A13_Predict_Unfreeze.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Wanita-8943/Main_Project/blob/main/A13_Predict_Unfreeze.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vYOj7qs68CC"
      },
      "source": [
        "##เรียกใช้ CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WlXPl93BpDM",
        "outputId": "eb6ac9f4-7459-4393-9eb4-1548df0d40cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "vCxtnct6BtlL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwAfx2vRCdxm",
        "outputId": "70cfb64d-27e8-4470-89a5-c59068efe10e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'efficientnet_keras_transfer_learning'...\n",
            "remote: Enumerating objects: 1085, done.\u001b[K\n",
            "remote: Counting objects: 100% (248/248), done.\u001b[K\n",
            "remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
            "remote: Total 1085 (delta 124), reused 241 (delta 121), pack-reused 837\u001b[K\n",
            "Receiving objects: 100% (1085/1085), 14.09 MiB | 7.52 MiB/s, done.\n",
            "Resolving deltas: 100% (621/621), done.\n",
            "/content/efficientnet_keras_transfer_learning\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "%cd /content\n",
        "if not os.path.isdir(\"efficientnet_keras_transfer_learning\"):\n",
        " !git clone https://github.com/Wanita-8943/efficientnet_keras_transfer_learning\n",
        "%cd efficientnet_keras_transfer_learning/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Sa_dq25vCUfN"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/cut_panoramic/Model/Regression/All/13Re_All_1G_1e-4_16_0.2_250_New_Unfreez.h5')\n",
        "\n",
        "from efficientnet.layers import Swish, DropConnect\n",
        "from efficientnet.model import ConvKernalInitializer\n",
        "from tensorflow.keras.utils import get_custom_objects\n",
        "\n",
        "get_custom_objects().update({\n",
        "    'ConvKernalInitializer': ConvKernalInitializer,\n",
        "    'Swish': Swish,\n",
        "    'DropConnect':DropConnect\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "dDq_RhxuB3US"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('/content/drive/MyDrive/cut_panoramic/Model/Regression/All/13Re_All_1G_1e-4_16_0.2_250_New_Unfreez.h5')\n",
        "height = width = model.input_shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "1RiTwdWRBvgU",
        "outputId": "5f1a6322-2796-4c53-9d37-e4cdba534cb2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "0           1               1          7  Y07F         1         V1.jpg   \n",
              "1           2               1          7  Y07F         1    Flip_V1.jpg   \n",
              "2           3               2          7  Y07F         1         V2.jpg   \n",
              "3           4               2          7  Y07F         1    Flip_V2.jpg   \n",
              "4           5               3          7  Y07F         1         V3.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25  Y25M        19  Flip_J463.jpg   \n",
              "4746      122              78         25  Y25M        19       J464.jpg   \n",
              "4747      123              78         25  Y25M        19  Flip_J464.jpg   \n",
              "4748      124              79         25  Y25M        19       J465.jpg   \n",
              "4749      125              79         25  Y25M        19  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "0     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "1     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "2     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "3     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "4     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "\n",
              "[4750 rows x 9 columns]"
            ],
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              "      <td>19</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
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              "      <td>Flip_J464.jpg</td>\n",
