12-4- Perceptron on Titanic dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43daaa31-4736-41bc-916b-bb556794efdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8f852b-d6ba-43a6-98af-ff8d3986922b",
   "metadata": {},
   "source": [
    "#### load and preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa9f77b4-d324-481d-8a45-ebf7db616b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 1)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "\n",
    "train_data = train_data.replace(['female', 'male'], [0, 1])\n",
    "train_data = train_data.replace(['S', 'C', 'Q'], [0, 1, 2])\n",
    "train_data = train_data.fillna(0)\n",
    "\n",
    "Y_train = train_data[['Survived']]\n",
    "print(Y_train.shape)\n",
    "\n",
    "X_train = train_data[['Pclass', 'Sex', 'Fare', 'Age', 'SibSp', 'Parch', 'Embarked']]\n",
    "\n",
    "Y_train = np.array(Y_train)\n",
    "Y_train = Y_train.reshape(-1)\n",
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9b59246-08f0-46fc-afb1-e38b6a385efb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "test_data = test_data.replace(['female', 'male'], [0, 1])\n",
    "test_data = test_data.replace(['S', 'C', 'Q'], [0, 1, 2])\n",
    "test_data = test_data.fillna(0)\n",
    "\n",
    "X_test = test_data[['Pclass', 'Sex', 'Fare', 'Age', 'SibSp', 'Parch', 'Embarked']]\n",
    "\n",
    "test_label = pd.read_csv('gender_submission.csv')\n",
    "Y_test = test_label[['Survived']]\n",
    "\n",
    "Y_test = np.array(Y_test)\n",
    "Y_test = Y_test.reshape(-1)\n",
    "print(Y_test.reshape(-1).shape)\n",
    "X_test = np.array(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f82e8ad-46fe-464b-bfc3-25f82dc1c78d",
   "metadata": {},
   "source": [
    "### Perceptron class, fit, predict and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bf94fab-a7ef-47d7-85ce-7aea11605ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Perceptron:\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initialize the Perceptron classifier.\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def fit(self, itr, X_train, Y_train):\n",
    "        \"\"\"\n",
    "        Train the Perceptron model.\n",
    "\n",
    "        Args:\n",
    "        itr (int): Number of iterations for training.\n",
    "        X_train (numpy.ndarray): Input features for training.\n",
    "        Y_train (numpy.ndarray): Output labels for training.\n",
    "\n",
    "        Returns:\n",
    "        list, list: Lists containing Mean Absolute Error (MAE) and Mean Squared Error (MSE) for each iteration.\n",
    "        \"\"\"\n",
    "        self.w = np.random.rand(7)\n",
    "        self.b = np.random.rand(1)\n",
    "        lr = 0.000001\n",
    "        MAE = []\n",
    "        MSE = []\n",
    "\n",
    "        for j in range(itr):\n",
    "            mae = []\n",
    "            mse = []\n",
    "\n",
    "            for i in range(X_train.shape[0]):\n",
    "                x_train = X_train[i].reshape(1, -1)\n",
    "                y_pred = np.matmul(x_train, self.w) + self.b\n",
    "\n",
    "                e = Y_train[i] - y_pred\n",
    "                \n",
    "                self.w = self.w + e * lr * X_train[i]\n",
    "                self.b = self.b + e * lr * 1\n",
    "         \n",
    "                mae.append(np.abs(e))\n",
    "                mse.append(np.square(e))\n",
    "\n",
    "            MAE.append(np.mean(mae))\n",
    "            MSE.append(np.mean(mse))\n",
    "        \n",
    "        np.save('w', self.w)\n",
    "        np.save('b', self.b)\n",
    "        return MAE, MSE\n",
    "\n",
    "    def predict(self, X_test):\n",
    "        \"\"\"\n",
    "        Predict the labels for test data using the trained model.\n",
    "\n",
    "        Args:\n",
    "        X_test (numpy.ndarray): Input features for testing.\n",
    "\n",
    "        Returns:\n",
    "        numpy.ndarray: Predicted labels for the test data.\n",
    "        \"\"\"\n",
    "        w = np.load('w.npy')\n",
    "        b = np.load('b.npy')\n",
    "        Y_pred = np.matmul(X_test, w) + b\n",
    "        Y_pred[Y_pred > 0.5] = 1\n",
    "        Y_pred[Y_pred <= 0.5] = 0\n",
    "\n",
    "        return Y_pred\n",
    "\n",
    "    def evaluate(self, X_test, Y_test):\n",
    "        \"\"\"\n",
    "        Evaluate the performance of the model on test data.\n",
    "\n",
    "        Args:\n",
    "        X_test (numpy.ndarray): Input features for testing.\n",
    "        Y_test (numpy.ndarray): True output labels for testing.\n",
    "\n",
    "        Returns:\n",
    "        float: Accuracy of the model on the test data.\n",
    "        \"\"\"\n",
    "        w = np.load('w.npy')\n",
    "        b = np.load('b.npy')\n",
    "        Y_pred = np.matmul(X_test, w) + b\n",
    "        \n",
    "        Y_pred[Y_pred > 0.5] = 1\n",
    "        Y_pred[Y_pred <= 0.5] = 0\n",
    "    \n",
    "        accuracy = np.count_nonzero(Y_pred == Y_test) / len(Y_test) * 100\n",
    "                                 \n",
    "        return accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac3b6eef-29f2-4f97-8efa-8927f04bdae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE in end of train:  2.6211897157274953 \n",
      "MSE in end of train: 9.532872313644846\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "\n",
    "itration = 5\n",
    "model = Perceptron()\n",
    "MAE, MSE = model.fit(itration, X_train, Y_train)\n",
    "print('MAE in end of train: ', np.min(MAE), '\\nMSE in end of train:', np.min(MSE))\n",
    "# print(model.w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62b779e3-bbdf-4b2b-9118-1fd2e4b77d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MAE during Training')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((np.arange(len(MAE))), MAE, c = 'b')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE')\n",
    "plt.title('MAE during Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04923be0-d541-4900-8594-423c393118b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MSE during Training')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(MSE, c = 'b')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MSE')\n",
    "plt.title('MSE during Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51bd62b2-2ec8-4e17-9724-678c41655302",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca03abb7-056f-40d5-a5f6-ee050434acc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy on X_test : 34.21052631578947\n"
     ]
    }
   ],
   "source": [
    "evaluate = model.evaluate(X_test, Y_test)\n",
    "print('accuracy on X_test :', evaluate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb5fef83-747b-43bc-b8fc-d07ee9b2436d",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}