material/deep_learning/treating_imbalanced_data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "treating_imbalanced_data.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pandas import DataFrame\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras import Sequential"
      ],
      "metadata": {
        "id": "BXZSqC0Od0bY"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 준비"
      ],
      "metadata": {
        "id": "JBDsVPeXjutv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "\n",
        "# 데이터 생성\n",
        "base_x, base_y = make_moons(n_samples=(1000,50), noise=0.2, random_state=32)\n",
        "\n",
        "\n",
        "# 데이터 그림 그리기\n",
        "df = DataFrame(dict(x=base_x[:,0], y=base_x[:,1], label=base_y))\n",
        "colors = {0:'red', 1:'blue'}\n",
        "fig, ax = plt.subplots()\n",
        "grouped = df.groupby('label')\n",
        "for key, group in grouped:\n",
        "    group.plot(ax=ax, kind='scatter', x='x', y='y', label=key, color=colors[key])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "BcFKzVpadvAR",
        "outputId": "116c4134-2fba-439a-856b-b877e793e1f5"
      },
      "execution_count": 2,
      "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": "markdown",
      "source": [
        "# 공통 실행 함수"
      ],
      "metadata": {
        "id": "psWnHrlxq3gV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "CLASS_0_LABEL = 0\n",
        "CLASS_1_LABEL = 1\n",
        "DEFAULT_CLASS_WEIGHT = {CLASS_0_LABEL:1.0, CLASS_1_LABEL:1.0}\n",
        "\n",
        "def do_classify(x, y, class_weight=DEFAULT_CLASS_WEIGHT):\n",
        "\n",
        "    train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.3)\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Input(2))\n",
        "    model.add(Dense(20, activation='relu'))\n",
        "    model.add(Dense(20, activation='relu'))\n",
        "    model.add(Dense(2, activation=\"softmax\"))\n",
        "    model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=[\"acc\"])\n",
        "\n",
        "    base_history = model.fit(\n",
        "        train_x, train_y, epochs=100, batch_size=128, verbose=0, validation_split=0.2,\n",
        "        class_weight=class_weight\n",
        "    )\n",
        "\n",
        "\n",
        "    plt.plot(base_history.history['loss'], label='train_loss')\n",
        "    plt.plot(base_history.history['val_loss'], label='val_loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    loss, acc = model.evaluate(test_x, test_y)\n",
        "    print(\"loss=\", loss)\n",
        "    print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "    y_ = model.predict(test_x)\n",
        "    predicted = np.argmax(y_, axis=-1)\n",
        "\n",
        "\n",
        "    cm = confusion_matrix(test_y, predicted)\n",
        "    print(cm)\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "\n",
        "    print(classification_report(test_y, predicted))\n",
        "    "
      ],
      "metadata": {
        "id": "y2_lg1lsmvyh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 처리 하지 않았을 때"
      ],
      "metadata": {
        "id": "_js1lcQFjsoB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = base_x.copy()\n",
        "y = base_y.copy()\n",
        "\n",
        "do_classify(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "yy7sk-GEms2R",
        "outputId": "35d62854-b2a6-4a8c-b940-51d96546372c"
      },
      "execution_count": 4,
      "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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step - loss: 0.0837 - acc: 0.9683\n",
            "loss= 0.08371222764253616\n",
            "acc= 0.9682539701461792\n",
            "[[300   4]\n",
            " [  6   5]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98       304\n",
            "           1       0.56      0.45      0.50        11\n",
            "\n",
            "    accuracy                           0.97       315\n",
            "   macro avg       0.77      0.72      0.74       315\n",
            "weighted avg       0.97      0.97      0.97       315\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 오버샘플링"
      ],
      "metadata": {
        "id": "zvSo0M4CjxsU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = base_x.copy()\n",
        "y = base_y.copy()"
      ],
      "metadata": {
        "id": "-3j5qyVYkfVX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class0_x = x[y==0]\n",
        "class0_y = y[y==0]\n",
        "class1_x = x[y==1]\n",
        "class1_y = y[y==1]\n",
        "\n",
        "print(len(class0_x), len(class1_x))\n",
        "imbalance_ratio = int(len(class0_x)/len(class1_x))\n",
        "print(imbalance_ratio)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZeGsUTTAdutu",
        "outputId": "4a239f8b-e330-4f7f-c287-c45e1d7e2c99"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000 50\n",