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              "      <td>124</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df = pd.read_csv (f'/content/drive/MyDrive/cut_panoramic/Data/All_Re.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = df[df['Fig_Age'].between(1,75)]\n",
        "test = df[df['Fig_Age'].between(101,125)]"
      ],
      "metadata": {
        "id": "-8k5KWBRxwf8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_PATH = \"/content/drive/My Drive/TVT_Gender\"\n",
        "os.chdir(DATA_PATH)\n",
        "train_dir = os.path.join(DATA_PATH, 'train')\n",
        "print(train_dir)\n",
        "test_dir = os.path.join(DATA_PATH, 'test')\n",
        "print(test_dir)"
      ],
      "metadata": {
        "id": "3jWpRK9HyW1q",
        "outputId": "c7147fbb-e165-45ea-f770-67c9d2ff75bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/TVT_Gender/train\n",
            "/content/drive/My Drive/TVT_Gender/test\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "IPwibaNFDfcS"
      },
      "outputs": [],
      "source": [
        "# base_dir = '/content/drive/MyDrive/TVT_Gender'\n",
        "# os.makedirs(base_dir, exist_ok=True)\n",
        "\n",
        "# # Directories for our training,\n",
        "# # validation and test splits\n",
        "# train_dir = os.path.join(base_dir, 'train')\n",
        "# os.makedirs(train_dir, exist_ok=True)\n",
        "# validation_dir = os.path.join(base_dir, 'validation')\n",
        "# os.makedirs(validation_dir, exist_ok=True)\n",
        "# test_dir = os.path.join(base_dir, 'test')\n",
        "# # os.makedirs(test_dir, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi1ozW49B_cT",
        "outputId": "e0a067d7-0e42-4868-a4c1-54c61209226d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2850 validated image filenames.\n"
          ]
        }
      ],
      "source": [
        "batch_size = 16\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255, #โมเดลส่วนใหญ่ต้องใช้ RGB ในช่วง 0–1\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "\n",
        "# Note that the validation data should not be augmented!\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_dataframe(\n",
        "        dataframe = train,\n",
        "        directory = train_dir,\n",
        "        x_col = 'Path_filename',\n",
        "        y_col = 'Class_Re',\n",
        "        # รูปภาพทั้งหมดจะถูกปรับขนาดตามความสูงและความกว้างของเป้าหมาย\n",
        "        target_size=(height, width),\n",
        "        batch_size=batch_size,\n",
        "        # Since we use categorical_crossentropy loss, we need categorical labels\n",
        "        #เนื่องจากเราใช้ categorical_crossentropy loss เราจึงต้องมีป้ายกำกับตามหมวดหมู่\n",
        "        class_mode = 'other')\n",
        "\n",
        "#label\n",
        "# labels = (train_generator.class_indices)\n",
        "# labels = dict((v,k) for k,v in labels.items())\n",
        "# print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "c1_BK5epD6r5",
        "outputId": "b30eea70-38aa-4de8-ec4e-94db244d370f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "100       101              51          7  Y07F         1        V67.jpg   \n",
              "101       102              51          7  Y07F         1   Flip_V67.jpg   \n",
              "102       103              52          7  Y07F         1        V68.jpg   \n",
              "103       104              52          7  Y07F         1   Flip_V68.jpg   \n",
              "104       105              53          7  Y07F         1        V69.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25  Y25M        19  Flip_J463.jpg   \n",
              "4746      122              78         25  Y25M        19       J464.jpg   \n",
              "4747      123              78         25  Y25M        19  Flip_J464.jpg   \n",
              "4748      124              79         25  Y25M        19       J465.jpg   \n",
              "4749      125              79         25  Y25M        19  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "100   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "101   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "102   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "103   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "104   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "\n",
