AssignmentWeek7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4c5bd85",
   "metadata": {},
   "source": [
    "# Week7 Practice\n",
    "\n",
    "## 1. Logistic Regression : 합격 여부 판단\n",
    "\n",
    "### 1) 로지스틱 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "36713e47",
   "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>ex1</th>\n",
       "      <th>ex2</th>\n",
       "      <th>Admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.623660</td>\n",
       "      <td>78.024693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.286711</td>\n",
       "      <td>43.894998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.847409</td>\n",
       "      <td>72.902198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.182599</td>\n",
       "      <td>86.308552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.032736</td>\n",
       "      <td>75.344376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>83.489163</td>\n",
       "      <td>48.380286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>42.261701</td>\n",
       "      <td>87.103851</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>99.315009</td>\n",
       "      <td>68.775409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>55.340018</td>\n",
       "      <td>64.931938</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>74.775893</td>\n",
       "      <td>89.529813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ex1        ex2  Admitted\n",
       "0   34.623660  78.024693         0\n",
       "1   30.286711  43.894998         0\n",
       "2   35.847409  72.902198         0\n",
       "3   60.182599  86.308552         1\n",
       "4   79.032736  75.344376         1\n",
       "..        ...        ...       ...\n",
       "95  83.489163  48.380286         1\n",
       "96  42.261701  87.103851         1\n",
       "97  99.315009  68.775409         1\n",
       "98  55.340018  64.931938         1\n",
       "99  74.775893  89.529813         1\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# (시험 1점수), (시험2 점수), (합격 여부)\n",
    "# Ng, Machine Learning, Course, ml-ex 중\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"admit.txt\", names=['ex1', 'ex2', 'Admitted'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7a83eaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2) (100, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.c_[data[\"ex1\"], data[\"ex2\"]]\n",
    "y = data[\"Admitted\"]\n",
    "m = len(data)\n",
    "\n",
    "# numpy array 형태로 변환, 형태 변환(m) -> (m, 1)\n",
    "X = np.array(X).reshape(m, 2)\n",
    "y = np.array(y).reshape(m, 1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcf00b3",
   "metadata": {},
   "source": [
    "### 2) 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f6d5459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4, 6, 7, 8, 9, 12, 13, 15, 16, 18, 19, 21, 24, 25, 26, 30, 31, 33, 37, 40, 42, 46, 47, 48, 49, 50, 51, 52, 56, 58, 59, 60, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 95, 96, 97, 98, 99]\n",
      "[0, 1, 2, 5, 10, 11, 14, 17, 20, 22, 23, 27, 28, 29, 32, 34, 35, 36, 38, 39, 41, 43, 44, 45, 53, 54, 55, 57, 61, 62, 63, 64, 65, 67, 70, 78, 79, 86, 89, 92]\n"
     ]
    }
   ],
   "source": [
    "pos = []\n",
    "neg = []\n",
    "\n",
    "for i, val in enumerate(y):\n",
    "    if val == 1:\n",
    "        pos.append(i)\n",
    "    else:\n",
    "        neg.append(i)\n",
    "print(pos)\n",
    "print(neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf0e3dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(X[pos, 0].reshape(-1), X[pos, 1].reshape(-1), 'b+', label=\"Admitted\")\n",
    "plt.plot(X[neg, 0].reshape(-1), X[neg, 1].reshape(-1), 'ro', label=\"Not admitted\")\n",
    "plt.xlabel(\"Exam 1 Score\")\n",
    "plt.ylabel(\"Exam 2 Score\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61193d0",
   "metadata": {},
   "source": [
    "### 3) 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5695b1bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, solver='liblinear')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression(solver=\"liblinear\", C=10) # C가 클수록 regularization 약해짐\n",
    "log_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30d182d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exam1 30, exam2 70\n",
    "# exam1 50, exam2 90\n",
    "# 합격? 불합격?\n",
    "log_reg.predict([[30, 70],\n",
    "                 [50, 90]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43babd2f",
   "metadata": {},
   "source": [
    "### 4) Decision Boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1a3e026",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/1607568763.py:12: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n"
     ]
    },
    {
     "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": [
    "# Plot the decision boundary. For thatm we will assign a color to each\n",
    "# points in the mesh [x_min, x_max] * [y_min, y_max]]\n",
    "exam1_min, exam1_max = X[:, 0].min(), X[:, 0].max()\n",
    "exam2_min, exam2_max = X[:, 1].min(), X[:, 1].max()\n",
    "h = .2 # step size in the mesh\n",
    "xx, yy = np.meshgrid(np.arange(exam1_min, exam1_max, h), np.arange(exam2_min, exam2_max, h))\n",
    "Z = log_reg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.plot(X[pos, 0].reshape(-1), X[pos, 1].reshape(-1), 'b+', label=\"Passed\")\n",
    "plt.plot(X[neg, 0].reshape(-1), X[neg, 1].reshape(-1), 'ro', label=\"Not passed\")\n",
    "plt.xlabel(\"Exam 1 Score\")\n",
    "plt.ylabel(\"Exam 2 Score\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks()\n",
    "plt.yticks()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4805e2df",
   "metadata": {},
   "source": [
    "## 2. 로지스틱 회귀 + 정규화 \n",
    "- 1) 합격 여부 데이터 읽기\n",
    "- 2) 그래프 그리기\n",