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class1_x = np.repeat(class1_x, imbalance_ratio, axis=0)\n",
        "class1_y = np.repeat(class1_y, imbalance_ratio, axis=0)\n",
        "print(len(class1_x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fJVv7x1durK",
        "outputId": "e53d13c0-5c05-4f08-b36f-127eda126137"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape)\n",
        "print(class1_x.shape)\n",
        "print(y.shape)\n",
        "print(class1_y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02OqivCmduk9",
        "outputId": "234a7e0c-09eb-4498-8b1e-1c4568f286dd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1050, 2)\n",
            "(1000, 2)\n",
            "(1050,)\n",
            "(1000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.vstack([class0_x, class1_x])\n",
        "y = np.hstack([class0_y, class1_y])\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz64kcBil2_s",
        "outputId": "12132c59-24b9-4613-c143-647df46147c7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2000, 2)\n",
            "(2000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "do_classify(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "vebV5awImqRv",
        "outputId": "3d708654-038b-4480-cb2d-1bb4b7009553"
      },
      "execution_count": 10,
      "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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 0s 2ms/step - loss: 0.1778 - acc: 0.9367\n",
            "loss= 0.17778272926807404\n",
            "acc= 0.9366666674613953\n",
            "[[280  14]\n",
            " [ 24 282]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.95      0.94       294\n",
            "           1       0.95      0.92      0.94       306\n",
            "\n",
            "    accuracy                           0.94       600\n",
            "   macro avg       0.94      0.94      0.94       600\n",
            "weighted avg       0.94      0.94      0.94       600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "hKerD0sYmqO9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 언더샘플링"
      ],
      "metadata": {
        "id": "z_gObmFtne9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = base_x.copy()\n",
        "y = base_y.copy()"
      ],
      "metadata": {
        "id": "7RyEjX1une96"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class0_x = x[y==0]\n",
        "class0_y = y[y==0]\n",
        "class1_x = x[y==1]\n",
        "class1_y = y[y==1]\n",
        "\n",
        "print(len(class0_x), len(class1_x))\n",
        "class1_count = len(class1_x)\n",
        "print(class1_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa0739b7-ea83-4767-dffb-c4850e851ffe",
        "id": "gpQHxVFUne97"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000 50\n",
            "50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class0_x = class0_x[:class1_count]\n",
        "class0_y = class0_y[:class1_count]\n",
        "\n",
        "print(len(class0_x), len(class1_x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1db8b6a-c45f-450a-e20e-144a331d4f5a",
        "id": "M3DrIAeDne97"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50 50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.vstack([class0_x, class1_x])\n",
        "y = np.hstack([class0_y, class1_y])\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b83c7586-1804-4d6b-f695-56ff211cfcca",
        "id": "oRrLGTzyne98"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 2)\n",
            "(100,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "do_classify(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "1c486c6d-69cd-4d8f-fa94-27755c5fed2a",
        "id": "9NLJHIunne98"
      },
      "execution_count": 15,
      "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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 18ms/step - loss: 0.5036 - acc: 0.7333\n",
            "loss= 0.5035920739173889\n",
            "acc= 0.7333333492279053\n",
            "[[10  3]\n",
            " [ 5 12]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.77      0.71        13\n",
            "           1       0.80      0.71      0.75        17\n",
            "\n",
            "    accuracy                           0.73        30\n",
            "   macro avg       0.73      0.74      0.73        30\n",
            "weighted avg       0.74      0.73      0.73        30\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# class weight"
      ],
      "metadata": {
        "id": "a-YXDLeCobl4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = base_x.copy()\n",
        "y = base_y.copy()"
      ],
      "metadata": {
        "id": "r7Y8jKlQpA5I"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "classes = np.unique(y)\n",
        "print(classes)\n",
        "\n",
