데이터분석_5일차_이론.ipynb

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  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "한림대 데이터분석 5일차",
      "provenance": [],
      "collapsed_sections": [],
      "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/younghoonNa/pythonDT/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_5%EC%9D%BC%EC%B0%A8_%EC%9D%B4%EB%A1%A0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2IJ7_VTq-yqr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "boston = load_boston()"
      ],
      "metadata": {
        "id": "5EMIxuOGQvaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준화 적용\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler() # 변환기 생성\n",
        "scaler.fit(boston['data']) # 각 컬럼의 평균과 표준편차를 알아내는 작업\n",
        "x_scaled = scaler.transform(boston['data']) # 표준화 적용\n",
        "pd.DataFrame(x_scaled).describe().round(2)"
      ],
      "metadata": {
        "id": "v6UPmWuGQ-n6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 최소 최대 정규화 적용\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "x_scaled = scaler.fit_transform(boston['data'])\n",
        "pd.DataFrame(x_scaled).describe().round(2)"
      ],
      "metadata": {
        "id": "qKfx2WkvSNwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, boston['target'], random_state=0)\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtglMpiISwBn",
        "outputId": "1ce08103-bce6-44f9-9518-ab5ff654c805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.7697699488741149, 0.6354638433202123)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 다항변환 적용\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "P = PolynomialFeatures(degree=2, include_bias=False)\n",
        "x_poly = P.fit_transform(boston['data']) # 다항변환 및 상호교차항 적용\n",
        "boston['data'].shape, x_poly.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ktbv9RXXTjE0",
        "outputId": "64466ce6-f5ef-4324-e749-f21ed94ac721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((506, 13), (506, 104))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "P.get_feature_names_out() # 변환된 컬럼명 정보 조회"
      ],
      "metadata": {
        "id": "Cb7vq18DXOgF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x_poly, boston['target'], random_state=0)\n",
        "model = LinearRegression().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNPs-BrXXTnc",
        "outputId": "4a84afb8-7ef3-4e68-9f80-e02e9afebc7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9520519609032728, 0.607472195965229)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "model = Ridge(alpha=2500).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CC13lWJqXq90",
        "outputId": "b5fbd218-d605-4424-e8a8-63700a48764c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.91725906854018, 0.7394691032074603)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "model = Lasso(alpha=1).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3LWoEgsfuY5",
        "outputId": "d33e3b1d-75c3-4f90-bc56-e1cc8a7f8611"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.749e+03, tolerance: 3.233e+00\n",
            "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9068684337013593, 0.7280316827205046)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(model.coef_), len(model.coef_[model.coef_ != 0 ])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-AQ1DmQhsk7",
        "outputId": "17fbf81e-6a1c-493c-f50e-8baecf7d87f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(104, 55)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([1,2,3,4,5,6,7,8,9,11,13])\n",
        "y = np.array([0,0,0,0,0,0,0,1,1,1,1])\n",
        "plt.scatter(x, y)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7TUjSTEXhw25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression().fit(x.reshape(-1, 1), y)\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, model.predict(x.reshape(-1, 1)), color='red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "j56LgV6ojXkT",
        "outputId": "23f8b5bc-fda7-4faf-c460-1af03a6185ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression().fit(x.reshape(-1, 1), y)\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, model.predict(x.reshape(-1, 1)), color='red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9u04YytSjdq3",
        "outputId": "b47af9e8-be22-4b8d-de7a-adb99e5a9817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer = load_breast_cancer() # 특성의 값을 이용해서 악성 종양 or 양성 종양 분류\n",
        "cancer['target'] # 0 or 1 > 범주형 > 분류 모델 "
      ],
      "metadata": {
        "id": "Z84ix2L-mA6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer['target_names']\n",
        "# 0 : 'malignant' > 악성\n",
        "# 1 : 'benign'    > 양성"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zrmuV3i7Ggj",
        "outputId": "601ce981-9cc4-4a7e-8c16-36141fd1e114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test =\\\n",
        "    train_test_split(cancer['data'], cancer['target'], random_state=0)"
      ],
      "metadata": {
        "id": "pyD3vONj7b_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(max_iter=100000).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)\n",
        "# ConvergenceWarning : 수렴 경고 > 학습 횟수를 증가"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVsOFYSk73ga",
        "outputId": "e32e0328-f60e-4a20-b2cb-7fdb7fd6f61f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.960093896713615, 0.951048951048951)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_test) # 예측 레이블\n",
        "model.predict_proba(x_test) # 예측 확률 (분류모델)\n",