    "- 3) 학습 \n",
    "- 4) decision boundary\n",
    "\n",
    "### 1) 불량 여부 데이터 읽기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96c9cdc5",
   "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>t1</th>\n",
       "      <th>t2</th>\n",
       "      <th>Passed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.699560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.684940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.692250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.502190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.465640</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>-0.720620</td>\n",
       "      <td>0.538740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>-0.593890</td>\n",
       "      <td>0.494880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>-0.484450</td>\n",
       "      <td>0.999270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>-0.006336</td>\n",
       "      <td>0.999270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.632650</td>\n",
       "      <td>-0.030612</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           t1        t2  Passed\n",
       "0    0.051267  0.699560       1\n",
       "1   -0.092742  0.684940       1\n",
       "2   -0.213710  0.692250       1\n",
       "3   -0.375000  0.502190       1\n",
       "4   -0.513250  0.465640       1\n",
       "..        ...       ...     ...\n",
       "113 -0.720620  0.538740       0\n",
       "114 -0.593890  0.494880       0\n",
       "115 -0.484450  0.999270       0\n",
       "116 -0.006336  0.999270       0\n",
       "117  0.632650 -0.030612       0\n",
       "\n",
       "[118 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"qa.txt\", names=[\"t1\", \"t2\", \"Passed\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0ce28b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118, 2) (118, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.c_[data[\"t1\"], data[\"t2\"]]\n",
    "y = data[\"Passed\"]\n",
    "m = len(data)\n",
    "\n",
    "X = np.array(X).reshape(m, 2)\n",
    "y = np.array(y).reshape(m, 1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ad4d93",
   "metadata": {},
   "source": [
    "### 2) 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d6df9d0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pos = [i for i, val in enumerate(y) if val == 1]\n",
    "neg = [i for i, val in enumerate(y) if val == 0]\n",
    "\n",
    "plt.plot(X[pos, 0].reshape(-1), X[pos, 1].reshape(-1), 'b+', label=\"Passed\")\n",
    "plt.plot(X[neg, 0].reshape(-1), X[neg, 1].reshape(-1), 'ro', label=\"Failed\")\n",
    "plt.xlabel(\"Microchip Test 1\")\n",
    "plt.ylabel(\"Microchip Test 2\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b8ac93",
   "metadata": {},
   "source": [
    "### 3) 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b33f11c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.051267 0.69956 ]\n",
      "(27,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly_features = PolynomialFeatures(degree=6, include_bias=False)\n",
    "X_poly = poly_features.fit_transform(X)\n",
    "print(X[0])\n",
    "print(X_poly[0].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e9bc6f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, solver='liblinear')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_features = PolynomialFeatures(degree=6, include_bias=False)\n",
    "X_poly = poly_features.fit_transform(X)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=1e-1)\n",
    "log_reg.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba0b9e0",
   "metadata": {},
   "source": [
    "### 4) Decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3eb76fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_plot():\n",
    "    u = np.linspace(-1, 1.5, 300)\n",
    "    v = np.linspace(-1, 1.5, 300)\n",
    "    z = np.zeros((len(u), len(v)))\n",
    "\n",
    "    for i in range(len(u)):\n",
    "        a = []\n",
    "        for j in range(len(v)):\n",
    "            a.append(np.array([u[i], v[j]]))\n",
    "        mydata = poly_features.fit_transform(a)\n",
    "        z[i] = log_reg.predict(mydata)\n",
    "\n",
    "    plt.contour(u, v, z, 0)\n",
    "\n",
    "    plt.plot(X[pos, 0].reshape(-1), X[pos, 1].reshape(-1), 'b+', label='Passed')\n",
    "    plt.plot(X[neg, 0].reshape(-1), X[neg, 1].reshape(-1), 'ro', label='Failed')\n",
    "    plt.xlabel(\"Microchip Test 1\")\n",
    "    plt.ylabel(\"Microchip Test 2\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a570abcd",
   "metadata": {},
   "source": [
    "#### C = 1e4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d644622e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/879285045.py:13: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour(u, v, z, 0)\n"
     ]
    },
    {
     "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": [
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=1e4)\n",
    "log_reg.fit(X_poly, y)\n",
    "draw_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c956773",
   "metadata": {},
   "source": [
    "#### C = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24496fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/879285045.py:13: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour(u, v, z, 0)\n"
     ]
    },
    {
     "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": [
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=100)\n",
    "log_reg.fit(X_poly, y)\n",
    "draw_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c88586b",
   "metadata": {},
   "source": [
    "#### C = 1e-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8127b2c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/879285045.py:13: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour(u, v, z, 0)\n"
