CNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7glsKGiOx6X"
      },
      "source": [
        "# Step1: Install Librarys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDrv5TdLO9-I",
        "outputId": "1a390b4c-c5ef-48e7-9f86-74e58dad6c0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "Installing collected packages: xxhash, requests, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.19.2 dill-0.3.8 multiprocess-0.70.16 requests-2.32.3 xxhash-3.4.1\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
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            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install scikit-learn\n",
        "!pip install numpy gensim scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-B3OQ-JOtcJ"
      },
      "source": [
        "# Step2: Import Librarys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "YOd8e1r_OsM6"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "from datasets import load_dataset\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.utils import to_categorical\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Embedding, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dense"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VR7ZgtLQ7GH"
      },
      "source": [
        "# Step3: Set Global Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sa8IduqWQ-Qj"
      },
      "outputs": [],
      "source": [
        "datasetLimit = 10000\n",
        "datasetLimitTest = int(datasetLimit*0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdh7NOQkRAz-"
      },
      "source": [
        "# Step4: Loading DataSets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VOd5arFCRDc-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1998f448-5eeb-423f-8622-18f600da2f8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "(train_data, train_label), (test_data, test_label) = imdb.load_data(num_words=25000)\n",
        "train_data = list(train_data[:datasetLimit])\n",
        "train_label = list(train_label[:datasetLimit])\n",
        "test_data = list(test_data[:datasetLimitTest])\n",
        "test_label = list(test_label[:datasetLimitTest])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWlrCiHWRyEk"
      },
      "source": [
        "# Step5: Add Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_CNN_model(train_data, test_data, train_labels, test_labels, max_pooling, CNN_size, CNN_window, Epochs, max_words=25000, embedding_dim=128, input_length=100, num_clusters=2):\n",
        "    train_padded = pad_sequences(train_data, maxlen=input_length)\n",
        "    test_padded = pad_sequences(test_data, maxlen=input_length)\n",
        "\n",
        "    train_labels_cat = to_categorical(train_labels, num_clusters)\n",
        "    test_labels_cat = to_categorical(test_labels, num_clusters)\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(max_words, embedding_dim, input_length=input_length))\n",
        "    model.add(Conv1D(CNN_size, CNN_window, activation='relu'))\n",
        "    if max_pooling:\n",
        "        model.add(MaxPooling1D(5))\n",
        "    model.add(Conv1D(CNN_size, CNN_window, activation='relu'))\n",
        "    model.add(GlobalMaxPooling1D())\n",
        "    model.add(Dense(num_clusters, activation='softmax'))\n",
        "\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    model.fit(train_padded, train_labels_cat, epochs=Epochs, batch_size=128, validation_split=0.2)\n",
        "\n",
        "    test_predictions = model.predict(test_padded)\n",
        "    predicted_labels = test_predictions.argmax(axis=1)\n",
        "\n",
        "    return predicted_labels"
      ],
      "metadata": {
        "id": "YRVabQix9Ajr"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictionsCNN = get_CNN_model(train_data, test_data, train_label, test_label, True, 32, 5, 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqTINzLPgsUf",
        "outputId": "dfb87957-f423-41d3-e681-a52043ba6e1f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "63/63 [==============================] - 16s 194ms/step - loss: 0.6872 - accuracy: 0.5484 - val_loss: 0.6655 - val_accuracy: 0.6875\n",
            "Epoch 2/50\n",
            "63/63 [==============================] - 8s 130ms/step - loss: 0.4952 - accuracy: 0.8240 - val_loss: 0.4041 - val_accuracy: 0.8075\n",
            "Epoch 3/50\n",
            "63/63 [==============================] - 7s 116ms/step - loss: 0.1973 - accuracy: 0.9289 - val_loss: 0.4674 - val_accuracy: 0.8060\n",
            "Epoch 4/50\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.0530 - accuracy: 0.9884 - val_loss: 0.5196 - val_accuracy: 0.8030\n",
            "Epoch 5/50\n",
            "63/63 [==============================] - 7s 112ms/step - loss: 0.0130 - accuracy: 0.9985 - val_loss: 0.5819 - val_accuracy: 0.8100\n",