        "model.decision_function(x_test) # 회귀식의 계산 결과 \n",
        "# 회귀식의 계산 결과  > sigmoid > 확률 > cutoff > 레이블"
      ],
      "metadata": {
        "id": "O5l19DB-8dDO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "def sigmoid(x):\n",
        "    return 1 / ( 1 + np.exp(-x))\n",
        "x_range = np.arange(-10, 10.1, 0.1)\n",
        "y_range = [sigmoid(x) for x in x_range]\n",
        "plt.plot(x_range, y_range)"
      ],
      "metadata": {
        "id": "XZlcmtI89Qm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.decision_function(x_test)[0] # -5.3699247474945935\n",
        "sigmoid(-5.3699247474945935)       # 0.0046329177283744435 > Y가 1일 확률\n",
        "model.predict_proba(x_test)[0]\n",
        "model.predict(x_test)[0]"
      ],
      "metadata": {
        "id": "zHMLbmcz-HrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_digits\n",
        "digits = load_digits() # 손글씨 데이터 세트 (0 ~ 9) > 총 범주 10개\n",
        "digits.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPWYaVRk-Pta",
        "outputId": "855671e9-3896-4a3e-be32-529e92ba6bac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(digits['data'], digits['target'], random_state=0)\n",
        "model = LogisticRegression(max_iter=10000).fit(x_train, y_train)\n",
        "model.coef_.shape # (10, 64) > (모델의개수, 특성의 개수)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4vfA-Xk_BHn",
        "outputId": "5a851cc9-2890-4673-d0c3-56c8451ac240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer = load_breast_cancer()\n",
        "x_train, x_test, y_train, y_test =\\\n",
        "    train_test_split(cancer['data'], cancer['target'], random_state=0)"
      ],
      "metadata": {
        "id": "w0JAMJEj_y78"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier(random_state=0).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)\n",
        "# DecisionTree : 학습을 할때마다 특성의 순서를 셔플 후 사용 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_WXXRVDLPTQ",
        "outputId": "12c180b0-e44e-4de5-fa38-56b9f4a326d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 0.8811188811188811)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 특성 중요도 \n",
        "model.feature_importances_"
      ],
      "metadata": {
        "id": "DH1cj6JdLXcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "fi = pd.Series(model.feature_importances_, index=cancer['feature_names'])\n",
        "fi[fi!=0].sort_values().plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "1TbyU3xJMCOV",
        "outputId": "e5825893-f7a3-4443-873f-5ad8d566a00d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff24bc1ead0>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## using graphviz \n",
        "!apt-get -qq install -y graphviz && pip install -q pydot\n",
        "import pydot\n",
        "## 아래 있는것은 필수는 아닌데, 가끔 에러가 생길 때가 있어서, 그냥 같이 해줌. \n",
        "!apt-get install graphviz libgraphviz-dev pkg-config\n",
        "!pip install pygraphviz\n",
        "import pygraphviz"
      ],
      "metadata": {
        "id": "G9U78GrYMThM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "export_graphviz(model, # 시각화할 모델\n",
        "                out_file='model.dot', # 저장할 파일명\n",
        "                feature_names=cancer['feature_names'], # 컬럼명\n",
        "                class_names=cancer['target_names'], # 레이블명\n",
        "                impurity=True, # 불순도 출력 유무\n",
        "                filled=True) # 노드에 과반 범주 색상 부여 유무"
      ],
      "metadata": {
        "id": "6S_LCdsxM7KD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "with open('model.dot') as f:\n",
        "    data = f.read()\n",
        "graphviz.Source(data).render('model') # pdf 저장\n",
        "graphviz.Source(data) # 출력창에 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "aAMANb88NvXe",
        "outputId": "d8401140-c3aa-4e4b-815a-a1c1d69aad52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'model.pdf'"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnsLcEFIOHFc",
        "outputId": "dcede5f0-31ab-455e-e802-38f485f16c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 0.972027972027972)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model = GradientBoostingClassifier().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfgFw5VQXr2g",
        "outputId": "f5c7e443-9830-4917-d862-94bbd5b2e9cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 0.972027972027972)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(boston['data'], boston['target'], random_state=0)\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression().fit(x_train, y_train)\n",
        "print('LinearRegression', model.score(x_train, y_train), model.score(x_test, y_test))\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)\n",
        "print('RandomForestRegressor', model.score(x_train, y_train), model.score(x_test, y_test))\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "model = GradientBoostingRegressor().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)\n",
        "print('GradientBoostingRegressor', model.score(x_train, y_train), model.score(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li5xHqkIX11O",
        "outputId": "2aa7d970-8429-4b5f-e5e4-a6e99b56a570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearRegression 0.7697699488741149 0.635463843320211\n",
            "RandomForestRegressor 0.9803919053376355 0.78628866061438\n",
            "GradientBoostingRegressor 0.9829864654169255 0.822792777833321\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "W3XfJEMBYKB9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}