4-1_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LlS4gyYqJVpn"
   },
   "source": [
    "# 로지스틱 회귀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m9MHDgx2JVpv"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/4-1.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQBDVUjBbIsL"
   },
   "source": [
    "## 럭키백의 확률"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ILi_LPl9JVpw"
   },
   "source": [
    "### 데이터 준비하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Mba6QeEmLn3r",
    "outputId": "0e6132fe-0404-404c-8fbf-b28b5e870fce"
   },
   "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>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diagonal</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species  Weight  Length  Diagonal   Height   Width\n",
       "0   Bream   242.0    25.4      30.0  11.5200  4.0200\n",
       "1   Bream   290.0    26.3      31.2  12.4800  4.3056\n",
       "2   Bream   340.0    26.5      31.1  12.3778  4.6961\n",
       "3   Bream   363.0    29.0      33.5  12.7300  4.4555\n",
       "4   Bream   430.0    29.0      34.0  12.4440  5.1340"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "fish = pd.read_csv('https://bit.ly/fish_csv_data')\n",
    "fish.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UWJWlRCHVWUg",
    "outputId": "12bcd63a-21f3-4b00-ae83-101f9f98c849"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n"
     ]
    }
   ],
   "source": [
    "print(pd.unique(fish['Species']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "8JjMXc9wVE7C"
   },
   "outputs": [],
   "source": [
    "fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1T6C1d5iMzb8",
    "outputId": "e4ff2e5f-821e-4c2b-e50f-1b9b03486684",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[242.      25.4     30.      11.52     4.02  ]\n",
      " [290.      26.3     31.2     12.48     4.3056]\n",
      " [340.      26.5     31.1     12.3778   4.6961]\n",
      " [363.      29.      33.5     12.73     4.4555]\n",
      " [430.      29.      34.      12.444    5.134 ]]\n"
     ]
    }
   ],
   "source": [
    "print(fish_input[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "aB2oHhojTfWE"
   },
   "outputs": [],
   "source": [
    "fish_target = fish['Species'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "dkllezAJW63K"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    fish_input, fish_target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "H0ujq0BjXpfp"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss = StandardScaler()\n",
    "ss.fit(train_input)\n",
    "train_scaled = ss.transform(train_input)\n",
    "test_scaled = ss.transform(test_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oAxk-V5kQcgc"
   },
   "source": [
    "### k-최근접 이웃 분류기의 확률 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BYWTNPOdXOfr",
    "outputId": "b9b209bc-0331-4a53-9a33-3a0abb8e5108"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8907563025210085\n",
      "0.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "kn = KNeighborsClassifier(n_neighbors=3)\n",
    "kn.fit(train_scaled, train_target)\n",
    "\n",
    "print(kn.score(train_scaled, train_target))\n",
    "print(kn.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a879-O42RhFO",
    "outputId": "8670d6db-7692-416d-a78b-445c6f3ecfc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
     ]
    }
   ],
   "source": [
    "print(kn.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EucmtF8HVOS_",
    "outputId": "bf8c96fb-6790-4fd8-ce66-1f531070129a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Perch' 'Smelt' 'Pike' 'Perch' 'Perch']\n"
     ]
    }
   ],
   "source": [
    "print(kn.predict(test_scaled[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OSDr8WSKXbUa",
    "outputId": "627f6ba0-1c2d-483a-a9db-13f53df4d6ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.     0.     1.     0.     0.     0.     0.    ]\n",
      " [0.     0.     0.     0.     0.     1.     0.    ]\n",
      " [0.     0.     0.     1.     0.     0.     0.    ]\n",
      " [0.     0.     0.6667 0.     0.3333 0.     0.    ]\n",
      " [0.     0.     0.6667 0.     0.3333 0.     0.    ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "proba = kn.predict_proba(test_scaled[:5])\n",
    "print(np.round(proba, decimals=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hk-ywsfKkf7t",
    "outputId": "92326fa0-d6f6-40b5-f4a1-92fe0ea73c81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Roach' 'Perch' 'Perch']]\n"
     ]
    }
   ],
   "source": [
    "distances, indexes = kn.kneighbors(test_scaled[3:4])\n",
    "print(train_target[indexes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q9_wuI_0tEqL"
   },
   "source": [
    "## 로지스틱 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "8rdDSaZ5uji2",
    "outputId": "34531e78-efc6-45ec-e4a1-8b8b4e0c61b5"
   },
   "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "z = np.arange(-5, 5, 0.1)\n",
    "phi = 1 / (1 + np.exp(-z))\n",
    "\n",
    "plt.plot(z, phi)\n",
    "plt.xlabel('z')\n",
    "plt.ylabel('phi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1J6LGKpUJbFE"
   },
   "source": [
    "### 로지스틱 회귀로 이진 분류 수행하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JeR5cA_fIe24",
    "outputId": "0c90e726-e1d6-4548-b900-9c6baf267ffd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'C']\n"
     ]
    }
   ],
   "source": [
    "char_arr = np.array(['A', 'B', 'C', 'D', 'E'])\n",
