G3_Predict_Unfreeze_250.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/G3_Predict_Unfreeze_250.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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WlXPl93BpDM",
        "outputId": "5e786f92-30a0-4f3e-bce3-085285392ead"
      },
      "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": null,
      "metadata": {
        "id": "vCxtnct6BtlL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwAfx2vRCdxm",
        "outputId": "6db6ef9c-f36a-4fc9-e9a5-d447e6373573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'efficientnet_keras_transfer_learning'...\n",
            "remote: Enumerating objects: 837, done.\u001b[K\n",
            "remote: Counting objects: 100% (359/359), done.\u001b[K\n",
            "remote: Compressing objects: 100% (124/124), done.\u001b[K\n",
            "remote: Total 837 (delta 255), reused 328 (delta 235), pack-reused 478\u001b[K\n",
            "Receiving objects: 100% (837/837), 13.82 MiB | 18.40 MiB/s, done.\n",
            "Resolving deltas: 100% (495/495), 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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1RiTwdWRBvgU",
        "outputId": "048d3aa1-a453-4ce7-a465-6112cb944f72"
      },
      "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         0         V1.jpg   \n",
              "1           2               1          7  Y07F         0    Flip_V1.jpg   \n",
              "2           3               2          7  Y07F         0         V2.jpg   \n",
              "3           4               2          7  Y07F         0    Flip_V2.jpg   \n",
              "4           5               3          7  Y07F         0         V3.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25  Y25M         1  Flip_J463.jpg   \n",
              "4746      122              78         25  Y25M         1       J464.jpg   \n",
              "4747      123              78         25  Y25M         1  Flip_J464.jpg   \n",
              "4748      124              79         25  Y25M         1       J465.jpg   \n",
              "4749      125              79         25  Y25M         1  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>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
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              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
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              "      <td>Both</td>\n",
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              "      <th>4748</th>\n",
              "      <td>124</td>\n",
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              "      <td>Y25M</td>\n",
              "      <td>1</td>\n",
              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "      <td>Y25M</td>\n",
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              "      <td>Flip_J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "<p>4750 rows × 9 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6db69e24-ef80-49b4-b1ed-40dd1dce53de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv (r'/content/drive/MyDrive/cut_panoramic/Data/New_Data_Gender.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sa_dq25vCUfN"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/cut_panoramic/Model/Classification/Gender/3_รอบที่3_Gender_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": null,
      "metadata": {
        "id": "dDq_RhxuB3US",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdf4ee27-5193-42f3-cbee-72990cbef464"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
            "Instructions for updating:\n",
            "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('/content/drive/My Drive/cut_panoramic/Model/Classification/Gender/3_รอบที่3_Gender_250_New_Unfreez.h5')\n",
        "height = width = model.input_shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi1ozW49B_cT",
        "outputId": "89b642f9-675a-4372-99dd-0ba8bc25fe37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2850 images belonging to 2 classes.\n",
            "{0: 'Female', 1: 'Male'}\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_directory(\n",
        "        # This is the target directory #ไดเรกเป้าหมาย\n",
        "        train_dir,\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='categorical')\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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "c1_BK5epD6r5",
        "outputId": "2f5d0b2a-bab9-44e0-c7a7-2cad875ad978"
      },
      "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         0        V67.jpg   \n",
              "101       102              51          7  Y07F         0   Flip_V67.jpg   \n",
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              "\n",
              "                                          Path_filename     Sex Floder  \n",
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              "...                                                 ...     ...    ...  \n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "test = df[df['Fig_Age'].between(101,125)]\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8TRhj_ncDbqq"
      },
      "outputs": [],
      "source": [
        "act = test['Sex'].tolist() #ค่าจริง\n",
        "path = test['Path_filename'].tolist() #path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eBCfOnOD8aX"
      },
      "outputs": [],
      "source": [
        "# act"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_xWno8PFb5K",
        "outputId": "e67c4f7d-6db1-4139-e8fd-0a9dcd107835"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 10s 10s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.8631348 , 0.13686515], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "predict_image(path[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "Vjx_4HEsEqsq",
        "outputId": "71feb968-76ac-4c9e-c2a8-8c33613247cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 33ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Female'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "no = np.argmax(predict_image(path[0]))\n",
        "labels[no]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX6FmOMzEjDk",
        "outputId": "6300e276-b966-4d6d-b7d5-d1577553da19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
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            "1/1 [==============================] - 0s 44ms/step\n"
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        }
      ],
      "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 = np.argmax(predict)\n",
        "    pred_list.append(labels[result])\n",
        "    prob_list.append(predict[result])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQqw0uH8-T4O",
        "outputId": "ae54bfb4-3c1c-4ede-e559-d812eb2aaf62"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
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              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
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              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Female',\n",
              " 'Female',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male',\n",
              " 'Male']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "pred_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1VKsxTELIIh",
        "outputId": "abd4e70e-5a18-4097-e7fc-1bf2f57fd21d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8631348"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "prob_list[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u_MQmuk3JiRj"
      },
      "outputs": [],
      "source": [
        "# data_train = valframe\n",
        "# #เช็คคลาสใน Predicted\n",
        "# pred_class = set(data_train['category_ov'])\n",
        "# print('Predicted : ',len(pred_class))\n",
        "# print(pred_class)\n",
        "# print('-'*50)\n",
        "# #เช็คคลาสใน Actual\n",
        "# classe = set(data_train['class'])\n",
        "# print('Actual : ',len(classe))\n",
        "# print(classe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ezbChl5EKQA7"
      },
      "outputs": [],
      "source": [
        "pred = np.array(pred_list) #แปลงเป็น array\n",
        "act = np.array(act)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pzpwmMAJwz-",
        "outputId": "a72be499-f993-4073-e5f0-ed7778075243"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier accuracy = 68.63157894736842%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Female       0.64      0.87      0.73       475\n",
            "        Male       0.79      0.50      0.62       475\n",
            "\n",
            "    accuracy                           0.69       950\n",
            "   macro avg       0.72      0.69      0.68       950\n",
            "weighted avg       0.72      0.69      0.68       950\n",
            "\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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "qCeaPS3pJzwt",
        "outputId": "21fe32cf-bc4f-4e61-bf54-734777623697"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 66.5, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 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",
      "execution_count": null,
      "metadata": {
        "id": "wv3kRyGxArop"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}