Module 4 Section 2 Lab 2 - Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4 Section 2 Lab 2 - Logistic Regression\n",
    "\n",
    "\n",
    "## Objektif\n",
    "- Apakah Linear Regression dapat digunakan untuk menyelesaikan tugas klasifikasi?\n",
    "- Decision Boundary\n",
    "- Fungsi Sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X, y = make_classification(n_samples=10, n_features=1, n_informative=1, n_redundant=0 , n_clusters_per_class=1, flip_y=0, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "plt.ylabel(\"Tumor Ganas {1: Yes  0: No}\")\n",
    "plt.xlabel(\"Ukuran Tumor\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apa jenis tugas ini?**\n",
    "- Output \"y\" memiliki dua kategori yaitu 1 (Ya) atau 0 (Tidak)\n",
    "- Jadi, seperti yang telah kita pelajari sebelumnya, ini adalah **Tugas Klasifikasi** di mana kita menggunakan variabel dependen kita, dalam hal ini Ukuran Tumor dan mendapatkan output **biner** yaitu 1 (Ya) atau 0 ( Tidak)!\n",
    "\n",
    "Mari kita dekati masalah ini dengan apa yang telah kita pelajari sejauh ini."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dapatkah Linear Regression digunakan untuk menyelesaikan tugas klasifikasi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)\n",
    "\n",
    "z = np.linspace(-2, 2, 1000)\n",
    "z_predict = lin_reg.intercept_ + (lin_reg.coef_ * z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "plt.plot(z, z_predict)\n",
    "plt.ylabel(\"Presence of Cancer {1: Yes  0: No}\")\n",
    "plt.xlabel(\"Tumor Size\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intuisi Decision Boundary\n",
    "Sebelumnya, kita telah membuat model *Linear Regression* yang diwakili oleh garis biru. Bagaimana kita mengubah keluaran kontinu menjadi keluaran diskrit? Salah satu cara yang bisa kita lakukan adalah menggunakan nilai ambang batas untuk output *Linear Regression*.\n",
    "\n",
    "Output kita adalah 1 atau 0, dan karena kita bisa mendapatkan nilai prediksi antara 0 dan 1 seperti 0.2, 0.6, pada garis regresi, kita perlu menemukan metode di mana output kita (0.2, 0.6, dll) **diubah** menjadi **0** atau **1**\n",
    "\n",
    "Ambang batas yang wajar untuk masalah biner (0 - 1) adalah 0.5. Jadi, jika nilai prediksi $y$ **lebih besar dari 0.5** akan di tetapkan sebagai **\"1\"** dan sebaliknya, jika $y$ **kurang dari 0.5** maka ditetapkan sebagai **\"0\"**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Target')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_critical = (0.5 - lin_reg.intercept_)/ lin_reg.coef_\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "plt.plot(z, z_predict)\n",
    "plt.axvline(x=x_critical, color='r', linestyle='--')\n",
    "plt.axhline(y=0.5, color='g', linestyle='--')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression - Bukan Ide Bagus!\n",
    "*Linear Regression* tidak kuat terhadap outlier dan itu mengubah kecocokan secara drastis dengan adanya outlier. Kita akan menambahkan outlier dan mempertahankan *decision boundary* yang sama seperti sebelumnya untuk melihat perbedaannya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_obs = 20\n",
    "X = np.vstack([X, new_obs])\n",
    "y = np.append(y, 1)\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)\n",
    "\n",
    "z = np.linspace(-2, new_obs, 1000)\n",
    "\n",
    "z_predict = lin_reg.intercept_ + (lin_reg.coef_ * z)\n",
    "x_critical2 = (0.5 - lin_reg.intercept_)/ lin_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "plt.plot(z, z_predict)\n",
    "plt.axvline(x=x_critical, color='r', linestyle='--')\n",
    "plt.axvline(x=x_critical2, color='y', linestyle='--')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada grafik diatas, kita dapat melihat bahwa *decision boundary* telah bergeser ke kanan (garis kuning). Akibatnya, kita mendapatkan beberapa prediksi $y$ yang salah (prediksi yang terletak di sebelah kiri garis kuning).\n",
    "\n",