              "[950 rows x 9 columns]"
            ],
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              "      <th>Fig_Age</th>\n",
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              "      <th>4745</th>\n",
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              "      <td>19</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
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              "<p>950 rows × 9 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-2c524ed0-9cd5-457a-84df-bb0ee47a3765 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-2c524ed0-9cd5-457a-84df-bb0ee47a3765');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "test = df[df['Fig_Age'].between(101,125)]\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8TRhj_ncDbqq"
      },
      "outputs": [],
      "source": [
        "act = test['Class_Re'].tolist() #ค่าจริง\n",
        "path = test['Path_filename'].tolist() #path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "tBFLv9UJD9m0"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "def predict_image(img_path):\n",
        "    # Read the image and resize it\n",
        "    img = image.load_img(img_path, target_size=(height, width)) #รูปทุกรูปมีขนาดไม่เท่ากันจึงตั้งไว้ว่าเวลาจะให้ model ดึงรูปมาทำนายให้ดึงรูปเข้ามาตามขนาดที่ตั้งไว้ตามพารามิตเตอร์\n",
        "    # Convert it to a Numpy array with target shape.\n",
        "    x = image.img_to_array(img)  #model ไม่สามารถทำนายรูปภาพโดยตรงได้ จึงเเปลงรูปภาพให้เป็น array เเล้วให้โมเดลทำนาย\n",
        "    # Reshape\n",
        "    x = x.reshape((1,) + x.shape) # เพิ่ม ไดเมนชั่นของโมเดล ให้เป็น 4 ได้ เมนชั่น โดยที่ 1, คือบอกให้โมเดลนำเข้าทีละ 1 รูปเเล้วค่อยทำนาย เเละ + ไดเมนชั่น คือ 150 ,150 ,3(สีของ RGB)\n",
        "    x /= 255.\n",
        "    result = model.predict([x])\n",
        "    return result[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_xWno8PFb5K",
        "outputId": "a613b5ef-00a5-4d54-b37b-0c6e72282ec2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.2478228"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "predict_image(path[0])[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qierSwCnFC8",
        "outputId": "2674b9c2-030d-4eb7-8277-24d9e71d97ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "1/1 [==============================] - 0s 81ms/step\n",
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            "1/1 [==============================] - 0s 63ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "1/1 [==============================] - 0s 64ms/step\n",
            "1/1 [==============================] - 0s 56ms/step\n",
            "1/1 [==============================] - 0s 63ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 67ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 110ms/step\n",
            "1/1 [==============================] - 0s 90ms/step\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "1/1 [==============================] - 0s 90ms/step\n",
            "1/1 [==============================] - 0s 89ms/step\n",
            "1/1 [==============================] - 0s 101ms/step\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "1/1 [==============================] - 0s 89ms/step\n",
            "1/1 [==============================] - 0s 56ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n"
          ]
        }
      ],
      "source": [
        "#Predict\n",
        "pred_list = list()\n",
        "prob_list = list()\n",
        "img_path= path\n",
        "for i in range(0,len(img_path)):\n",
        "    predict = predict_image(img_path[i])\n",
        "    result = predict[0]\n",
        "    pred_list.append(result)\n",
        "    # prob_list.append(predict[result])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQqw0uH8-T4O",
        "outputId": "4e52c911-d674-41ee-9546-d79f82294b0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 15.922818,\n",
