4_실습.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyP1pxbK33PDrD7vJVk871P/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gammapasta/machineLearning/blob/main/4_%EC%8B%A4%EC%8A%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#데이터 준비\n",
        "riris 데이터 세트 (다중분류)"
      ],
      "metadata": {
        "id": "TUQn0D9_Du4y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pvuKrZi8DqCU"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "iris = pd.read_csv('https://raw.githubusercontent.com/jmnote/zdata/master/R/iris.csv')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#클래스 추출\n",
        "데이터에 어떤 클래스가 있는지 확인해보자"
      ],
      "metadata": {
        "id": "-CIxAQ4pEAbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris)\n",
        "print(pd.unique(iris['Species']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1A6Gbhu4EWZJ",
        "outputId": "faa476ec-3ff9-4981-9743-a923357e104e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
            "0             5.1          3.5           1.4          0.2     setosa\n",
            "1             4.9          3.0           1.4          0.2     setosa\n",
            "2             4.7          3.2           1.3          0.2     setosa\n",
            "3             4.6          3.1           1.5          0.2     setosa\n",
            "4             5.0          3.6           1.4          0.2     setosa\n",
            "..            ...          ...           ...          ...        ...\n",
            "145           6.7          3.0           5.2          2.3  virginica\n",
            "146           6.3          2.5           5.0          1.9  virginica\n",
            "147           6.5          3.0           5.2          2.0  virginica\n",
            "148           6.2          3.4           5.4          2.3  virginica\n",
            "149           5.9          3.0           5.1          1.8  virginica\n",
            "\n",
            "[150 rows x 5 columns]\n",
            "['setosa' 'versicolor' 'virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#입력 데이터와 정답 데이터를 생성하자\n",
        "입력 데이터는 iris_input  \n",
        "정답 데이터는 iris_target  \n",
        "5번쨰까지 데이터를 각각 출력"
      ],
      "metadata": {
        "id": "wnAE4_-YEdzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris_input = iris[['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width']].to_numpy()\n",
        "iris_target = iris['Species'].to_numpy()\n",
        "\n",
        "print(iris_input[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iv8iME4dEcV9",
        "outputId": "b917368a-7054-4f6e-eba5-59f2544e7d6c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#훈련 세트와 테스트 세트로 나누자\n",
        "train_input, test_input, train_target, test_target  \n",
        "</br>\n",
        "train_input, train_target의 형태를 출력하자  \n",
        "\n",
        "데이터 석어야함 stratify"
      ],
      "metadata": {
        "id": "2-3_us2JEtEm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target   = train_test_split(iris_input, iris_target, random_state=42, stratify=iris_target)\n",
        "\n",
        "print(train_input.shape, train_target.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWhbwjnUISIs",
        "outputId": "c4cb7c70-4417-4490-d7a3-df8223a9b1f0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(112, 4) (112,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#데이터 표준화\n"
      ],
      "metadata": {
        "id": "3xoiefSrFnnM"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "JsvHUsRUJCp1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# k최근접 이웃 알고리즘을 이용하여 훈련하고 훈련 세트와 테스트 세트에 대해 평가"
      ],
      "metadata": {
        "id": "6eA_18G_Ez2_"
      }
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5nqSw7SJMEC",
        "outputId": "421302d9-fc5c-4718-92f4-7c1d0026f387"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9642857142857143\n",
            "0.9210526315789473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#테스트 세트의 5개의 데이터에 대해 예측과 확률을 알아보자"
      ],
      "metadata": {
        "id": "Z8MAvWYpE0TV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(kn.predict(test_scaled[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6POzJoIyKOw3",
