material/deep_learning/roc_auc_confusion_matric.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "roc_auc_confusion_matric.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giMgHzNY8861"
      },
      "source": [
        "# ROC, AUC"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from numpy import interp\n",
        "import matplotlib.pyplot as plt\n",
        "from itertools import cycle\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "def draw_ROC_and_get_AUC(test_y, y_score, n_classes=2):\n",
        "\n",
        "  test_y = np.eye(n_classes)[test_y]\n",
        "  y_score = np.eye(n_classes)[y_score]\n",
        "\n",
        "  # Plot linewidth.\n",
        "  lw = 2\n",
        "\n",
        "  # Compute ROC curve and ROC area for each class\n",
        "  fpr = dict()\n",
        "  tpr = dict()\n",
        "  roc_auc = dict()\n",
        "  for i in range(n_classes):\n",
        "      fpr[i], tpr[i], _ = roc_curve(test_y[:, i], y_score[:, i])\n",
        "      roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "  # Compute micro-average ROC curve and ROC area\n",
        "  fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(test_y.ravel(), y_score.ravel())\n",
        "  roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "  # Compute macro-average ROC curve and ROC area\n",
        "\n",
        "  # First aggregate all false positive rates\n",
        "  all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
        "\n",
        "  # Then interpolate all ROC curves at this points\n",
        "  mean_tpr = np.zeros_like(all_fpr)\n",
        "  for i in range(n_classes):\n",
        "      mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
        "\n",
        "  # Finally average it and compute AUC\n",
        "  mean_tpr /= n_classes\n",
        "\n",
        "  fpr[\"macro\"] = all_fpr\n",
        "  tpr[\"macro\"] = mean_tpr\n",
        "  roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
        "\n",
        "  # Plot all ROC curves\n",
        "  plt.figure(1)\n",
        "  plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
        "          label='ROC curve (area = {0:0.2f})'\n",
        "                ''.format(roc_auc[\"macro\"]),\n",
        "          color='navy', linestyle=':', linewidth=4)\n",
        "\n",
        "  colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
        "  for i, color in zip(range(n_classes), colors):\n",
        "      plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
        "              label='ROC curve of class {0} (area = {1:0.2f})'\n",
        "              ''.format(i, roc_auc[i]))\n",
        "\n",
        "  plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
        "  plt.xlim([0.0, 1.0])\n",
        "  plt.ylim([0.0, 1.05])\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.show()\n",
        "\n",
        "  return roc_auc[\"macro\"]"
      ],
      "metadata": {
        "id": "bC9FeOdpuH7a"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2개 클래스"
      ],
      "metadata": {
        "id": "RVBHGKcOtbD1"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDu8wpKXq7kV"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROMsisKxw8MC"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwMz9NaYwx5e",
        "outputId": "8a88c1ca-7655-4bc5-edf2-484b343238bd"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/data/sonar.csv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-01 08:16:14--  https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/data/sonar.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 87776 (86K) [text/plain]\n",
            "Saving to: ‘sonar.csv’\n",
            "\n",
            "\rsonar.csv             0%[                    ]       0  --.-KB/s               \rsonar.csv           100%[===================>]  85.72K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2022-07-01 08:16:14 (5.16 MB/s) - ‘sonar.csv’ saved [87776/87776]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VckhCYiwxAZj",
        "outputId": "64083d27-4265-4e6f-9cc2-ed0afd225874",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "raw_data = pd.read_csv(\"sonar.csv\")\n",
        "data = raw_data.to_numpy()\n",
        "np.random.shuffle(data)\n",
        "\n",
        "x = data[:,:60]\n",
        "y = data[:,60].astype(np.int16)\n",
        "\n",
        "split_index = int(len(x)*0.8)\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvaZhPiSq5Gc"
      },
      "source": [
        "## 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-wBUWBH_mTQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "outputId": "d2e83bb2-0f35-4f7c-b5cb-a02d016d0511"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.metrics import AUC, Precision, Recall\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(60,)))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "model.compile(optimizer=\"Adam\", loss=\"binary_crossentropy\", metrics=[\"acc\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=200, verbose=0, batch_size=128, validation_split=0.1)\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = (y_>0.5).astype(np.int16).squeeze()\n",
        "\n",
        "plt.plot(test_y, \"o\")\n",
        "plt.plot(predicted, \".\")\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 10)                610       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 731\n",
            "Trainable params: 731\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5021 - acc: 0.7381\n",
            "loss= 0.5020686388015747\n",
            "acc= 0.738095223903656\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:36: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC 그리기, AUC 출력"
      ],
      "metadata": {
        "id": "N9QZY6Ni15Sp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auc = draw_ROC_and_get_AUC(test_y, predicted, n_classes=2)\n",
        "print(\"AUC \", auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "-ywAwrFpwwgs",
        "outputId": "11414f78-cd8a-4524-bf58-29d55029b216"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC  0.738095238095238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Pkxkygw2yKvQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matric"
      ],
      "metadata": {
        "id": "g6fwqeQo3L5i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(test_y, predicted)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d476efb-32e5-47a7-e9e3-cfe86e453bee",