            "Epoch 6/50\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.0046 - accuracy: 0.9999 - val_loss: 0.6316 - val_accuracy: 0.8125\n",
            "Epoch 7/50\n",
            "63/63 [==============================] - 7s 115ms/step - loss: 0.0027 - accuracy: 0.9999 - val_loss: 0.6653 - val_accuracy: 0.8085\n",
            "Epoch 8/50\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.0017 - accuracy: 0.9999 - val_loss: 0.6956 - val_accuracy: 0.8095\n",
            "Epoch 9/50\n",
            "63/63 [==============================] - 7s 115ms/step - loss: 8.1178e-04 - accuracy: 1.0000 - val_loss: 0.7237 - val_accuracy: 0.8040\n",
            "Epoch 10/50\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 5.9043e-04 - accuracy: 1.0000 - val_loss: 0.7450 - val_accuracy: 0.8090\n",
            "Epoch 11/50\n",
            "63/63 [==============================] - 7s 115ms/step - loss: 4.5462e-04 - accuracy: 1.0000 - val_loss: 0.7643 - val_accuracy: 0.8070\n",
            "Epoch 12/50\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 3.6537e-04 - accuracy: 1.0000 - val_loss: 0.7824 - val_accuracy: 0.8050\n",
            "Epoch 13/50\n",
            "63/63 [==============================] - 7s 112ms/step - loss: 3.0141e-04 - accuracy: 1.0000 - val_loss: 0.7976 - val_accuracy: 0.8055\n",
            "Epoch 14/50\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 2.5272e-04 - accuracy: 1.0000 - val_loss: 0.8120 - val_accuracy: 0.8060\n",
            "Epoch 15/50\n",
            "63/63 [==============================] - 8s 125ms/step - loss: 2.1532e-04 - accuracy: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.8055\n",
            "Epoch 16/50\n",
            "63/63 [==============================] - 7s 105ms/step - loss: 1.8565e-04 - accuracy: 1.0000 - val_loss: 0.8380 - val_accuracy: 0.8060\n",
            "Epoch 17/50\n",
            "63/63 [==============================] - 7s 116ms/step - loss: 1.6147e-04 - accuracy: 1.0000 - val_loss: 0.8497 - val_accuracy: 0.8045\n",
            "Epoch 18/50\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 1.4198e-04 - accuracy: 1.0000 - val_loss: 0.8609 - val_accuracy: 0.8065\n",
            "Epoch 19/50\n",
            "63/63 [==============================] - 7s 116ms/step - loss: 1.2558e-04 - accuracy: 1.0000 - val_loss: 0.8716 - val_accuracy: 0.8050\n",
            "Epoch 20/50\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 1.1196e-04 - accuracy: 1.0000 - val_loss: 0.8820 - val_accuracy: 0.8060\n",
            "Epoch 21/50\n",
            "63/63 [==============================] - 7s 113ms/step - loss: 9.9990e-05 - accuracy: 1.0000 - val_loss: 0.8915 - val_accuracy: 0.8065\n",
            "Epoch 22/50\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 9.0047e-05 - accuracy: 1.0000 - val_loss: 0.9006 - val_accuracy: 0.8060\n",
            "Epoch 23/50\n",
            "63/63 [==============================] - 7s 118ms/step - loss: 8.1469e-05 - accuracy: 1.0000 - val_loss: 0.9096 - val_accuracy: 0.8060\n",
            "Epoch 24/50\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 7.3901e-05 - accuracy: 1.0000 - val_loss: 0.9182 - val_accuracy: 0.8060\n",
            "Epoch 25/50\n",
            "63/63 [==============================] - 7s 116ms/step - loss: 6.7412e-05 - accuracy: 1.0000 - val_loss: 0.9265 - val_accuracy: 0.8055\n",
            "Epoch 26/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 6.1618e-05 - accuracy: 1.0000 - val_loss: 0.9342 - val_accuracy: 0.8075\n",
            "Epoch 27/50\n",
            "63/63 [==============================] - 7s 114ms/step - loss: 5.6580e-05 - accuracy: 1.0000 - val_loss: 0.9420 - val_accuracy: 0.8055\n",
            "Epoch 28/50\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 5.1967e-05 - accuracy: 1.0000 - val_loss: 0.9496 - val_accuracy: 0.8055\n",
            "Epoch 29/50\n",
            "63/63 [==============================] - 7s 112ms/step - loss: 4.7928e-05 - accuracy: 1.0000 - val_loss: 0.9568 - val_accuracy: 0.8060\n",
            "Epoch 30/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 4.4294e-05 - accuracy: 1.0000 - val_loss: 0.9637 - val_accuracy: 0.8060\n",
            "Epoch 31/50\n",
            "63/63 [==============================] - 7s 118ms/step - loss: 4.1055e-05 - accuracy: 1.0000 - val_loss: 0.9708 - val_accuracy: 0.8065\n",
            "Epoch 32/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 3.8111e-05 - accuracy: 1.0000 - val_loss: 0.9777 - val_accuracy: 0.8055\n",
            "Epoch 33/50\n",
            "63/63 [==============================] - 7s 117ms/step - loss: 3.5454e-05 - accuracy: 1.0000 - val_loss: 0.9842 - val_accuracy: 0.8065\n",
            "Epoch 34/50\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 3.3001e-05 - accuracy: 1.0000 - val_loss: 0.9905 - val_accuracy: 0.8070\n",