        "outputId": "d5579036-f089-46b1-9e2a-a102f92e4ad1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['setosa' 'versicolor' 'versicolor' 'versicolor' 'setosa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#최적화\n",
        "\n"
      ],
      "metadata": {
        "id": "pCW8QGM5E0ZE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 1\n",
        "while i < 20:\n",
        "  print(\"i= \", i)\n",
        "  kn = KNeighborsClassifier(n_neighbors=i)\n",
        "  kn.fit(train_scaled, train_target)\n",
        "  print(kn.score(train_scaled, train_target))\n",
        "  print(kn.score(test_scaled, test_target))\n",
        "  i = i + 1\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_w6_GWWeKszW",
        "outputId": "514fbcae-1f6e-4db0-8fa1-dda78c5dee1b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "i=  1\n",
            "1.0\n",
            "0.9473684210526315\n",
            "i=  2\n",
            "0.9732142857142857\n",
            "0.9210526315789473\n",
            "i=  3\n",
            "0.9642857142857143\n",
            "0.9210526315789473\n",
            "i=  4\n",
            "0.9642857142857143\n",
            "0.9210526315789473\n",
            "i=  5\n",
            "0.9821428571428571\n",
            "0.9210526315789473\n",
            "i=  6\n",
            "0.9642857142857143\n",
            "0.9210526315789473\n",
            "i=  7\n",
            "0.9732142857142857\n",
            "0.9473684210526315\n",
            "i=  8\n",
            "0.9642857142857143\n",
            "0.9210526315789473\n",
            "i=  9\n",
            "0.9642857142857143\n",
            "0.9736842105263158\n",
            "i=  10\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  11\n",
            "0.9553571428571429\n",
            "0.9736842105263158\n",
            "i=  12\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  13\n",
            "0.9642857142857143\n",
            "0.9736842105263158\n",
            "i=  14\n",
            "0.9732142857142857\n",
            "0.9473684210526315\n",
            "i=  15\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  16\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  17\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  18\n",
            "0.9732142857142857\n",
            "0.9736842105263158\n",
            "i=  19\n",
            "0.9732142857142857\n",
            "0.9473684210526315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  #10일떄 가장 높음\n",
        "  kn = KNeighborsClassifier(n_neighbors=10)\n",
        "  kn.fit(train_scaled, train_target)\n",
        "  print(kn.score(train_scaled, train_target))\n",
        "  print(kn.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWfiSpwdL4f5",
        "outputId": "cf83c2c9-6970-4374-85e4-2d7eea141fb0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9732142857142857\n",
            "0.9736842105263158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#로지스틱 회귀를 이용하여 다중 분류 훈련 및 최적화하기"
      ],
      "metadata": {
        "id": "PU-QmcPNKfi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression(C = 20, max_iter=1000) #매개변수 넣기\n",
        "\n",
        "#객체생성, 훈련\n",
        "lr.fit(train_scaled, train_target)\n",
        "print(lr.score(train_scaled, train_target))\n",
        "print(lr.score(test_scaled, test_target))\n",
        "\n",
        "#5까지 예측\n",
        "print(lr.predict(test_scaled[:5]))\n",
        "\n",
        "import numpy as np\n",
        "proba = lr.predict_proba(test_scaled[:5]) #예측한 확률을 숫자로 출력\n",
        "print(np.round(proba, decimals=3))\n",
        "\n",
        "print(lr.classes_)\n",
        "print(lr.coef_.shape, lr.intercept_.shape) #클래스마다 계수와 절편을 만든다 5개의 특성을 사용하므로 열은 5개 타깃은 7개이므로 7개\n",
        "\n",
        "decision = lr.decision_function(test_scaled[:5])\n",
        "print(np.round(decision, decimals=2))\n",
        "\n",
        "from scipy.special import softmax\n",
        "\n",
        "proba = softmax(decision, axis=1) #가로로 row 를 사용해서 시험!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
        "print(np.round(proba, decimals=3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFnUacdvMo7f",
        "outputId": "f6187df1-4586-4f54-f1b2-4aae45da8b5e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "['setosa' 'versicolor' 'versicolor' 'versicolor' 'setosa']\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "['setosa' 'versicolor' 'virginica']\n",