        "id": "BSUdf_2M3L5i"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[13  8]\n",
            " [ 3 18]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test_y, predicted, target_names=['0','1']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a46aa75f-f926-4472-d309-2a4b4a12706e",
        "id": "YbeZ_QDc3L5j"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.62      0.70        21\n",
            "           1       0.69      0.86      0.77        21\n",
            "\n",
            "    accuracy                           0.74        42\n",
            "   macro avg       0.75      0.74      0.73        42\n",
            "weighted avg       0.75      0.74      0.73        42\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "figure = plt.figure(figsize=(2, 2))\n",
        "sns.heatmap(cm, annot=True, cmap=plt.cm.Blues)\n",
        "plt.tight_layout()\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "ced3854a-5c7e-4a20-c98a-51c881a71294",
        "id": "VzZnYhk33L5j"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EqoYnm1T3L5j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# n개 클래스"
      ],
      "metadata": {
        "id": "172g03PW1_UV"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9tjK5mf1_UV"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255\n",
        "test_x = raw_test_x/255\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLLMtYtg2G9x",
        "outputId": "493ff996-34ea-4e17-8e53-8229fc75900f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-Wb-MeJ1_UW"
      },
      "source": [
        "## 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5ca85be-b7ed-4c75-c0f0-79d96d17317c",
        "id": "cz3YVUPO1_UX"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input, Reshape\n",
        "from tensorflow.keras.metrics import *\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Reshape((28,28,1)))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "hist = model.fit(train_x, train_y, epochs=5, verbose=1, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " reshape (Reshape)           (None, 28, 28, 1)         0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                16010     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 35,046\n",
            "Trainable params: 35,046\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "422/422 [==============================] - 14s 4ms/step - loss: 0.6061 - accuracy: 0.8134 - val_loss: 0.1825 - val_accuracy: 0.9492\n",
            "Epoch 2/5\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.1730 - accuracy: 0.9503 - val_loss: 0.1130 - val_accuracy: 0.9673\n",
            "Epoch 3/5\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.1229 - accuracy: 0.9635 - val_loss: 0.0887 - val_accuracy: 0.9742\n",
            "Epoch 4/5\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.1017 - accuracy: 0.9699 - val_loss: 0.0778 - val_accuracy: 0.9792\n",
            "Epoch 5/5\n",
            "422/422 [==============================] - 2s 4ms/step - loss: 0.0848 - accuracy: 0.9747 - val_loss: 0.0760 - val_accuracy: 0.9778\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0832 - accuracy: 0.9744\n",
            "loss= 0.08324328809976578\n",
            "acc= 0.974399983882904\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC 그리기, AUC 출력"
      ],
      "metadata": {
        "id": "K2Jhr9Q81_UX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auc = draw_ROC_and_get_AUC(test_y, predicted, n_classes=10)\n",
        "print(\"AUC \", auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "e2814bd0-6065-4ed8-f2cd-917f1bede957",
        "id": "dXrDuJzt1_UX"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC  0.985725678951989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matric"
      ],
      "metadata": {
        "id": "9SVpeZeX3GnK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(test_y, predicted)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpibgvS-1_UX",
        "outputId": "9422d477-69f2-409d-c83d-95825c99c980"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 963    0    1    0    1    1    6    3    3    2]\n",
            " [   0 1111    1    1    1    1    4    7    9    0]\n",
            " [   1    2  989    3    3    0    0   11   23    0]\n",
            " [   0    1   12  984    0    1    0    3    6    3]\n",
            " [   0    1    2    0  962    0    3    0    2   12]\n",
            " [   2    0    0   10    1  857   13    1    6    2]\n",
            " [   3    1    0    0    5    1  945    0    3    0]\n",
            " [   0    0   15    1    4    0    0 1004    1    3]\n",
            " [   6    1    4    2    2    0    5    3  943    8]\n",
            " [   5    3    0    3    3    0    0    4    5  986]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test_y, predicted, target_names=['0','1','2','3','4','5','6','7','8','9']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7MV_iiY2-3z",
        "outputId": "ac56db8c-b2d4-4e25-9556-b34744821e5c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       980\n",
            "           1       0.99      0.98      0.99      1135\n",
            "           2       0.97      0.96      0.96      1032\n",
            "           3       0.98      0.97      0.98      1010\n",
            "           4       0.98      0.98      0.98       982\n",
            "           5       1.00      0.96      0.98       892\n",
            "           6       0.97      0.99      0.98       958\n",
            "           7       0.97      0.98      0.97      1028\n",
            "           8       0.94      0.97      0.95       974\n",
            "           9       0.97      0.98      0.97      1009\n",
            "\n",
            "    accuracy                           0.97     10000\n",
            "   macro avg       0.97      0.97      0.97     10000\n",
            "weighted avg       0.97      0.97      0.97     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "figure = plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(cm, annot=True, cmap=plt.cm.Blues)\n",
        "plt.tight_layout()\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "_4PMa4rb3CpJ",
        "outputId": "9ef28a12-325d-454d-9466-26c3c9e1f10a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Q2Lij5UF3E0d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}