     ]
    },
    {
     "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": [
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=1e-1)\n",
    "log_reg.fit(X_poly, y)\n",
    "draw_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2becdb42",
   "metadata": {},
   "source": [
    "#### C = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77a9196e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/879285045.py:13: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour(u, v, z, 0)\n"
     ]
    },
    {
     "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": [
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=1e-2)\n",
    "log_reg.fit(X_poly, y)\n",
    "draw_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c631d90c",
   "metadata": {},
   "source": [
    "#### C = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b7d3b3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machine-learning/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/var/folders/l4/0n86l_692hx8xxqz3fqkk6mm0000gn/T/ipykernel_2630/879285045.py:13: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour(u, v, z, 0)\n"
     ]
    },
    {
     "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": [
    "log_reg = LogisticRegression(penalty=\"l2\", solver=\"liblinear\", C=10)\n",
    "log_reg.fit(X_poly, y)\n",
    "draw_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ea6816",
   "metadata": {},
   "source": [
    "## 3. 로지스틱 회귀의 성능 측정법\n",
    "\n",
    "- 편의상 trainset에 대하여 prediction함\n",
    "- 원래는 validation, testset에 대하여 진행해야함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2cee26f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. y값 prediction\n",
    "y_pred = log_reg.predict(X_poly)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3bc2d10a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[48 12]\n",
      " [ 7 51]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "conf_mat = confusion_matrix(y, y_pred)\n",
    "print(conf_mat)\n",
    "plt.matshow(conf_mat, cmap=plt.cm.gray)\n",
    "plt.xlabel(\"True      False\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fac99148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score\t: 0.8095238095238095\n",
      "recall score \t: 0.8793103448275862\n"
     ]
    }
   ],
   "source": [
    "# 3. precision & recall\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "print(f\"precision score\\t: {precision_score(y, y_pred)}\")\n",
    "print(f\"recall score \\t: {recall_score(y, y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a404ef3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score.  \t: 0.8429752066115702\n"
     ]
    }
   ],
   "source": [
    "# 4. F1 score\n",
    "from sklearn.metrics import f1_score\n",
    "print(f\"F1 Score.  \\t: {f1_score(y, y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1d29f065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.63843998,  1.94870322,  1.83153079,  2.07479026,  1.20384302,\n",
       "        0.91958736,  1.31539768,  0.80459852,  0.82760904,  0.34247932,\n",
       "        0.26874529,  0.39113702,  1.15018989,  0.3675054 ,  1.00655759,\n",
       "        1.68656454,  2.48685032,  0.57866268,  1.82488372,  1.02837843,\n",
       "       -0.0467156 ,  0.03289813, -0.42938371, -0.39917066,  0.41712539,\n",
       "       -0.25910028,  0.43110646, -0.46895755,  2.65489212,  0.95626797,\n",
       "       -1.54699759,  0.88657635,  2.1577756 ,  2.50719834,  2.1878541 ,\n",
       "        1.81771385,  1.02809592,  1.81395712,  2.20602113,  1.23912949,\n",
       "        2.17463203,  2.0701022 ,  1.34295993,  2.51972163,  1.58133983,\n",
       "        1.78062333, -0.65522793,  2.41551423,  0.09682876,  0.64037237,\n",
       "        2.35303768,  3.07165023,  2.66452047,  2.80652746,  2.59639042,\n",
       "        1.59857899,  1.99978436,  1.11263191, -2.56980435,  0.04554951,\n",
       "        1.49571632, -1.77163854, -2.55435503, -0.53134378, -1.80424451,\n",
       "       -4.35511438, -1.34643544, -4.22341218, -3.42362307, -1.55362034,\n",
       "       -2.4618821 , -1.91360004, -2.27353326, -1.96926169, -1.89932451,\n",
       "       -0.13578388,  1.17309895,  0.14045748, -2.03190512, -2.3859142 ,\n",
       "       -0.17617805, -0.03353184, -1.33151297, -0.10258769, -3.97613009,\n",
       "       -1.60323447, -1.92397178, -1.83435175,  1.10741273, -1.06290096,\n",
       "       -1.64451959,  0.01681645,  0.60501163, -1.46931545, -2.37478516,\n",
       "       -5.79299648, -8.43684357, -1.93637032, -6.6681795 , -5.12561674,\n",
       "       -1.9676757 , -8.52924806, -0.8225354 , -2.41213515,  0.47660951,\n",
       "       -1.15069142,  0.07325011,  1.52813046, -0.96019769, -2.44248778,\n",
       "       -3.20037027, -2.61604957, -3.47052933, -1.53424241,  0.37018053,\n",
       "       -3.11546635, -2.96877245,  1.57702728])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y probability\n",
    "y_scores = log_reg.decision_function(X_poly)\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e3c9ea4f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 5. ROC Curve\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, threshold = roc_curve(y, y_scores)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel(\"FPR\")\n",
    "    plt.ylabel(\"TPR\")\n",
    "\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "15ffd8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score : 0.8396551724137932\n"
     ]
    }
   ],
   "source": [
    "# 6. AUC \n",
    "from sklearn.metrics import roc_auc_score\n",
    "print(f\"roc_auc_score : {roc_auc_score(y, y_pred)}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "machine-leraning",
   "language": "python",
   "name": "machine-leraning"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}