",
    "    \"\"\"\n",
    "    return np.mean(targets == predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401adfd1",
   "metadata": {},
   "source": [
    "* I'll apply the gradient descent of the heart_disease data. \n",
    "* The data is from kaggle: https://www.kaggle.com/datasets/zeeshanmulla/heart-disease-dataset\n",
    "\n",
    "**Dataset features**\n",
    "* age = age in years\n",
    "* sex = (1 = male; 0 = female)\n",
    "* cp= chest pain type\n",
    "* trestbps= resting blood pressure (in mm Hg on admission to the hospital)\n",
    "* chol = serum cholestoral in mg/dl\n",
    "* fbs = ( fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "* restecg = resting electrocardiographic results\n",
    "* thalach = maximum heart rate achieved\n",
    "* exang = exercise induced angina (1 = yes; 0 = no)\n",
    "* oldpeak = ST depression induced by exercise relative to rest\n",
    "* slope = the slope of the peak exercise ST segment\n",
    "* ca = number of major vessels (0-3) colored by flourosopy\n",
    "* thal3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "* target = 1 or 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0a092a6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
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       "      <td>150</td>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
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       "      <td>1</td>\n",
       "      <td>187</td>\n",
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       "      <td>0</td>\n",
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       "      <td>172</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('heart-2.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18fe9ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "433fbaa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d870a9eb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "664ede66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60a8793c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    166\n",
       "3    117\n",
       "1     18\n",
       "0      2\n",
       "Name: thal, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.thal.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "916b14a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qkE/LSFKDDHdJapDhLkkNMtwlqUGGuyQ16P8AE5Z9BUA5gqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.sex.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89a20e3f",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dataset.sex, dataset.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a2b9d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(dataset.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e261558",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the feature matrix and the targets\n",
    "X = dataset.iloc[:, :-1]\n",
    "Y = dataset.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24605d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10e7fd84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e3f6797",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb6ee5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_feature_matrix = np.array(x_train)\n",
    "training_labels = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ab1d6bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (242,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_feature_matrix.shape, training_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb1eb889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 14)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column of ones to the feature matrix\n",
    "\n",
    "N = training_feature_matrix.shape[0]\n",
    "ones_vec = np.ones(N).reshape((N, 1))\n",
    "\n",
    "training_matrix = np.hstack((ones_vec, training_feature_matrix))\n",
    "training_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6dda6694",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the targets to -1 and 1 instaed of 0 and 1\n",
    "for i in range(len(training_labels)):\n",
    "    if training_labels[i] == 0:\n",
    "        training_labels[i] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "585247bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1, -1,  1, -1,  1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c4ced74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.001, 0.0001, 0.00001, 0.000001]\n",
    "accuracies = []\n",
    "lambda_ = 0.05\n",
    "epochs = 1000\n",
    "\n",
    "for rate in learning_rates:\n",
    "    optimized_theta = gradient_descent(training_matrix, training_labels, epochs, rate, lambda_)\n",
    "    y_pred_train = predict(training_matrix, optimized_theta)\n",
    "    accuracy = linear_classifier_accuracy(y_pred_train, training_labels)\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "plt.plot(learning_rates, accuracies)\n",
    "plt.title('Accuracy versus Learning Rate')\n",
    "plt.xlabel('Learning Rate')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd95bf44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-8.22990037e-05 -6.82228577e-03 -1.39200867e-03  3.75446827e-03\n",
      " -1.60652444e-02 -6.50847010e-03  8.15096718e-05  5.32955675e-04\n",
      "  2.96887372e-02 -1.76503849e-03 -3.47637500e-03  1.34392750e-03\n",
      " -2.68779336e-03 -1.50611430e-03]\n"
     ]
    }
   ],
   "source": [
    "theta_vec = gradient_descent(training_matrix, training_labels, 1000, 0.00001, 0.05)\n",
    "print(theta_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "672deabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Training Accuracy: 0.6611570247933884\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = predict(training_matrix, theta_vec)\n",
    "accuracy = linear_classifier_accuracy(y_pred_train, training_labels)\n",
    "print(f' Training Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c8371f",
   "metadata": {},
   "source": [
    "**Let's evaluate these results on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3faa2717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 14)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column of ones to the test set\n",
    "\n",
    "test_feature_matrix = np.array(x_test)\n",
    "test_labels = np.array(y_test)\n",
    "\n",
    "\n",
    "N = test_feature_matrix.shape[0]\n",
    "ones_vec2 = np.ones(N).reshape((N, 1))\n",
    "\n",
    "test_matrix = np.hstack((ones_vec2, test_feature_matrix))\n",
    "test_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f7beff10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1, -1, -1, -1, -1, -1,  1, -1,  1])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for j in range(len(test_labels)):\n",
    "    if test_labels[j] == 0:\n",
    "        test_labels[j] = -1\n",
    "test_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b23a4938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.6229508196721312\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = predict(test_matrix, theta_vec)\n",
    "test_accuracy = linear_classifier_accuracy(y_pred_test, test_labels)\n",
    "print(f'Test accuracy: {test_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01c68195",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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