            "Epoch 35/50\n",
            "63/63 [==============================] - 8s 120ms/step - loss: 3.0816e-05 - accuracy: 1.0000 - val_loss: 0.9968 - val_accuracy: 0.8065\n",
            "Epoch 36/50\n",
            "63/63 [==============================] - 6s 100ms/step - loss: 2.8768e-05 - accuracy: 1.0000 - val_loss: 1.0029 - val_accuracy: 0.8060\n",
            "Epoch 37/50\n",
            "63/63 [==============================] - 7s 119ms/step - loss: 2.6924e-05 - accuracy: 1.0000 - val_loss: 1.0089 - val_accuracy: 0.8070\n",
            "Epoch 38/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 2.5253e-05 - accuracy: 1.0000 - val_loss: 1.0149 - val_accuracy: 0.8060\n",
            "Epoch 39/50\n",
            "63/63 [==============================] - 7s 117ms/step - loss: 2.3670e-05 - accuracy: 1.0000 - val_loss: 1.0208 - val_accuracy: 0.8060\n",
            "Epoch 40/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 2.2250e-05 - accuracy: 1.0000 - val_loss: 1.0264 - val_accuracy: 0.8065\n",
            "Epoch 41/50\n",
            "63/63 [==============================] - 7s 116ms/step - loss: 2.0921e-05 - accuracy: 1.0000 - val_loss: 1.0321 - val_accuracy: 0.8060\n",
            "Epoch 42/50\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 1.9697e-05 - accuracy: 1.0000 - val_loss: 1.0376 - val_accuracy: 0.8055\n",
            "Epoch 43/50\n",
            "63/63 [==============================] - 7s 119ms/step - loss: 1.8580e-05 - accuracy: 1.0000 - val_loss: 1.0429 - val_accuracy: 0.8050\n",
            "Epoch 44/50\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 1.7534e-05 - accuracy: 1.0000 - val_loss: 1.0482 - val_accuracy: 0.8045\n",
            "Epoch 45/50\n",
            "63/63 [==============================] - 7s 115ms/step - loss: 1.6536e-05 - accuracy: 1.0000 - val_loss: 1.0536 - val_accuracy: 0.8055\n",
            "Epoch 46/50\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 1.5629e-05 - accuracy: 1.0000 - val_loss: 1.0588 - val_accuracy: 0.8050\n",
            "Epoch 47/50\n",
            "63/63 [==============================] - 7s 117ms/step - loss: 1.4798e-05 - accuracy: 1.0000 - val_loss: 1.0638 - val_accuracy: 0.8050\n",
            "Epoch 48/50\n",
            "63/63 [==============================] - 7s 106ms/step - loss: 1.3991e-05 - accuracy: 1.0000 - val_loss: 1.0689 - val_accuracy: 0.8055\n",
            "Epoch 49/50\n",
            "63/63 [==============================] - 9s 141ms/step - loss: 1.3257e-05 - accuracy: 1.0000 - val_loss: 1.0737 - val_accuracy: 0.8050\n",
            "Epoch 50/50\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 1.2569e-05 - accuracy: 1.0000 - val_loss: 1.0787 - val_accuracy: 0.8060\n",
            "63/63 [==============================] - 1s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_7lLO-SVEmy"
      },
      "source": [
        "# Step6: Create Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "V_SxzEbwVFhZ",
        "colab": {
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          "height": 1000
        },
        "outputId": "45f36504-6f0e-426e-8a85-6b7b7f968b8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   Data  Label\n",
              "0     [1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...      0\n",
              "1     [1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...      1\n",
              "2     [1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...      1\n",
              "3     [1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...      0\n",
              "4     [1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...      1\n",
              "...                                                 ...    ...\n",
              "1995  [1, 6, 52, 21, 24, 87, 22, 4, 87, 8434, 9, 125...      1\n",
              "1996  [1, 14, 22, 9, 691, 6, 338, 193, 23, 4, 154, 2...      0\n",
              "1997  [1, 14, 9, 527, 22, 12, 9, 501, 995, 86, 4, 64...      0\n",
              "1998  [1, 61, 1640, 65, 39, 2, 39, 4, 2, 2, 1532, 16...      1\n",
              "1999  [1, 543, 1986, 9, 2789, 682, 2536, 1386, 18, 6...      1\n",
              "\n",
              "[2000 rows x 2 columns]"
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              "      <th>1995</th>\n",
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              "      <th>1997</th>\n",
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              "      <td>0</td>\n",
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              "      <th>1998</th>\n",
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              "      <td>1</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "confusion_matrix_CNN",
              "summary": "{\n  \"name\": \"confusion_matrix_CNN\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "confusion_matrix_CNN = pd.DataFrame({\n",