              " 17.368216,\n",
              " 15.222794,\n",
              " 14.725529,\n",
              " 10.814434,\n",
              " 11.340272,\n",
              " 15.817713,\n",
              " 15.6347065,\n",
              " 12.137744,\n",
              " 14.893997,\n",
              " 13.458473,\n",
              " 14.66761,\n",
              " 15.150494,\n",
              " 15.84252,\n",
              " 16.2393,\n",
              " 15.349625,\n",
              " 15.983712,\n",
              " 15.793457,\n",
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              " 17.014275,\n",
              " 17.240238,\n",
              " 15.825351,\n",
              " 15.722662]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "pred_list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(pred_list))"
      ],
      "metadata": {
        "id": "UcNyZKXzHEED",
        "outputId": "abbbd962-c4ad-437c-85a0-da2a8df4e42e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_int = np.around(pred_list)"
      ],
      "metadata": {
        "id": "tWblsHwTEpaV"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ezbChl5EKQA7"
      },
      "outputs": [],
      "source": [
        "pred = np.array(pred_int) #แปลงเป็น array\n",
        "act = np.array(act)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred"
      ],
      "metadata": {
        "id": "ODV31M8k8sK0",
        "outputId": "b68f10c1-b517-423c-c58a-6f8f16ce3985",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6.,  6.,  6.,  6.,  6.,  6.,  6.,  2.,  6.,  6.,  6.,  6.,  6.,\n",
              "        6., 10., 10.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6., 10.,  3.,\n",
              "        2.,  6.,  6.,  6.,  6.,  5.,  6.,  3.,  2.,  6.,  2.,  2.,  2.,\n",
              "        6.,  6.,  6.,  6.,  6.,  5.,  6.,  6.,  6.,  6.,  3.,  4.,  5.,\n",
              "        3.,  3.,  4.,  4.,  2.,  3.,  4.,  3.,  2.,  2.,  2.,  3.,  3.,\n",
              "        3.,  3.,  2.,  2.,  2.,  2.,  3.,  2.,  3.,  3.,  3.,  4.,  4.,\n",
              "        5.,  5.,  2.,  3.,  2.,  2.,  5.,  5.,  2.,  2.,  4.,  4.,  4.,\n",
              "        3.,  2.,  2.,  4.,  3.,  2.,  2.,  3.,  3.,  4.,  4.,  4.,  4.,\n",
              "        5.,  4.,  5.,  3.,  3.,  5.,  5.,  5.,  5.,  4.,  4.,  4.,  6.,\n",
              "        5.,  5.,  5.,  5.,  6.,  5.,  6.,  6.,  6.,  5.,  5.,  5.,  4.,\n",
              "        6.,  6.,  5.,  5.,  5.,  5.,  5.,  4.,  5.,  6.,  6.,  5.,  5.,\n",
              "        5.,  6.,  6.,  6.,  5.,  5.,  6.,  7.,  7.,  6.,  6.,  7.,  7.,\n",
              "        5.,  4.,  8.,  8.,  4.,  5.,  8.,  7.,  7.,  7.,  8.,  8.,  8.,\n",
              "        6.,  6.,  7.,  7.,  7.,  7., 10.,  6.,  6., 10., 10.,  7.,  7.,\n",
              "        7.,  9.,  8.,  9.,  7.,  6.,  5.,  5.,  5.,  5.,  9.,  8.,  7.,\n",
              "        7.,  7.,  9.,  7.,  7.,  5.,  6.,  6., 11., 10., 11.,  7.,  6.,\n",
              "       16.,  8.,  8., 10.,  7.,  7.,  8.,  8.,  7.,  6.,  8., 10.,  7.,\n",
              "        7., 10., 11.,  6., 10.,  8.,  8.,  7.,  7., 10.,  9.,  8.,  9.,\n",
              "       10., 10., 10., 11., 11.,  9., 10.,  9.,  7., 14., 12.,  9., 10.,\n",
              "        8., 10.,  8.,  9.,  9.,  9.,  9., 10.,  9., 11., 11., 13., 11.,\n",
              "       13., 14.,  8.,  8.,  8., 11.,  8., 14., 12.,  9.,  7., 10., 10.,\n",
              "        9., 10., 15.,  7., 11., 14., 14., 15., 10., 10., 10., 15., 15.,\n",
              "       13., 14., 10.,  8., 10., 10., 12., 15., 11., 15., 12., 11., 17.,\n",
              "       10., 15., 12.,  7., 18., 14., 13., 16., 12., 17., 17., 16., 11.,\n",
              "        7., 14., 11., 12., 11., 10., 14.,  6., 14., 13., 15., 16., 14.,\n",
              "       16.,  8., 12., 15., 12., 11., 13., 11., 18., 18., 10., 12., 11.,\n",
              "       11., 12., 11., 16., 15., 11., 11., 13., 17., 17., 15., 16., 11.,\n",
              "       11., 13., 16., 14., 12., 16., 15., 15., 12., 11., 11., 17., 16.,\n",
              "       13., 15., 14., 10., 12., 13., 15., 15., 14., 13., 15., 18., 18.,\n",