    "print(char_arr[[True, False, True, False, False]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Khxh-3t5-2Tk"
   },
   "outputs": [],
   "source": [
    "bream_smelt_indexes = (train_target == 'Bream') | (train_target == 'Smelt')\n",
    "train_bream_smelt = train_scaled[bream_smelt_indexes]\n",
    "target_bream_smelt = train_target[bream_smelt_indexes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jEzP0aeXANra",
    "outputId": "2e73610b-6d2e-48fe-cd23-1e5f29d6d8ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(train_bream_smelt, target_bream_smelt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VtEWtsB7EIgm",
    "outputId": "604d2478-331c-4231-dd9d-f09216308ab9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bream' 'Smelt' 'Bream' 'Bream' 'Bream']\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict(train_bream_smelt[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3H_qieV-_CTt",
    "outputId": "a5f9ba31-c408-48d2-8231-31ddfdb9ea4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.99759855 0.00240145]\n",
      " [0.02735183 0.97264817]\n",
      " [0.99486072 0.00513928]\n",
      " [0.98584202 0.01415798]\n",
      " [0.99767269 0.00232731]]\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict_proba(train_bream_smelt[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mm60bpr7EQKU",
    "outputId": "77267b3d-69d7-42e5-9dc8-a44f652cc06f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bream' 'Smelt']\n"
     ]
    }
   ],
   "source": [
    "print(lr.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1mvoYhUVQmFY",
    "outputId": "265ed14d-106d-4675-ee81-51b826175992"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.4037798  -0.57620209 -0.66280298 -1.01290277 -0.73168947]] [-2.16155132]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SxrRy9m8A5Hy",
    "outputId": "bf42de57-9f93-4f4b-fc1a-a04eb19be25d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.02927744  3.57123907 -5.26568906 -4.24321775 -6.0607117 ]\n"
     ]
    }
   ],
   "source": [
    "decisions = lr.decision_function(train_bream_smelt[:5])\n",
    "print(decisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SeuhSRuiA9yZ",
    "outputId": "f70c9be9-54df-48e3-e0d7-752cb47a17d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00240145 0.97264817 0.00513928 0.01415798 0.00232731]\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import expit\n",
    "\n",
    "print(expit(decisions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ee-s4l7EuVo"
   },
   "source": [
    "### 로지스틱 회귀로 다중 분류 수행하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7QugsbD2X8bf",
    "outputId": "9d2a151d-ec02-4127-f68a-8a82ff8be318"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9327731092436975\n",
      "0.925\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(C=20, max_iter=1000)\n",
    "lr.fit(train_scaled, train_target)\n",
    "\n",
    "print(lr.score(train_scaled, train_target))\n",
    "print(lr.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0taO0XnF9dha",
    "outputId": "f9e92d6e-5eef-4fc7-a962-847f83b3d2ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Perch' 'Smelt' 'Pike' 'Roach' 'Perch']\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict(test_scaled[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pqZosYezZOi3",
    "outputId": "d4bc2673-e966-4933-81ec-1a26b3d83a8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
      " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
      " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
      " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
      " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
     ]
    }
   ],
   "source": [
    "proba = lr.predict_proba(test_scaled[:5])\n",
    "print(np.round(proba, decimals=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CXASv4WU87UF",
    "outputId": "f65f2234-5fe4-4d6c-a9ab-08627c730fbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
     ]
    }
   ],
   "source": [
    "print(lr.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1swPv6ZOZTjg",
    "outputId": "bb129ce3-7500-4df2-dede-c06cdfe22041"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 5) (7,)\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_.shape, lr.intercept_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s9iRz1iAd7Oe",
    "outputId": "646831e9-4077-4d05-a899-1d341487be7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -6.5    1.03   5.16  -2.73   3.34   0.33  -0.63]\n",
      " [-10.86   1.93   4.77  -2.4    2.98   7.84  -4.26]\n",
      " [ -4.34  -6.23   3.17   6.49   2.36   2.42  -3.87]\n",
      " [ -0.68   0.45   2.65  -1.19   3.26  -5.75   1.26]\n",
      " [ -6.4   -1.99   5.82  -0.11   3.5   -0.11  -0.71]]\n"
     ]
    }
   ],
   "source": [
    "decision = lr.decision_function(test_scaled[:5])\n",
    "print(np.round(decision, decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "49CcsDHZeJma",
    "outputId": "c707d169-6860-47c3-dd2c-35d650b7a875"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
      " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
      " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
      " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
      " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import softmax\n",
    "\n",
    "proba = softmax(decision, axis=1)\n",
    "print(np.round(proba, decimals=3))"
   ]
  }
 ],
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  "kernelspec": {
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