            "(3, 4) (3,)\n",
            "[[ 14.49   7.22 -21.71]\n",
            " [ -2.88   2.82   0.06]\n",
            " [ -1.46   3.99  -2.52]\n",
            " [ -3.4    4.44  -1.04]\n",
            " [ 13.45   7.44 -20.9 ]]\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 1\n",
        "arr = [[],[],[]]\n",
        "while i < 30:\n",
        "  lr = LogisticRegression(C = 20, max_iter=1000) #매개변수 넣기\n",
        "\n",
        "#객체생성, 훈련\n",
        "  lr.fit(train_scaled, train_target)\n",
        "  print(lr.score(train_scaled, train_target))\n",
        "  print(lr.score(test_scaled, test_target))\n",
        "\n",
        "  arr[0].append(i)\n",
        "  arr[1].append(lr.score(train_scaled, train_target))\n",
        "  arr[2].append(lr.score(test_scaled, test_target))\n",
        "  #배열에 넣는다\n",
        "\n",
        "  proba = lr.predict_proba(test_scaled[:5]) #예측한 확률을 숫자로 출력\n",
        "\n",
        "\n",
        "  decision = lr.decision_function(test_scaled[:5])\n",
        "\n",
        "\n",
        "  proba = softmax(decision, axis=1) #가로로 row 를 사용해서 시험!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
        "  print(np.round(proba, decimals=3))\n",
        "  i+=1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22iO5TW2Yysm",
        "outputId": "e6efcd57-589d-40bd-dba8-45b1d6b7b6cf"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
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            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n",
            "0.9821428571428571\n",
            "0.9473684210526315\n",
            "[[0.999 0.001 0.   ]\n",
            " [0.003 0.938 0.059]\n",
            " [0.004 0.994 0.001]\n",
            " [0.    0.995 0.004]\n",
            " [0.998 0.002 0.   ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array(arr[1])\n",
        "y2 = np.array(arr[2])\n",
        "x = np.array(arr[0])\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-nVz1zIYbD3O",
        "outputId": "4cb490ec-f635-45a0-d290-b98e9bdb5d47"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#확률적 경사 하강법을 이용하여 다중 분류 훈련 밑 최적화 하기"
      ],
      "metadata": {
        "id": "5B72GKDMKjd5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "#객체 생성\n",
        "sc = SGDClassifier(loss='log_loss', max_iter=100, random_state=42) #100에포크씩 훈련\n",
        "\n",
        "#훈련\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "#평가\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))\n",
        "print(sc.coef_, sc.intercept_) #모델 파라미터 fit쓰면 지우고 다시 하지만 partialfit은 아님\n",
        "\n",
        "sc = SGDClassifier(loss='log_loss', random_state=42)\n",
        "\n",
        "train_score = []\n",
        "test_score = []\n",
        "\n",
        "classes = np.unique(train_target)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for _ in range(0, 200):\n",
        "    sc.partial_fit(train_scaled, train_target, classes=classes)\n",
        "\n",
        "    train_score.append(sc.score(train_scaled, train_target))\n",
        "    test_score.append(sc.score(test_scaled, test_target))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(train_score)\n",
        "plt.plot(test_score)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()\n",
        "#갭이 가장 작은거 선"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "mvJoUm2DNsuW",
        "outputId": "e4d1c247-69a0-47fe-c7d2-2422f811bcd6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8035714285714286\n",
            "0.7894736842105263\n",
            "[[ -1.7869281    2.95630265  -3.40604012  -3.24188287]\n",
            " [ -4.99018037  -4.40722501   9.69137377  -7.79752907]\n",
            " [ -0.84386405 -11.2561908   27.79121832  31.79025786]] [ -4.59859683 -11.36007382 -31.47659983]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#42에서 작다\n",
        "sc = SGDClassifier(loss='log_loss', max_iter=47, random_state=42) #100에포크씩 훈련\n",
        "\n",
        "#훈련\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "#추가 훈련 47번\n",
        "for _ in range(0, 47):\n",
        "    sc.partial_fit(train_scaled, train_target, classes=classes)\n",
        "\n",
        "    train_score.append(sc.score(train_scaled, train_target))\n",
        "    test_score.append(sc.score(test_scaled, test_target))\n",