    "Karena data diberi label, kita tahu bahwa awalnya adalah bernilai \"1\". Tetapi, karena menunjukkan berada di sebelah kiri *decision boundary*, data tersebut harus bernilai \"0\"\n",
    "\n",
    "Jadi, *Linear Regression* tidak cocok untuk tugas klasifikasi karena sangat sensitif terhadap outlier. Kita  membutuhkan model yang lebih baik untuk menyelesaikan masalah klasifikasi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "Untuk mengatasi keterbatasan *Linear Regression* kita akan menggunakan model lain yang cocok untuk klasifikasi yaitu, *Logistic Regression*.Apa yang kita butuhkan? Kita membutuhkan $0 <= y <= 1$.\n",
    "  \n",
    "Untuk itu, kita perlu menggunakan fungsi yang berbeda (dan bukan fungsi pada *Linear Regression*:  $y = mx + b$). Fungsi ini adalah **Fungsi Sigmoid**\n",
    "\n",
    "## Fungsi Sigmoid\n",
    "Kita perlu mendapatkan nilai dalam kisaran (0,1). Mari kita lihat grafik Fungsi Sigmoid dibawah ini:\n",
    "\n",
    "<center><img src=\"https://raw.githubusercontent.com/bluedataconsulting/AIMasteryProgram/main/Lab_Exercises/Module4/images/sigmoid.png\" style=\"width: 450px;\"/></center>\n",
    "\n",
    "Baca selanjutnya: https://github.com./drbilo/logistic-regression\n",
    "\n",
    "- Fungsi grafik di atas adalah $g(z) = \\frac {1} {(1 + e^(-z))}$ untuk nilai $z$ mulai dari - tak terhingga , + tak terhingga\n",
    "- $g(z)$ juga disebut sebagai \"Sigmoid dari 'z' atau hanya $sig(z)$\"\n",
    "\n",
    "Sekali lagi, mari kita mulai dengan memasukkan ujung jangkauannya seperti dibawah ini:\n",
    "\n",
    "$$g(-infinity) = \\frac {1} {(1 + infinity)} = ~0$$\n",
    " \n",
    "$$g( infinity) = \\frac {1} {(1 + ~0)} = ~1$$\n",
    "\n",
    "Berapapun nilai $z$, kita akan selalu mendapatkan jawaban di kisaran **[0,1]**.\n",
    "\n",
    "## Modifikasi\n",
    "Sekarang, daripada $sig(z)$ mari kita pertimbangkan $sig(y)$, dimana $y$ adalah respon. Tetapi, seperti yang dibahas sebelumnya:\n",
    " \n",
    "$$y = \\theta_0 + \\theta_1(x1) + \\theta_2(x_2) + .. + \\theta_n(x_n)$$\n",
    " \n",
    "Jadi, \n",
    "\n",
    "$$g(y) = g(h_{\\theta}(x)) = 1/(1 + e^{-(\\theta* X)})$$\n",
    "\n",
    "## Interprestasi Sederhana\n",
    "$h_θ(x)$ adalah perkiraan probabilitas bahwa $y=1$ pada input $x$. Contoh: $h_θ(x)$ = 0,7 dalam masalah tumor berarti ada 70% kemungkinan tumor menjadi ganas.\n",
    " \n",
    "Secara matematis, $h_θ(x) = P(y=1 | x; )$ berarti probabilitas bahwa $y = 1$ yang diberikan $x$ dan diparameterisasi oleh $\\theta$. Jadi, $P(y=1 | x;θ) + P(y=0 | x;θ) = 1$.\n",
    "\n",
    "Cukup matematikanya, sekarang coba kita terapkan pada masalah diatas.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X, y = make_classification(n_samples=10, n_features=1, n_informative=1, n_redundant=0 , n_clusters_per_class=1, flip_y=0, random_state=7)\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Target')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "xt = np.linspace(-3, 3, 1000).reshape(1000,1)\n",
    "yt = log_reg.predict(xt)\n",
    "plt.plot(xt, yt)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dalam kasus tanpa outlier, *Logistic Regression* melakukan pekerjaan yang baik dalam memisahkan kelas. Sekarang kita akan menambahkan outlier dan memeriksa apakah masih berhasil melakukan hal yang sama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Target')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_obs = 20\n",
    "X = np.vstack([X, new_obs])\n",
    "y = np.append(y, 1)\n",
    "\n",
    "log_reg.fit(X, y)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(X, y, c='r', marker='x')\n",
    "xt = np.linspace(-5, 25, 1000).reshape(1000,1)\n",
    "yt = log_reg.predict(xt)\n",
    "plt.plot(xt, yt)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bahkan setelah menambahkan outlier, *Logistic Regression* memisahkan kelas dengan baik. Maka, *Logistic Regression* jauh lebih cocok untuk tugas klasifikasi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}