        "class_weight_ratio = compute_class_weight(\n",
        "    class_weight=\"balanced\", classes=classes, y=y.squeeze())\n",
        "print(class_weight_ratio)\n",
        "\n",
        "class_weight = { k:v for k, v in zip(classes, class_weight_ratio) }\n",
        "print(class_weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fehe02nJobY8",
        "outputId": "a392052c-5e83-4cc0-ae2e-20e0708e6d9f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n",
            "[ 0.525 10.5  ]\n",
            "{0: 0.525, 1: 10.5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "do_classify(x, y, class_weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "P4Dfa_00pqvY",
        "outputId": "241ef949-5443-4bcb-97dc-9ce11445e96d"
      },
      "execution_count": 19,
      "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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step - loss: 0.3229 - acc: 0.8952\n",
            "loss= 0.32285207509994507\n",
            "acc= 0.8952381014823914\n",
            "[[275  31]\n",
            " [  2   7]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.90      0.94       306\n",
            "           1       0.18      0.78      0.30         9\n",
            "\n",
            "    accuracy                           0.90       315\n",
            "   macro avg       0.59      0.84      0.62       315\n",
            "weighted avg       0.97      0.90      0.92       315\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-fold 교차 검증"
      ],
      "metadata": {
        "id": "bneAFQhwoIW7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "all_test_y = []\n",
        "all_predicted = []\n",
        "\n",
        "i = 1\n",
        "for train_index, test_index in KFold(n_splits=5).split(x, y):\n",
        "    print()\n",
        "    print(f\"=========================== {i}th K-Fold ==============================\")\n",
        "    print()\n",
        "    i += 1\n",
        "\n",
        "    train_x, test_x = x[train_index], x[test_index]\n",
        "    train_y, test_y = y[train_index], y[test_index]\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Input(2))\n",
        "    model.add(Dense(20, activation='relu'))\n",
        "    model.add(Dense(20, activation='relu'))\n",
        "    model.add(Dense(2, activation=\"softmax\"))\n",
        "    model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=[\"acc\"])\n",
        "\n",
        "    base_history = model.fit(train_x, train_y, epochs=100, batch_size=128, verbose=0, validation_split=0.2)\n",
        "\n",
        "\n",
        "    plt.plot(base_history.history['loss'], label='train_loss')\n",
        "    plt.plot(base_history.history['val_loss'], label='val_loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    loss, acc = model.evaluate(test_x, test_y)\n",
        "    print(\"loss=\", loss)\n",
        "    print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "    y_ = model.predict(test_x)\n",
        "    predicted = np.argmax(y_, axis=-1)\n",
        "\n",
        "    cm = confusion_matrix(test_y, predicted)\n",
        "    print(cm)\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "    plt.show()\n",
        "\n",
        "    print(classification_report(test_y, predicted))\n",
        "    all_test_y.extend(test_y)\n",
        "    all_predicted.extend(predicted)\n",
        "\n",
        "\n",
        "print()\n",
        "print(f\"=========================== Aggregated ==============================\")\n",
        "print()\n",
        "\n",
        "cm = confusion_matrix(all_test_y, all_predicted)\n",
        "print(cm)\n",
        "plt.show()\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "\n",
        "print(classification_report(all_test_y, all_predicted))\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9uu5jyVed-bV",
        "outputId": "da06178e-c2c3-45d6-fe96-2a1a5800dd8a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=========================== 1th K-Fold ==============================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.1111 - acc: 0.9619\n",
            "loss= 0.11107105016708374\n",
            "acc= 0.961904764175415\n",
            "[[198   1]\n",
            " [  7   4]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98       199\n",
            "           1       0.80      0.36      0.50        11\n",
            "\n",
            "    accuracy                           0.96       210\n",
            "   macro avg       0.88      0.68      0.74       210\n",
            "weighted avg       0.96      0.96      0.96       210\n",
            "\n",
            "\n",
            "=========================== 2th K-Fold ==============================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.0450 - acc: 0.9905\n",
            "loss= 0.04503537714481354\n",
            "acc= 0.9904761910438538\n",