              "       15., 14., 17., 17., 13., 14., 13., 13., 17., 16., 13.,  5., 15.,\n",
              "       16.,  9., 16., 11., 13., 16., 17., 17., 12., 16., 13., 14., 15.,\n",
              "       15., 16., 16., 13., 16., 13., 14.,  7.,  9., 11.,  6., 16., 16.,\n",
              "       15., 16., 19., 16., 16., 15., 16., 16., 16., 15., 16., 13., 11.,\n",
              "       17., 17., 18., 18., 15., 13., 16., 15., 14., 15., 18., 17., 10.,\n",
              "       13., 12., 16., 19., 18., 15., 14., 13., 11., 16., 12., 13., 14.,\n",
              "       15., 17., 17., 17., 16., 16., 17., 16., 14., 13., 16., 11., 16.,\n",
              "        8., 19., 17., 13., 16., 16.,  8.,  6.,  6.,  6.,  2.,  6.,  6.,\n",
              "        6.,  6.,  6.,  6.,  6.,  6.,  6., 10.,  6.,  6., 10.,  6.,  6.,\n",
              "        6.,  6.,  6.,  2.,  5.,  6.,  2.,  2.,  2.,  6.,  2.,  6.,  2.,\n",
              "        2.,  2.,  3.,  4.,  6.,  2.,  2.,  6.,  6.,  2.,  6.,  2.,  2.,\n",
              "        6.,  6.,  6.,  2.,  2.,  4.,  2.,  3.,  2.,  2.,  2.,  2.,  2.,\n",
              "        3.,  2.,  3.,  2.,  2.,  3.,  3.,  2.,  2.,  2.,  2.,  3.,  3.,\n",
              "        6.,  2.,  2.,  2.,  2.,  4.,  3.,  3.,  2.,  2.,  6.,  6.,  4.,\n",
              "        4.,  3.,  2.,  2.,  2.,  3.,  3.,  3.,  4.,  4.,  4.,  4.,  4.,\n",
              "        3.,  3.,  4.,  5.,  4.,  4.,  6.,  4.,  5.,  3.,  2.,  2.,  5.,\n",
              "        4.,  4.,  3.,  2.,  4.,  4.,  5.,  5.,  6.,  5.,  5.,  5.,  5.,\n",
              "        4.,  4.,  5.,  6.,  4.,  5.,  6.,  7.,  6.,  6.,  5.,  4.,  6.,\n",
              "        6.,  4.,  7.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  7.,  7.,\n",
              "        6.,  6.,  7.,  6.,  6.,  6.,  5.,  5.,  6.,  5.,  8.,  5.,  6.,\n",
              "        6.,  6.,  6.,  8.,  8.,  6.,  7.,  9.,  8.,  7.,  6.,  7.,  8.,\n",
              "        6.,  6.,  6.,  6.,  4.,  4.,  6.,  6.,  5.,  5.,  6.,  6.,  6.,\n",
              "        5.,  6.,  5.,  5.,  5.,  5.,  4.,  4.,  6.,  6.,  6.,  5.,  6.,\n",
              "        6.,  8.,  8.,  6.,  5., 10.,  7.,  6.,  9.,  6.,  7.,  6.,  6.,\n",
              "        6.,  6.,  6.,  8., 10.,  9.,  5.,  6.,  4.,  4.,  5.,  7.,  9.,\n",
              "        6.,  8.,  6.,  9.,  7.,  9.,  9.,  9.,  9., 10., 10.,  8.,  8.,\n",
              "        8.,  9.,  6.,  8., 11., 11.,  9.,  9.,  8.,  8.,  9.,  9., 12.,\n",
              "       11., 10., 10.,  7., 14., 11., 16., 16.,  7., 10., 11., 13., 11.,\n",
              "       11., 13., 11., 11.,  6.,  7., 12., 13.,  8., 10., 10., 10., 10.,\n",
              "       13., 13., 12., 15., 10.,  9., 11., 10., 10., 10., 12., 12., 12.,\n",
              "       12., 15., 12., 14., 13., 14., 13., 15., 15., 14., 10., 11., 16.,\n",
              "       10., 11., 12., 11., 14., 18.,  8., 11., 16., 16., 13., 13., 19.,\n",
              "       19., 15., 15., 16., 15., 11.,  9.,  5.,  9., 14., 16., 16., 16.,\n",
              "       16., 12., 16., 17., 16., 15., 12., 12.,  8.,  8., 13., 14., 11.,\n",
              "       12.,  8.,  5., 17., 13., 13., 19., 17., 18., 18., 18., 18., 14.,\n",
              "       12., 14., 17., 13., 11., 15., 14., 12., 14., 17., 13., 16., 16.,\n",
              "       15., 12., 17., 17., 15., 14., 16., 13., 17., 18., 14., 12., 15.,\n",
              "       13., 14., 14., 12., 12., 15., 16., 10., 12., 11., 16., 17., 17.,\n",
              "       15., 10., 10., 11., 14., 14., 16., 15., 17., 17., 16., 17., 15.,\n",
              "       16., 16., 11., 12., 13., 15., 16., 16., 12., 14., 12., 17., 15.,\n",
              "       11., 13., 16., 17., 17., 16., 15., 13., 17., 17., 14., 18., 18.,\n",
              "       14., 14., 18., 17., 14., 17., 12., 14., 14., 11., 16., 15., 13.,\n",
              "       17., 16., 13., 16., 17., 15., 15., 11., 11., 16., 16., 12., 15.,\n",
              "       13., 15., 15., 16., 16., 15., 16., 16., 17., 18., 17., 17., 16.,\n",
              "       16.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#confusion_matrix"
      ],
      "metadata": {