        "    'Data': test_data,\n",
        "    'Label': test_label\n",
        "})\n",
        "confusion_matrix_CNN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_labels_CNN = list(predictionsCNN)\n",
        "confusion_matrix_CNN['Predicted'] = predicted_labels_CNN\n",
        "confusion_matrix_CNN"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1K8UMiko5LUe",
        "outputId": "bf8c5480-232c-49ee-8998-a8a23c0f28a8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   Data  Label  Predicted\n",
              "0     [1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 1...      0          0\n",
              "1     [1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 267...      1          1\n",
              "2     [1, 111, 748, 4368, 1133, 2, 24563, 4, 87, 155...      1          1\n",
              "3     [1, 13, 1228, 119, 14, 552, 7, 20, 190, 14, 58...      0          0\n",
              "4     [1, 40, 49, 85, 84, 1040, 146, 6, 783, 254, 43...      1          1\n",
              "...                                                 ...    ...        ...\n",
              "1995  [1, 6, 52, 21, 24, 87, 22, 4, 87, 8434, 9, 125...      1          1\n",
              "1996  [1, 14, 22, 9, 691, 6, 338, 193, 23, 4, 154, 2...      0          0\n",
              "1997  [1, 14, 9, 527, 22, 12, 9, 501, 995, 86, 4, 64...      0          1\n",
              "1998  [1, 61, 1640, 65, 39, 2, 39, 4, 2, 2, 1532, 16...      1          1\n",
              "1999  [1, 543, 1986, 9, 2789, 682, 2536, 1386, 18, 6...      1          1\n",
              "\n",
              "[2000 rows x 3 columns]"
            ],
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              "      <td>...</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "confusion_matrix_CNN",
              "summary": "{\n  \"name\": \"confusion_matrix_CNN\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ivl9qtbFVTU5"
      },
      "outputs": [],
      "source": [
        "def compute_confusion_matrix_results(confusion_matrix):\n",
        "    conditions = [\n",
        "        (confusion_matrix['Label'] == 1) & (confusion_matrix['Predicted'] == 1),  # True Positive (TP)\n",
        "        (confusion_matrix['Label'] == 0) & (confusion_matrix['Predicted'] == 0),  # True Negative (TN)\n",
        "        (confusion_matrix['Label'] == 0) & (confusion_matrix['Predicted'] == 1),  # False Positive (FP)\n",
        "        (confusion_matrix['Label'] == 1) & (confusion_matrix['Predicted'] == 0)   # False Negative (FN)\n",
        "    ]\n",
        "\n",
        "    choices = ['TP', 'TN', 'FP', 'FN']\n",
        "\n",
        "    confusion_matrix['result'] = np.select(conditions, choices, default='')\n",
        "\n",
        "    return confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix_CNN = compute_confusion_matrix_results(confusion_matrix_CNN)"
      ],
      "metadata": {
        "id": "DNcZbEuQ5T8G"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfP6mRiMVYn4"
      },
      "source": [
        "# Step7: Calculate Measurments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "SimAIdm9VZZb"
      },
      "outputs": [],
      "source": [
        "def calculate_metrics(confusion_matrix):\n",
        "    TP = confusion_matrix[confusion_matrix['result'] == 'TP'].shape[0]\n",
        "    FP = confusion_matrix[confusion_matrix['result'] == 'FP'].shape[0]\n",
        "    TN = confusion_matrix[confusion_matrix['result'] == 'TN'].shape[0]\n",
        "    FN = confusion_matrix[confusion_matrix['result'] == 'FN'].shape[0]\n",
        "\n",
        "    precision = TP / (TP + FP) if TP + FP != 0 else 0\n",
        "    recall = TP / (TP + FN) if TP + FN != 0 else 0\n",
        "    accuracy = (TP + TN) / (TP + TN + FP + FN) if TP + TN + FP + FN != 0 else 0\n",
        "\n",
        "    return precision, recall, accuracy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_metrics(confusion_matrix):\n",
        "    precision, recall, accuracy = calculate_metrics(confusion_matrix)\n",
        "    print(f\"Precision: {precision}\")\n",
        "    print(f\"Recall: {recall}\")\n",
        "    print(f\"Accuracy: {accuracy}\")\n",
        "    print(\"-----------------------\")"
      ],
      "metadata": {
        "id": "REazgGgY_4Qh"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "bE_uwbJ1Vbzh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaaf35a8-360f-4222-cdff-51d6c268036a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.7688492063492064\n",
            "Recall: 0.8132214060860441\n",
            "Accuracy: 0.7945\n",
            "-----------------------\n"
          ]
        }
      ],
      "source": [
        "print_metrics(confusion_matrix_CNN)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat_minor": 0
}