            "[[204   0]\n",
            " [  2   4]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      1.00       204\n",
            "           1       1.00      0.67      0.80         6\n",
            "\n",
            "    accuracy                           0.99       210\n",
            "   macro avg       1.00      0.83      0.90       210\n",
            "weighted avg       0.99      0.99      0.99       210\n",
            "\n",
            "\n",
            "=========================== 3th K-Fold ==============================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.0898 - acc: 0.9667\n",
            "loss= 0.08978809416294098\n",
            "acc= 0.9666666388511658\n",
            "[[191   5]\n",
            " [  2  12]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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r0tlf0bijT9bGjW+lDqfh1Wsbmu3BkgZExOvZ689LulDSbEmnSbo0+/mbWs9RrgIeKukISS9vHpukh2s9aX8z6tMH6ZST/1UL//S0WuffLUn6/vcv1Z2/vTdxZOhtv556uUYdcrC2+8gQPfn0/frJj3+ps7/VrEGDBumWWe3/Q9na+qS+e87kxJE2rkLU7X+6h0q63bbUnitvjIjf2p4v6SbbEyWtlHRirSdwdBGs7amSro6IBzs4dmNEnFTuBLQg0JEhW26dOgT0QS++usTdXeOkXY+vOOfcuPL2bp+vO7qsgCOi028MVJJ8AaC38VVkAEikkb6KTAIGkCs8EQMAEqEFAQCJ1HEXRI8jAQPIFVoQAJAIF+EAIBF6wACQCC0IAEikq2/39jUkYAC5wmPpASARWhAAkAgtCABIhAoYABJhGxoAJMJXkQEgEVoQAJAICRgAEmEXBAAk0kgV8IDUAQBAPUUV/3TF9s6277P9tO2nbJ+dzf/Q9mrbC7JxdK2xUgEDyJVC1O2GlG2Svh0Rf7S9jaTHbc/Njl0REZd19wQkYAC5Uq8ecESskbQme/267Wck7VSXxTO0IADkSlFR8bDdbLu1ZDR3tKbt3SR9XNIfsqlJthfanmZ7SK2xkoAB5Eo1PeCIaImIkSWjZfP1bG8t6VZJ34yI1yRdJWlPSU1qr5AvrzVWWhAAcqVYx21otrdQe/K9ISJuk6SIWFtyfIqkObWuTwUMIFfquAvCkqZKeiYiflYyP6zkY8dLWlRrrFTAAHKljrsgRkk6RdKfbC/I5s6XNMF2k6SQ9KykM2o9AQkYQK7UqwUREQ9KcgeH7qjLCUQCBpAz3I4SABKp50W4nkYCBpArVMAAkEghCqlDqBgJGECucDtKAEikkW5HSQIGkCtUwACQCLsgACARdkEAQCJ1/CpyjyMBA8gVesAAkAg9YABIhAoYABJhHzAAJEIFDACJsAsCABLhIhwAJEILAgAS4ZtwAJAIFTAAJNJIPWA30n8tGp3t5ohoSR0H+hb+LvqvAakD6GeaUweAPom/i36KBAwAiZCAASAREnDvos+HjvB30U9xEQ4AEqECBoBESMAAkAgJuJfYPtL2EtvLbJ+XOh6kZ3ua7XW2F6WOBWmQgHuB7Q9I+i9JR0naR9IE2/ukjQp9wDWSjkwdBNIhAfeOgyUti4gVEfF/kmZIGpc4JiQWEfMkrU8dB9IhAfeOnSQ9X/J+VTYHoB8jAQNAIiTg3rFa0s4l74dncwD6MRJw75gvaYTt3W0PkjRe0uzEMQFIjATcCyKiTdIkSXdJekbSTRHxVNqokJrt6ZIekbS37VW2J6aOCb2LryIDQCJUwACQCAkYABIhAQNAIiRgAEiEBAwAiZCAASAREjAAJPL/2RsmX+FR6BsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.97      0.98       196\n",
            "           1       0.71      0.86      0.77        14\n",
            "\n",
            "    accuracy                           0.97       210\n",
            "   macro avg       0.85      0.92      0.88       210\n",
            "weighted avg       0.97      0.97      0.97       210\n",
            "\n",
            "\n",
            "=========================== 4th K-Fold ==============================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.1704 - acc: 0.9619\n",
            "loss= 0.17043709754943848\n",
            "acc= 0.961904764175415\n",
            "[[196   0]\n",
            " [  8   6]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98       196\n",
            "           1       1.00      0.43      0.60        14\n",
            "\n",
            "    accuracy                           0.96       210\n",
            "   macro avg       0.98      0.71      0.79       210\n",
            "weighted avg       0.96      0.96      0.95       210\n",
            "\n",
            "\n",
            "=========================== 5th K-Fold ==============================\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.0732 - acc: 0.9810\n",
            "loss= 0.07315201312303543\n",
            "acc= 0.9809523820877075\n",
            "[[204   1]\n",
            " [  3   2]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       205\n",
            "           1       0.67      0.40      0.50         5\n",
            "\n",
            "    accuracy                           0.98       210\n",
            "   macro avg       0.83      0.70      0.75       210\n",
            "weighted avg       0.98      0.98      0.98       210\n",
            "\n",
            "\n",
            "=========================== Aggregated ==============================\n",
            "\n",
            "[[993   7]\n",
            " [ 22  28]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      1000\n",
            "           1       0.80      0.56      0.66        50\n",
            "\n",
            "    accuracy                           0.97      1050\n",
            "   macro avg       0.89      0.78      0.82      1050\n",
            "weighted avg       0.97      0.97      0.97      1050\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 리샘플링 앙상블"
      ],
      "metadata": {
        "id": "_-PSnwUnoNBd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ThkSytz4oM8b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nxxg790FoM4n"
      }
    }
  ]
}