        "id": "BJqjIzTMZVLB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pzpwmMAJwz-",
        "outputId": "8572a08d-ce00-4080-adc1-ecd5faffc9df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier accuracy = 18.842105263157894%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        50\n",
            "           2       0.29      0.38      0.33        50\n",
            "           3       0.45      0.36      0.40        50\n",
            "           4       0.29      0.30      0.30        50\n",
            "           5       0.29      0.40      0.33        50\n",
            "           6       0.16      0.48      0.24        50\n",
            "           7       0.31      0.30      0.30        50\n",
            "           8       0.04      0.04      0.04        50\n",
            "           9       0.06      0.04      0.05        50\n",
            "          10       0.18      0.20      0.19        50\n",
            "          11       0.20      0.22      0.21        50\n",
            "          12       0.19      0.16      0.17        50\n",
            "          13       0.08      0.08      0.08        50\n",
            "          14       0.04      0.04      0.04        50\n",
            "          15       0.16      0.18      0.17        50\n",
            "          16       0.11      0.16      0.13        50\n",
            "          17       0.09      0.08      0.09        50\n",
            "          18       0.37      0.14      0.20        50\n",
            "          19       0.17      0.02      0.04        50\n",
            "\n",
            "    accuracy                           0.19       950\n",
            "   macro avg       0.18      0.19      0.17       950\n",
            "weighted avg       0.18      0.19      0.17       950\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# act = data_train['class'].array\n",
        "# pred = data_train['category_ov'].array\n",
        "\n",
        "cmat = confusion_matrix(act, pred)\n",
        "print('classifier accuracy = {}%'.format((100.*np.trace(cmat))/(np.sum(cmat))))\n",
        "\n",
        "#Marking the Confusion Matrix\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(act, pred))#performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "qCeaPS3pJzwt",
        "outputId": "6e4c7ec7-36b8-432f-80ee-8f5a2d29713c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 76.24999999999999, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#create CF \n",
        "data = {'Actual': act,'Predicted' : pred}\n",
        "df = pd.DataFrame(data, columns=['Actual','Predicted'])\n",
        "conf_mat = pd.crosstab(df['Actual'],df['Predicted'],rownames=['Actual'],colnames=['Predicted'])\n",
        "\n",
        "#Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "cm = confusion_matrix(act, pred)\n",
        "\n",
        "#plot Confusion matrix\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "\n",
        "ax = sns.heatmap(conf_mat, annot=True, fmt=\"d\", cmap=\"YlGnBu\") #Blues,Oranges,Reds\n",
        "ax.set_title('Confusion matrix',fontsize=20)\n",
        "ax.set_ylabel('True label',fontsize=18)\n",
        "ax.set_xlabel('Predicted label',fontsize=18)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn import metrics"
      ],
      "metadata": {
        "id": "Yks_Ln7sCOtg"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MAE, MSE and RMSE"
      ],
      "metadata": {
        "id": "-mwNrpt2Y4m4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "  \n",
        "mae = mean_absolute_error(act,pred)\n",
        "mse = mean_squared_error(act,pred)\n",
        "rmse = mean_squared_error(act,pred,squared=False)\n",
        "\n",
        "print(\"MAE:\",mae)\n",
        "print(\"MSE:\",mse)\n",
        "print(\"RMSE:\",rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJL2lEHmA4cJ",
        "outputId": "ec07ea06-97fd-489b-acbb-9be92a52db93"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 2.1178947368421053\n",
            "MSE: 8.138947368421052\n",
            "RMSE: 2.8528840439844467\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}