        "#평가\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGmvnq6ZcA4I",
        "outputId": "1493eba4-140f-4775-b3ad-30c68bf2546c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9732142857142857\n",
            "0.9473684210526315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#와인 퀄리티 데이터"
      ],
      "metadata": {
        "id": "wRBuWujlQ-Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVgVNObzRAOK",
        "outputId": "d89de94d-896a-425d-ea86-77db1432bd23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/winequality-red.csv', sep=';')\n",
        "wine.head()\n",
        "\n",
        "#타겟 데이터 없음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "HA_Rpm_ARJBO",
        "outputId": "99100ca4-ef20-44a9-f64a-9c3837b06756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51675543-36c5-4951-8085-6628540b67f7\" class=\"colab-df-container\">\n",
              "    <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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-51675543-36c5-4951-8085-6628540b67f7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-51675543-36c5-4951-8085-6628540b67f7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-58a6f69b-981e-4de1-ba53-c98516f626d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.unique(wine['quality']))\n",
        "\n",
        "#빈값 있는지\n",
        "print(wine.isnull().any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3sgxSVGRaAt",
        "outputId": "3f4b5364-3bf1-4f2a-b163-9e199906ed85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 6 7 4 8 3]\n",
            "fixed acidity           False\n",
            "volatile acidity        False\n",
            "citric acid             False\n",
            "residual sugar          False\n",
            "chlorides               False\n",
            "free sulfur dioxide     False\n",
            "total sulfur dioxide    False\n",
            "density                 False\n",
            "pH                      False\n",
            "sulphates               False\n",
            "alcohol                 False\n",
            "quality                 False\n",
            "dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine_input = wine[['fixed acidity','volatile acidity', 'citric acid', 'residual sugar',\t'chlorides',\t'free sulfur dioxide',\t'total sulfur dioxide',\t'density',\t'pH',\t'sulphates',\t'alcohol']].to_numpy()\n",
        "wine_target = wine['quality'].to_numpy()\n",
        "\n",
        "print(wine_input[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THZux8vOR8OG",
        "outputId": "9cc6d049-c06f-4cf5-dac3-eaaabb5e9489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[7.400e+00 7.000e-01 0.000e+00 1.900e+00 7.600e-02 1.100e+01 3.400e+01\n",
            "  9.978e-01 3.510e+00 5.600e-01 9.400e+00]\n",
            " [7.800e+00 8.800e-01 0.000e+00 2.600e+00 9.800e-02 2.500e+01 6.700e+01\n",
            "  9.968e-01 3.200e+00 6.800e-01 9.800e+00]\n",
            " [7.800e+00 7.600e-01 4.000e-02 2.300e+00 9.200e-02 1.500e+01 5.400e+01\n",
            "  9.970e-01 3.260e+00 6.500e-01 9.800e+00]\n",
            " [1.120e+01 2.800e-01 5.600e-01 1.900e+00 7.500e-02 1.700e+01 6.000e+01\n",
            "  9.980e-01 3.160e+00 5.800e-01 9.800e+00]\n",
            " [7.400e+00 7.000e-01 0.000e+00 1.900e+00 7.600e-02 1.100e+01 3.400e+01\n",
            "  9.978e-01 3.510e+00 5.600e-01 9.400e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input1, test_input1, train_target1, test_target1 = train_test_split(wine_input, wine_target,random_state=42, stratify=wine_target )\n",
        "\n",
        "print(train_input1.shape, train_target1.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gryVU-9zTEvI",
        "outputId": "38048e95-608e-42e1-db49-d1e0dc2a8991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1199, 11) (1199,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import SingleScalar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "BcAyAwvWUej9",
        "outputId": "8db28fd8-18c9-4528-d675-7f0de9d421ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-f463ba866180>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSingleScalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'SingleScalar' from 'sklearn.preprocessing' (/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    }
  ]
}